Spaces:
Paused
Paused
File size: 6,327 Bytes
a48ca26 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 |
package common
import (
"errors"
"fmt"
logger "genspark2api/common/loggger"
"genspark2api/model"
"github.com/pkoukk/tiktoken-go"
"strings"
)
// tokenEncoderMap won't grow after initialization
var tokenEncoderMap = map[string]*tiktoken.Tiktoken{}
var defaultTokenEncoder *tiktoken.Tiktoken
func InitTokenEncoders() {
logger.SysLog("initializing token encoders...")
gpt35TokenEncoder, err := tiktoken.EncodingForModel("gpt-3.5-turbo")
if err != nil {
logger.FatalLog(fmt.Sprintf("failed to get gpt-3.5-turbo token encoder: %s", err.Error()))
}
defaultTokenEncoder = gpt35TokenEncoder
gpt4oTokenEncoder, err := tiktoken.EncodingForModel("gpt-4o")
if err != nil {
logger.FatalLog(fmt.Sprintf("failed to get gpt-4o token encoder: %s", err.Error()))
}
gpt4TokenEncoder, err := tiktoken.EncodingForModel("gpt-4")
if err != nil {
logger.FatalLog(fmt.Sprintf("failed to get gpt-4 token encoder: %s", err.Error()))
}
for _, model := range DefaultOpenaiModelList {
if strings.HasPrefix(model, "gpt-3.5") {
tokenEncoderMap[model] = gpt35TokenEncoder
} else if strings.HasPrefix(model, "gpt-4o") {
tokenEncoderMap[model] = gpt4oTokenEncoder
} else if strings.HasPrefix(model, "gpt-4") {
tokenEncoderMap[model] = gpt4TokenEncoder
} else {
tokenEncoderMap[model] = nil
}
}
logger.SysLog("token encoders initialized.")
}
func getTokenEncoder(model string) *tiktoken.Tiktoken {
tokenEncoder, ok := tokenEncoderMap[model]
if ok && tokenEncoder != nil {
return tokenEncoder
}
if ok {
tokenEncoder, err := tiktoken.EncodingForModel(model)
if err != nil {
//logger.SysError(fmt.Sprintf("[IGNORE] | failed to get token encoder for model %s: %s, using encoder for gpt-3.5-turbo", model, err.Error()))
tokenEncoder = defaultTokenEncoder
}
tokenEncoderMap[model] = tokenEncoder
return tokenEncoder
}
return defaultTokenEncoder
}
func getTokenNum(tokenEncoder *tiktoken.Tiktoken, text string) int {
return len(tokenEncoder.Encode(text, nil, nil))
}
func CountTokenMessages(messages []model.OpenAIChatMessage, model string) int {
tokenEncoder := getTokenEncoder(model)
// Reference:
// https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
// https://github.com/pkoukk/tiktoken-go/issues/6
//
// Every message follows <|start|>{role/name}\n{content}<|end|>\n
var tokensPerMessage int
if model == "gpt-3.5-turbo-0301" {
tokensPerMessage = 4
} else {
tokensPerMessage = 3
}
tokenNum := 0
for _, message := range messages {
tokenNum += tokensPerMessage
switch v := message.Content.(type) {
case string:
tokenNum += getTokenNum(tokenEncoder, v)
case []any:
for _, it := range v {
m := it.(map[string]any)
switch m["type"] {
case "text":
if textValue, ok := m["text"]; ok {
if textString, ok := textValue.(string); ok {
tokenNum += getTokenNum(tokenEncoder, textString)
}
}
case "image_url":
imageUrl, ok := m["image_url"].(map[string]any)
if ok {
url := imageUrl["url"].(string)
detail := ""
if imageUrl["detail"] != nil {
detail = imageUrl["detail"].(string)
}
imageTokens, err := countImageTokens(url, detail, model)
if err != nil {
logger.SysError("error counting image tokens: " + err.Error())
} else {
tokenNum += imageTokens
}
}
}
}
}
tokenNum += getTokenNum(tokenEncoder, message.Role)
}
tokenNum += 3 // Every reply is primed with <|start|>assistant<|message|>
return tokenNum
}
const (
lowDetailCost = 85
highDetailCostPerTile = 170
additionalCost = 85
// gpt-4o-mini cost higher than other model
gpt4oMiniLowDetailCost = 2833
gpt4oMiniHighDetailCost = 5667
gpt4oMiniAdditionalCost = 2833
)
// https://platform.openai.com/docs/guides/vision/calculating-costs
// https://github.com/openai/openai-cookbook/blob/05e3f9be4c7a2ae7ecf029a7c32065b024730ebe/examples/How_to_count_tokens_with_tiktoken.ipynb
func countImageTokens(url string, detail string, model string) (_ int, err error) {
// Reference: https://platform.openai.com/docs/guides/vision/low-or-high-fidelity-image-understanding
// detail == "auto" is undocumented on how it works, it just said the model will use the auto setting which will look at the image input size and decide if it should use the low or high setting.
// According to the official guide, "low" disable the high-res model,
// and only receive low-res 512px x 512px version of the image, indicating
// that image is treated as low-res when size is smaller than 512px x 512px,
// then we can assume that image size larger than 512px x 512px is treated
// as high-res. Then we have the following logic:
// if detail == "" || detail == "auto" {
// width, height, err = image.GetImageSize(url)
// if err != nil {
// return 0, err
// }
// fetchSize = false
// // not sure if this is correct
// if width > 512 || height > 512 {
// detail = "high"
// } else {
// detail = "low"
// }
// }
// However, in my test, it seems to be always the same as "high".
// The following image, which is 125x50, is still treated as high-res, taken
// 255 tokens in the response of non-stream chat completion api.
// https://upload.wikimedia.org/wikipedia/commons/1/10/18_Infantry_Division_Messina.jpg
if detail == "" || detail == "auto" {
// assume by test, not sure if this is correct
detail = "low"
}
switch detail {
case "low":
if strings.HasPrefix(model, "gpt-4o-mini") {
return gpt4oMiniLowDetailCost, nil
}
return lowDetailCost, nil
default:
return 0, errors.New("invalid detail option")
}
}
func CountTokenInput(input any, model string) int {
switch v := input.(type) {
case string:
return CountTokenText(v, model)
case []string:
text := ""
for _, s := range v {
text += s
}
return CountTokenText(text, model)
}
return 0
}
func CountTokenText(text string, model string) int {
tokenEncoder := getTokenEncoder(model)
return getTokenNum(tokenEncoder, text)
}
func CountToken(text string) int {
return CountTokenInput(text, "gpt-3.5-turbo")
}
|