mirror of
https://github.com/ggerganov/llama.cpp.git
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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
166 lines
6.7 KiB
Python
166 lines
6.7 KiB
Python
from __future__ import annotations
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import logging
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import json
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import os
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from pathlib import Path
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from typing import Any, Callable, Sequence, Mapping, Iterable
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from .gguf_writer import GGUFWriter
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logger = logging.getLogger(__name__)
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class SpecialVocab:
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merges: list[str]
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add_special_token: dict[str, bool]
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special_token_ids: dict[str, int]
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chat_template: str | Sequence[Mapping[str, str]] | None
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def __init__(
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self, path: str | os.PathLike[str], load_merges: bool = False,
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special_token_types: Iterable[str] | None = None,
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n_vocab: int | None = None,
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):
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self.special_token_ids = {}
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self.add_special_token = {}
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self.n_vocab = n_vocab
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self.load_merges = load_merges
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self.merges = []
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self.chat_template = None
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if special_token_types is not None:
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self.special_token_types = special_token_types
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else:
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self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad', 'cls', 'mask')
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self._load(Path(path))
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def __repr__(self) -> str:
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return '<SpecialVocab with {} merges, special tokens {}, add special tokens {}>'.format(
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len(self.merges), self.special_token_ids or "unset", self.add_special_token or "unset",
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)
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def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None:
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if self.merges:
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if not quiet:
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logger.info(f'Adding {len(self.merges)} merge(s).')
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gw.add_token_merges(self.merges)
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elif self.load_merges:
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logger.warning('Adding merges requested but no merges found, output may be non-functional.')
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for typ, tokid in self.special_token_ids.items():
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id_handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None)
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if id_handler is None:
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logger.warning(f'No handler for special token type {typ} with id {tokid} - skipping')
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continue
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if not quiet:
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logger.info(f'Setting special token type {typ} to {tokid}')
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id_handler(tokid)
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for typ, value in self.add_special_token.items():
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add_handler: Callable[[bool], None] | None = getattr(gw, f'add_add_{typ}_token', None)
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if add_handler is None:
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logger.warning(f'No handler for add_{typ}_token with value {value} - skipping')
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continue
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if not quiet:
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logger.info(f'Setting add_{typ}_token to {value}')
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add_handler(value)
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if self.chat_template is not None:
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if not quiet:
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logger.info(f'Setting chat_template to {self.chat_template}')
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gw.add_chat_template(self.chat_template)
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def _load(self, path: Path) -> None:
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self._try_load_from_tokenizer_json(path)
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self._try_load_from_config_json(path)
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if self.load_merges and not self.merges:
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self._try_load_merges_txt(path)
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def _try_load_merges_txt(self, path: Path) -> bool:
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merges_file = path / 'merges.txt'
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if not merges_file.is_file():
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return False
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with open(merges_file, 'r', encoding = 'utf-8') as fp:
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first_line = next(fp, '').strip()
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if not first_line.startswith('#'):
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fp.seek(0)
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line_num = 0
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else:
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line_num = 1
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merges = []
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for line in fp:
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line_num += 1
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line = line.strip()
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if not line:
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continue
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parts = line.split(None, 3)
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if len(parts) != 2:
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logger.warning(f'{merges_file.name}: Line {line_num}: Entry malformed, ignoring')
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continue
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merges.append(f'{parts[0]} {parts[1]}')
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self.merges = merges
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return True
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def _set_special_token(self, typ: str, tid: Any) -> None:
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if not isinstance(tid, int):
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return
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if tid < 0:
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raise ValueError(f'invalid value for special token type {typ}: {tid}')
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if self.n_vocab is None or tid < self.n_vocab:
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if typ in self.special_token_ids:
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return
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self.special_token_ids[typ] = tid
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return
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logger.warning(f'Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping')
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def _try_load_from_tokenizer_json(self, path: Path) -> bool:
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tokenizer_file = path / 'tokenizer.json'
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if tokenizer_file.is_file():
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with open(tokenizer_file, encoding = 'utf-8') as f:
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tokenizer = json.load(f)
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if self.load_merges:
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merges = tokenizer.get('model', {}).get('merges')
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if isinstance(merges, list) and merges and isinstance(merges[0], str):
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self.merges = merges
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added_tokens = tokenizer.get('added_tokens', {})
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else:
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added_tokens = {}
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tokenizer_config_file = path / 'tokenizer_config.json'
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if not tokenizer_config_file.is_file():
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return True
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with open(tokenizer_config_file, encoding = 'utf-8') as f:
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tokenizer_config = json.load(f)
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chat_template = tokenizer_config.get('chat_template')
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if chat_template is None or isinstance(chat_template, (str, list)):
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self.chat_template = chat_template
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else:
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logger.warning(f'Bad type for chat_template field in {tokenizer_config_file!r} - ignoring')
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for typ in self.special_token_types:
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add_entry = tokenizer_config.get(f'add_{typ}_token')
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if isinstance(add_entry, bool):
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self.add_special_token[typ] = add_entry
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entry = tokenizer_config.get(f'{typ}_token')
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if isinstance(entry, str):
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tc_content = entry
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elif isinstance(entry, dict):
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entry_content = entry.get('content')
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if not isinstance(entry_content, str):
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continue
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tc_content = entry_content
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else:
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continue
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# We only need the first match here.
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maybe_token_id = next(
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(atok.get('id') for atok in added_tokens if atok.get('content') == tc_content),
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None,
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)
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self._set_special_token(typ, maybe_token_id)
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return True
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def _try_load_from_config_json(self, path: Path) -> bool:
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config_file = path / 'config.json'
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if not config_file.is_file():
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return False
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with open(config_file, encoding = 'utf-8') as f:
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config = json.load(f)
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for typ in self.special_token_types:
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self._set_special_token(typ, config.get(f'{typ}_token_id'))
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return True
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