mirror of
https://github.com/SillyTavern/SillyTavern.git
synced 2024-12-15 18:59:07 +01:00
512 lines
16 KiB
JavaScript
512 lines
16 KiB
JavaScript
import { characters, main_api, nai_settings, online_status, this_chid } from "../script.js";
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import { power_user, registerDebugFunction } from "./power-user.js";
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import { chat_completion_sources, model_list, oai_settings } from "./openai.js";
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import { groups, selected_group } from "./group-chats.js";
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import { getStringHash } from "./utils.js";
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import { kai_flags } from "./kai-settings.js";
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export const CHARACTERS_PER_TOKEN_RATIO = 3.35;
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const TOKENIZER_WARNING_KEY = 'tokenizationWarningShown';
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export const tokenizers = {
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NONE: 0,
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GPT2: 1,
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OPENAI: 2,
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LLAMA: 3,
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NERD: 4,
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NERD2: 5,
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API: 6,
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MISTRAL: 7,
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BEST_MATCH: 99,
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};
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const objectStore = new localforage.createInstance({ name: "SillyTavern_ChatCompletions" });
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let tokenCache = {};
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/**
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* Guesstimates the token count for a string.
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* @param {string} str String to tokenize.
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* @returns {number} Token count.
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*/
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export function guesstimate(str) {
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return Math.ceil(str.length / CHARACTERS_PER_TOKEN_RATIO);
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}
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async function loadTokenCache() {
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try {
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console.debug('Chat Completions: loading token cache')
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tokenCache = await objectStore.getItem('tokenCache') || {};
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} catch (e) {
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console.log('Chat Completions: unable to load token cache, using default value', e);
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tokenCache = {};
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}
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}
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export async function saveTokenCache() {
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try {
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console.debug('Chat Completions: saving token cache')
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await objectStore.setItem('tokenCache', tokenCache);
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} catch (e) {
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console.log('Chat Completions: unable to save token cache', e);
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}
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}
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async function resetTokenCache() {
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try {
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console.debug('Chat Completions: resetting token cache');
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Object.keys(tokenCache).forEach(key => delete tokenCache[key]);
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await objectStore.removeItem('tokenCache');
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toastr.success('Token cache cleared. Please reload the chat to re-tokenize it.');
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} catch (e) {
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console.log('Chat Completions: unable to reset token cache', e);
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}
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}
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/**
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* Gets the friendly name of the current tokenizer.
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* @param {string} forApi API to get the tokenizer for. Defaults to the main API.
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* @returns { { tokenizerName: string, tokenizerId: number } } Tokenizer info
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*/
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export function getFriendlyTokenizerName(forApi) {
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if (!forApi) {
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forApi = main_api;
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}
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const tokenizerOption = $("#tokenizer").find(':selected');
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let tokenizerId = Number(tokenizerOption.val());
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let tokenizerName = tokenizerOption.text();
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if (forApi !== 'openai' && tokenizerId === tokenizers.BEST_MATCH) {
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tokenizerId = getTokenizerBestMatch(forApi);
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tokenizerName = $(`#tokenizer option[value="${tokenizerId}"]`).text();
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}
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tokenizerName = forApi == 'openai'
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? getTokenizerModel()
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: tokenizerName;
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tokenizerId = forApi == 'openai'
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? tokenizers.OPENAI
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: tokenizerId;
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return { tokenizerName, tokenizerId };
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}
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/**
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* Gets the best tokenizer for the current API.
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* @param {string} forApi API to get the tokenizer for. Defaults to the main API.
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* @returns {number} Tokenizer type.
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*/
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export function getTokenizerBestMatch(forApi) {
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if (!forApi) {
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forApi = main_api;
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}
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if (forApi === 'novel') {
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if (nai_settings.model_novel.includes('clio')) {
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return tokenizers.NERD;
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}
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if (nai_settings.model_novel.includes('kayra')) {
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return tokenizers.NERD2;
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}
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}
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if (forApi === 'kobold' || forApi === 'textgenerationwebui' || forApi === 'koboldhorde') {
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// Try to use the API tokenizer if possible:
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// - API must be connected
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// - Kobold must pass a version check
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// - Tokenizer haven't reported an error previously
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if (kai_flags.can_use_tokenization && !sessionStorage.getItem(TOKENIZER_WARNING_KEY) && online_status !== 'no_connection') {
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return tokenizers.API;
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}
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return tokenizers.LLAMA;
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}
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return tokenizers.NONE;
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}
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/**
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* Calls the underlying tokenizer model to the token count for a string.
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* @param {number} type Tokenizer type.
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* @param {string} str String to tokenize.
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* @param {number} padding Number of padding tokens.
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* @returns {number} Token count.
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*/
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function callTokenizer(type, str, padding) {
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switch (type) {
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case tokenizers.NONE:
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return guesstimate(str) + padding;
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case tokenizers.GPT2:
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return countTokensRemote('/api/tokenize/gpt2', str, padding);
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case tokenizers.LLAMA:
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return countTokensRemote('/api/tokenize/llama', str, padding);
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case tokenizers.NERD:
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return countTokensRemote('/api/tokenize/nerdstash', str, padding);
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case tokenizers.NERD2:
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return countTokensRemote('/api/tokenize/nerdstash_v2', str, padding);
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case tokenizers.MISTRAL:
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return countTokensRemote('/api/tokenize/mistral', str, padding);
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case tokenizers.API:
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return countTokensRemote('/tokenize_via_api', str, padding);
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default:
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console.warn("Unknown tokenizer type", type);
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return callTokenizer(tokenizers.NONE, str, padding);
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}
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}
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/**
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* Gets the token count for a string using the current model tokenizer.
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* @param {string} str String to tokenize
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* @param {number | undefined} padding Optional padding tokens. Defaults to 0.
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* @returns {number} Token count.
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*/
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export function getTokenCount(str, padding = undefined) {
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if (typeof str !== 'string' || !str?.length) {
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return 0;
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}
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let tokenizerType = power_user.tokenizer;
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if (main_api === 'openai') {
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if (padding === power_user.token_padding) {
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// For main "shadow" prompt building
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tokenizerType = tokenizers.NONE;
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} else {
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// For extensions and WI
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return counterWrapperOpenAI(str);
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}
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}
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if (tokenizerType === tokenizers.BEST_MATCH) {
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tokenizerType = getTokenizerBestMatch(main_api);
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}
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if (padding === undefined) {
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padding = 0;
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}
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const cacheObject = getTokenCacheObject();
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const hash = getStringHash(str);
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const cacheKey = `${tokenizerType}-${hash}+${padding}`;
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if (typeof cacheObject[cacheKey] === 'number') {
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return cacheObject[cacheKey];
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}
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const result = callTokenizer(tokenizerType, str, padding);
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if (isNaN(result)) {
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console.warn("Token count calculation returned NaN");
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return 0;
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}
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cacheObject[cacheKey] = result;
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return result;
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}
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/**
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* Gets the token count for a string using the OpenAI tokenizer.
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* @param {string} text Text to tokenize.
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* @returns {number} Token count.
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*/
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function counterWrapperOpenAI(text) {
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const message = { role: 'system', content: text };
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return countTokensOpenAI(message, true);
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}
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export function getTokenizerModel() {
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// OpenAI models always provide their own tokenizer
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if (oai_settings.chat_completion_source == chat_completion_sources.OPENAI) {
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return oai_settings.openai_model;
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}
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const turbo0301Tokenizer = 'gpt-3.5-turbo-0301';
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const turboTokenizer = 'gpt-3.5-turbo';
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const gpt4Tokenizer = 'gpt-4';
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const gpt2Tokenizer = 'gpt2';
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const claudeTokenizer = 'claude';
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const llamaTokenizer = 'llama';
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const mistralTokenizer = 'mistral';
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// Assuming no one would use it for different models.. right?
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if (oai_settings.chat_completion_source == chat_completion_sources.SCALE) {
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return gpt4Tokenizer;
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}
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// Select correct tokenizer for WindowAI proxies
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if (oai_settings.chat_completion_source == chat_completion_sources.WINDOWAI && oai_settings.windowai_model) {
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if (oai_settings.windowai_model.includes('gpt-4')) {
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return gpt4Tokenizer;
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}
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else if (oai_settings.windowai_model.includes('gpt-3.5-turbo-0301')) {
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return turbo0301Tokenizer;
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}
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else if (oai_settings.windowai_model.includes('gpt-3.5-turbo')) {
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return turboTokenizer;
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}
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else if (oai_settings.windowai_model.includes('claude')) {
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return claudeTokenizer;
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}
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else if (oai_settings.windowai_model.includes('GPT-NeoXT')) {
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return gpt2Tokenizer;
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}
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}
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// And for OpenRouter (if not a site model, then it's impossible to determine the tokenizer)
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if (oai_settings.chat_completion_source == chat_completion_sources.OPENROUTER && oai_settings.openrouter_model) {
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const model = model_list.find(x => x.id === oai_settings.openrouter_model);
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if (model?.architecture?.tokenizer === 'Llama2') {
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return llamaTokenizer;
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}
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else if (model?.architecture?.tokenizer === 'Mistral') {
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return mistralTokenizer;
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}
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else if (oai_settings.openrouter_model.includes('gpt-4')) {
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return gpt4Tokenizer;
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}
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else if (oai_settings.openrouter_model.includes('gpt-3.5-turbo-0301')) {
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return turbo0301Tokenizer;
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}
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else if (oai_settings.openrouter_model.includes('gpt-3.5-turbo')) {
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return turboTokenizer;
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}
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else if (oai_settings.openrouter_model.includes('claude')) {
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return claudeTokenizer;
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}
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else if (oai_settings.openrouter_model.includes('GPT-NeoXT')) {
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return gpt2Tokenizer;
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}
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}
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if (oai_settings.chat_completion_source == chat_completion_sources.CLAUDE) {
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return claudeTokenizer;
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}
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// Default to Turbo 3.5
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return turboTokenizer;
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}
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/**
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* @param {any[] | Object} messages
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*/
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export function countTokensOpenAI(messages, full = false) {
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const shouldTokenizeAI21 = oai_settings.chat_completion_source === chat_completion_sources.AI21 && oai_settings.use_ai21_tokenizer;
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const cacheObject = getTokenCacheObject();
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if (!Array.isArray(messages)) {
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messages = [messages];
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}
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let token_count = -1;
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for (const message of messages) {
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const model = getTokenizerModel();
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if (model === 'claude' || shouldTokenizeAI21) {
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full = true;
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}
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const hash = getStringHash(JSON.stringify(message));
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const cacheKey = `${model}-${hash}`;
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const cachedCount = cacheObject[cacheKey];
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if (typeof cachedCount === 'number') {
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token_count += cachedCount;
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}
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else {
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jQuery.ajax({
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async: false,
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type: 'POST', //
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url: shouldTokenizeAI21 ? '/api/tokenize/ai21' : `/api/tokenize/openai?model=${model}`,
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data: JSON.stringify([message]),
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dataType: "json",
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contentType: "application/json",
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success: function (data) {
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token_count += Number(data.token_count);
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cacheObject[cacheKey] = Number(data.token_count);
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}
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});
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}
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}
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if (!full) token_count -= 2;
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return token_count;
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}
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/**
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* Gets the token cache object for the current chat.
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* @returns {Object} Token cache object for the current chat.
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*/
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function getTokenCacheObject() {
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let chatId = 'undefined';
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try {
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if (selected_group) {
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chatId = groups.find(x => x.id == selected_group)?.chat_id;
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}
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else if (this_chid !== undefined) {
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chatId = characters[this_chid].chat;
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}
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} catch {
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console.log('No character / group selected. Using default cache item');
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}
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if (typeof tokenCache[chatId] !== 'object') {
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tokenCache[chatId] = {};
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}
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return tokenCache[String(chatId)];
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}
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/**
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* Counts token using the remote server API.
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* @param {string} endpoint API endpoint.
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* @param {string} str String to tokenize.
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* @param {number} padding Number of padding tokens.
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* @returns {number} Token count with padding.
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*/
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function countTokensRemote(endpoint, str, padding) {
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let tokenCount = 0;
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jQuery.ajax({
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async: false,
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type: 'POST',
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url: endpoint,
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data: JSON.stringify({ text: str }),
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dataType: "json",
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contentType: "application/json",
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success: function (data) {
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if (typeof data.count === 'number') {
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tokenCount = data.count;
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} else {
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tokenCount = guesstimate(str);
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console.error("Error counting tokens");
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if (!sessionStorage.getItem(TOKENIZER_WARNING_KEY)) {
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toastr.warning(
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"Your selected API doesn't support the tokenization endpoint. Using estimated counts.",
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"Error counting tokens",
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{ timeOut: 10000, preventDuplicates: true },
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);
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sessionStorage.setItem(TOKENIZER_WARNING_KEY, String(true));
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}
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}
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}
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});
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return tokenCount + padding;
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}
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/**
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* Calls the underlying tokenizer model to encode a string to tokens.
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* @param {string} endpoint API endpoint.
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* @param {string} str String to tokenize.
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* @param {string} model Tokenizer model.
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* @returns {number[]} Array of token ids.
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*/
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function getTextTokensRemote(endpoint, str, model = '') {
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if (model) {
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endpoint += `?model=${model}`;
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}
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let ids = [];
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jQuery.ajax({
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async: false,
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type: 'POST',
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url: endpoint,
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data: JSON.stringify({ text: str }),
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dataType: "json",
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contentType: "application/json",
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success: function (data) {
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ids = data.ids;
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// Don't want to break reverse compatibility, so sprinkle in some of the JS magic
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if (Array.isArray(data.chunks)) {
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Object.defineProperty(ids, 'chunks', { value: data.chunks });
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}
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}
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});
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return ids;
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}
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/**
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* Calls the underlying tokenizer model to decode token ids to text.
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* @param {string} endpoint API endpoint.
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* @param {number[]} ids Array of token ids
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*/
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function decodeTextTokensRemote(endpoint, ids) {
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let text = '';
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jQuery.ajax({
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async: false,
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type: 'POST',
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url: endpoint,
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data: JSON.stringify({ ids: ids }),
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dataType: "json",
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contentType: "application/json",
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success: function (data) {
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text = data.text;
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}
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});
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return text;
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}
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/**
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* Encodes a string to tokens using the remote server API.
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* @param {number} tokenizerType Tokenizer type.
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* @param {string} str String to tokenize.
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* @returns {number[]} Array of token ids.
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*/
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export function getTextTokens(tokenizerType, str) {
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switch (tokenizerType) {
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case tokenizers.GPT2:
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return getTextTokensRemote('/api/tokenize/gpt2', str);
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case tokenizers.LLAMA:
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return getTextTokensRemote('/api/tokenize/llama', str);
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case tokenizers.NERD:
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return getTextTokensRemote('/api/tokenize/nerdstash', str);
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case tokenizers.NERD2:
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return getTextTokensRemote('/api/tokenize/nerdstash_v2', str);
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case tokenizers.MISTRAL:
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return getTextTokensRemote('/api/tokenize/mistral', str);
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case tokenizers.OPENAI:
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const model = getTokenizerModel();
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return getTextTokensRemote('/api/tokenize/openai-encode', str, model);
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default:
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console.warn("Calling getTextTokens with unsupported tokenizer type", tokenizerType);
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return [];
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}
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}
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/**
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* Decodes token ids to text using the remote server API.
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* @param {number} tokenizerType Tokenizer type.
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* @param {number[]} ids Array of token ids
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*/
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export function decodeTextTokens(tokenizerType, ids) {
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switch (tokenizerType) {
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case tokenizers.GPT2:
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return decodeTextTokensRemote('/api/decode/gpt2', ids);
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case tokenizers.LLAMA:
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return decodeTextTokensRemote('/api/decode/llama', ids);
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case tokenizers.NERD:
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return decodeTextTokensRemote('/api/decode/nerdstash', ids);
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case tokenizers.NERD2:
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return decodeTextTokensRemote('/api/decode/nerdstash_v2', ids);
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case tokenizers.MISTRAL:
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return decodeTextTokensRemote('/api/decode/mistral', ids);
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default:
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console.warn("Calling decodeTextTokens with unsupported tokenizer type", tokenizerType);
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return '';
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}
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}
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export async function initTokenizers() {
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await loadTokenCache();
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registerDebugFunction('resetTokenCache', 'Reset token cache', 'Purges the calculated token counts. Use this if you want to force a full re-tokenization of all chats or suspect the token counts are wrong.', resetTokenCache);
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}
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