llama.cpp/gguf-py/gguf/gguf_reader.py

#
# GGUF file reading/modification support. For API usage information,
# please see the files scripts/ for some fairly simple examples.
#
from __future__ import annotations

import logging
import os
from collections import OrderedDict
from typing import Any, Literal, NamedTuple, TypeVar, Union

import numpy as np
import numpy.typing as npt

if __name__ == "__main__":
    import sys
    from pathlib import Path

    # Allow running file in package as a script.
    sys.path.insert(0, str(Path(__file__).parent.parent))

from gguf.constants import (
    GGML_QUANT_SIZES,
    GGUF_DEFAULT_ALIGNMENT,
    GGUF_MAGIC,
    GGUF_VERSION,
    GGMLQuantizationType,
    GGUFValueType,
)

logger = logging.getLogger(__name__)

READER_SUPPORTED_VERSIONS = [2, GGUF_VERSION]


class ReaderField(NamedTuple):
    # Offset to start of this field.
    offset: int

    # Name of the field (not necessarily from file data).
    name: str

    # Data parts. Some types have multiple components, such as strings
    # that consist of a length followed by the string data.
    parts: list[npt.NDArray[Any]] = []

    # Indexes into parts that we can call the actual data. For example
    # an array of strings will be populated with indexes to the actual
    # string data.
    data: list[int] = [-1]

    types: list[GGUFValueType] = []


class ReaderTensor(NamedTuple):
    name: str
    tensor_type: GGMLQuantizationType
    shape: npt.NDArray[np.uint32]
    n_elements: int
    n_bytes: int
    data_offset: int
    data: npt.NDArray[Any]
    field: ReaderField


class GGUFReader:
    # I - same as host, S - swapped
    byte_order: Literal['I'] | Literal['S'] = 'I'
    alignment: int = GGUF_DEFAULT_ALIGNMENT

    # Note: Internal helper, API may change.
    gguf_scalar_to_np: dict[GGUFValueType, type[np.generic]] = {
        GGUFValueType.UINT8:   np.uint8,
        GGUFValueType.INT8:    np.int8,
        GGUFValueType.UINT16:  np.uint16,
        GGUFValueType.INT16:   np.int16,
        GGUFValueType.UINT32:  np.uint32,
        GGUFValueType.INT32:   np.int32,
        GGUFValueType.FLOAT32: np.float32,
        GGUFValueType.UINT64:  np.uint64,
        GGUFValueType.INT64:   np.int64,
        GGUFValueType.FLOAT64: np.float64,
        GGUFValueType.BOOL:    np.bool_,
    }

    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
        if self._get(offs, np.uint32, override_order = '<')[0] != GGUF_MAGIC:
            raise ValueError('GGUF magic invalid')
        offs += 4
        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
            # the opposite byte order of the machine this script is running on.
            self.byte_order = 'S'
            temp_version = temp_version.newbyteorder(self.byte_order)
        version = temp_version[0]
        if version not in READER_SUPPORTED_VERSIONS:
            raise ValueError(f'Sorry, file appears to be version {version} which we cannot handle')
        self.fields: OrderedDict[str, ReaderField] = OrderedDict()
        self.tensors: list[ReaderTensor] = []
        offs += self._push_field(ReaderField(offs, 'GGUF.version', [temp_version], [0], [GGUFValueType.UINT32]))
        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)
        new_align = self.fields.get('general.alignment')
        if new_align is not None:
            if new_align.types != [GGUFValueType.UINT32]:
                raise ValueError('Bad type for general.alignment field')
            self.alignment = new_align.parts[-1][0]
        padding = offs % self.alignment
        if padding != 0:
            offs += self.alignment - padding
        self._build_tensors(offs, tensors_fields)

    _DT = TypeVar('_DT', bound = npt.DTypeLike)

    # Fetch a key/value metadata field by key.
    def get_field(self, key: str) -> Union[ReaderField, None]:
        return self.fields.get(key, None)

    # Fetch a tensor from the list by index.
    def get_tensor(self, idx: int) -> ReaderTensor:
        return self.tensors[idx]

    def _get(
        self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I'] | Literal['S'] | Literal['<'] = None,
    ) -> npt.NDArray[Any]:
        count = int(count)
        itemsize = int(np.empty([], dtype = dtype).itemsize)
        end_offs = offset + itemsize * count
        return (
            self.data[offset:end_offs]
            .view(dtype = dtype)[:count]
            .newbyteorder(override_order or self.byte_order)
        )

    def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int:
        if field.name in self.fields:
            # TODO: add option to generate error on duplicate keys
            # raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}')

            logger.warning(f'Duplicate key {field.name} at offset {field.offset}')
            self.fields[field.name + '_{}'.format(field.offset)] = field
        else:
            self.fields[field.name] = field
        return 0 if skip_sum else sum(int(part.nbytes) for part in field.parts)

    def _get_str(self, offset: int) -> tuple[npt.NDArray[np.uint64], npt.NDArray[np.uint8]]:
        slen = self._get(offset, np.uint64)
        return slen, self._get(offset + 8, np.uint8, slen[0])

    def _get_field_parts(
        self, orig_offs: int, raw_type: int,
    ) -> tuple[int, list[npt.NDArray[Any]], list[int], list[GGUFValueType]]:
        offs = orig_offs
        types: list[GGUFValueType] = []
        gtype = GGUFValueType(raw_type)
        types.append(gtype)
        # Handle strings.
        if gtype == GGUFValueType.STRING:
            sparts: list[npt.NDArray[Any]] = list(self._get_str(offs))
            size = sum(int(part.nbytes) for part in sparts)
            return size, sparts, [1], types
        # Check if it's a simple scalar type.
        nptype = self.gguf_scalar_to_np.get(gtype)
        if nptype is not None:
            val = self._get(offs, nptype)
            return int(val.nbytes), [val], [0], types
        # Handle arrays.
        if gtype == GGUFValueType.ARRAY:
            raw_itype = self._get(offs, np.uint32)
            offs += int(raw_itype.nbytes)
            alen = self._get(offs, np.uint64)
            offs += int(alen.nbytes)
            aparts: list[npt.NDArray[Any]] = [raw_itype, alen]
            data_idxs: list[int] = []
            for idx in range(alen[0]):
                curr_size, curr_parts, curr_idxs, curr_types = self._get_field_parts(offs, raw_itype[0])
                if idx == 0:
                    types += curr_types
                idxs_offs = len(aparts)
                aparts += curr_parts
                data_idxs += (idx + idxs_offs for idx in curr_idxs)
                offs += curr_size
            return offs - orig_offs, aparts, data_idxs, types
        # We can't deal with this one.
        raise ValueError('Unknown/unhandled field type {gtype}')

    def _get_tensor(self, orig_offs: int) -> ReaderField:
        offs = orig_offs
        name_len, name_data = self._get_str(offs)
        offs += int(name_len.nbytes + name_data.nbytes)
        n_dims = self._get(offs, np.uint32)
        offs += int(n_dims.nbytes)
        dims = self._get(offs, np.uint64, n_dims[0])
        offs += int(dims.nbytes)
        raw_dtype = self._get(offs, np.uint32)
        offs += int(raw_dtype.nbytes)
        offset_tensor = self._get(offs, np.uint64)
        offs += int(offset_tensor.nbytes)
        return ReaderField(
            orig_offs,
            str(bytes(name_data), encoding = 'utf-8'),
            [name_len, name_data, n_dims, dims, raw_dtype, offset_tensor],
            [1, 3, 4, 5],
        )

    def _build_fields(self, offs: int, count: int) -> int:
        for _ in range(count):
            orig_offs = offs
            kv_klen, kv_kdata = self._get_str(offs)
            offs += int(kv_klen.nbytes + kv_kdata.nbytes)
            raw_kv_type = self._get(offs, np.uint32)
            offs += int(raw_kv_type.nbytes)
            parts: list[npt.NDArray[Any]] = [kv_klen, kv_kdata, raw_kv_type]
            idxs_offs = len(parts)
            field_size, field_parts, field_idxs, field_types = self._get_field_parts(offs, raw_kv_type[0])
            parts += field_parts
            self._push_field(ReaderField(
                orig_offs,
                str(bytes(kv_kdata), encoding = 'utf-8'),
                parts,
                [idx + idxs_offs for idx in field_idxs],
                field_types,
            ), skip_sum = True)
            offs += field_size
        return offs

    def _build_tensors_fields(self, offs: int, count: int) -> tuple[int, list[ReaderField]]:
        tensor_fields = []
        for _ in range(count):
            field = self._get_tensor(offs)
            offs += sum(int(part.nbytes) for part in field.parts)
            tensor_fields.append(field)
        return offs, tensor_fields

    def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None:
        tensors = []
        tensor_names = set() # keep track of name to prevent duplicated tensors
        for field in fields:
            _name_len, name_data, _n_dims, dims, raw_dtype, offset_tensor = field.parts
            # check if there's any tensor having same name already in the list
            tensor_name = str(bytes(name_data), encoding = 'utf-8')
            if tensor_name in tensor_names:
                raise ValueError(f'Found duplicated tensor with name {tensor_name}')
            tensor_names.add(tensor_name)
            ggml_type = GGMLQuantizationType(raw_dtype[0])
            n_elems = int(np.prod(dims))
            block_size, type_size = GGML_QUANT_SIZES[ggml_type]
            n_bytes = n_elems * type_size // block_size
            data_offs = int(start_offs + offset_tensor[0])
            item_type: npt.DTypeLike
            if ggml_type == GGMLQuantizationType.F16:
                item_count = n_elems
                item_type = np.float16
            elif ggml_type == GGMLQuantizationType.F32:
                item_count = n_elems
                item_type = np.float32
            elif ggml_type == GGMLQuantizationType.F64:
                item_count = n_elems
                item_type = np.float64
            elif ggml_type == GGMLQuantizationType.I8:
                item_count = n_elems
                item_type = np.int8
            elif ggml_type == GGMLQuantizationType.I16:
                item_count = n_elems
                item_type = np.int16
            elif ggml_type == GGMLQuantizationType.I32:
                item_count = n_elems
                item_type = np.int32
            elif ggml_type == GGMLQuantizationType.I64:
                item_count = n_elems
                item_type = np.int64
            else:
                item_count = n_bytes
                item_type = np.uint8
            tensors.append(ReaderTensor(
                name = tensor_name,
                tensor_type = ggml_type,
                shape = dims,
                n_elements = n_elems,
                n_bytes = n_bytes,
                data_offset = data_offs,
                data = self._get(data_offs, item_type, item_count),
                field = field,
            ))
        self.tensors = tensors