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
import sys
from collections import OrderedDict
from typing import Any, Literal, NamedTuple, TypeVar, Union

import numpy as np
import numpy.typing as npt

from .quants import quant_shape_to_byte_shape

if __name__ == "__main__":
    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,
    GGUFEndian,
)

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] = []

    def contents(self, index_or_slice: int | slice = slice(None)) -> Any:
        if self.types:
            to_string = lambda x: str(x.tobytes(), encoding='utf-8') # noqa: E731
            main_type = self.types[0]

            if main_type == GGUFValueType.ARRAY:
                sub_type = self.types[-1]

                if sub_type == GGUFValueType.STRING:
                    indices = self.data[index_or_slice]

                    if isinstance(index_or_slice, int):
                        return to_string(self.parts[indices]) # type: ignore
                    else:
                        return [to_string(self.parts[idx]) for idx in indices] # type: ignore
                else:
                    # FIXME: When/if _get_field_parts() support multi-dimensional arrays, this must do so too

                    # Check if it's unsafe to perform slice optimization on data
                    # if any(True for idx in self.data if len(self.parts[idx]) != 1):
                    #     optim_slice = slice(None)
                    # else:
                    #     optim_slice = index_or_slice
                    #     index_or_slice = slice(None)

                    # if isinstance(optim_slice, int):
                    #     return self.parts[self.data[optim_slice]].tolist()[0]
                    # else:
                    #     return [pv for idx in self.data[optim_slice] for pv in self.parts[idx].tolist()][index_or_slice]

                    if isinstance(index_or_slice, int):
                        return self.parts[self.data[index_or_slice]].tolist()[0]
                    else:
                        return [pv for idx in self.data[index_or_slice] for pv in self.parts[idx].tolist()]

            if main_type == GGUFValueType.STRING:
                return to_string(self.parts[-1])
            else:
                return self.parts[-1].tolist()[0]

        return None


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', '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]] = {
        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', 'r+', '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
            # the opposite byte order of the machine this script is running on.
            self.byte_order = 'S'
            temp_version = temp_version.view(temp_version.dtype.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')
        if sys.byteorder == "little":
            # Host is little endian
            host_endian = GGUFEndian.LITTLE
            swapped_endian = GGUFEndian.BIG
        else:
            # Sorry PDP or other weird systems that don't use BE or LE.
            host_endian = GGUFEndian.BIG
            swapped_endian = GGUFEndian.LITTLE
        self.endianess = swapped_endian if self.byte_order == "S" else host_endian
        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)

        # 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]:
                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.data_offset = offs
        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', 'S', '<'] = None,
    ) -> npt.NDArray[Any]:
        count = int(count)
        itemsize = int(np.empty([], dtype = dtype).itemsize)
        end_offs = offset + itemsize * count
        arr = self.data[offset:end_offs].view(dtype=dtype)[:count]
        return arr.view(arr.dtype.newbyteorder(self.byte_order if override_order is None else override_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] = []
            # FIXME: Handle multi-dimensional arrays properly instead of flattening
            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(f'Unknown/unhandled field type {gtype}')

    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'),
            [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_tensor_info(self, offs: int, count: int) -> tuple[int, list[ReaderField]]:
        tensor_fields = []
        for _ in range(count):
            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

    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))
            np_dims = tuple(reversed(dims.tolist()))
            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
                np_dims = quant_shape_to_byte_shape(np_dims, ggml_type)
            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).reshape(np_dims),
                field = field,
            ))
        self.tensors = tensors