Here's how OpenAI Token count is computed in Tiktokenizer

In this arcticle, we will review how OpenAI Token count is computed in Tiktokenizer. We will look at:

Tokenizer interface
TiktokenTokenizer

For more context, read the part 1.

Tokenizer interface

In tiktokenizer/src/models/tokenizer.ts, at line 20, you will find the following code


export interface Tokenizer {
  name: string;
  tokenize(text: string): TokenizerResult;
  free?(): void;
}

This interface is implemented by a class named Tiktokentokenizer as shown below


export class TiktokenTokenizer implements Tokenizer {

TiktokenTokenizer

You will find the following code at line 26 in tiktokenizer/src/models/tokenizer.ts.


export class TiktokenTokenizer implements Tokenizer {
  private enc: Tiktoken;
  name: string;
  constructor(model: z.infer<typeof oaiModels> | z.infer<typeof oaiEncodings>) {
    const isModel = oaiModels.safeParse(model);
    const isEncoding = oaiEncodings.safeParse(model);
    console.log(isModel.success, isEncoding.success, model)
    if (isModel.success) {
 
      if (
        model === "text-embedding-3-small" ||
        model === "text-embedding-3-large"
      ) {
        throw new Error("Model may be too new");
      }
 
      const enc =
        model === "gpt-3.5-turbo" || model === "gpt-4" || model === "gpt-4-32k"
          ? get_encoding("cl100k_base", {
              "<|im_start|>": 100264,
              "<|im_end|>": 100265,
              "<|im_sep|>": 100266,
            })
          : model === "gpt-4o"
          ? get_encoding("o200k_base", {
              "<|im_start|>": 200264,
              "<|im_end|>": 200265,
              "<|im_sep|>": 200266,
            })
          : // @ts-expect-error r50k broken?
            encoding_for_model(model);
      this.name = enc.name ?? model;
      this.enc = enc;
    } else if (isEncoding.success) {
      this.enc = get_encoding(isEncoding.data);
      this.name = isEncoding.data;
    } else {
      throw new Error("Invalid model or encoding");
    }
  }
 
  tokenize(text: string): TokenizerResult {
    const tokens = [...(this.enc?.encode(text, "all") ?? [])];
    return {
      name: this.name,
      tokens,
      segments: getTiktokenSegments(this.enc, text),
      count: tokens.length,
    };
  }
 
  free(): void {
    this.enc.free();
  }
}

constructor

This below code is picked from the TiktokenTokenizer class.


 constructor(model: z.infer<typeof oaiModels> | z.infer<typeof oaiEncodings>) {
    const isModel = oaiModels.safeParse(model);
    const isEncoding = oaiEncodings.safeParse(model);
    console.log(isModel.success, isEncoding.success, model)
    if (isModel.success) {
 
      if (
        model === "text-embedding-3-small" ||
        model === "text-embedding-3-large"
      ) {
        throw new Error("Model may be too new");
      }
 
      const enc =
        model === "gpt-3.5-turbo" || model === "gpt-4" || model === "gpt-4-32k"
          ? get_encoding("cl100k_base", {
              "<|im_start|>": 100264,
              "<|im_end|>": 100265,
              "<|im_sep|>": 100266,
            })
          : model === "gpt-4o"
          ? get_encoding("o200k_base", {
              "<|im_start|>": 200264,
              "<|im_end|>": 200265,
              "<|im_sep|>": 200266,
            })
          : // @ts-expect-error r50k broken?
            encoding_for_model(model);
      this.name = enc.name ?? model;
      this.enc = enc;
    } else if (isEncoding.success) {
      this.enc = get_encoding(isEncoding.data);
      this.name = isEncoding.data;
    } else {
      throw new Error("Invalid model or encoding");
    }
  }

This constructor is used to set this.name and this.enc.

At line 42, you will find the following code


 const enc =
        model === "gpt-3.5-turbo" || model === "gpt-4" || model === "gpt-4-32k"
          ? get_encoding("cl100k_base", {
              "<|im_start|>": 100264,
              "<|im_end|>": 100265,
              "<|im_sep|>": 100266,
            })
          : model === "gpt-4o"
          ? get_encoding("o200k_base", {
              "<|im_start|>": 200264,
              "<|im_end|>": 200265,
              "<|im_sep|>": 200266,
            })
          : // @ts-expect-error r50k broken?
            encoding_for_model(model);

get_encoding returns a value that is assigned to enc.

get_encoding is imported as shown below:


import { get_encoding, encoding_for_model, type Tiktoken } from "tiktoken";

Tiktoken is fast BPE tokenizer for use with openAI models.

Learn more about tiktoken

tokenize

In the same class, tokenize is defined as following:


 tokenize(text: string): TokenizerResult {
    const tokens = [...(this.enc?.encode(text, "all") ?? [])];
    return {
      name: this.name,
      tokens,
      segments: getTiktokenSegments(this.enc, text),
      count: tokens.length,
    };
  }

This returns an object that contains name, token, count and segments.

This is the function that is used in tiktokenizer/src/pages/index.tsx at line 48.


  const tokens = tokenizer.data?.tokenize(inputText);

free

free has the following code


  free(): void {
    this.enc.free();
  }

About me:

Hey, my name is Ramu Narasinga. I study codebase architecture in large open-source projects.

Email: ramu.narasinga@gmail.com

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Here's how OpenAI Token count is computed in Tiktokenizer - Part 2