Meta Llama 3 文本编码为 token

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Meta Llama 3 文本编码为 token

flyfish

tiktoken 是一个用于 OpenAI 模型的快速 BPE 分词器，这里用在Meta Llama 3上。主要功能包括将文本编码为token，以及将token解码回文本。这个过程通常使用BPE（Byte Pair Encoding）算法或其他类似的子词分割方法。

参考网址

https://github.com/openai/tiktoken
https://github.com/karpathy/minbpe

什么是BPE（Byte Pair Encoding）？
BPE（Byte Pair Encoding）是一种用于文本分词的子词（subword）分割算法。它通过逐步合并最常见的字符或字符序列来减少词汇表的大小，从而能够更高效地处理和表示文本数据。

BPE在tiktoken中的应用

简单的应用

import tiktoken# 获取GPT-2编码器
enc = tiktoken.get_encoding("gpt2")# 示例文本
text = "This is an example text."# 将文本编码为tokens
tokens = enc.encode(text)
print(f"Encoded tokens: {tokens}")# 将tokens解码为原文本
decoded_text = enc.decode(tokens)
print(f"Decoded text: {decoded_text}")

Meta Llama 3的使用方式 - load_tiktoken_bpe函数

在tiktoken库中，BPE用于将文本编码成模型可以处理的tokens。load_tiktoken_bpe函数会加载BPE编码的词汇表和规则，以便将文本分解成子词单位。

代码示例

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_bpelogger = getLogger(__name__)Role = Literal["system", "user", "assistant"]class Message(TypedDict):role: Rolecontent: strDialog = Sequence[Message]class Tokenizer:"""Tokenizing and encoding/decoding text using the Tiktoken tokenizer."""special_tokens: Dict[str, int]num_reserved_special_tokens = 256pat_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+"  # noqa: E501def __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_pathmergeable_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|>",  # end of turn] + [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# BOS / EOS token IDsself.bos_id: int = self.special_tokens["<|begin_of_text|>"]self.eos_id: int = self.special_tokens["<|end_of_text|>"]self.pad_id: int = -1self.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]:assert type(s) is strTIKTOKEN_MAX_ENCODE_CHARS = 400_000MAX_NO_WHITESPACES_CHARS = 25_000substrs = (substrfor 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 tdef decode(self, t: Sequence[int]) -> str:return self.model.decode(cast(List[int], t))@staticmethoddef _split_whitespaces_or_nonwhitespaces(s: str, max_consecutive_slice_len: int) -> Iterator[str]:current_slice_len = 0current_slice_is_space = s[0].isspace() if len(s) > 0 else Falseslice_start = 0for i in range(len(s)):is_now_space = s[i].isspace()if current_slice_is_space ^ is_now_space:current_slice_len = 1current_slice_is_space = is_now_spaceelse:current_slice_len += 1if current_slice_len > max_consecutive_slice_len:yield s[slice_start:i]slice_start = icurrent_slice_len = 1yield s[slice_start:]model_path = "Meta-Llama-3-8B-Instruct/tokenizer.model"
tokenizer = Tokenizer(model_path)print(tokenizer.encode( "This is a test sentence.", bos=True,eos=True))print(tokenizer.decode( [128000, 2028, 374, 264, 1296, 11914, 13, 128001]))输出[128000, 2028, 374, 264, 1296, 11914, 13, 128001]
<|begin_of_text|>This is a test sentence.<|end_of_text|>

再测试一个

print(tokenizer.encode( "This is Ji'nan in the winter", bos=True,eos=True))
print(tokenizer.decode( [128000, 2028, 374, 55551, 6, 19285, 304, 279, 12688, 128001]))

输出

[128000, 2028, 374, 55551, 6, 19285, 304, 279, 12688, 128001]
<|begin_of_text|>This is Ji'nan in the winter<|end_of_text|>

在这个例子中，load_tiktoken_bpe函数加载了一个预训练的BPE词汇表和规则，然后使用这些规则将输入的文本分割成tokens。之后，这些tokens可以被解码回原文本。

扩展

import tiktoken
cl100k_base = tiktoken.get_encoding("cl100k_base")# In production, load the arguments directly instead of accessing private attributes
# See openai_public.py for examples of arguments for specific encodings
enc = tiktoken.Encoding(# If you're changing the set of special tokens, make sure to use a different name# It should be clear from the name what behaviour to expect.name="cl100k_im",pat_str=cl100k_base._pat_str,mergeable_ranks=cl100k_base._mergeable_ranks,special_tokens={**cl100k_base._special_tokens,"<|im_start|>": 100264,"<|im_end|>": 100265,}
)print(enc)#<Encoding 'cl100k_im'>