# I - same as host, S - swapped
byte_order: Literal['I'] | Literal['S'] = 'I'
alignment: int = GGUF_DEFAULT_ALIGNMENT
+ data_offset: int
# Note: Internal helper, API may change.
gguf_scalar_to_np: dict[GGUFValueType, type[np.generic]] = {
def __init__(self, path: os.PathLike[str] | str, mode: Literal['r'] | Literal['r+'] | Literal['c'] = 'r'):
self.data = np.memmap(path, mode = mode)
offs = 0
+
+ # Check for GGUF magic
if self._get(offs, np.uint32, override_order = '<')[0] != GGUF_MAGIC:
raise ValueError('GGUF magic invalid')
offs += 4
+
+ # Check GGUF version
temp_version = self._get(offs, np.uint32)
if temp_version[0] & 65535 == 0:
# If we get 0 here that means it's (probably) a GGUF file created for
self.fields: OrderedDict[str, ReaderField] = OrderedDict()
self.tensors: list[ReaderTensor] = []
offs += self._push_field(ReaderField(offs, 'GGUF.version', [temp_version], [0], [GGUFValueType.UINT32]))
+
+ # Check tensor count and kv count
temp_counts = self._get(offs, np.uint64, 2)
offs += self._push_field(ReaderField(offs, 'GGUF.tensor_count', [temp_counts[:1]], [0], [GGUFValueType.UINT64]))
offs += self._push_field(ReaderField(offs, 'GGUF.kv_count', [temp_counts[1:]], [0], [GGUFValueType.UINT64]))
tensor_count, kv_count = temp_counts
offs = self._build_fields(offs, kv_count)
- offs, tensors_fields = self._build_tensors_fields(offs, tensor_count)
+
+ # Build Tensor Info Fields
+ offs, tensors_fields = self._build_tensor_info(offs, tensor_count)
new_align = self.fields.get('general.alignment')
if new_align is not None:
if new_align.types != [GGUFValueType.UINT32]:
padding = offs % self.alignment
if padding != 0:
offs += self.alignment - padding
+ self.data_offset = offs
self._build_tensors(offs, tensors_fields)
_DT = TypeVar('_DT', bound = npt.DTypeLike)
# We can't deal with this one.
raise ValueError('Unknown/unhandled field type {gtype}')
- def _get_tensor(self, orig_offs: int) -> ReaderField:
+ def _get_tensor_info_field(self, orig_offs: int) -> ReaderField:
offs = orig_offs
+
+ # Get Tensor Name
name_len, name_data = self._get_str(offs)
offs += int(name_len.nbytes + name_data.nbytes)
+
+ # Get Tensor Dimensions Count
n_dims = self._get(offs, np.uint32)
offs += int(n_dims.nbytes)
+
+ # Get Tensor Dimension Array
dims = self._get(offs, np.uint64, n_dims[0])
offs += int(dims.nbytes)
+
+ # Get Tensor Encoding Scheme Type
raw_dtype = self._get(offs, np.uint32)
offs += int(raw_dtype.nbytes)
+
+ # Get Tensor Offset
offset_tensor = self._get(offs, np.uint64)
offs += int(offset_tensor.nbytes)
+
return ReaderField(
orig_offs,
str(bytes(name_data), encoding = 'utf-8'),
offs += field_size
return offs
- def _build_tensors_fields(self, offs: int, count: int) -> tuple[int, list[ReaderField]]:
+ def _build_tensor_info(self, offs: int, count: int) -> tuple[int, list[ReaderField]]:
tensor_fields = []
for _ in range(count):
- field = self._get_tensor(offs)
+ field = self._get_tensor_info_field(offs)
offs += sum(int(part.nbytes) for part in field.parts)
tensor_fields.append(field)
return offs, tensor_fields
markdown_content += "\n"
+ markdown_content += "### Tensor Data Offset\n"
+ markdown_content += '\n'
+ markdown_content += 'This table contains the offset and data segment relative to start of file\n'
+ markdown_content += '\n'
+
+ tensor_mapping_table: list[dict[str, str | int]] = []
+ for key, tensor in enumerate(reader.tensors):
+ data_offset_pretty = '{0:#16x}'.format(tensor.data_offset)
+ data_size_pretty = '{0:#16x}'.format(tensor.n_bytes)
+ tensor_mapping_table.append({"t_id":key, "layer_name":tensor.name, "data_offset":data_offset_pretty, "data_size":data_size_pretty})
+
+ tensors_mapping_table_header_map = [
+ {'key_name':'t_id', 'header_name':'T_ID', 'align':'right'},
+ {'key_name':'layer_name', 'header_name':'Tensor Layer Name', 'align':'left'},
+ {'key_name':'data_offset', 'header_name':'Data Offset (B)', 'align':'right'},
+ {'key_name':'data_size', 'header_name':'Data Size (B)', 'align':'right'},
+ ]
+
+ markdown_content += markdown_table_with_alignment_support(tensors_mapping_table_header_map, tensor_mapping_table)
+ markdown_content += "\n"
+
for group in tensor_prefix_order:
tensors = tensor_groups[group]
group_elements = sum(tensor.n_elements for tensor in tensors)
parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata")
parser.add_argument("--json", action="store_true", help="Produce JSON output")
parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)")
+ parser.add_argument("--data-offset", action="store_true", help="Start of data offset")
+ parser.add_argument("--data-alignment", action="store_true", help="Data alignment applied globally to data field")
parser.add_argument("--markdown", action="store_true", help="Produce markdown output")
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
- if not args.json and not args.markdown:
+ if not args.json and not args.markdown and not args.data_offset and not args.data_alignment:
logger.info(f'* Loading: {args.model}')
reader = GGUFReader(args.model, 'r')
dump_metadata_json(reader, args)
elif args.markdown:
dump_markdown_metadata(reader, args)
+ elif args.data_offset:
+ print(reader.data_offset) # noqa: NP100
+ elif args.data_alignment:
+ print(reader.alignment) # noqa: NP100
else:
dump_metadata(reader, args)