547 lines
22 KiB
Python
547 lines
22 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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import time
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from enum import IntEnum
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from typing import TYPE_CHECKING, Any, Sequence, Mapping
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from string import ascii_letters, digits
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from argparse import Namespace
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from math import ceil
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from collections import deque
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import numpy as np
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if TYPE_CHECKING:
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from typing_extensions import TypeAlias
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from .constants import (
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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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from .gguf_writer import GGUFWriter
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SHARD_NAME_FORMAT = "{:s}-{:05d}-of-{:05d}.gguf"
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LLM_KV_SPLIT_NO = "split.no"
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LLM_KV_SPLIT_COUNT = "split.count"
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LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"
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SplitTensorsPerFile: TypeAlias = deque[tuple[os.PathLike[str], deque[tuple[str, Any]], GGUFWriter]] # [(outfile name, [(tensor name, tensor data)] for each tensor in file, filewriter)]
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KVTempData: TypeAlias = dict[str, tuple[Any, GGUFValueType]] # {key: (value, type)}
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TensorTempData: TypeAlias = tuple[str, np.ndarray[Any, Any], GGMLQuantizationType] # (tensor name, tensor data, tensor dtype), aka LazyModel
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class SplitStyle(IntEnum):
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NONE = 0
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TENSORS = 1
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SIZE = 2
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class SplitArguments:
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split: bool
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dry_run: bool
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small_first_shard: bool
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split_max_tensors: int
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split_max_size: int
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split_style: SplitStyle
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def __init__(self, args: Namespace = None) -> None:
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self.split = args.split if args else False
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self.split_max_tensors = args.split_max_tensors if args else 0
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self.split_max_size = SplitStrategy.split_str_to_n_bytes(args.split_max_size) if args and args.split_max_size else 0
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self.dry_run = args.dry_run if args else False
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self.small_first_shard = not args.large_first_shard if args else False
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self.split_style = SplitStyle.NONE if not self.split or not args \
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else SplitStyle.TENSORS if self.split_max_tensors \
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else SplitStyle.SIZE
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class SplitStrategy(deque):
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data: SplitTensorsPerFile
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def __init__(self, fname_out: os.PathLike[str], model: list[TensorTempData], arch: str,
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split_arguments: SplitArguments, use_temp_file: bool = True, endianess: GGUFEndian = GGUFEndian.LITTLE,
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):
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super().__init__()
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if split_arguments.split_style == SplitStyle.NONE:
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self.append((fname_out, model, GGUFWriter(fname_out, arch, use_temp_file=use_temp_file, endianess=endianess)))
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elif split_arguments.split_style == SplitStyle.TENSORS:
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total_shards = ceil(len(model) / split_arguments.split_max_tensors) + split_arguments.small_first_shard
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shard_files = [fname_out.with_name(SHARD_NAME_FORMAT.format(fname_out.stem, i + 1, total_shards)) for i in range(total_shards)]
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if split_arguments.small_first_shard:
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self.append((shard_files[0], None, GGUFWriter(shard_files[0], arch, use_temp_file=use_temp_file, endianess=endianess)))
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for i, shard in enumerate(shard_files[split_arguments.small_first_shard:]):
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start = i * split_arguments.split_max_tensors
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stop = min((i + 1) * split_arguments.split_max_tensors, len(model))
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self.append((shard, model[start:stop], GGUFWriter(shard, arch, use_temp_file=use_temp_file, endianess=endianess)))
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elif split_arguments.split_style == SplitStyle.SIZE:
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shards = deque()
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# we have to determine the shards first to determine how many shards there will be in total - two passes
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for i, shard in enumerate(model):
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if i == 0:
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shards.append([shard])
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continue
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if SplitStrategy.get_tensor_size(shard[1]) + sum(SplitStrategy.get_tensor_size(t[1]) for t in shards[-1]) > split_arguments.split_max_size:
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shards.append([shard])
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else:
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shards[-1].append(shard)
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total_shards = len(shards) + split_arguments.small_first_shard
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shard_offset = 1
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if split_arguments.small_first_shard:
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outname = fname_out.with_name(SHARD_NAME_FORMAT.format(fname_out.stem, shard_offset, total_shards))
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self.append((outname, None, GGUFWriter(outname, arch, use_temp_file=use_temp_file, endianess=endianess)))
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shard_offset += 1
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for i, shard in enumerate(shards):
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outname = fname_out.with_name(SHARD_NAME_FORMAT.format(fname_out.stem, i + shard_offset, total_shards))
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self.append((outname, deque(shard), GGUFWriter(outname, arch, use_temp_file=use_temp_file, endianess=endianess)))
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@staticmethod
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def get_tensor_size(tensor) -> int:
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# we don't have the LazyTensor class here from convert.py but we can try
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try:
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return tensor.data_type.elements_to_bytes(np.prod(tensor.shape))
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except AttributeError: # numpy ndarray[Any, Any]
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return tensor.nbytes
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except: # this should never happen
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raise ValueError(f"Invalid tensor type: {type(tensor)}")
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@staticmethod
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def split_str_to_n_bytes(split_str: str) -> int:
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if split_str.endswith("K"):
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n = int(split_str[:-1]) * 1024
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elif split_str.endswith("M"):
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n = int(split_str[:-1]) * 1024 * 1024
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elif split_str.endswith("G"):
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n = int(split_str[:-1]) * 1024 * 1024 * 1024
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elif split_str.isnumeric():
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n = int(split_str)
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else:
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raise ValueError(f"Invalid split size: {split_str}, must be a number, optionally followed by K, M, or G")
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if n <= 0:
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raise ValueError(f"Invalid split size: {split_str}, must be positive")
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return n
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@staticmethod
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def format_n_bytes_to_str(num: int) -> str:
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num = float(num)
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for unit in ("", "K", "M", "G"):
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if abs(num) < 1024.0:
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return f"{num:3.1f}{unit}"
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num /= 1024.0
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return f"{num:.1f}T - over 1TB, --split recommended"
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# ideally this has most of the same signatures as GGUFWriter so it's nearly a drop-in replacement
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class GGUFManager:
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kv_data: KVTempData
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tensors: deque[TensorTempData]
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split_arguments: SplitArguments
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split_strategy: SplitStrategy
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dtype: GGMLQuantizationType
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def __init__(self, path: os.PathLike[str] | str, arch: str, split_arguments: SplitArguments,
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use_temp_file: bool = True, endianess: GGUFEndian = GGUFEndian.LITTLE
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) -> None:
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self.arch = arch
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self.path = path
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self.endianess = endianess
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self.offset_tensor = 0
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self.kv_data = {}
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self.tensors = deque()
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self.split_strategy = None
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self.total_shards = None
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self.total_tensors = None
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self.use_temp_file = use_temp_file
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self.split_arguments = split_arguments
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self.add_architecture()
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# have to consolidate because we need to know kv data count and tensor count before we can write the header
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# and we need to write tensor info before we can write metadata
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# these all kinda show up around the same places anyway so it's not a huge deal?
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def write_to_file(self, meta_only: bool = False) -> None:
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# here is the first place you can assume you have all tensors written and you can establish the size of the file - so logic goes here
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self.total_tensors = len(self.tensors)
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total_size = sum(SplitStrategy.get_tensor_size(tensor[1]) for tensor in self.tensors)
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if self.split_arguments.split_max_tensors and self.total_tensors < self.split_arguments.split_max_tensors:
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print("Model has fewer tensors than the split threshold, not splitting")
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self.split_style = SplitStyle.NONE
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if self.split_arguments.split_max_size and total_size < self.split_arguments.split_max_size:
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print("Model has smaller size than the split threshold, not splitting")
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self.split_style = SplitStyle.NONE
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self.split_strategy = SplitStrategy(self.path, self.tensors, self.arch, self.split_arguments,
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use_temp_file=self.use_temp_file, endianess=self.endianess)
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del self.tensors
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self.total_shards = len(self.split_strategy)
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# only the first shard needs all the KV data
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for key, (value, etype) in self.kv_data.items():
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self.split_strategy[0][2].add_key(key)
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self.split_strategy[0][2].add_val(value, etype)
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if self.split_arguments.split_style != SplitStyle.NONE:
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for i, (_, _, writer) in enumerate(self.split_strategy):
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writer.add_uint16(LLM_KV_SPLIT_NO, i)
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writer.add_uint16(LLM_KV_SPLIT_COUNT, self.total_shards)
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writer.add_int32(LLM_KV_SPLIT_TENSORS_COUNT, self.total_tensors)
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# metadata/vocab only can write and return here
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if meta_only:
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for i, (_, _, writer) in enumerate(self.split_strategy):
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writer.write_header_to_file()
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writer.write_kv_data_to_file()
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return
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# tensor writing code starts here
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print("\nWriting the following files:")
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for (shard_path, shard_tensors, _) in self.split_strategy:
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size = SplitStrategy.format_n_bytes_to_str(sum(SplitStrategy.get_tensor_size(t[1]) for t in shard_tensors)) if shard_tensors else "negligible - metadata only"
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print(f" {shard_path}: n_tensors = {len(shard_tensors) if shard_tensors else 0}, total_size = {size}")
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if self.split_arguments.dry_run:
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print("\nDry run, not writing files")
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# instantiating GGUFWriters creates files
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for name, _, _ in self.split_strategy:
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os.remove(name)
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return
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# run add_tensor_info, write data, then write_tensor_data - taken from convert.py
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running_total = self.total_tensors
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ct = 0
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while True:
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try:
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(_, tensors, writer) = self.split_strategy.popleft()
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except IndexError:
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break
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shard_num_tensors = len(tensors) if tensors else 0
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if tensors:
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while True:
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try:
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(name, tensor, dtype) = tensors.popleft()
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except IndexError:
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break
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writer.add_tensor(name, tensor, raw_dtype=dtype)
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print(f"Writing to shard {ct + 1}/{self.total_shards} with {shard_num_tensors}/{running_total} remaining tensors (of {self.total_tensors} total)")
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running_total -= shard_num_tensors
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writer.write_header_to_file()
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writer.write_kv_data_to_file()
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writer.write_tensors_to_file(progress=True)
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ct = ct + 1
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del tensors
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def add_uint8(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.UINT8)
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def add_int8(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.INT8)
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def add_uint16(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.UINT16)
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def add_int16(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.INT16)
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def add_uint32(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.UINT32)
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def add_int32(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.INT32)
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def add_float32(self, key: str, val: float) -> None:
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self.kv_data[key] = (val, GGUFValueType.FLOAT32)
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def add_uint64(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.UINT64)
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def add_int64(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.INT64)
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def add_float64(self, key: str, val: float) -> None:
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self.kv_data[key] = (val, GGUFValueType.FLOAT64)
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def add_bool(self, key: str, val: bool) -> None:
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self.kv_data[key] = (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.kv_data[key] = (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(f'Expected a sequence for {key}, got {type(val)}')
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self.kv_data[key] = (val, GGUFValueType.ARRAY)
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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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# TODO reimplement temp file
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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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self.tensors.append((name, tensor, 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 close(self) -> None:
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for _, _, writer in self.split_strategy:
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writer.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(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)
|
|
|
|
def add_rope_dimension_count(self, count: int) -> None:
|
|
self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count)
|
|
|
|
def add_rope_freq_base(self, value: float) -> None:
|
|
self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value)
|
|
|
|
def add_rope_scaling_type(self, value: RopeScalingType) -> None:
|
|
self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value)
|
|
|
|
def add_rope_scaling_factor(self, value: float) -> None:
|
|
self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value)
|
|
|
|
def add_rope_scaling_orig_ctx_len(self, value: int) -> None:
|
|
self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value)
|
|
|
|
def add_rope_scaling_finetuned(self, value: bool) -> None:
|
|
self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value)
|
|
|
|
def add_ssm_conv_kernel(self, value: int) -> None:
|
|
self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value)
|
|
|
|
def add_ssm_inner_size(self, value: int) -> None:
|
|
self.add_uint32(Keys.SSM.INNER_SIZE.format(arch=self.arch), value)
|
|
|
|
def add_ssm_state_size(self, value: int) -> None:
|
|
self.add_uint32(Keys.SSM.STATE_SIZE.format(arch=self.arch), value)
|
|
|
|
def add_ssm_time_step_rank(self, value: int) -> None:
|
|
self.add_uint32(Keys.SSM.TIME_STEP_RANK.format(arch=self.arch), value)
|
|
|
|
def add_tokenizer_model(self, model: str) -> None:
|
|
self.add_string(Keys.Tokenizer.MODEL, model)
|
|
|
|
def add_tokenizer_pre(self, pre: str) -> None:
|
|
self.add_string(Keys.Tokenizer.PRE, pre)
|
|
|
|
def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
|
|
self.add_array(Keys.Tokenizer.LIST, tokens)
|
|
|
|
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 isinstance(value, list):
|
|
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) |