mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 19:34:35 +00:00
444 lines
16 KiB
Python
444 lines
16 KiB
Python
from __future__ import annotations
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import os
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import shutil
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import struct
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import tempfile
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from enum import Enum, auto
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from io import BufferedWriter
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from typing import IO, Any, Sequence
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import numpy as np
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from .constants import (
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GGUF_DEFAULT_ALIGNMENT,
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GGUF_MAGIC,
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GGUF_VERSION,
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GGMLQuantizationType,
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GGUFEndian,
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GGUFValueType,
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Keys,
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RopeScalingType,
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PoolingType,
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TokenType,
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)
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class WriterState(Enum):
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EMPTY = auto()
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HEADER = auto()
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KV_DATA = auto()
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TI_DATA = auto()
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class GGUFWriter:
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fout: BufferedWriter
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temp_file: tempfile.SpooledTemporaryFile[bytes] | None
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tensors: list[np.ndarray[Any, Any]]
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_simple_value_packing = {
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GGUFValueType.UINT8: "B",
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GGUFValueType.INT8: "b",
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GGUFValueType.UINT16: "H",
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GGUFValueType.INT16: "h",
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GGUFValueType.UINT32: "I",
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GGUFValueType.INT32: "i",
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GGUFValueType.FLOAT32: "f",
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GGUFValueType.UINT64: "Q",
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GGUFValueType.INT64: "q",
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GGUFValueType.FLOAT64: "d",
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GGUFValueType.BOOL: "?",
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}
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def __init__(
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self, path: os.PathLike[str] | str, arch: str, use_temp_file: bool = True,
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endianess: GGUFEndian = GGUFEndian.LITTLE,
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):
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self.fout = open(path, "wb")
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self.arch = arch
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self.endianess = endianess
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self.offset_tensor = 0
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self.data_alignment = GGUF_DEFAULT_ALIGNMENT
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self.kv_data = bytearray()
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self.kv_data_count = 0
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self.ti_data = bytearray()
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self.ti_data_count = 0
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self.use_temp_file = use_temp_file
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self.temp_file = None
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self.tensors = []
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print("gguf: This GGUF file is for {0} Endian only".format(
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"Big" if self.endianess == GGUFEndian.BIG else "Little",
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))
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self.state = WriterState.EMPTY
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self.add_architecture()
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def write_header_to_file(self) -> None:
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if self.state is not WriterState.EMPTY:
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raise ValueError(f'Expected output file to be empty, got {self.state}')
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self._write_packed("<I", GGUF_MAGIC, skip_pack_prefix = True)
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self._write_packed("I", GGUF_VERSION)
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self._write_packed("Q", self.ti_data_count)
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self._write_packed("Q", self.kv_data_count)
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self.flush()
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self.state = WriterState.HEADER
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def write_kv_data_to_file(self) -> None:
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if self.state is not WriterState.HEADER:
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raise ValueError(f'Expected output file to contain the header, got {self.state}')
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self.fout.write(self.kv_data)
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self.flush()
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self.state = WriterState.KV_DATA
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def write_ti_data_to_file(self) -> None:
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if self.state is not WriterState.KV_DATA:
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raise ValueError(f'Expected output file to contain KV data, got {self.state}')
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self.fout.write(self.ti_data)
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self.flush()
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self.state = WriterState.TI_DATA
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def add_key(self, key: str) -> None:
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self.add_val(key, GGUFValueType.STRING, add_vtype=False)
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def add_uint8(self, key: str, val: int) -> None:
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self.add_key(key)
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self.add_val(val, GGUFValueType.UINT8)
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def add_int8(self, key: str, val: int) -> None:
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self.add_key(key)
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self.add_val(val, GGUFValueType.INT8)
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def add_uint16(self, key: str, val: int) -> None:
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self.add_key(key)
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self.add_val(val, GGUFValueType.UINT16)
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def add_int16(self, key: str, val: int) -> None:
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self.add_key(key)
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self.add_val(val, GGUFValueType.INT16)
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def add_uint32(self, key: str, val: int) -> None:
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self.add_key(key)
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self.add_val(val, GGUFValueType.UINT32)
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def add_int32(self, key: str, val: int) -> None:
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self.add_key(key)
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self.add_val(val, GGUFValueType.INT32)
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def add_float32(self, key: str, val: float) -> None:
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self.add_key(key)
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self.add_val(val, GGUFValueType.FLOAT32)
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def add_uint64(self, key: str, val: int) -> None:
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self.add_key(key)
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self.add_val(val, GGUFValueType.UINT64)
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def add_int64(self, key: str, val: int) -> None:
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self.add_key(key)
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self.add_val(val, GGUFValueType.INT64)
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def add_float64(self, key: str, val: float) -> None:
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self.add_key(key)
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self.add_val(val, GGUFValueType.FLOAT64)
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def add_bool(self, key: str, val: bool) -> None:
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self.add_key(key)
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self.add_val(val, GGUFValueType.BOOL)
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def add_string(self, key: str, val: str) -> None:
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if not val:
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return
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self.add_key(key)
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self.add_val(val, GGUFValueType.STRING)
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def add_array(self, key: str, val: Sequence[Any]) -> None:
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if not isinstance(val, Sequence):
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raise ValueError("Value must be a sequence for array type")
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self.add_key(key)
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self.add_val(val, GGUFValueType.ARRAY)
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def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True) -> None:
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if vtype is None:
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vtype = GGUFValueType.get_type(val)
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if add_vtype:
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self.kv_data += self._pack("I", vtype)
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self.kv_data_count += 1
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pack_fmt = self._simple_value_packing.get(vtype)
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if pack_fmt is not None:
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self.kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL)
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elif vtype == GGUFValueType.STRING:
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encoded_val = val.encode("utf8") if isinstance(val, str) else val
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self.kv_data += self._pack("Q", len(encoded_val))
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self.kv_data += encoded_val
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elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val:
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ltype = GGUFValueType.get_type(val[0])
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if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]):
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raise ValueError("All items in a GGUF array should be of the same type")
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self.kv_data += self._pack("I", ltype)
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self.kv_data += self._pack("Q", len(val))
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for item in val:
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self.add_val(item, add_vtype=False)
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else:
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raise ValueError("Invalid GGUF metadata value type or value")
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@staticmethod
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def ggml_pad(x: int, n: int) -> int:
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return ((x + n - 1) // n) * n
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def add_tensor_info(
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self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32],
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tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None,
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) -> None:
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if self.state is not WriterState.EMPTY:
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raise ValueError(f'Expected output file to be empty, got {self.state}')
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if raw_dtype is None and tensor_dtype not in (np.float32, np.float16):
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raise ValueError("Only F32 and F16 tensors are supported for now")
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encoded_name = name.encode("utf8")
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self.ti_data += self._pack("Q", len(encoded_name))
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self.ti_data += encoded_name
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n_dims = len(tensor_shape)
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self.ti_data += self._pack("I", n_dims)
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for i in range(n_dims):
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self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i])
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if raw_dtype is None:
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dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
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else:
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dtype = raw_dtype
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self.ti_data += self._pack("I", dtype)
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self.ti_data += self._pack("Q", self.offset_tensor)
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self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
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self.ti_data_count += 1
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def add_tensor(
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self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None,
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raw_dtype: GGMLQuantizationType | None = None,
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) -> None:
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if self.endianess == GGUFEndian.BIG:
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tensor.byteswap(inplace=True)
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if self.use_temp_file and self.temp_file is None:
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fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256 * 1024 * 1024)
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fp.seek(0)
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self.temp_file = fp
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shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape
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self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype)
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if self.temp_file is None:
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self.tensors.append(tensor)
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return
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tensor.tofile(self.temp_file)
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self.write_padding(self.temp_file, tensor.nbytes)
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def write_padding(self, fp: IO[bytes], n: int, align: int | None = None) -> None:
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pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n
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if pad != 0:
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fp.write(bytes([0] * pad))
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def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None:
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if self.state is not WriterState.TI_DATA:
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raise ValueError(f'Expected output file to contain tensor info, got {self.state}')
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if self.endianess == GGUFEndian.BIG:
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tensor.byteswap(inplace=True)
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self.write_padding(self.fout, self.fout.tell())
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tensor.tofile(self.fout)
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self.write_padding(self.fout, tensor.nbytes)
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def write_tensors_to_file(self) -> None:
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self.write_ti_data_to_file()
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self.write_padding(self.fout, self.fout.tell())
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if self.temp_file is None:
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while True:
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try:
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tensor = self.tensors.pop(0)
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except IndexError:
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break
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tensor.tofile(self.fout)
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self.write_padding(self.fout, tensor.nbytes)
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return
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self.temp_file.seek(0)
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shutil.copyfileobj(self.temp_file, self.fout)
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self.flush()
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self.temp_file.close()
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def flush(self) -> None:
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self.fout.flush()
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def close(self) -> None:
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self.fout.close()
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def add_architecture(self) -> None:
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self.add_string(Keys.General.ARCHITECTURE, self.arch)
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def add_author(self, author: str) -> None:
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self.add_string(Keys.General.AUTHOR, author)
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def add_tensor_data_layout(self, layout: str) -> None:
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self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
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def add_url(self, url: str) -> None:
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self.add_string(Keys.General.URL, url)
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def add_description(self, description: str) -> None:
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self.add_string(Keys.General.DESCRIPTION, description)
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def add_source_url(self, url: str) -> None:
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self.add_string(Keys.General.SOURCE_URL, url)
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def add_source_hf_repo(self, repo: str) -> None:
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self.add_string(Keys.General.SOURCE_HF_REPO, repo)
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def add_file_type(self, ftype: int) -> None:
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self.add_uint32(Keys.General.FILE_TYPE, ftype)
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def add_name(self, name: str) -> None:
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self.add_string(Keys.General.NAME, name)
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def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None:
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self.add_uint32(
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Keys.General.QUANTIZATION_VERSION, quantization_version)
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def add_custom_alignment(self, alignment: int) -> None:
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self.data_alignment = alignment
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self.add_uint32(Keys.General.ALIGNMENT, alignment)
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def add_context_length(self, length: int) -> None:
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self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length)
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def add_embedding_length(self, length: int) -> None:
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self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length)
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def add_block_count(self, length: int) -> None:
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self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length)
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def add_feed_forward_length(self, length: int) -> None:
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self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
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def add_parallel_residual(self, use: bool) -> None:
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self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
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def add_head_count(self, count: int) -> None:
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self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
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def add_head_count_kv(self, count: int) -> None:
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self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count)
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def add_key_length(self, length: int) -> None:
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self.add_uint32(Keys.Attention.KEY_LENGTH.format(arch=self.arch), length)
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def add_value_length(self, length: int) -> None:
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self.add_uint32(Keys.Attention.VALUE_LENGTH.format(arch=self.arch), length)
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def add_max_alibi_bias(self, bias: float) -> None:
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self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias)
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def add_clamp_kqv(self, value: float) -> None:
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self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
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def add_expert_count(self, count: int) -> None:
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self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count)
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def add_expert_used_count(self, count: int) -> None:
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self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count)
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def add_layer_norm_eps(self, value: float) -> None:
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self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)
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def add_layer_norm_rms_eps(self, value: float) -> None:
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self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value)
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def add_causal_attention(self, value: bool) -> None:
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self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)
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def add_pooling_type(self, value: PoolingType) -> None:
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self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
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def add_rope_dimension_count(self, count: int) -> None:
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self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count)
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def add_rope_freq_base(self, value: float) -> None:
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self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value)
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def add_rope_scaling_type(self, value: RopeScalingType) -> None:
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self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value)
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def add_rope_scaling_factor(self, value: float) -> None:
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self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value)
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def add_rope_scaling_orig_ctx_len(self, value: int) -> None:
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self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value)
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def add_rope_scaling_finetuned(self, value: bool) -> None:
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self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value)
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def add_tokenizer_model(self, model: str) -> None:
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self.add_string(Keys.Tokenizer.MODEL, model)
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def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
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self.add_array(Keys.Tokenizer.LIST, tokens)
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def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
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self.add_array(Keys.Tokenizer.MERGES, merges)
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def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None:
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self.add_array(Keys.Tokenizer.TOKEN_TYPE, types)
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def add_token_type_count(self, value: int) -> None:
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self.add_uint32(Keys.Tokenizer.TOKEN_TYPE_COUNT, value)
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def add_token_scores(self, scores: Sequence[float]) -> None:
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self.add_array(Keys.Tokenizer.SCORES, scores)
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def add_bos_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.BOS_ID, id)
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def add_eos_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.EOS_ID, id)
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def add_unk_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.UNK_ID, id)
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def add_sep_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.SEP_ID, id)
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def add_pad_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.PAD_ID, id)
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def add_cls_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.CLS_ID, id)
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def add_mask_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.MASK_ID, id)
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def add_add_bos_token(self, value: bool) -> None:
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self.add_bool(Keys.Tokenizer.ADD_BOS, value)
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def add_add_eos_token(self, value: bool) -> None:
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self.add_bool(Keys.Tokenizer.ADD_EOS, value)
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def add_add_space_prefix(self, value: bool) -> None:
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self.add_bool(Keys.Tokenizer.ADD_PREFIX, value)
|
|
|
|
def add_chat_template(self, value: str) -> None:
|
|
self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value)
|
|
|
|
def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes:
|
|
pack_prefix = ''
|
|
if not skip_pack_prefix:
|
|
pack_prefix = '<' if self.endianess == GGUFEndian.LITTLE else '>'
|
|
return struct.pack(f'{pack_prefix}{fmt}', value)
|
|
|
|
def _write_packed(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> None:
|
|
self.fout.write(self._pack(fmt, value, skip_pack_prefix))
|