OpenAI - tiktoken ⏳ | fast BPE tokeniser

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关于 ⏳ tiktoken

tiktoken is a fast BPE tokeniser for use with OpenAI’s models.
初看这个名字，以为是跟 tiktok 相关，没想到是 openai 下面的，这取名还真是有趣呢。

• github https://github.com/openai/tiktoken
• openai-cookbook / examples / How_to_count_tokens_with_tiktoken.ipynb
  https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb

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性能表现

tiktoken 比其他开源 tokeniser 快 3-6 倍
基于 1GB 文本进行测试，使用 GPT-2 tokeniser，使用 GPT2TokenizerFast from tokenizers==0.13.2, transformers==4.24.0 and tiktoken==0.2.0。

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安装

pip install tiktoken 

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tiktoken 如何计算 token

给定一个文本字符："tiktoken is great!"，和一个 encoding，比如 "cl100k_base"。
一个 tokenizer 可以讲文本字符串分割成一系列 tokens，如： ["t", "ik", "token", " is", " great", "!"]

GPT 模型使用这种类型的 token。
知道文本字符串中有多少令牌，可以告诉你（a）字符串是否太长，文本模型无法处理，以及（b）OpenAI API调用的成本（因为使用是按令牌定价的）。

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Encodings

编码指定如何将文本转换为标记。不同的模型使用不同的编码。

OpenAI models 使用 tiktoken 支持下面三种编码：

Encoding name	OpenAI models
cl100k_base	gpt-4, gpt-3.5-turbo, text-embedding-ada-002
p50k_base	Codex models, text-davinci-002, text-davinci-003
r50k_base (or gpt2)	GPT-3 models like davinci

您可以获取一个模型的编码 ，使用 tiktoken.encoding_for_model() 如下：

encoding = tiktoken.encoding_for_model('gpt-3.5-turbo')

注意，p50k_base 与 r50k_base 基本类似，对于非代码应用程序，它们通常会给出相同的令牌。

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Tokenizer libraries 对不同编程语言的支持

对于 cl100k_base 和 p50k_base encodings:

• Python: tiktoken
• .NET / C#: SharpToken

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对于 r50k_base (gpt2) encodings, tokenizers are available in many languages.

• Python: tiktoken (or alternatively GPT2TokenizerFast)
• JavaScript: gpt-3-encoder
• .NET / C#: GPT Tokenizer
• Java: gpt2-tokenizer-java
• PHP: GPT-3-Encoder-PHP

（OpenAI不对第三方库进行背书或保证。）

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How strings are typically tokenized

In English, tokens commonly range in length from one character to one word (e.g., "t" or " great"), though in some languages tokens can be shorter than one character or longer than one word. Spaces are usually grouped with the starts of words (e.g., " is" instead of "is " or " "+"is"). You can quickly check how a string is tokenized at the OpenAI Tokenizer.

在英语中，tokens的长度通常从一个字符到一个单词（例如，t 或 great ），尽管在一些语言中，tokens 可以短于一个字符或长于一个单词。
空格通常以单词的开头分组（例如， is 而不是 is 或 + is。
您可以在[OpenAI Tokenizer]快速检查字符串是如何tokenize的。

OpenAI Tokenizer : https://beta.openai.com/tokenizer

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使用

编解码

import tiktoken

# 使用名字加载 encoding
# 第一次运行时，可能需要连接互联网来下载；下一次不需要联网
encoding = tiktoken.get_encoding("cl100k_base")
 

# 对于给定的模型名，自动加载正确的 encoding 
encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")


# 将文本转化为 tokens 列表
encoding.encode("tiktoken is great!")
# [83, 1609, 5963, 374, 2294, 0]

# 计算 encode 返回列表的长度
def num_tokens_from_string(string: str, encoding_name: str) -> int:
    """Returns the number of tokens in a text string."""
    encoding = tiktoken.get_encoding(encoding_name)
    num_tokens = len(encoding.encode(string))
    return num_tokens

num_tokens_from_string("tiktoken is great!", "cl100k_base")  # 6

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# 将 tokens 转化为 文本
encoding.decode([83, 1609, 5963, 374, 2294, 0])
# 'tiktoken is great!'

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警告：尽管 .decode() 可以应用于单个令牌，但要注意，对于不在utf-8边界上的令牌，它可能会有损耗。
对于单个 tokens，.decode_single_token_bytes() 方法安全地将单个整数令牌转换为它所代表的字节。

[encoding.decode_single_token_bytes(token) for token in [83, 1609, 5963, 374, 2294, 0]]
# [b't', b'ik', b'token', b' is', b' great', b'!']

（字符串前面的 b 表示字符串是字节字符串。）

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比较 encodings

不同的编码在拆分单词、组空格和处理非英语字符的方式上各不相同。使用上面的方法，我们可以比较几个示例字符串的不同编码。

def compare_encodings(example_string: str) -> None:
    """Prints a comparison of three string encodings."""
    # print the example string
    print(f'\nExample string: "{example_string}"')
    # for each encoding, print the # of tokens, the token integers, and the token bytes
    for encoding_name in ["gpt2", "p50k_base", "cl100k_base"]:
        encoding = tiktoken.get_encoding(encoding_name)
        token_integers = encoding.encode(example_string)
        num_tokens = len(token_integers)
        token_bytes = [encoding.decode_single_token_bytes(token) for token in token_integers]
        print()
        print(f"{encoding_name}: {num_tokens} tokens")
        print(f"token integers: {token_integers}")
        print(f"token bytes: {token_bytes}")
        

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compare_encodings("antidisestablishmentarianism")

Example string: "antidisestablishmentarianism"

gpt2: 5 tokens
token integers: [415, 29207, 44390, 3699, 1042]
token bytes: [b'ant', b'idis', b'establishment', b'arian', b'ism']

p50k_base: 5 tokens
token integers: [415, 29207, 44390, 3699, 1042]
token bytes: [b'ant', b'idis', b'establishment', b'arian', b'ism']

cl100k_base: 6 tokens
token integers: [519, 85342, 34500, 479, 8997, 2191]
token bytes: [b'ant', b'idis', b'establish', b'ment', b'arian', b'ism']

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compare_encodings("2 + 2 = 4")

Example string: "2 + 2 = 4"

gpt2: 5 tokens
token integers: [17, 1343, 362, 796, 604]
token bytes: [b'2', b' +', b' 2', b' =', b' 4']

p50k_base: 5 tokens
token integers: [17, 1343, 362, 796, 604]
token bytes: [b'2', b' +', b' 2', b' =', b' 4']

cl100k_base: 7 tokens
token integers: [17, 489, 220, 17, 284, 220, 19]
token bytes: [b'2', b' +', b' ', b'2', b' =', b' ', b'4']

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compare_encodings("お誕生日おめでとう")

Example string: "お誕生日おめでとう"

gpt2: 14 tokens
token integers: [2515, 232, 45739, 243, 37955, 33768, 98, 2515, 232, 1792, 223, 30640, 30201, 29557]
token bytes: [b'\xe3\x81', b'\x8a', b'\xe8\xaa', b'\x95', b'\xe7\x94\x9f', b'\xe6\x97', b'\xa5', b'\xe3\x81', b'\x8a', b'\xe3\x82', b'\x81', b'\xe3\x81\xa7', b'\xe3\x81\xa8', b'\xe3\x81\x86']

p50k_base: 14 tokens
token integers: [2515, 232, 45739, 243, 37955, 33768, 98, 2515, 232, 1792, 223, 30640, 30201, 29557]
token bytes: [b'\xe3\x81', b'\x8a', b'\xe8\xaa', b'\x95', b'\xe7\x94\x9f', b'\xe6\x97', b'\xa5', b'\xe3\x81', b'\x8a', b'\xe3\x82', b'\x81', b'\xe3\x81\xa7', b'\xe3\x81\xa8', b'\xe3\x81\x86']

cl100k_base: 9 tokens
token integers: [33334, 45918, 243, 21990, 9080, 33334, 62004, 16556, 78699]
token bytes: [b'\xe3\x81\x8a', b'\xe8\xaa', b'\x95', b'\xe7\x94\x9f', b'\xe6\x97\xa5', b'\xe3\x81\x8a', b'\xe3\x82\x81', b'\xe3\x81\xa7', b'\xe3\x81\xa8\xe3\x81\x86']

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计算chat API调用的tokens

ChatGPT models like gpt-3.5-turbo and gpt-4 use tokens in the same way as older completions models, but because of their message-based formatting, it’s more difficult to count how many tokens will be used by a conversation.

Below is an example function for counting tokens for messages passed to gpt-3.5-turbo-0301 or gpt-4-0314.

Note that the exact way that tokens are counted from messages may change from model to model. Consider the counts from the function below an estimate, not a timeless guarantee.

像 gpt-3.5-turbo 和 gpt-4 这样的ChatGPT模型使用tokens 的方式与旧的完成模型相同，但由于它们基于消息的格式，很难计算会话将使用多少tokens。
下面是一个示例函数，用于对传递到 gpt-3.5-turbo-0301 或 gpt-4-0314 的消息的tokens进行计数。
请注意，从消息中计算tokens的确切方式可能会因模型而异。将函数中的计数视为一个估计值，而不是一个永恒的保证。

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def num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301"):
    """Returns the number of tokens used by a list of messages."""
    try:
        encoding = tiktoken.encoding_for_model(model)
    except KeyError:
        print("Warning: model not found. Using cl100k_base encoding.")
        encoding = tiktoken.get_encoding("cl100k_base")
    if model == "gpt-3.5-turbo":
        print("Warning: gpt-3.5-turbo may change over time. Returning num tokens assuming gpt-3.5-turbo-0301.")
        return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301")
    elif model == "gpt-4":
        print("Warning: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
        return num_tokens_from_messages(messages, model="gpt-4-0314")
    elif model == "gpt-3.5-turbo-0301":
        tokens_per_message = 4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
        tokens_per_name = -1  # if there's a name, the role is omitted
    elif model == "gpt-4-0314":
        tokens_per_message = 3
        tokens_per_name = 1
    else:
        raise NotImplementedError(f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""")
    num_tokens = 0
    for message in messages:
        num_tokens += tokens_per_message
        for key, value in message.items():
            num_tokens += len(encoding.encode(value))
            if key == "name":
                num_tokens += tokens_per_name
    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
    return num_tokens

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# let's verify the function above matches the OpenAI API response

import openai

example_messages = [
    {
        "role": "system",
        "content": "You are a helpful, pattern-following assistant that translates corporate jargon into plain English.",
    },
    {
        "role": "system",
        "name": "example_user",
        "content": "New synergies will help drive top-line growth.",
    },
    {
        "role": "system",
        "name": "example_assistant",
        "content": "Things working well together will increase revenue.",
    },
    {
        "role": "system",
        "name": "example_user",
        "content": "Let's circle back when we have more bandwidth to touch base on opportunities for increased leverage.",
    },
    {
        "role": "system",
        "name": "example_assistant",
        "content": "Let's talk later when we're less busy about how to do better.",
    },
    {
        "role": "user",
        "content": "This late pivot means we don't have time to boil the ocean for the client deliverable.",
    },
]

for model in ["gpt-3.5-turbo-0301", "gpt-4-0314"]:
    print(model)
    # example token count from the function defined above
    print(f"{num_tokens_from_messages(example_messages, model)} prompt tokens counted by num_tokens_from_messages().")
    # example token count from the OpenAI API
    response = openai.ChatCompletion.create(
        model=model,
        messages=example_messages,
        temperature=0,
        max_tokens=1  # we're only counting input tokens here, so let's not waste tokens on the output
    )
    print(f'{response["usage"]["prompt_tokens"]} prompt tokens counted by the OpenAI API.')
    print()

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gpt-3.5-turbo-0301
127 prompt tokens counted by num_tokens_from_messages().
127 prompt tokens counted by the OpenAI API.

gpt-4-0314
129 prompt tokens counted by num_tokens_from_messages().
129 prompt tokens counted by the OpenAI API.

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拓展 tiktoken

您可能希望扩展 tiktoken 以支持新的编码。有两种方法可以做到这一点。
按照您想要的方式创建Encoding对象，然后简单地传递它。

方式一：

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,
    }
)

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方式二：
使用 tiktoken_ext 插件机制 向tiktoken注册Encoding对象。
只有当您需要 tiktoken.get_encoding 来查找您的编码时，这才有用，否则更适合上面方式1。
要做到这一点，您需要在 tiktoken_ext 下创建一个命名空间包。
这样布局你的项目，确保省略 tiktoken_ext/__init__.py文件：

my_tiktoken_extension
├── tiktoken_ext
│   └── my_encodings.py
└── setup.py

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my_encodings.py 应该是一个包含名为 ENCODING_CONSTRUCTORS 的变量的模块。
这是一个从编码名称到函数的字典，该函数不接受参数，并返回可以传递给 tiktoken.encoding 的参数来构造该编码。
例如，请参阅 tiktoken_ext/openai_public.py。有关详细信息，请参阅 tiktoken/registry.py 。
你的setup.py 应该是这样的：

from setuptools import setup, find_namespace_packages

setup(
    name="my_tiktoken_extension",
    packages=find_namespace_packages(include=['tiktoken_ext*']),
    install_requires=["tiktoken"],
    ...
)

然后简单地执行 pip install ./my_tiktoken_extension，您应该能够使用自定义编码！请确保不要使用可编辑安装。

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2023-03-31(五)