tiktoken-go

OpenAI's tiktoken in Go.

Tiktoken is a fast BPE tokeniser for use with OpenAI's models.

This is a port of the original tiktoken.

Usage

Install

go get github.com/shapor/tiktoken-go

Examples

Get Token By Encoding

package main

import (
    "fmt"
    "github.com/shapor/tiktoken-go"
)

func main()  {
	text := "Hello, world!"
	encoding := "cl100k_base"

	tke, err := tiktoken.GetEncoding(encoding)
	if err != nil {
		err = fmt.Errorf("getEncoding: %v", err)
		return
	}

	// encode
	token := tke.Encode(text, nil, nil)

	//tokens
	fmt.Println((token))
	// num_tokens
	fmt.Println(len(token))
}

Get Token By Model

package main

import (
    "fmt"
    "github.com/shapor/tiktoken-go"
)

func main()  {
	text := "Hello, world!"
	encoding := "gpt-3.5-turbo"

	tkm, err := tiktoken.EncodingForModel(encoding)
	if err != nil {
		err = fmt.Errorf("getEncoding: %v", err)
		return
	}

	// encode
	token := tkm.Encode(text, nil, nil)

	// tokens
	fmt.Println(token)
	// num_tokens
	fmt.Println(len(token))
}

Counting Tokens For Chat API Calls

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

The following code was written based on openai-cookbook examples at Wednesday, 28 June 2023.

Please note that the token calculation method for the message may change at any time, so this code may not necessarily be applicable in the future.

If you need accurate calculation, please refer to the official documentation.

If you find that this code is no longer applicable, please feel free to submit a PR or Issue.

package main

import (
	"fmt"

	"github.com/shapor/tiktoken-go"
	"github.com/sashabaranov/go-openai"
)

// OpenAI Cookbook: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
func NumTokensFromMessages(messages []openai.ChatCompletionMessage, model string) (numTokens int) {
	tkm, err := tiktoken.EncodingForModel(model)
	if err != nil {
		err = fmt.Errorf("encoding for model: %v", err)
		log.Println(err)
		return
	}

	var tokensPerMessage, tokensPerName int
	switch model {
	case "gpt-3.5-turbo-0613",
		"gpt-3.5-turbo-16k-0613",
		"gpt-4-0314",
		"gpt-4-32k-0314",
		"gpt-4-0613",
		"gpt-4-32k-0613":
		tokensPerMessage = 3
		tokensPerName = 1
	case "gpt-3.5-turbo-0301":
		tokensPerMessage = 4 // every message follows <|start|>{role/name}\n{content}<|end|>\n
		tokensPerName = -1   // if there's a name, the role is omitted
	default:
		if strings.Contains(model, "gpt-3.5-turbo") {
			log.Println("warning: gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613.")
			return NumTokensFromMessages(messages, "gpt-3.5-turbo-0613")
		} else if strings.Contains(model, "gpt-4") {
			log.Println("warning: gpt-4 may update over time. Returning num tokens assuming gpt-4-0613.")
			return NumTokensFromMessages(messages, "gpt-4-0613")
		} else {
			err = fmt.Errorf("num_tokens_from_messages() is not implemented for model %s. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.", model)
			log.Println(err)
			return
		}
	}

	for _, message := range messages {
		numTokens += tokensPerMessage
		numTokens += len(tkm.Encode(message.Content, nil, nil))
		numTokens += len(tkm.Encode(message.Role, nil, nil))
		numTokens += len(tkm.Encode(message.Name, nil, nil))
		if message.Name != "" {
			numTokens += tokensPerName
		}
	}
	numTokens += 3 // every reply is primed with <|start|>assistant<|message|>
	return numTokens
}

Available Encodings

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`

Available Models

Model name	OpenAI models
gpt-4-*	cl100k_base
gpt-3.5-turbo-*	cl100k_base
gpt-4	cl100k_base
gpt-3.5-turbo	cl100k_base
text-davinci-003	p50k_base
text-davinci-002	p50k_base
text-davinci-001	r50k_base
text-curie-001	r50k_base
text-babbage-001	r50k_base
text-ada-001	r50k_base
davinci	r50k_base
curie	r50k_base
babbage	r50k_base
ada	r50k_base
code-davinci-002	p50k_base
code-davinci-001	p50k_base
code-cushman-002	p50k_base
code-cushman-001	p50k_base
davinci-codex	p50k_base
cushman-codex	p50k_base
text-davinci-edit-001	p50k_edit
code-davinci-edit-001	p50k_edit
text-embedding-ada-002	cl100k_base
text-similarity-davinci-001	r50k_base
text-similarity-curie-001	r50k_base
text-similarity-babbage-001	r50k_base
text-similarity-ada-001	r50k_base
text-search-davinci-doc-001	r50k_base
text-search-curie-doc-001	r50k_base
text-search-babbage-doc-001	r50k_base
text-search-ada-doc-001	r50k_base
code-search-babbage-code-001	r50k_base
code-search-ada-code-001	r50k_base
gpt2	gpt2

Test

you can run test in test folder

compare with original tiktoken

get token by encoding

result

get token by model

result

Benchmark

you can run benchmark in test folder

Benchmark result

name	time/op	os	cpu	text	times
tiktoken-go	8795ns	macOS 13.2	Apple M1	UDHR	100000
tiktoken	8838ns	macOS 13.2	Apple M1	UDHR	100000

It looks like the performance is almost the same.

Maybe the difference is due to the difference in the performance of the machine.

Or maybe my benchmark method is not appropriate.

If you have better benchmark method or if you want add your benchmark result, please feel free to submit a PR.

License

MIT