mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 19:34:35 +00:00
f98eb31c51
* convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
589 lines
22 KiB
Python
589 lines
22 KiB
Python
from __future__ import annotations
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import logging
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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, Callable, Sequence, Mapping
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from string import ascii_letters, digits
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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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logger = logging.getLogger(__name__)
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class LazyTensor:
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data: Callable[[], np.ndarray[Any, Any]]
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# to avoid too deep recursion
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functions: list[Callable[[np.ndarray[Any, Any]], np.ndarray[Any, Any]]]
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dtype: np.dtype[Any]
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shape: tuple[int, ...]
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def __init__(self, data: Callable[[], np.ndarray[Any, Any]], *, dtype: type, shape: tuple[int, ...]):
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self.data = data
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self.functions = []
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self.dtype = np.dtype(dtype)
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self.shape = shape
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def astype(self, dtype: type, **kwargs) -> LazyTensor:
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self.functions.append(lambda n: n.astype(dtype, **kwargs))
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self.dtype = np.dtype(dtype)
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return self
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@property
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def nbytes(self) -> int:
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size = 1
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for n in self.shape:
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size *= n
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return size * self.dtype.itemsize
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def tofile(self, *args, **kwargs) -> None:
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data = self.data()
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for f in self.functions:
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data = f(data)
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assert data.shape == self.shape
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assert data.dtype == self.dtype
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assert data.nbytes == self.nbytes
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self.functions = []
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self.data = lambda: data
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data.tofile(*args, **kwargs)
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def byteswap(self, *args, **kwargs) -> LazyTensor:
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self.functions.append(lambda n: n.byteswap(*args, **kwargs))
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return self
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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] | LazyTensor]
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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.ti_names = set()
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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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logger.info("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("utf-8") 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 name in self.ti_names:
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raise ValueError(f'Duplicated tensor name {name}')
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self.ti_names.add(name)
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encoded_name = name.encode("utf-8")
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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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if tensor_dtype == np.float16:
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dtype = GGMLQuantizationType.F16
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elif tensor_dtype == np.float32:
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dtype = GGMLQuantizationType.F32
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elif tensor_dtype == np.float64:
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dtype = GGMLQuantizationType.F64
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elif tensor_dtype == np.int8:
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dtype = GGMLQuantizationType.I8
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elif tensor_dtype == np.int16:
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dtype = GGMLQuantizationType.I16
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elif tensor_dtype == np.int32:
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dtype = GGMLQuantizationType.I32
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elif tensor_dtype == np.int64:
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dtype = GGMLQuantizationType.I64
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else:
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raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now")
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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] | LazyTensor, 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] | LazyTensor) -> 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, *, progress: bool = False) -> 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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self.tensors.reverse() # to pop from the "beginning" in constant time
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if progress:
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from tqdm import tqdm
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total_bytes = sum(t.nbytes for t in self.tensors)
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bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True)
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while True:
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try:
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tensor = self.tensors.pop()
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except IndexError:
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break
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tensor.tofile(self.fout)
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bar.update(tensor.nbytes)
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self.write_padding(self.fout, tensor.nbytes)
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return
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while True:
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try:
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tensor = self.tensors.pop()
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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_version(self, version: str) -> None:
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self.add_string(Keys.General.VERSION, version)
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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_licence(self, licence: str) -> None:
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self.add_string(Keys.General.LICENSE, licence)
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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_vocab_size(self, size: int) -> None:
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self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size)
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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_logit_scale(self, value: float) -> None:
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self.add_float32(Keys.LLM.LOGIT_SCALE.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_ssm_conv_kernel(self, value: int) -> None:
|
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self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value)
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def add_ssm_inner_size(self, value: int) -> None:
|
|
self.add_uint32(Keys.SSM.INNER_SIZE.format(arch=self.arch), value)
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def add_ssm_state_size(self, value: int) -> None:
|
|
self.add_uint32(Keys.SSM.STATE_SIZE.format(arch=self.arch), value)
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|
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def add_ssm_time_step_rank(self, value: int) -> None:
|
|
self.add_uint32(Keys.SSM.TIME_STEP_RANK.format(arch=self.arch), value)
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def add_tokenizer_model(self, model: str) -> None:
|
|
self.add_string(Keys.Tokenizer.MODEL, model)
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|
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def add_tokenizer_pre(self, pre: str) -> None:
|
|
self.add_string(Keys.Tokenizer.PRE, pre)
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|
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def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
|
|
self.add_array(Keys.Tokenizer.LIST, tokens)
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|
|
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def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
|
|
self.add_array(Keys.Tokenizer.MERGES, merges)
|
|
|
|
def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None:
|
|
self.add_array(Keys.Tokenizer.TOKEN_TYPE, types)
|
|
|
|
def add_token_type_count(self, value: int) -> None:
|
|
self.add_uint32(Keys.Tokenizer.TOKEN_TYPE_COUNT, value)
|
|
|
|
def add_token_scores(self, scores: Sequence[float]) -> None:
|
|
self.add_array(Keys.Tokenizer.SCORES, scores)
|
|
|
|
def add_bos_token_id(self, id: int) -> None:
|
|
self.add_uint32(Keys.Tokenizer.BOS_ID, id)
|
|
|
|
def add_eos_token_id(self, id: int) -> None:
|
|
self.add_uint32(Keys.Tokenizer.EOS_ID, id)
|
|
|
|
def add_unk_token_id(self, id: int) -> None:
|
|
self.add_uint32(Keys.Tokenizer.UNK_ID, id)
|
|
|
|
def add_sep_token_id(self, id: int) -> None:
|
|
self.add_uint32(Keys.Tokenizer.SEP_ID, id)
|
|
|
|
def add_pad_token_id(self, id: int) -> None:
|
|
self.add_uint32(Keys.Tokenizer.PAD_ID, id)
|
|
|
|
def add_cls_token_id(self, id: int) -> None:
|
|
self.add_uint32(Keys.Tokenizer.CLS_ID, id)
|
|
|
|
def add_mask_token_id(self, id: int) -> None:
|
|
self.add_uint32(Keys.Tokenizer.MASK_ID, id)
|
|
|
|
def add_add_bos_token(self, value: bool) -> None:
|
|
self.add_bool(Keys.Tokenizer.ADD_BOS, value)
|
|
|
|
def add_add_eos_token(self, value: bool) -> None:
|
|
self.add_bool(Keys.Tokenizer.ADD_EOS, value)
|
|
|
|
def add_add_space_prefix(self, value: bool) -> None:
|
|
self.add_bool(Keys.Tokenizer.ADD_PREFIX, value)
|
|
|
|
def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None:
|
|
if not isinstance(value, str):
|
|
template_default = None
|
|
template_names = set()
|
|
|
|
for choice in value:
|
|
name = choice.get('name', '')
|
|
template = choice.get('template')
|
|
|
|
# Allowing non-alphanumerical characters in template name is probably not a good idea, so filter it
|
|
name = ''.join((c if c in ascii_letters + digits else '_' for c in name))
|
|
|
|
if name and template is not None:
|
|
if name == 'default':
|
|
template_default = template
|
|
else:
|
|
template_names.add(name)
|
|
self.add_string(Keys.Tokenizer.CHAT_TEMPLATE_N.format(name=name), template)
|
|
|
|
if template_names:
|
|
self.add_array(Keys.Tokenizer.CHAT_TEMPLATES, list(template_names))
|
|
|
|
if template_default is None:
|
|
return
|
|
|
|
value = template_default
|
|
|
|
self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value)
|
|
|
|
def add_prefix_token_id(self, id: int) -> None:
|
|
self.add_uint32(Keys.Tokenizer.PREFIX_ID, id)
|
|
|
|
def add_suffix_token_id(self, id: int) -> None:
|
|
self.add_uint32(Keys.Tokenizer.SUFFIX_ID, id)
|
|
|
|
def add_middle_token_id(self, id: int) -> None:
|
|
self.add_uint32(Keys.Tokenizer.MIDDLE_ID, id)
|
|
|
|
def add_eot_token_id(self, id: int) -> None:
|
|
self.add_uint32(Keys.Tokenizer.EOT_ID, id)
|
|
|
|
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))
|