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GGUF Metadata Extraction for AIBOM Generator
This module extracts metadata from GGUF files without downloading the full file.
It uses HTTP range requests to fetch only the header portion (typically 2-8MB)
of potentially multi-GB model files.
"""
import struct
import logging
from typing import Dict, Any, Optional, List, OrderedDict
from collections import OrderedDict as OrderedDictType
from urllib.parse import quote
logger = logging.getLogger(__name__)
GGUF_MAGIC = 0x46554747
_STRUCT_UINT8 = struct.Struct("<B")
_STRUCT_INT8 = struct.Struct("<b")
_STRUCT_UINT16 = struct.Struct("<H")
_STRUCT_INT16 = struct.Struct("<h")
_STRUCT_UINT32 = struct.Struct("<I")
_STRUCT_INT32 = struct.Struct("<i")
_STRUCT_UINT64 = struct.Struct("<Q")
_STRUCT_INT64 = struct.Struct("<q")
_STRUCT_FLOAT32 = struct.Struct("<f")
_STRUCT_FLOAT64 = struct.Struct("<d")
class GGUFParseError(Exception):
"""Base exception for GGUF parsing errors."""
pass
class BufferUnderrunError(GGUFParseError):
"""Raised when buffer doesn't contain enough data to parse."""
def __init__(self, message: str = "buffer underrun", *, required_bytes: Optional[int] = None):
super().__init__(message)
self.required_bytes = required_bytes
class InvalidMagicError(GGUFParseError):
"""Raised when file doesn't have valid GGUF magic number."""
pass
class GGUFValueType:
UINT8 = 0
INT8 = 1
UINT16 = 2
INT16 = 3
UINT32 = 4
INT32 = 5
FLOAT32 = 6
BOOL = 7
STRING = 8
ARRAY = 9
UINT64 = 10
INT64 = 11
FLOAT64 = 12
class GGUFMetadata:
"""Parsed GGUF file metadata."""
def __init__(
self,
version: int,
tensor_count: int,
kv_count: int,
metadata: Dict[str, Any],
header_length: int,
filename: str = "",
):
self.version = version
self.tensor_count = tensor_count
self.kv_count = kv_count
self.metadata = metadata
self.header_length = header_length
self.filename = filename
class GGUFModelInfo:
"""Model information extracted from GGUF metadata for AIBOM."""
def __init__(
self,
filename: str,
architecture: Optional[str] = None,
name: Optional[str] = None,
quantization_version: Optional[int] = None,
file_type: Optional[int] = None,
tokenizer_model: Optional[str] = None,
vocab_size: Optional[int] = None,
context_length: Optional[int] = None,
embedding_length: Optional[int] = None,
block_count: Optional[int] = None,
attention_head_count: Optional[int] = None,
attention_head_count_kv: Optional[int] = None,
feed_forward_length: Optional[int] = None,
rope_dimension_count: Optional[int] = None,
description: Optional[str] = None,
license: Optional[str] = None,
author: Optional[str] = None,
raw_metadata: Optional[Dict[str, Any]] = None,
):
self.filename = filename
self.architecture = architecture
self.name = name
self.quantization_version = quantization_version
self.file_type = file_type
self.tokenizer_model = tokenizer_model
self.vocab_size = vocab_size
self.context_length = context_length
self.embedding_length = embedding_length
self.block_count = block_count
self.attention_head_count = attention_head_count
self.attention_head_count_kv = attention_head_count_kv
self.feed_forward_length = feed_forward_length
self.rope_dimension_count = rope_dimension_count
self.description = description
self.license = license
self.author = author
self.raw_metadata = raw_metadata or {}
class _ByteReader:
"""Helper for reading structured binary data from a buffer."""
__slots__ = ("_view", "_offset")
def __init__(self, buffer: bytes) -> None:
self._view = memoryview(buffer)
self._offset = 0
@property
def offset(self) -> int:
return self._offset
def _require(self, size: int) -> None:
if self._offset + size > len(self._view):
raise BufferUnderrunError(
f"need {size} bytes at offset {self._offset}, but only {len(self._view) - self._offset} available",
required_bytes=self._offset + size
)
def read(self, size: int) -> memoryview:
self._require(size)
start = self._offset
self._offset += size
return self._view[start:self._offset]
def read_uint8(self) -> int:
return _STRUCT_UINT8.unpack_from(self.read(_STRUCT_UINT8.size))[0]
def read_int8(self) -> int:
return _STRUCT_INT8.unpack_from(self.read(_STRUCT_INT8.size))[0]
def read_uint16(self) -> int:
return _STRUCT_UINT16.unpack_from(self.read(_STRUCT_UINT16.size))[0]
def read_int16(self) -> int:
return _STRUCT_INT16.unpack_from(self.read(_STRUCT_INT16.size))[0]
def read_uint32(self) -> int:
return _STRUCT_UINT32.unpack_from(self.read(_STRUCT_UINT32.size))[0]
def read_int32(self) -> int:
return _STRUCT_INT32.unpack_from(self.read(_STRUCT_INT32.size))[0]
def read_uint64(self) -> int:
return _STRUCT_UINT64.unpack_from(self.read(_STRUCT_UINT64.size))[0]
def read_int64(self) -> int:
return _STRUCT_INT64.unpack_from(self.read(_STRUCT_INT64.size))[0]
def read_float32(self) -> float:
return _STRUCT_FLOAT32.unpack_from(self.read(_STRUCT_FLOAT32.size))[0]
def read_float64(self) -> float:
return _STRUCT_FLOAT64.unpack_from(self.read(_STRUCT_FLOAT64.size))[0]
def read_bool(self) -> bool:
return self.read_uint8() != 0
def read_string(self) -> str:
length = self.read_uint64()
if length > 10_000_000:
raise GGUFParseError(f"string length {length} exceeds sanity limit")
raw = self.read(length)
return raw.tobytes().decode("utf-8")
def _read_value(reader: _ByteReader, value_type: int) -> Any:
"""Parse a GGUF metadata value based on its type."""
if value_type == GGUFValueType.UINT8:
return reader.read_uint8()
elif value_type == GGUFValueType.INT8:
return reader.read_int8()
elif value_type == GGUFValueType.UINT16:
return reader.read_uint16()
elif value_type == GGUFValueType.INT16:
return reader.read_int16()
elif value_type == GGUFValueType.UINT32:
return reader.read_uint32()
elif value_type == GGUFValueType.INT32:
return reader.read_int32()
elif value_type == GGUFValueType.UINT64:
return reader.read_uint64()
elif value_type == GGUFValueType.INT64:
return reader.read_int64()
elif value_type == GGUFValueType.FLOAT32:
return reader.read_float32()
elif value_type == GGUFValueType.FLOAT64:
return reader.read_float64()
elif value_type == GGUFValueType.BOOL:
return reader.read_bool()
elif value_type == GGUFValueType.STRING:
return reader.read_string()
elif value_type == GGUFValueType.ARRAY:
element_type = reader.read_uint32()
count = reader.read_uint64()
if count > 1_000_000:
raise GGUFParseError(f"array count {count} exceeds sanity limit")
return [_read_value(reader, element_type) for _ in range(count)]
else:
raise GGUFParseError(f"unknown GGUF value type: {value_type}")
def parse_gguf_metadata(buffer: bytes, filename: str = "") -> GGUFMetadata:
"""Parse GGUF metadata from a byte buffer."""
reader = _ByteReader(buffer)
magic = reader.read_uint32()
if magic != GGUF_MAGIC:
raise InvalidMagicError(f"invalid magic: 0x{magic:08x}, expected 0x{GGUF_MAGIC:08x}")
version = reader.read_uint32()
tensor_count = reader.read_uint64()
kv_count = reader.read_uint64()
if kv_count > 100_000:
raise GGUFParseError(f"kv_count {kv_count} exceeds sanity limit")
metadata: OrderedDictType[str, Any] = OrderedDictType()
for _ in range(kv_count):
key = reader.read_string()
value_type = reader.read_uint32()
value = _read_value(reader, value_type)
metadata[key] = value
return GGUFMetadata(
version=version,
tensor_count=tensor_count,
kv_count=kv_count,
metadata=metadata,
header_length=reader.offset,
filename=filename
)
def extract_model_info(gguf_metadata: GGUFMetadata) -> GGUFModelInfo:
"""Extract AIBOM-relevant model information from GGUF metadata."""
meta = gguf_metadata.metadata
arch = meta.get("general.architecture", "")
def get_arch_key(suffix: str) -> Optional[Any]:
if arch:
val = meta.get(f"{arch}.{suffix}")
if val is not None:
return val
return None
return GGUFModelInfo(
filename=gguf_metadata.filename,
architecture=arch or None,
name=meta.get("general.name"),
quantization_version=meta.get("general.quantization_version"),
file_type=meta.get("general.file_type"),
tokenizer_model=meta.get("tokenizer.ggml.model"),
vocab_size=len(meta.get("tokenizer.ggml.tokens", [])) or None,
context_length=get_arch_key("context_length"),
embedding_length=get_arch_key("embedding_length"),
block_count=get_arch_key("block_count"),
attention_head_count=get_arch_key("attention.head_count"),
attention_head_count_kv=get_arch_key("attention.head_count_kv"),
feed_forward_length=get_arch_key("feed_forward_length"),
rope_dimension_count=get_arch_key("rope.dimension_count"),
description=meta.get("general.description"),
license=meta.get("general.license"),
author=meta.get("general.author"),
raw_metadata=dict(meta)
)
def build_huggingface_url(repo_id: str, filename: str, revision: str = "main") -> str:
"""Build a HuggingFace download URL for a file."""
if not repo_id or "/" not in repo_id:
raise ValueError("repo_id must be in format 'owner/repo'")
owner, repo = repo_id.split("/", 1)
owner_quoted = quote(owner, safe="-_.~")
repo_quoted = quote(repo, safe="-_.~")
revision_quoted = quote(revision, safe="-_.~")
filename_quoted = "/".join(quote(part, safe="-_.~/") for part in filename.split("/"))
return f"https://huggingface.co/{owner_quoted}/{repo_quoted}/resolve/{revision_quoted}/{filename_quoted}"
def fetch_gguf_metadata_from_url(
url: str,
filename: str = "",
*,
hf_token: Optional[str] = None,
initial_request_size: int = 8 * 1024 * 1024,
max_request_size: int = 64 * 1024 * 1024,
timeout: float = 60.0,
) -> GGUFMetadata:
"""Fetch and parse GGUF metadata from a URL using HTTP range requests."""
try:
import httpx
except ImportError:
raise ImportError("httpx is required for remote GGUF fetching. Install with: pip install httpx")
headers = {
"User-Agent": "OWASP-AIBOM-Generator/1.0",
"Accept": "application/octet-stream",
}
if hf_token:
headers["Authorization"] = f"Bearer {hf_token}"
with httpx.Client(timeout=timeout, follow_redirects=False) as client:
current_url = url
for _ in range(5):
response = client.head(current_url, headers=headers)
if response.status_code in (301, 302, 303, 307, 308):
current_url = response.headers.get("location", current_url)
logger.debug(f"Redirecting to: {current_url}")
else:
break
actual_url = current_url
buffer = bytearray()
request_size = initial_request_size
with httpx.Client(timeout=timeout, follow_redirects=True) as client:
range_header = f"bytes=0-{request_size - 1}"
request_headers = {**headers, "Range": range_header}
logger.info(f"Fetching first {request_size // (1024*1024)}MB of GGUF metadata...")
response = client.get(actual_url, headers=request_headers)
response.raise_for_status()
buffer.extend(response.content)
max_retries = 5
for retry in range(max_retries):
try:
return parse_gguf_metadata(bytes(buffer), filename)
except BufferUnderrunError as exc:
if retry >= max_retries - 1:
raise
if exc.required_bytes:
needed = max(exc.required_bytes + 2 * 1024 * 1024, len(buffer) * 2)
else:
needed = len(buffer) * 2
additional_size = min(needed - len(buffer), max_request_size - len(buffer))
if additional_size <= 0 or len(buffer) >= max_request_size:
raise GGUFParseError(f"unable to parse metadata within {max_request_size} bytes")
logger.info(f"Need more data (retry {retry + 1}), fetching additional {additional_size // 1024}KB...")
range_header = f"bytes={len(buffer)}-{len(buffer) + additional_size - 1}"
request_headers = {**headers, "Range": range_header}
response = client.get(actual_url, headers=request_headers)
response.raise_for_status()
buffer.extend(response.content)
logger.info(f"Buffer now {len(buffer) // 1024}KB")
def fetch_gguf_metadata_from_repo(
repo_id: str,
filename: str,
*,
revision: str = "main",
hf_token: Optional[str] = None,
**kwargs
) -> GGUFModelInfo:
"""Fetch and extract AIBOM-relevant metadata from a GGUF file in a HuggingFace repo."""
url = build_huggingface_url(repo_id, filename, revision)
logger.info(f"Fetching GGUF metadata from {repo_id}/{filename}")
gguf_metadata = fetch_gguf_metadata_from_url(
url,
filename=filename,
hf_token=hf_token,
**kwargs
)
return extract_model_info(gguf_metadata)
def list_gguf_files(repo_id: str, hf_token: Optional[str] = None) -> List[str]:
"""List GGUF files in a HuggingFace repository."""
from huggingface_hub import list_repo_files
files = list_repo_files(repo_id, token=hf_token)
return [f for f in files if f.endswith('.gguf')]
def extract_all_gguf_metadata(
repo_id: str,
*,
hf_token: Optional[str] = None,
**kwargs
) -> List[GGUFModelInfo]:
"""Extract metadata from all GGUF files in a repository."""
gguf_files = list_gguf_files(repo_id, hf_token)
if not gguf_files:
logger.debug(f"No GGUF files found in {repo_id}")
return []
logger.info(f"Found {len(gguf_files)} GGUF files in {repo_id}")
results = []
for filename in gguf_files:
try:
info = fetch_gguf_metadata_from_repo(
repo_id,
filename,
hf_token=hf_token,
**kwargs
)
results.append(info)
logger.info(f" {filename}: architecture={info.architecture}")
except Exception as e:
logger.warning(f" {filename}: failed to extract metadata: {e}")
return results
def _map_core_fields(gguf_info: GGUFModelInfo) -> Dict[str, Any]:
"""Map basic model identity and tokenizer fields."""
metadata = {}
if gguf_info.architecture:
metadata["model_type"] = gguf_info.architecture
metadata["typeOfModel"] = gguf_info.architecture
if gguf_info.name:
metadata["name"] = gguf_info.name
if gguf_info.tokenizer_model:
metadata["tokenizer_class"] = gguf_info.tokenizer_model
if gguf_info.vocab_size:
metadata["vocab_size"] = gguf_info.vocab_size
if gguf_info.context_length:
metadata["context_length"] = gguf_info.context_length
metadata["gguf_filename"] = gguf_info.filename
return metadata
def _map_supplementary_fields(gguf_info: GGUFModelInfo) -> Dict[str, Any]:
"""Map optional descriptive fields from GGUF."""
metadata = {}
if gguf_info.description:
metadata["description"] = gguf_info.description
if gguf_info.author:
metadata["suppliedBy"] = gguf_info.author
if gguf_info.license:
metadata["gguf_license"] = gguf_info.license
return metadata
def _map_quantization(gguf_info: GGUFModelInfo) -> Dict[str, Any]:
"""Map quantization metadata."""
quantization = {}
if gguf_info.quantization_version:
quantization["version"] = gguf_info.quantization_version
if gguf_info.file_type:
quantization["file_type"] = gguf_info.file_type
return {"quantization": quantization} if quantization else {}
def _map_hyperparameters(gguf_info: GGUFModelInfo) -> Dict[str, Any]:
"""Map inference-shape hyperparameters."""
hyperparams = {}
if gguf_info.context_length:
hyperparams["context_length"] = gguf_info.context_length
if gguf_info.embedding_length:
hyperparams["embedding_length"] = gguf_info.embedding_length
if gguf_info.block_count:
hyperparams["block_count"] = gguf_info.block_count
if gguf_info.attention_head_count:
hyperparams["attention_head_count"] = gguf_info.attention_head_count
if gguf_info.attention_head_count_kv:
hyperparams["attention_head_count_kv"] = gguf_info.attention_head_count_kv
if gguf_info.feed_forward_length:
hyperparams["feed_forward_length"] = gguf_info.feed_forward_length
if gguf_info.rope_dimension_count:
hyperparams["rope_dimension_count"] = gguf_info.rope_dimension_count
return {"hyperparameter": hyperparams} if hyperparams else {}
def map_to_metadata(gguf_info: GGUFModelInfo) -> Dict[str, Any]:
metadata = _map_core_fields(gguf_info)
metadata |= _map_supplementary_fields(gguf_info)
metadata |= _map_quantization(gguf_info)
metadata |= _map_hyperparameters(gguf_info)
# TODO: add chat template field mapping
return metadata
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