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import os
from logging import getLogger
from pathlib import Path
from typing import (
AbstractSet,
cast,
Collection,
Dict,
Iterator,
List,
Literal,
Sequence,
TypedDict,
Union,
)
import tiktoken
from tiktoken.load import load_tiktoken_bpe
logger = getLogger(__name__)
Role = Literal["system", "user", "assistant"]
class Message(TypedDict):
role: Role
content: str
Dialog = Sequence[Message]
class Tokenizer:
"""
Tokenizing and encoding/decoding text using the Tiktoken tokenizer.
"""
special_tokens: Dict[str, int]
num_reserved_special_tokens = 256
pat_str = r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"
def __init__(self, model_path: str):
"""
Initializes the Tokenizer with a Tiktoken model.
Args:
model_path (str): The path to the Tiktoken model file.
"""
assert os.path.isfile(model_path), model_path
mergeable_ranks = load_tiktoken_bpe(model_path)
num_base_tokens = len(mergeable_ranks)
special_tokens = [
"<|begin_of_text|>",
"<|end_of_text|>",
"<|reserved_special_token_0|>",
"<|reserved_special_token_1|>",
"<|reserved_special_token_2|>",
"<|reserved_special_token_3|>",
"<|start_header_id|>",
"<|end_header_id|>",
"<|reserved_special_token_4|>",
"<|eot_id|>",
] + [
f"<|reserved_special_token_{i}|>"
for i in range(5, self.num_reserved_special_tokens - 5)
]
self.special_tokens = {
token: num_base_tokens + i for i, token in enumerate(special_tokens)
}
self.model = tiktoken.Encoding(
name=Path(model_path).name,
pat_str=self.pat_str,
mergeable_ranks=mergeable_ranks,
special_tokens=self.special_tokens,
)
logger.info(f"Reloaded tiktoken model from {model_path}")
self.n_words: int = self.model.n_vocab
self.bos_id: int = self.special_tokens["<|begin_of_text|>"]
self.eos_id: int = self.special_tokens["<|end_of_text|>"]
self.pad_id: int = -1
self.stop_tokens = {
self.special_tokens["<|end_of_text|>"],
self.special_tokens["<|eot_id|>"],
}
logger.info(
f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
)
def encode(
self,
s: str,
*,
bos: bool,
eos: bool,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = (),
) -> List[int]:
"""
Encodes a string into a list of token IDs.
Args:
s (str): The input string to be encoded.
bos (bool): Whether to prepend the beginning-of-sequence token.
eos (bool): Whether to append the end-of-sequence token.
allowed_tokens ("all"|set[str]): allowed special tokens in string
disallowed_tokens ("all"|set[str]): special tokens that raise an error when in string
Returns:
list[int]: A list of token IDs.
By default, setting disallowed_special=() encodes a string by ignoring
special tokens. Specifically:
- Setting `disallowed_special` to () will cause all text corresponding
to special tokens to be encoded as natural text (insteading of raising
an error).
- Setting `allowed_special` to "all" will treat all text corresponding
to special tokens to be encoded as special tokens.
"""
assert type(s) is str
TIKTOKEN_MAX_ENCODE_CHARS = 400_000
MAX_NO_WHITESPACES_CHARS = 25_000
substrs = (
substr
for i in range(0, len(s), TIKTOKEN_MAX_ENCODE_CHARS)
for substr in self._split_whitespaces_or_nonwhitespaces(
s[i : i + TIKTOKEN_MAX_ENCODE_CHARS], MAX_NO_WHITESPACES_CHARS
)
)
t: List[int] = []
for substr in substrs:
t.extend(
self.model.encode(
substr,
allowed_special=allowed_special,
disallowed_special=disallowed_special,
)
)
if bos:
t.insert(0, self.bos_id)
if eos:
t.append(self.eos_id)
return t
def decode(self, t: Sequence[int]) -> str:
"""
Decodes a list of token IDs into a string.
Args:
t (List[int]): The list of token IDs to be decoded.
Returns:
str: The decoded string.
"""
return self.model.decode(cast(List[int], t))
@staticmethod
def _split_whitespaces_or_nonwhitespaces(
s: str, max_consecutive_slice_len: int
) -> Iterator[str]:
"""
Splits the string `s` so that each substring contains no more than `max_consecutive_slice_len`
consecutive whitespaces or consecutive non-whitespaces.
"""
current_slice_len = 0
current_slice_is_space = s[0].isspace() if len(s) > 0 else False
slice_start = 0
for i in range(len(s)):
is_now_space = s[i].isspace()
if current_slice_is_space ^ is_now_space:
current_slice_len = 1
current_slice_is_space = is_now_space
else:
current_slice_len += 1
if current_slice_len > max_consecutive_slice_len:
yield s[slice_start:i]
slice_start = i
current_slice_len = 1
yield s[slice_start:]
class ChatFormat:
def __init__(self, tokenizer: Tokenizer):
self.tokenizer = tokenizer
def encode_header(self, message: Message) -> List[int]:
tokens = []
tokens.append(self.tokenizer.special_tokens["<|start_header_id|>"])
tokens.extend(self.tokenizer.encode(message["role"], bos=False, eos=False))
tokens.append(self.tokenizer.special_tokens["<|end_header_id|>"])
tokens.extend(self.tokenizer.encode("\n\n", bos=False, eos=False))
return tokens
def encode_message(self, message: Message) -> List[int]:
tokens = self.encode_header(message)
tokens.extend(
self.tokenizer.encode(message["content"].strip(), bos=False, eos=False)
)
tokens.append(self.tokenizer.special_tokens["<|eot_id|>"])
return tokens
def encode_dialog_prompt(self, dialog: Dialog) -> List[int]:
tokens = []
tokens.append(self.tokenizer.special_tokens["<|begin_of_text|>"])
for message in dialog:
tokens.extend(self.encode_message(message))
tokens.extend(self.encode_header({"role": "assistant", "content": ""}))
return tokens
|
