mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-29 12:54:35 +00:00
9c4c9cc83f
* Move convert.py to examples/convert-no-torch.py * Fix CI, scripts, readme files * convert-no-torch -> convert-legacy-llama * Move vocab thing to vocab.py * Fix convert-no-torch -> convert-legacy-llama * Fix lost convert.py in ci/run.sh * Fix imports * Fix gguf not imported correctly * Fix flake8 complaints * Fix check-requirements.sh * Get rid of ADDED_TOKENS_FILE, FAST_TOKENIZER_FILE * Review fixes
466 lines
19 KiB
Python
466 lines
19 KiB
Python
from __future__ import annotations
|
|
|
|
import re
|
|
import logging
|
|
import json
|
|
import os
|
|
from pathlib import Path
|
|
from typing import Any, Callable, Sequence, Mapping, Iterable, Protocol, ClassVar, runtime_checkable
|
|
|
|
from sentencepiece import SentencePieceProcessor
|
|
|
|
import gguf
|
|
|
|
from .gguf_writer import GGUFWriter
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class SpecialVocab:
|
|
merges: list[str]
|
|
add_special_token: dict[str, bool]
|
|
special_token_ids: dict[str, int]
|
|
chat_template: str | Sequence[Mapping[str, str]] | None
|
|
|
|
def __init__(
|
|
self, path: str | os.PathLike[str], load_merges: bool = False,
|
|
special_token_types: Iterable[str] | None = None,
|
|
n_vocab: int | None = None,
|
|
):
|
|
self.special_token_ids = {}
|
|
self.add_special_token = {}
|
|
self.n_vocab = n_vocab
|
|
self.load_merges = load_merges
|
|
self.merges = []
|
|
self.chat_template = None
|
|
if special_token_types is not None:
|
|
self.special_token_types = special_token_types
|
|
else:
|
|
self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad', 'cls', 'mask')
|
|
self._load(Path(path))
|
|
|
|
def __repr__(self) -> str:
|
|
return '<SpecialVocab with {} merges, special tokens {}, add special tokens {}>'.format(
|
|
len(self.merges), self.special_token_ids or "unset", self.add_special_token or "unset",
|
|
)
|
|
|
|
def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None:
|
|
if self.merges:
|
|
if not quiet:
|
|
logger.info(f'Adding {len(self.merges)} merge(s).')
|
|
gw.add_token_merges(self.merges)
|
|
elif self.load_merges:
|
|
logger.warning('Adding merges requested but no merges found, output may be non-functional.')
|
|
for typ, tokid in self.special_token_ids.items():
|
|
id_handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None)
|
|
if id_handler is None:
|
|
logger.warning(f'No handler for special token type {typ} with id {tokid} - skipping')
|
|
continue
|
|
if not quiet:
|
|
logger.info(f'Setting special token type {typ} to {tokid}')
|
|
id_handler(tokid)
|
|
for typ, value in self.add_special_token.items():
|
|
add_handler: Callable[[bool], None] | None = getattr(gw, f'add_add_{typ}_token', None)
|
|
if add_handler is None:
|
|
logger.warning(f'No handler for add_{typ}_token with value {value} - skipping')
|
|
continue
|
|
if not quiet:
|
|
logger.info(f'Setting add_{typ}_token to {value}')
|
|
add_handler(value)
|
|
if self.chat_template is not None:
|
|
if not quiet:
|
|
logger.info(f'Setting chat_template to {self.chat_template}')
|
|
gw.add_chat_template(self.chat_template)
|
|
|
|
def _load(self, path: Path) -> None:
|
|
self._try_load_from_tokenizer_json(path)
|
|
self._try_load_from_config_json(path)
|
|
if self.load_merges and not self.merges:
|
|
self._try_load_merges_txt(path)
|
|
|
|
def _try_load_merges_txt(self, path: Path) -> bool:
|
|
merges_file = path / 'merges.txt'
|
|
if not merges_file.is_file():
|
|
return False
|
|
with open(merges_file, 'r', encoding = 'utf-8') as fp:
|
|
first_line = next(fp, '').strip()
|
|
if not first_line.startswith('#'):
|
|
fp.seek(0)
|
|
line_num = 0
|
|
else:
|
|
line_num = 1
|
|
merges = []
|
|
for line in fp:
|
|
line_num += 1
|
|
line = line.strip()
|
|
if not line:
|
|
continue
|
|
parts = line.split(None, 3)
|
|
if len(parts) != 2:
|
|
logger.warning(f'{merges_file.name}: Line {line_num}: Entry malformed, ignoring')
|
|
continue
|
|
merges.append(f'{parts[0]} {parts[1]}')
|
|
self.merges = merges
|
|
return True
|
|
|
|
def _set_special_token(self, typ: str, tid: Any) -> None:
|
|
if not isinstance(tid, int):
|
|
return
|
|
if tid < 0:
|
|
raise ValueError(f'invalid value for special token type {typ}: {tid}')
|
|
if self.n_vocab is None or tid < self.n_vocab:
|
|
if typ in self.special_token_ids:
|
|
return
|
|
self.special_token_ids[typ] = tid
|
|
return
|
|
logger.warning(f'Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping')
|
|
|
|
def _try_load_from_tokenizer_json(self, path: Path) -> bool:
|
|
tokenizer_file = path / 'tokenizer.json'
|
|
if tokenizer_file.is_file():
|
|
with open(tokenizer_file, encoding = 'utf-8') as f:
|
|
tokenizer = json.load(f)
|
|
if self.load_merges:
|
|
merges = tokenizer.get('model', {}).get('merges')
|
|
if isinstance(merges, list) and merges and isinstance(merges[0], str):
|
|
self.merges = merges
|
|
added_tokens = tokenizer.get('added_tokens', {})
|
|
else:
|
|
added_tokens = {}
|
|
tokenizer_config_file = path / 'tokenizer_config.json'
|
|
if not tokenizer_config_file.is_file():
|
|
return True
|
|
with open(tokenizer_config_file, encoding = 'utf-8') as f:
|
|
tokenizer_config = json.load(f)
|
|
chat_template = tokenizer_config.get('chat_template')
|
|
if chat_template is None or isinstance(chat_template, (str, list)):
|
|
self.chat_template = chat_template
|
|
else:
|
|
logger.warning(f'Bad type for chat_template field in {tokenizer_config_file!r} - ignoring')
|
|
for typ in self.special_token_types:
|
|
add_entry = tokenizer_config.get(f'add_{typ}_token')
|
|
if isinstance(add_entry, bool):
|
|
self.add_special_token[typ] = add_entry
|
|
entry = tokenizer_config.get(f'{typ}_token')
|
|
if isinstance(entry, str):
|
|
tc_content = entry
|
|
elif isinstance(entry, dict):
|
|
entry_content = entry.get('content')
|
|
if not isinstance(entry_content, str):
|
|
continue
|
|
tc_content = entry_content
|
|
else:
|
|
continue
|
|
# We only need the first match here.
|
|
maybe_token_id = next(
|
|
(atok.get('id') for atok in added_tokens if atok.get('content') == tc_content),
|
|
None,
|
|
)
|
|
self._set_special_token(typ, maybe_token_id)
|
|
return True
|
|
|
|
def _try_load_from_config_json(self, path: Path) -> bool:
|
|
config_file = path / 'config.json'
|
|
if not config_file.is_file():
|
|
return False
|
|
with open(config_file, encoding = 'utf-8') as f:
|
|
config = json.load(f)
|
|
for typ in self.special_token_types:
|
|
self._set_special_token(typ, config.get(f'{typ}_token_id'))
|
|
return True
|
|
|
|
|
|
@runtime_checkable
|
|
class BaseVocab(Protocol):
|
|
tokenizer_model: ClassVar[str]
|
|
name: ClassVar[str]
|
|
|
|
|
|
@runtime_checkable
|
|
class Vocab(BaseVocab, Protocol):
|
|
vocab_size: int
|
|
added_tokens_dict: dict[str, int]
|
|
added_tokens_list: list[str]
|
|
fname_tokenizer: Path
|
|
|
|
def __init__(self, base_path: Path): ...
|
|
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ...
|
|
|
|
|
|
class NoVocab(BaseVocab):
|
|
tokenizer_model = "no_vocab"
|
|
name = "no_vocab"
|
|
|
|
def __repr__(self) -> str:
|
|
return "<NoVocab for a model without integrated vocabulary>"
|
|
|
|
|
|
class BpeVocab(Vocab):
|
|
tokenizer_model = "gpt2"
|
|
name = "bpe"
|
|
|
|
def __init__(self, base_path: Path):
|
|
added_tokens: dict[str, int] = {}
|
|
|
|
if (fname_tokenizer := base_path / 'vocab.json').exists():
|
|
# "slow" tokenizer
|
|
with open(fname_tokenizer, encoding="utf-8") as f:
|
|
self.vocab = json.load(f)
|
|
|
|
try:
|
|
# FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
|
|
with open(base_path / 'added_tokens.json', encoding="utf-8") as f:
|
|
added_tokens = json.load(f)
|
|
except FileNotFoundError:
|
|
pass
|
|
else:
|
|
# "fast" tokenizer
|
|
fname_tokenizer = base_path / 'tokenizer.json'
|
|
|
|
# if this fails, FileNotFoundError propagates to caller
|
|
with open(fname_tokenizer, encoding="utf-8") as f:
|
|
tokenizer_json = json.load(f)
|
|
|
|
tokenizer_model: dict[str, Any] = tokenizer_json['model']
|
|
if (
|
|
tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False)
|
|
or tokenizer_json['decoder']['type'] != 'ByteLevel'
|
|
):
|
|
raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer')
|
|
|
|
self.vocab = tokenizer_model["vocab"]
|
|
|
|
if (added := tokenizer_json.get('added_tokens')) is not None:
|
|
# Added tokens here can be duplicates of the main vocabulary.
|
|
added_tokens = {item['content']: item['id']
|
|
for item in added
|
|
if item['content'] not in self.vocab}
|
|
|
|
vocab_size = len(self.vocab)
|
|
expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
|
|
actual_ids = sorted(added_tokens.values())
|
|
if expected_ids != actual_ids:
|
|
expected_end_id = vocab_size + len(actual_ids) - 1
|
|
raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range "
|
|
f"{vocab_size} - {expected_end_id}; got {actual_ids}")
|
|
|
|
items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
|
|
self.added_tokens_dict = added_tokens
|
|
self.added_tokens_list = [text for (text, idx) in items]
|
|
self.vocab_size_base = vocab_size
|
|
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
|
|
self.fname_tokenizer = fname_tokenizer
|
|
|
|
def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
|
reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}
|
|
|
|
for i, _ in enumerate(self.vocab):
|
|
yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL
|
|
|
|
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
|
for text in self.added_tokens_list:
|
|
score = -1000.0
|
|
yield text.encode("utf-8"), score, gguf.TokenType.CONTROL
|
|
|
|
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
|
yield from self.bpe_tokens()
|
|
yield from self.added_tokens()
|
|
|
|
def __repr__(self) -> str:
|
|
return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
|
|
|
|
|
|
class SentencePieceVocab(Vocab):
|
|
tokenizer_model = "llama"
|
|
name = "spm"
|
|
|
|
def __init__(self, base_path: Path):
|
|
added_tokens: dict[str, int] = {}
|
|
if (fname_tokenizer := base_path / 'tokenizer.model').exists():
|
|
# normal location
|
|
try:
|
|
with open(base_path / 'added_tokens.json', encoding="utf-8") as f:
|
|
added_tokens = json.load(f)
|
|
except FileNotFoundError:
|
|
pass
|
|
elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists():
|
|
# not found in alternate location either
|
|
raise FileNotFoundError('Cannot find tokenizer.model')
|
|
|
|
self.sentencepiece_tokenizer = SentencePieceProcessor()
|
|
self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer))
|
|
vocab_size = self.sentencepiece_tokenizer.vocab_size()
|
|
|
|
new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
|
|
expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
|
|
actual_new_ids = sorted(new_tokens.keys())
|
|
|
|
if expected_new_ids != actual_new_ids:
|
|
raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
|
|
|
|
# Token pieces that were added to the base vocabulary.
|
|
self.added_tokens_dict = added_tokens
|
|
self.added_tokens_list = [new_tokens[id] for id in actual_new_ids]
|
|
self.vocab_size_base = vocab_size
|
|
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
|
|
self.fname_tokenizer = fname_tokenizer
|
|
|
|
def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
|
tokenizer = self.sentencepiece_tokenizer
|
|
for i in range(tokenizer.vocab_size()):
|
|
piece = tokenizer.IdToPiece(i)
|
|
text = piece.encode("utf-8")
|
|
score: float = tokenizer.GetScore(i)
|
|
|
|
toktype = gguf.TokenType.NORMAL
|
|
if tokenizer.IsUnknown(i):
|
|
toktype = gguf.TokenType.UNKNOWN
|
|
if tokenizer.IsControl(i):
|
|
toktype = gguf.TokenType.CONTROL
|
|
|
|
# NOTE: I think added_tokens are user defined.
|
|
# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
|
|
# if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED
|
|
|
|
if tokenizer.IsUnused(i):
|
|
toktype = gguf.TokenType.UNUSED
|
|
if tokenizer.IsByte(i):
|
|
toktype = gguf.TokenType.BYTE
|
|
|
|
yield text, score, toktype
|
|
|
|
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
|
for text in self.added_tokens_list:
|
|
score = -1000.0
|
|
yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED
|
|
|
|
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
|
yield from self.sentencepiece_tokens()
|
|
yield from self.added_tokens()
|
|
|
|
def __repr__(self) -> str:
|
|
return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
|
|
|
|
|
|
class LlamaHfVocab(Vocab):
|
|
tokenizer_model = "llama"
|
|
name = "hfft"
|
|
|
|
def __init__(self, base_path: Path):
|
|
fname_tokenizer = base_path / 'tokenizer.json'
|
|
# if this fails, FileNotFoundError propagates to caller
|
|
with open(fname_tokenizer, encoding='utf-8') as f:
|
|
tokenizer_json = json.load(f)
|
|
|
|
# pre-check so we know if we need transformers
|
|
tokenizer_model: dict[str, Any] = tokenizer_json['model']
|
|
is_llama3 = (
|
|
tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
|
|
and not tokenizer_model.get('byte_fallback', True)
|
|
)
|
|
if is_llama3:
|
|
raise TypeError('Llama 3 must be converted with BpeVocab')
|
|
|
|
if not is_llama3 and (
|
|
tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
|
|
or tokenizer_json['decoder']['type'] != 'Sequence'
|
|
):
|
|
raise FileNotFoundError('Cannot find Llama BPE tokenizer')
|
|
|
|
try:
|
|
from transformers import AutoTokenizer
|
|
except ImportError as e:
|
|
raise ImportError(
|
|
"To use LlamaHfVocab, please install the `transformers` package. "
|
|
"You can install it with `pip install transformers`."
|
|
) from e
|
|
|
|
# Allow the tokenizer to default to slow or fast versions.
|
|
# Explicitly set tokenizer to use local paths.
|
|
self.tokenizer = AutoTokenizer.from_pretrained(
|
|
base_path,
|
|
cache_dir=base_path,
|
|
local_files_only=True,
|
|
)
|
|
assert self.tokenizer.is_fast # assume tokenizer.json is used
|
|
|
|
# Initialize lists and dictionaries for added tokens
|
|
self.added_tokens_list = []
|
|
self.added_tokens_dict = dict()
|
|
self.added_tokens_ids = set()
|
|
|
|
# Process added tokens
|
|
for tok, tokidx in sorted(
|
|
self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]
|
|
):
|
|
# Only consider added tokens that are not in the base vocabulary
|
|
if tokidx >= self.tokenizer.vocab_size:
|
|
self.added_tokens_list.append(tok)
|
|
self.added_tokens_dict[tok] = tokidx
|
|
self.added_tokens_ids.add(tokidx)
|
|
|
|
# Store special tokens and their IDs
|
|
self.specials = {
|
|
tok: self.tokenizer.get_vocab()[tok]
|
|
for tok in self.tokenizer.all_special_tokens
|
|
}
|
|
self.special_ids = set(self.tokenizer.all_special_ids)
|
|
|
|
# Set vocabulary sizes
|
|
self.vocab_size_base = self.tokenizer.vocab_size
|
|
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
|
|
|
|
self.fname_tokenizer = fname_tokenizer
|
|
|
|
def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
|
reverse_vocab = {
|
|
id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()
|
|
}
|
|
|
|
for token_id in range(self.vocab_size_base):
|
|
# Skip processing added tokens here
|
|
if token_id in self.added_tokens_ids:
|
|
continue
|
|
|
|
# Convert token text to bytes
|
|
token_text = reverse_vocab[token_id].encode("utf-8")
|
|
|
|
# Yield token text, score, and type
|
|
yield token_text, self.get_token_score(token_id), self.get_token_type(
|
|
token_id, token_text, self.special_ids # Reuse already stored special IDs
|
|
)
|
|
|
|
def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType:
|
|
# Special case for byte tokens
|
|
if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
|
|
return gguf.TokenType.BYTE
|
|
|
|
# Determine token type based on whether it's a special token
|
|
return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL
|
|
|
|
def get_token_score(self, token_id: int) -> float:
|
|
# Placeholder for actual logic to determine the token's score
|
|
# This needs to be implemented based on specific requirements
|
|
return -1000.0 # Default score
|
|
|
|
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
|
for text in self.added_tokens_list:
|
|
if text in self.specials:
|
|
toktype = self.get_token_type(self.specials[text], b'', self.special_ids)
|
|
score = self.get_token_score(self.specials[text])
|
|
else:
|
|
toktype = gguf.TokenType.USER_DEFINED
|
|
score = -1000.0
|
|
|
|
yield text.encode("utf-8"), score, toktype
|
|
|
|
def has_newline_token(self):
|
|
return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab
|
|
|
|
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
|
yield from self.hf_tokens()
|
|
yield from self.added_tokens()
|
|
|
|
def __repr__(self) -> str:
|
|
return f"<LlamaHfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
|