File size: 3,556 Bytes
f039f41 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | from __future__ import annotations
import hashlib
from pathlib import Path
from typing import Any, Protocol
class TokenizerProtocol(Protocol):
def encode(self, text: str) -> list[int]: ...
def decode(self, token_ids: list[int]) -> str: ...
def render_chat(self, messages: list[dict[str, Any]]) -> str: ...
def token_offset(self, rendered_text: str, substring: str) -> int: ...
@property
def fingerprint(self) -> str: ...
class TransformersTokenizer:
def __init__(
self,
model_path: str | Path,
*,
enable_thinking: bool = True,
reasoning_effort: str | None = "medium",
preserve_thinking: bool = True,
):
try:
from transformers import AutoTokenizer
except ImportError as exc:
raise RuntimeError(
"Install shiftedx-bench[tokenizers] to use a Transformers tokenizer"
) from exc
self.model_path = Path(model_path).expanduser().resolve()
self._tokenizer = AutoTokenizer.from_pretrained(
self.model_path, local_files_only=True, trust_remote_code=True
)
self.enable_thinking = enable_thinking
self.reasoning_effort = reasoning_effort
self.preserve_thinking = preserve_thinking
digest = hashlib.sha256()
for name in ("tokenizer.json", "tokenizer_config.json", "special_tokens_map.json"):
path = self.model_path / name
if path.exists():
digest.update(name.encode())
digest.update(path.read_bytes())
digest.update(
repr((self.enable_thinking, self.reasoning_effort, self.preserve_thinking)).encode()
)
self._fingerprint = digest.hexdigest()
def encode(self, text: str) -> list[int]:
return list(self._tokenizer.encode(text, add_special_tokens=False))
def decode(self, token_ids: list[int]) -> str:
return self._tokenizer.decode(
token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
)
def render_chat(self, messages: list[dict[str, Any]]) -> str:
kwargs: dict[str, Any] = {
"tokenize": False,
"add_generation_prompt": True,
"enable_thinking": self.enable_thinking,
"preserve_thinking": self.preserve_thinking,
}
if self.reasoning_effort:
kwargs["reasoning_effort"] = self.reasoning_effort
try:
value = self._tokenizer.apply_chat_template(messages, **kwargs)
except TypeError:
value = self._tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
if not isinstance(value, str):
raise TypeError("Tokenizer chat template did not return text")
return value
def token_offset(self, rendered_text: str, substring: str) -> int:
char_offset = rendered_text.find(substring)
if char_offset < 0:
raise ValueError(f"Substring not present in rendered prompt: {substring!r}")
encoded = self._tokenizer(
rendered_text,
add_special_tokens=False,
return_offsets_mapping=True,
)
for index, (start, end) in enumerate(encoded["offset_mapping"]):
if start <= char_offset < end or start == char_offset:
return index
raise ValueError("Could not map substring to a token offset")
@property
def fingerprint(self) -> str:
return self._fingerprint
|