#!/usr/bin/python3 #==================================================================# # KoboldAI # Version: 1.19.2 # By: The KoboldAI Community #==================================================================# # External packages import eventlet eventlet.monkey_patch(all=True, thread=False, os=False) import os os.system("") __file__ = os.path.dirname(os.path.realpath(__file__)) os.chdir(__file__) os.environ['EVENTLET_THREADPOOL_SIZE'] = '1' os.environ['TOKENIZERS_PARALLELISM'] = 'false' from eventlet import tpool import logging from logger import logger, set_logger_verbosity, quiesce_logger logging.getLogger("urllib3").setLevel(logging.ERROR) from os import path, getcwd import time import re import json import collections import zipfile import packaging import packaging.version import contextlib import traceback import threading import markdown import bleach import itertools import bisect import functools import traceback import inspect import warnings from collections.abc import Iterable from typing import Any, Callable, TypeVar, Tuple, Union, Dict, Set, List, Optional, Type import requests import html import argparse import sys import gc import lupa import importlib # KoboldAI import fileops import gensettings from utils import debounce import utils import structures import torch from transformers import StoppingCriteria, GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM, AutoTokenizer, PreTrainedModel, modeling_utils from transformers import __version__ as transformers_version import transformers try: from transformers.models.opt.modeling_opt import OPTDecoder except: pass import transformers.generation_utils global tpu_mtj_backend if lupa.LUA_VERSION[:2] != (5, 4): logger.error(f"Please install lupa==1.10. You have lupa {lupa.__version__}.") patch_causallm_patched = False # Make sure tqdm progress bars display properly in Colab from tqdm.auto import tqdm old_init = tqdm.__init__ def new_init(self, *args, **kwargs): old_init(self, *args, **kwargs) if(self.ncols == 0 and kwargs.get("ncols") != 0): self.ncols = 99 tqdm.__init__ = new_init # Fix some issues with the OPT tokenizer from transformers import PreTrainedTokenizerBase old_pretrainedtokenizerbase_from_pretrained = PreTrainedTokenizerBase.from_pretrained.__func__ @classmethod def new_pretrainedtokenizerbase_from_pretrained(cls, *args, **kwargs): tokenizer = old_pretrainedtokenizerbase_from_pretrained(cls, *args, **kwargs) tokenizer._koboldai_header = tokenizer.encode("") tokenizer.add_bos_token = False tokenizer.add_prefix_space = False return tokenizer PreTrainedTokenizerBase.from_pretrained = new_pretrainedtokenizerbase_from_pretrained #==================================================================# # Variables & Storage #==================================================================# # Terminal tags for colored text class colors: PURPLE = '\033[95m' BLUE = '\033[94m' CYAN = '\033[96m' GREEN = '\033[92m' YELLOW = '\033[93m' RED = '\033[91m' END = '\033[0m' UNDERLINE = '\033[4m' # AI models Menu # This is a dict of lists where they key is the menu name, and the list is the menu items. # Each item takes the 4 elements, 1: Text to display, 2: Model Name (var.model) or menu name (Key name for another menu), # 3: the memory requirement for the model, 4: if the item is a menu or not (True/False) model_menu = { 'mainmenu': [ ["Load a model from its directory", "NeoCustom", "", False], ["Load an old GPT-2 model (eg CloverEdition)", "GPT2Custom", "", False], ["Adventure Models", "adventurelist", "", True], ["Novel Models", "novellist", "", True], ["NSFW Models", "nsfwlist", "", True], ["Untuned OPT", "optlist", "", True], ["Untuned GPT-Neo/J", "gptneolist", "", True], ["Untuned Pythia", "pythialist", "", True], ["Untuned Fairseq Dense", "fsdlist", "", True], ["Untuned Bloom", "bloomlist", "", True], ["Untuned XGLM", "xglmlist", "", True], ["Untuned GPT2", "gpt2list", "", True], ["Online Services", "apilist", "", True], ["Read Only (No AI)", "ReadOnly", "", False] ], 'adventurelist': [ ["Skein 20B", "KoboldAI/GPT-NeoX-20B-Skein", "64GB", False], ["Nerys OPT 13B V2 (Hybrid)", "KoboldAI/OPT-13B-Nerys-v2", "32GB", False], ["Nerys FSD 13B V2 (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys-v2", "32GB", False], ["Nerys FSD 13B (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys", "32GB", False], ["Skein 6B", "KoboldAI/GPT-J-6B-Skein", "16GB", False], ["OPT Nerys 6B V2 (Hybrid)", "KoboldAI/OPT-6B-nerys-v2", "16GB", False], ["Adventure 6B", "KoboldAI/GPT-J-6B-Adventure", "16GB", False], ["Nerys FSD 2.7B (Hybrid)", "KoboldAI/fairseq-dense-2.7B-Nerys", "8GB", False], ["Adventure 2.7B", "KoboldAI/GPT-Neo-2.7B-AID", "8GB", False], ["Adventure 1.3B", "KoboldAI/GPT-Neo-1.3B-Adventure", "6GB", False], ["Adventure 125M (Mia)", "Merry/AID-Neo-125M", "2GB", False], ["Return to Main Menu", "mainmenu", "", True], ], 'novellist': [ ["Nerys OPT 13B V2 (Hybrid)", "KoboldAI/OPT-13B-Nerys-v2", "32GB", False], ["Nerys FSD 13B V2 (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys-v2", "32GB", False], ["Janeway FSD 13B", "KoboldAI/fairseq-dense-13B-Janeway", "32GB", False], ["Nerys FSD 13B (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys", "32GB", False], ["OPT Nerys 6B V2 (Hybrid)", "KoboldAI/OPT-6B-nerys-v2", "16GB", False], ["Janeway FSD 6.7B", "KoboldAI/fairseq-dense-6.7B-Janeway", "16GB", False], ["Janeway Neo 6B", "KoboldAI/GPT-J-6B-Janeway", "16GB", False], ["Qilin Lit 6B (SFW)", "rexwang8/qilin-lit-6b", "16GB", False], ["Janeway Neo 2.7B", "KoboldAI/GPT-Neo-2.7B-Janeway", "8GB", False], ["Janeway FSD 2.7B", "KoboldAI/fairseq-dense-2.7B-Janeway", "8GB", False], ["Nerys FSD 2.7B (Hybrid)", "KoboldAI/fairseq-dense-2.7B-Nerys", "8GB", False], ["Horni-LN 2.7B", "KoboldAI/GPT-Neo-2.7B-Horni-LN", "8GB", False], ["Picard 2.7B (Older Janeway)", "KoboldAI/GPT-Neo-2.7B-Picard", "8GB", False], ["Return to Main Menu", "mainmenu", "", True], ], 'nsfwlist': [ ["Erebus 20B (NSFW)", "KoboldAI/GPT-NeoX-20B-Erebus", "64GB", False], ["Erebus 13B (NSFW)", "KoboldAI/OPT-13B-Erebus", "32GB", False], ["Shinen FSD 13B (NSFW)", "KoboldAI/fairseq-dense-13B-Shinen", "32GB", False], ["Erebus 6.7B (NSFW)", "KoboldAI/OPT-6.7B-Erebus", "16GB", False], ["Shinen FSD 6.7B (NSFW)", "KoboldAI/fairseq-dense-6.7B-Shinen", "16GB", False], ["Lit V2 6B (NSFW)", "hakurei/litv2-6B-rev3", "16GB", False], ["Lit 6B (NSFW)", "hakurei/lit-6B", "16GB", False], ["Shinen 6B (NSFW)", "KoboldAI/GPT-J-6B-Shinen", "16GB", False], ["Erebus 2.7B (NSFW)", "KoboldAI/OPT-2.7B-Erebus", "8GB", False], ["Horni 2.7B (NSFW)", "KoboldAI/GPT-Neo-2.7B-Horni", "8GB", False], ["Shinen 2.7B (NSFW)", "KoboldAI/GPT-Neo-2.7B-Shinen", "8GB", False], ["Return to Main Menu", "mainmenu", "", True], ], 'chatlist': [ ["Convo 6B (Chatbot)", "hitomi-team/convo-6B", "16GB", False], ["C1 6B (Chatbot)", "hakurei/c1-6B", "16GB", False], ["C1 1.3B (Chatbot)", "iokru/c1-1.3B", "6GB", False], ["Return to Main Menu", "mainmenu", "", True], ], 'gptneolist': [ ["GPT-NeoX 20B", "EleutherAI/gpt-neox-20b", "64GB", False], ["Pythia 13B (NeoX, Same dataset)", "EleutherAI/pythia-13b", "32GB", False], ["GPT-J 6B", "EleutherAI/gpt-j-6B", "16GB", False], ["GPT-Neo 2.7B", "EleutherAI/gpt-neo-2.7B", "8GB", False], ["GPT-Neo 1.3B", "EleutherAI/gpt-neo-1.3B", "6GB", False], ["Pythia 800M (NeoX, Same dataset)", "EleutherAI/pythia-800m", "4GB", False], ["Pythia 350M (NeoX, Same dataset)", "EleutherAI/pythia-350m", "2GB", False], ["GPT-Neo 125M", "EleutherAI/gpt-neo-125M", "2GB", False], ["Return to Main Menu", "mainmenu", "", True], ], 'pythialist': [ ["Pythia 13B Deduped", "EleutherAI/pythia-13b-deduped", "32GB", False], ["Pythia 13B", "EleutherAI/pythia-13b", "32GB", False], ["Pythia 6.7B Deduped", "EleutherAI/pythia-6.7b-deduped", "16GB", False], ["Pythia 6.7B", "EleutherAI/pythia-6.7b", "16GB", False], ["Pythia 1.3B Deduped", "EleutherAI/pythia-1.3b-deduped", "6GB", False], ["Pythia 1.3B", "EleutherAI/pythia-1.3b", "6GB", False], ["Pythia 800M", "EleutherAI/pythia-800m", "4GB", False], ["Pythia 350M Deduped", "EleutherAI/pythia-350m-deduped", "2GB", False], ["Pythia 350M", "EleutherAI/pythia-350m", "2GB", False], ["Pythia 125M Deduped", "EleutherAI/pythia-125m-deduped", "2GB", False], ["Pythia 125M", "EleutherAI/pythia-125m", "2GB", False], ["Pythia 19M Deduped", "EleutherAI/pythia-19m-deduped", "1GB", False], ["Pythia 19M", "EleutherAI/pythia-19m", "1GB", False], ["Return to Main Menu", "mainmenu", "", True], ], 'gpt2list': [ ["GPT-2 XL", "gpt2-xl", "6GB", False], ["GPT-2 Large", "gpt2-large", "4GB", False], ["GPT-2 Med", "gpt2-medium", "2GB", False], ["GPT-2", "gpt2", "2GB", False], ["Return to Main Menu", "mainmenu", "", True], ], 'bloomlist': [ ["Bloom 176B", "bigscience/bloom", "", False], ["Bloom 7.1B", "bigscience/bloom-7b1", "", False], ["Bloom 3B", "bigscience/bloom-3b", "", False], ["Bloom 1.7B", "bigscience/bloom-1b7", "", False], ["Bloom 560M", "bigscience/bloom-560m", "", False], ["Return to Main Menu", "mainmenu", "", True], ], 'optlist': [ ["OPT 66B", "facebook/opt-66b", "128GB", False], ["OPT 30B", "facebook/opt-30b", "64GB", False], ["OPT 13B", "facebook/opt-13b", "32GB", False], ["OPT 6.7B", "facebook/opt-6.7b", "16GB", False], ["OPT 2.7B", "facebook/opt-2.7b", "8GB", False], ["OPT 1.3B", "facebook/opt-1.3b", "4GB", False], ["OPT 350M", "facebook/opt-350m", "2GB", False], ["OPT 125M", "facebook/opt-125m", "1GB", False], ["Return to Main Menu", "mainmenu", "", True], ], 'fsdlist': [ ["Fairseq Dense 13B", "KoboldAI/fairseq-dense-13B", "32GB", False], ["Fairseq Dense 6.7B", "KoboldAI/fairseq-dense-6.7B", "16GB", False], ["Fairseq Dense 2.7B", "KoboldAI/fairseq-dense-2.7B", "8GB", False], ["Fairseq Dense 1.3B", "KoboldAI/fairseq-dense-1.3B", "4GB", False], ["Fairseq Dense 355M", "KoboldAI/fairseq-dense-355M", "2GB", False], ["Fairseq Dense 125M", "KoboldAI/fairseq-dense-125M", "1GB", False], ["Return to Main Menu", "mainmenu", "", True], ], 'xglmlist': [ ["XGLM 4.5B (Larger Dataset)", "facebook/xglm-4.5B", "12GB", False], ["XGLM 7.5B", "facebook/xglm-7.5B", "18GB", False], ["XGLM 2.9B", "facebook/xglm-2.9B", "10GB", False], ["XGLM 1.7B", "facebook/xglm-1.7B", "6GB", False], ["XGLM 564M", "facebook/xglm-564M", "4GB", False], ["Return to Main Menu", "mainmenu", "", True], ], 'apilist': [ ["GooseAI API (requires API key)", "GooseAI", "", False], ["OpenAI API (requires API key)", "OAI", "", False], ["InferKit API (requires API key)", "InferKit", "", False], # ["KoboldAI Server API (Old Google Colab)", "Colab", "", False], ["KoboldAI API", "API", "", False], ["KoboldAI Horde", "CLUSTER", "", False], ["Return to Main Menu", "mainmenu", "", True], ] } class TokenStreamQueue: def __init__(self): self.probability_buffer = None self.queue = [] def add_text(self, text): self.queue.append({ "decoded": text, "probabilities": self.probability_buffer }) self.probability_buffer = None # Variables class vars: lastact = "" # The last action received from the user submission = "" # Same as above, but after applying input formatting lastctx = "" # The last context submitted to the generator model = "ReadOnly" # Model ID string chosen at startup online_model = "" # Used when Model ID is an online service, and there is a secondary option for the actual model name model_selected = "" #selected model in UI model_type = "" # Model Type (Automatically taken from the model config) noai = False # Runs the script without starting up the transformers pipeline aibusy = False # Stops submissions while the AI is working max_length = 1024 # Maximum number of tokens to submit per action ikmax = 3000 # Maximum number of characters to submit to InferKit genamt = 80 # Amount of text for each action to generate ikgen = 200 # Number of characters for InferKit to generate rep_pen = 1.1 # Default generator repetition_penalty rep_pen_slope = 0.7 # Default generator repetition penalty slope rep_pen_range = 1024 # Default generator repetition penalty range temp = 0.5 # Default generator temperature top_p = 0.9 # Default generator top_p top_k = 0 # Default generator top_k top_a = 0.0 # Default generator top-a tfs = 1.0 # Default generator tfs (tail-free sampling) typical = 1.0 # Default generator typical sampling threshold numseqs = 1 # Number of sequences to ask the generator to create full_determinism = False # Whether or not full determinism is enabled seed_specified = False # Whether or not the current RNG seed was specified by the user (in their settings file) seed = None # The current RNG seed (as an int), or None if unknown gamestarted = False # Whether the game has started (disables UI elements) gamesaved = True # Whether or not current game is saved serverstarted = False # Whether or not the Flask server has started prompt = "" # Prompt memory = "" # Text submitted to memory field authornote = "" # Text submitted to Author's Note field authornotetemplate = "[Author's note: <|>]" # Author's note template setauthornotetemplate = authornotetemplate # Saved author's note template in settings andepth = 3 # How far back in history to append author's note actions = structures.KoboldStoryRegister() # Actions submitted by user and AI actions_metadata = {} # List of dictonaries, one dictonary for every action that contains information about the action like alternative options. # Contains at least the same number of items as actions. Back action will remove an item from actions, but not actions_metadata # Dictonary keys are: # Selected Text: (text the user had selected. None when this is a newly generated action) # Alternative Generated Text: {Text, Pinned, Previous Selection, Edited} # worldinfo = [] # List of World Info key/value objects worldinfo_i = [] # List of World Info key/value objects sans uninitialized entries worldinfo_u = {} # Dictionary of World Info UID - key/value pairs wifolders_d = {} # Dictionary of World Info folder UID-info pairs wifolders_l = [] # List of World Info folder UIDs wifolders_u = {} # Dictionary of pairs of folder UID - list of WI UID modelconfig = {} # Raw contents of the model's config.json, or empty dictionary if none found lua_state = None # Lua state of the Lua scripting system lua_koboldbridge = None # `koboldbridge` from bridge.lua lua_kobold = None # `kobold` from` bridge.lua lua_koboldcore = None # `koboldcore` from bridge.lua lua_logname = ... # Name of previous userscript that logged to terminal lua_running = False # Whether or not Lua is running (i.e. wasn't stopped due to an error) lua_edited = set() # Set of chunk numbers that were edited from a Lua generation modifier lua_deleted = set() # Set of chunk numbers that were deleted from a Lua generation modifier generated_tkns = 0 # If using a backend that supports Lua generation modifiers, how many tokens have already been generated, otherwise 0 abort = False # Whether or not generation was aborted by clicking on the submit button during generation compiling = False # If using a TPU Colab, this will be set to True when the TPU backend starts compiling and then set to False again checking = False # Whether or not we are actively checking to see if TPU backend is compiling or not sp_changed = False # This gets set to True whenever a userscript changes the soft prompt so that check_for_sp_change() can alert the browser that the soft prompt has changed spfilename = "" # Filename of soft prompt to load, or an empty string if not using a soft prompt userscripts = [] # List of userscripts to load last_userscripts = [] # List of previous userscript filenames from the previous time userscripts were send via usstatitems corescript = "default.lua" # Filename of corescript to load # badwords = [] # Array of str/chr values that should be removed from output badwordsids = [] badwordsids_default = [[13460], [6880], [50256], [42496], [4613], [17414], [22039], [16410], [27], [29], [38430], [37922], [15913], [24618], [28725], [58], [47175], [36937], [26700], [12878], [16471], [37981], [5218], [29795], [13412], [45160], [3693], [49778], [4211], [20598], [36475], [33409], [44167], [32406], [29847], [29342], [42669], [685], [25787], [7359], [3784], [5320], [33994], [33490], [34516], [43734], [17635], [24293], [9959], [23785], [21737], [28401], [18161], [26358], [32509], [1279], [38155], [18189], [26894], [6927], [14610], [23834], [11037], [14631], [26933], [46904], [22330], [25915], [47934], [38214], [1875], [14692], [41832], [13163], [25970], [29565], [44926], [19841], [37250], [49029], [9609], [44438], [16791], [17816], [30109], [41888], [47527], [42924], [23984], [49074], [33717], [31161], [49082], [30138], [31175], [12240], [14804], [7131], [26076], [33250], [3556], [38381], [36338], [32756], [46581], [17912], [49146]] # Tokenized array of badwords used to prevent AI artifacting badwordsids_neox = [[0], [1], [44162], [9502], [12520], [31841], [36320], [49824], [34417], [6038], [34494], [24815], [26635], [24345], [3455], [28905], [44270], [17278], [32666], [46880], [7086], [43189], [37322], [17778], [20879], [49821], [3138], [14490], [4681], [21391], [26786], [43134], [9336], [683], [48074], [41256], [19181], [29650], [28532], [36487], [45114], [46275], [16445], [15104], [11337], [1168], [5647], [29], [27482], [44965], [43782], [31011], [42944], [47389], [6334], [17548], [38329], [32044], [35487], [2239], [34761], [7444], [1084], [12399], [18990], [17636], [39083], [1184], [35830], [28365], [16731], [43467], [47744], [1138], [16079], [40116], [45564], [18297], [42368], [5456], [18022], [42696], [34476], [23505], [23741], [39334], [37944], [45382], [38709], [33440], [26077], [43600], [34418], [36033], [6660], [48167], [48471], [15775], [19884], [41533], [1008], [31053], [36692], [46576], [20095], [20629], [31759], [46410], [41000], [13488], [30952], [39258], [16160], [27655], [22367], [42767], [43736], [49694], [13811], [12004], [46768], [6257], [37471], [5264], [44153], [33805], [20977], [21083], [25416], [14277], [31096], [42041], [18331], [33376], [22372], [46294], [28379], [38475], [1656], [5204], [27075], [50001], [16616], [11396], [7748], [48744], [35402], [28120], [41512], [4207], [43144], [14767], [15640], [16595], [41305], [44479], [38958], [18474], [22734], [30522], [46267], [60], [13976], [31830], [48701], [39822], [9014], [21966], [31422], [28052], [34607], [2479], [3851], [32214], [44082], [45507], [3001], [34368], [34758], [13380], [38363], [4299], [46802], [30996], [12630], [49236], [7082], [8795], [5218], [44740], [9686], [9983], [45301], [27114], [40125], [1570], [26997], [544], [5290], [49193], [23781], [14193], [40000], [2947], [43781], [9102], [48064], [42274], [18772], [49384], [9884], [45635], [43521], [31258], [32056], [47686], [21760], [13143], [10148], [26119], [44308], [31379], [36399], [23983], [46694], [36134], [8562], [12977], [35117], [28591], [49021], [47093], [28653], [29013], [46468], [8605], [7254], [25896], [5032], [8168], [36893], [38270], [20499], [27501], [34419], [29547], [28571], [36586], [20871], [30537], [26842], [21375], [31148], [27618], [33094], [3291], [31789], [28391], [870], [9793], [41361], [47916], [27468], [43856], [8850], [35237], [15707], [47552], [2730], [41449], [45488], [3073], [49806], [21938], [24430], [22747], [20924], [46145], [20481], [20197], [8239], [28231], [17987], [42804], [47269], [29972], [49884], [21382], [46295], [36676], [34616], [3921], [26991], [27720], [46265], [654], [9855], [40354], [5291], [34904], [44342], [2470], [14598], [880], [19282], [2498], [24237], [21431], [16369], [8994], [44524], [45662], [13663], [37077], [1447], [37786], [30863], [42854], [1019], [20322], [4398], [12159], [44072], [48664], [31547], [18736], [9259], [31], [16354], [21810], [4357], [37982], [5064], [2033], [32871], [47446], [62], [22158], [37387], [8743], [47007], [17981], [11049], [4622], [37916], [36786], [35138], [29925], [14157], [18095], [27829], [1181], [22226], [5709], [4725], [30189], [37014], [1254], [11380], [42989], [696], [24576], [39487], [30119], [1092], [8088], [2194], [9899], [14412], [21828], [3725], [13544], [5180], [44679], [34398], [3891], [28739], [14219], [37594], [49550], [11326], [6904], [17266], [5749], [10174], [23405], [9955], [38271], [41018], [13011], [48392], [36784], [24254], [21687], [23734], [5413], [41447], [45472], [10122], [17555], [15830], [47384], [12084], [31350], [47940], [11661], [27988], [45443], [905], [49651], [16614], [34993], [6781], [30803], [35869], [8001], [41604], [28118], [46462], [46762], [16262], [17281], [5774], [10943], [5013], [18257], [6750], [4713], [3951], [11899], [38791], [16943], [37596], [9318], [18413], [40473], [13208], [16375]] badwordsids_opt = [[44717], [46613], [48513], [49923], [50185], [48755], [8488], [43303], [49659], [48601], [49817], [45405], [48742], [49925], [47720], [11227], [48937], [48784], [50017], [42248], [49310], [48082], [49895], [50025], [49092], [49007], [8061], [44226], [0], [742], [28578], [15698], [49784], [46679], [39365], [49281], [49609], [48081], [48906], [46161], [48554], [49670], [48677], [49721], [49632], [48610], [48462], [47457], [10975], [46077], [28696], [48709], [43839], [49798], [49154], [48203], [49625], [48395], [50155], [47161], [49095], [48833], [49420], [49666], [48443], [22176], [49242], [48651], [49138], [49750], [40389], [48021], [21838], [49070], [45333], [40862], [1], [49915], [33525], [49858], [50254], [44403], [48992], [48872], [46117], [49853], [47567], [50206], [41552], [50068], [48999], [49703], [49940], [49329], [47620], [49868], [49962], [2], [44082], [50236], [31274], [50260], [47052], [42645], [49177], [17523], [48691], [49900], [49069], [49358], [48794], [47529], [46479], [48457], [646], [49910], [48077], [48935], [46386], [48902], [49151], [48759], [49803], [45587], [48392], [47789], [48654], [49836], [49230], [48188], [50264], [46844], [44690], [48505], [50161], [27779], [49995], [41833], [50154], [49097], [48520], [50018], [8174], [50084], [49366], [49526], [50193], [7479], [49982], [3]] fp32_model = False # Whether or not the most recently loaded HF model was in fp32 format deletewi = None # Temporary storage for UID to delete wirmvwhtsp = True # Whether to remove leading whitespace from WI entries widepth = 3 # How many historical actions to scan for WI hits mode = "play" # Whether the interface is in play, memory, or edit mode editln = 0 # Which line was last selected in Edit Mode gpu_device = 0 # Which PyTorch device to use when using pure GPU generation url = "https://api.inferkit.com/v1/models/standard/generate" # InferKit API URL oaiurl = "" # OpenAI API URL oaiengines = "https://api.openai.com/v1/engines" colaburl = "" # Ngrok url for Google Colab mode apikey = "" # API key to use for InferKit API calls oaiapikey = "" # API key to use for OpenAI API calls cluster_requested_models = [] # The models which we allow to generate during cluster mode savedir = getcwd()+"\\stories" hascuda = False # Whether torch has detected CUDA on the system usegpu = False # Whether to launch pipeline with GPU support custmodpth = "" # Filesystem location of custom model to run formatoptns = {'frmttriminc': True, 'frmtrmblln': False, 'frmtrmspch': False, 'frmtadsnsp': True, 'singleline': False} # Container for state of formatting options importnum = -1 # Selection on import popup list importjs = {} # Temporary storage for import data loadselect = "" # Temporary storage for story filename to load spselect = "" # Temporary storage for soft prompt filename to load spmeta = None # Metadata of current soft prompt, or None if not using a soft prompt sp = None # Current soft prompt tensor (as a NumPy array) sp_length = 0 # Length of current soft prompt in tokens, or 0 if not using a soft prompt has_genmod = False # Whether or not at least one loaded Lua userscript has a generation modifier svowname = "" # Filename that was flagged for overwrite confirm saveow = False # Whether or not overwrite confirm has been displayed autosave = False # Whether or not to automatically save after each action genseqs = [] # Temporary storage for generated sequences recentback = False # Whether Back button was recently used without Submitting or Retrying after recentrng = None # If a new random game was recently generated without Submitting after, this is the topic used (as a string), otherwise this is None recentrngm = None # If a new random game was recently generated without Submitting after, this is the memory used (as a string), otherwise this is None useprompt = False # Whether to send the full prompt with every submit action breakmodel = False # For GPU users, whether to use both system RAM and VRAM to conserve VRAM while offering speedup compared to CPU-only bmsupported = False # Whether the breakmodel option is supported (GPT-Neo/GPT-J/XGLM/OPT only, currently) nobreakmodel = False # Something specifically requested Breakmodel to be disabled (For example a models config) smandelete = False # Whether stories can be deleted from inside the browser smanrename = False # Whether stories can be renamed from inside the browser allowsp = False # Whether we are allowed to use soft prompts (by default enabled if we're using GPT-2, GPT-Neo or GPT-J) modeldim = -1 # Embedding dimension of your model (e.g. it's 4096 for GPT-J-6B and 2560 for GPT-Neo-2.7B) laststory = None # Filename (without extension) of most recent story JSON file we loaded regex_sl = re.compile(r'\n*(?<=.) *\n(.|\n)*') # Pattern for limiting the output to a single line acregex_ai = re.compile(r'\n* *>(.|\n)*') # Pattern for matching adventure actions from the AI so we can remove them acregex_ui = re.compile(r'^ *(>.*)$', re.MULTILINE) # Pattern for matching actions in the HTML-escaped story so we can apply colouring, etc (make sure to encase part to format in parentheses) comregex_ai = re.compile(r'(?:\n<\|(?:.|\n)*?\|>(?=\n|$))|(?:<\|(?:.|\n)*?\|>\n?)') # Pattern for matching comments to remove them before sending them to the AI comregex_ui = re.compile(r'(<\|(?:.|\n)*?\|>)') # Pattern for matching comments in the editor sampler_order = utils.default_sampler_order.copy() rng_states = {} # Used by the POST /generate endpoint to store sampler RNG states chatmode = False chatname = "You" adventure = False actionmode = 1 dynamicscan = False host = False nopromptgen = False rngpersist = False nogenmod = False welcome = False # Custom Welcome Text (False is default) newlinemode = "ns" quiet = False # If set will suppress any story text from being printed to the console (will only be seen on the client web page) debug = False # If set to true, will send debug information to the client for display lazy_load = True # Whether or not to use torch_lazy_loader.py for transformers models in order to reduce CPU memory usage use_colab_tpu = os.environ.get("COLAB_TPU_ADDR", "") != "" or os.environ.get("TPU_NAME", "") != "" # Whether or not we're in a Colab TPU instance or Kaggle TPU instance and are going to use the TPU rather than the CPU revision = None standalone = False api_tokenizer_id = None disable_set_aibusy = False disable_input_formatting = False disable_output_formatting = False output_streaming = True token_stream_queue = TokenStreamQueue() # Queue for the token streaming show_probs = False # Whether or not to show token probabilities show_budget = False # Whether or not to show token probabilities configname = None utils.vars = vars class Send_to_socketio(object): def write(self, bar): print(bar, end="") time.sleep(0.01) try: gui_msg = bar.replace(f"{colors.PURPLE}INIT{colors.END} | ","").replace(" ", " ") emit('from_server', {'cmd': 'model_load_status', 'data': gui_msg}, broadcast=True) except: pass # Set logging level to reduce chatter from Flask import logging log = logging.getLogger('werkzeug') log.setLevel(logging.ERROR) from flask import Flask, render_template, Response, request, copy_current_request_context, send_from_directory, session, jsonify, abort, redirect from flask_socketio import SocketIO from flask_socketio import emit as _emit from flask_session import Session from werkzeug.exceptions import HTTPException, NotFound, InternalServerError import secrets app = Flask(__name__, root_path=os.getcwd()) app.secret_key = secrets.token_hex() app.config['SESSION_TYPE'] = 'filesystem' app.config['TEMPLATES_AUTO_RELOAD'] = True socketio = SocketIO(app, async_method="eventlet") old_socketio_on = socketio.on def new_socketio_on(*a, **k): decorator = old_socketio_on(*a, **k) def new_decorator(f): @functools.wraps(f) def g(*a, **k): if args.no_ui: return return f(*a, **k) return decorator(g) return new_decorator socketio.on = new_socketio_on def emit(*args, **kwargs): try: return _emit(*args, **kwargs) except AttributeError: return socketio.emit(*args, **kwargs) utils.emit = emit # marshmallow/apispec setup from apispec import APISpec from apispec.ext.marshmallow import MarshmallowPlugin from apispec.ext.marshmallow.field_converter import make_min_max_attributes from apispec_webframeworks.flask import FlaskPlugin from marshmallow import Schema, fields, validate, EXCLUDE from marshmallow.exceptions import ValidationError class KoboldSchema(Schema): pass def new_make_min_max_attributes(validators, min_attr, max_attr) -> dict: # Patched apispec function that creates "exclusiveMinimum"/"exclusiveMaximum" OpenAPI attributes insteaed of "minimum"/"maximum" when using validators.Range or validators.Length with min_inclusive=False or max_inclusive=False attributes = {} min_list = [validator.min for validator in validators if validator.min is not None] max_list = [validator.max for validator in validators if validator.max is not None] min_inclusive_list = [getattr(validator, "min_inclusive", True) for validator in validators if validator.min is not None] max_inclusive_list = [getattr(validator, "max_inclusive", True) for validator in validators if validator.max is not None] if min_list: if min_attr == "minimum" and not min_inclusive_list[max(range(len(min_list)), key=min_list.__getitem__)]: min_attr = "exclusiveMinimum" attributes[min_attr] = max(min_list) if max_list: if min_attr == "maximum" and not max_inclusive_list[min(range(len(max_list)), key=max_list.__getitem__)]: min_attr = "exclusiveMaximum" attributes[max_attr] = min(max_list) return attributes make_min_max_attributes.__code__ = new_make_min_max_attributes.__code__ def api_format_docstring(f): f.__doc__ = eval('f"""{}"""'.format(f.__doc__.replace("\\", "\\\\"))) return f def api_catch_out_of_memory_errors(f): @functools.wraps(f) def decorated(*args, **kwargs): try: return f(*args, **kwargs) except Exception as e: if any (s in traceback.format_exc().lower() for s in ("out of memory", "not enough memory")): for line in reversed(traceback.format_exc().split("\n")): if any(s in line.lower() for s in ("out of memory", "not enough memory")) and line.count(":"): line = line.split(":", 1)[1] line = re.sub(r"\[.+?\] +data\.", "", line).strip() raise KoboldOutOfMemoryError("KoboldAI ran out of memory: " + line, type="out_of_memory.gpu.cuda" if "cuda out of memory" in line.lower() else "out_of_memory.gpu.hip" if "hip out of memory" in line.lower() else "out_of_memory.tpu.hbm" if "memory space hbm" in line.lower() else "out_of_memory.cpu.default_memory_allocator" if "defaultmemoryallocator" in line.lower() else "out_of_memory.unknown.unknown") raise KoboldOutOfMemoryError(type="out_of_memory.unknown.unknown") raise e return decorated def api_schema_wrap(f): try: input_schema: Type[Schema] = next(iter(inspect.signature(f).parameters.values())).annotation except: HAS_SCHEMA = False else: HAS_SCHEMA = inspect.isclass(input_schema) and issubclass(input_schema, Schema) f = api_format_docstring(f) f = api_catch_out_of_memory_errors(f) @functools.wraps(f) def decorated(*args, **kwargs): if HAS_SCHEMA: body = request.get_json() schema = input_schema.from_dict(input_schema().load(body)) response = f(schema, *args, **kwargs) else: response = f(*args, **kwargs) if not isinstance(response, Response): response = jsonify(response) return response return decorated @app.errorhandler(HTTPException) def handler(e): if request.path != "/api" and not request.path.startswith("/api/"): return e resp = jsonify(detail={"msg": str(e), "type": "generic.error_" + str(e.code)}) if e.code == 405 and e.valid_methods is not None: resp.headers["Allow"] = ", ".join(e.valid_methods) return resp, e.code class KoboldOutOfMemoryError(HTTPException): code = 507 description = "KoboldAI ran out of memory." type = "out_of_memory.unknown.unknown" def __init__(self, *args, type=None, **kwargs): super().__init__(*args, **kwargs) if type is not None: self.type = type @app.errorhandler(KoboldOutOfMemoryError) def handler(e): if request.path != "/api" and not request.path.startswith("/api/"): return InternalServerError() return jsonify(detail={"type": e.type, "msg": e.description}), e.code @app.errorhandler(ValidationError) def handler(e): if request.path != "/api" and not request.path.startswith("/api/"): return InternalServerError() return jsonify(detail=e.messages), 422 @app.errorhandler(NotImplementedError) def handler(e): if request.path != "/api" and not request.path.startswith("/api/"): return InternalServerError() return jsonify(detail={"type": "not_implemented", "msg": str(e).strip()}), 501 api_versions: List[str] = [] class KoboldAPISpec(APISpec): class KoboldFlaskPlugin(FlaskPlugin): def __init__(self, api: "KoboldAPISpec", *args, **kwargs): self._kobold_api_spec = api super().__init__(*args, **kwargs) def path_helper(self, *args, **kwargs): return super().path_helper(*args, **kwargs)[len(self._kobold_api_spec._prefixes[0]):] def __init__(self, *args, title: str = "KoboldAI API", openapi_version: str = "3.0.3", version: str = "1.0.0", prefixes: List[str] = None, **kwargs): plugins = [KoboldAPISpec.KoboldFlaskPlugin(self), MarshmallowPlugin()] self._prefixes = prefixes if prefixes is not None else [""] self._kobold_api_spec_version = version api_versions.append(version) api_versions.sort(key=lambda x: [int(e) for e in x.split(".")]) super().__init__(*args, title=title, openapi_version=openapi_version, version=version, plugins=plugins, servers=[{"url": self._prefixes[0]}], **kwargs) for prefix in self._prefixes: app.route(prefix, endpoint="~KoboldAPISpec~" + prefix)(lambda: redirect(request.path + "/docs/")) app.route(prefix + "/", endpoint="~KoboldAPISpec~" + prefix + "/")(lambda: redirect("docs/")) app.route(prefix + "/docs", endpoint="~KoboldAPISpec~" + prefix + "/docs")(lambda: redirect("docs/")) app.route(prefix + "/docs/", endpoint="~KoboldAPISpec~" + prefix + "/docs/")(lambda: render_template("swagger-ui.html", url=self._prefixes[0] + "/openapi.json")) app.route(prefix + "/openapi.json", endpoint="~KoboldAPISpec~" + prefix + "/openapi.json")(lambda: jsonify(self.to_dict())) def route(self, rule: str, methods=["GET"], **kwargs): __F = TypeVar("__F", bound=Callable[..., Any]) if "strict_slashes" not in kwargs: kwargs["strict_slashes"] = False def new_decorator(f: __F) -> __F: @functools.wraps(f) def g(*args, **kwargs): global api_version api_version = self._kobold_api_spec_version try: return f(*args, **kwargs) finally: api_version = None for prefix in self._prefixes: g = app.route(prefix + rule, methods=methods, **kwargs)(g) with app.test_request_context(): self.path(view=g, **kwargs) return g return new_decorator def get(self, rule: str, **kwargs): return self.route(rule, methods=["GET"], **kwargs) def post(self, rule: str, **kwargs): return self.route(rule, methods=["POST"], **kwargs) def put(self, rule: str, **kwargs): return self.route(rule, methods=["PUT"], **kwargs) def patch(self, rule: str, **kwargs): return self.route(rule, methods=["PATCH"], **kwargs) def delete(self, rule: str, **kwargs): return self.route(rule, methods=["DELETE"], **kwargs) tags = [ {"name": "info", "description": "Metadata about this API"}, {"name": "generate", "description": "Text generation endpoints"}, {"name": "model", "description": "Information about the current text generation model"}, {"name": "story", "description": "Endpoints for managing the story in the KoboldAI GUI"}, {"name": "world_info", "description": "Endpoints for managing the world info in the KoboldAI GUI"}, {"name": "config", "description": "Allows you to get/set various setting values"}, ] api_version = None # This gets set automatically so don't change this value api_v1 = KoboldAPISpec( version="1.2.1", prefixes=["/api/v1", "/api/latest"], tags=tags, ) # Returns the expected config filename for the current setup. # If the model_name is specified, it returns what the settings file would be for that model def get_config_filename(model_name = None): if model_name: return(f"settings/{model_name.replace('/', '_')}.settings") elif args.configname: return(f"settings/{args.configname.replace('/', '_')}.settings") elif vars.configname != '': return(f"settings/{vars.configname.replace('/', '_')}.settings") else: logger.warning(f"Empty configfile name sent back. Defaulting to ReadOnly") return(f"settings/ReadOnly.settings") #==================================================================# # Function to get model selection at startup #==================================================================# def sendModelSelection(menu="mainmenu", folder="./models"): #If we send one of the manual load options, send back the list of model directories, otherwise send the menu if menu in ('NeoCustom', 'GPT2Custom'): (paths, breadcrumbs) = get_folder_path_info(folder) if vars.host: breadcrumbs = [] menu_list = [[folder, menu, "", False] for folder in paths] menu_list.append(["Return to Main Menu", "mainmenu", "", True]) if os.path.abspath("{}/models".format(os.getcwd())) == os.path.abspath(folder): showdelete=True else: showdelete=False emit('from_server', {'cmd': 'show_model_menu', 'data': menu_list, 'menu': menu, 'breadcrumbs': breadcrumbs, "showdelete": showdelete}, broadcast=True) else: emit('from_server', {'cmd': 'show_model_menu', 'data': model_menu[menu], 'menu': menu, 'breadcrumbs': [], "showdelete": False}, broadcast=True) def get_folder_path_info(base): if base == 'This PC': breadcrumbs = [['This PC', 'This PC']] paths = [["{}:\\".format(chr(i)), "{}:\\".format(chr(i))] for i in range(65, 91) if os.path.exists("{}:".format(chr(i)))] else: path = os.path.abspath(base) if path[-1] == "\\": path = path[:-1] breadcrumbs = [] for i in range(len(path.replace("/", "\\").split("\\"))): breadcrumbs.append(["\\".join(path.replace("/", "\\").split("\\")[:i+1]), path.replace("/", "\\").split("\\")[i]]) if len(breadcrumbs) == 1: breadcrumbs = [["{}:\\".format(chr(i)), "{}:\\".format(chr(i))] for i in range(65, 91) if os.path.exists("{}:".format(chr(i)))] else: if len([["{}:\\".format(chr(i)), "{}:\\".format(chr(i))] for i in range(65, 91) if os.path.exists("{}:".format(chr(i)))]) > 0: breadcrumbs.insert(0, ['This PC', 'This PC']) paths = [] base_path = os.path.abspath(base) for item in os.listdir(base_path): if os.path.isdir(os.path.join(base_path, item)): paths.append([os.path.join(base_path, item), item]) # Paths/breadcrumbs is a list of lists, where the first element in the sublist is the full path and the second is the folder name return (paths, breadcrumbs) def getModelSelection(modellist): print(" # Model\t\t\t\t\t\tVRAM\n ========================================================") i = 1 for m in modellist: print(" {0} - {1}\t\t\t{2}".format("{:<2}".format(i), m[0].ljust(25), m[2])) i += 1 print(" "); modelsel = 0 vars.model = '' while(vars.model == ''): modelsel = input("Model #> ") if(modelsel.isnumeric() and int(modelsel) > 0 and int(modelsel) <= len(modellist)): vars.model = modellist[int(modelsel)-1][1] else: print("{0}Please enter a valid selection.{1}".format(colors.RED, colors.END)) # Model Lists try: getModelSelection(eval(vars.model)) except Exception as e: if(vars.model == "Return"): getModelSelection(mainmenu) # If custom model was selected, get the filesystem location and store it if(vars.model == "NeoCustom" or vars.model == "GPT2Custom"): print("{0}Please choose the folder where pytorch_model.bin is located:{1}\n".format(colors.CYAN, colors.END)) modpath = fileops.getdirpath(getcwd() + "/models", "Select Model Folder") if(modpath): # Save directory to vars vars.custmodpth = modpath else: # Print error and retry model selection print("{0}Model select cancelled!{1}".format(colors.RED, colors.END)) print("{0}Select an AI model to continue:{1}\n".format(colors.CYAN, colors.END)) getModelSelection(mainmenu) def check_if_dir_is_model(path): return os.path.exists(os.path.join(path, 'config.json')) #==================================================================# # Return all keys in tokenizer dictionary containing char #==================================================================# #def gettokenids(char): # keys = [] # for key in vocab_keys: # if(key.find(char) != -1): # keys.append(key) # return keys #==================================================================# # Return Model Name #==================================================================# def getmodelname(): if(vars.online_model != ''): return(f"{vars.model}/{vars.online_model}") if(vars.model in ("NeoCustom", "GPT2Custom", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")): modelname = os.path.basename(os.path.normpath(vars.custmodpth)) return modelname else: modelname = vars.model return modelname #==================================================================# # Get hidden size from model #==================================================================# def get_hidden_size_from_model(model): return model.get_input_embeddings().embedding_dim #==================================================================# # Breakmodel configuration functions #==================================================================# def device_list(n_layers, primary=None, selected=None): device_count = torch.cuda.device_count() if(device_count < 2): primary = None gpu_blocks = breakmodel.gpu_blocks + (device_count - len(breakmodel.gpu_blocks))*[0] print(f"{colors.YELLOW} DEVICE ID | LAYERS | DEVICE NAME{colors.END}") for i in range(device_count): name = torch.cuda.get_device_name(i) if(len(name) > 47): name = "..." + name[-44:] row_color = colors.END sep_color = colors.YELLOW print(f"{row_color}{colors.YELLOW + '->' + row_color if i == selected else ' '} {'(primary)' if i == primary else ' '*9} {i:3} {sep_color}|{row_color} {gpu_blocks[i]:3} {sep_color}|{row_color} {name}{colors.END}") row_color = colors.END sep_color = colors.YELLOW if(utils.HAS_ACCELERATE): print(f"{row_color}{colors.YELLOW + '->' + row_color if -1 == selected else ' '} {' '*9} N/A {sep_color}|{row_color} {breakmodel.disk_blocks:3} {sep_color}|{row_color} (Disk cache){colors.END}") print(f"{row_color} {' '*9} N/A {sep_color}|{row_color} {n_layers:3} {sep_color}|{row_color} (CPU){colors.END}") def device_config(config): global breakmodel, generator import breakmodel n_layers = utils.num_layers(config) if args.cpu: breakmodel.gpu_blocks = [0]*n_layers return elif(args.breakmodel_gpulayers is not None or (utils.HAS_ACCELERATE and args.breakmodel_disklayers is not None)): try: if(not args.breakmodel_gpulayers): breakmodel.gpu_blocks = [] else: breakmodel.gpu_blocks = list(map(int, args.breakmodel_gpulayers.split(','))) assert len(breakmodel.gpu_blocks) <= torch.cuda.device_count() s = n_layers for i in range(len(breakmodel.gpu_blocks)): if(breakmodel.gpu_blocks[i] <= -1): breakmodel.gpu_blocks[i] = s break else: s -= breakmodel.gpu_blocks[i] assert sum(breakmodel.gpu_blocks) <= n_layers n_layers -= sum(breakmodel.gpu_blocks) if(args.breakmodel_disklayers is not None): assert args.breakmodel_disklayers <= n_layers breakmodel.disk_blocks = args.breakmodel_disklayers n_layers -= args.breakmodel_disklayers except: logger.warning("--breakmodel_gpulayers is malformatted. Please use the --help option to see correct usage of --breakmodel_gpulayers. Defaulting to all layers on device 0.") breakmodel.gpu_blocks = [n_layers] n_layers = 0 elif(args.breakmodel_layers is not None): breakmodel.gpu_blocks = [n_layers - max(0, min(n_layers, args.breakmodel_layers))] n_layers -= sum(breakmodel.gpu_blocks) elif(args.model is not None): logger.info("Breakmodel not specified, assuming GPU 0") breakmodel.gpu_blocks = [n_layers] n_layers = 0 else: device_count = torch.cuda.device_count() if(device_count > 1): print(colors.CYAN + "\nPlease select one of your GPUs to be your primary GPU.") print("VRAM usage in your primary GPU will be higher than for your other ones.") print("It is recommended you make your fastest GPU your primary GPU.") device_list(n_layers) while(True): primaryselect = input("device ID> ") if(primaryselect.isnumeric() and 0 <= int(primaryselect) < device_count): breakmodel.primary_device = int(primaryselect) break else: print(f"{colors.RED}Please enter an integer between 0 and {device_count-1}.{colors.END}") else: breakmodel.primary_device = 0 print(colors.PURPLE + "\nIf you don't have enough VRAM to run the model on a single GPU") print("you can split the model between your CPU and your GPU(s), or between") print("multiple GPUs if you have more than one.") print("By putting more 'layers' on a GPU or CPU, more computations will be") print("done on that device and more VRAM or RAM will be required on that device") print("(roughly proportional to number of layers).") print("It should be noted that GPUs are orders of magnitude faster than the CPU.") print(f"This model has{colors.YELLOW} {n_layers} {colors.PURPLE}layers.{colors.END}\n") for i in range(device_count): device_list(n_layers, primary=breakmodel.primary_device, selected=i) print(f"{colors.CYAN}\nHow many of the remaining{colors.YELLOW} {n_layers} {colors.CYAN}layers would you like to put into device {i}?\nYou can also enter -1 to allocate all remaining layers to this device.{colors.END}\n") while(True): layerselect = input("# of layers> ") if((layerselect.isnumeric() or layerselect.strip() == '-1') and -1 <= int(layerselect) <= n_layers): layerselect = int(layerselect) layerselect = n_layers if layerselect == -1 else layerselect breakmodel.gpu_blocks.append(layerselect) n_layers -= layerselect break else: print(f"{colors.RED}Please enter an integer between -1 and {n_layers}.{colors.END}") if(n_layers == 0): break if(utils.HAS_ACCELERATE and n_layers > 0): device_list(n_layers, primary=breakmodel.primary_device, selected=-1) print(f"{colors.CYAN}\nHow many of the remaining{colors.YELLOW} {n_layers} {colors.CYAN}layers would you like to put into the disk cache?\nYou can also enter -1 to allocate all remaining layers to this device.{colors.END}\n") while(True): layerselect = input("# of layers> ") if((layerselect.isnumeric() or layerselect.strip() == '-1') and -1 <= int(layerselect) <= n_layers): layerselect = int(layerselect) layerselect = n_layers if layerselect == -1 else layerselect breakmodel.disk_blocks = layerselect n_layers -= layerselect break else: print(f"{colors.RED}Please enter an integer between -1 and {n_layers}.{colors.END}") logger.init_ok("Final device configuration:", status="Info") device_list(n_layers, primary=breakmodel.primary_device) # If all layers are on the same device, use the old GPU generation mode while(len(breakmodel.gpu_blocks) and breakmodel.gpu_blocks[-1] == 0): breakmodel.gpu_blocks.pop() if(len(breakmodel.gpu_blocks) and breakmodel.gpu_blocks[-1] in (-1, utils.num_layers(config))): vars.breakmodel = False vars.usegpu = True vars.gpu_device = len(breakmodel.gpu_blocks)-1 return if(not breakmodel.gpu_blocks): logger.warning("Nothing assigned to a GPU, reverting to CPU only mode") import breakmodel breakmodel.primary_device = "cpu" vars.breakmodel = False vars.usegpu = False return def move_model_to_devices(model): global generator if(not utils.HAS_ACCELERATE and not vars.breakmodel): if(vars.usegpu): model = model.half().to(vars.gpu_device) else: model = model.to('cpu').float() generator = model.generate return import breakmodel if(utils.HAS_ACCELERATE): import accelerate.utils for key, value in model.state_dict().items(): target_dtype = torch.float32 if breakmodel.primary_device == "cpu" else torch.float16 if(value.dtype is not target_dtype): accelerate.utils.set_module_tensor_to_device(model, key, target_dtype) disk_blocks = breakmodel.disk_blocks gpu_blocks = breakmodel.gpu_blocks ram_blocks = len(utils.layers_module_names) - sum(gpu_blocks) cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks)) device_map = {} for name in utils.layers_module_names: layer = int(name.rsplit(".", 1)[1]) device = ("disk" if layer < disk_blocks else "cpu") if layer < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, layer - ram_blocks) device_map[name] = device for name in utils.get_missing_module_names(model, list(device_map.keys())): device_map[name] = breakmodel.primary_device breakmodel.dispatch_model_ex(model, device_map, main_device=breakmodel.primary_device, offload_buffers=True, offload_dir="accelerate-disk-cache") gc.collect() generator = model.generate return model.half() gc.collect() if(hasattr(model, "transformer")): model.transformer.wte.to(breakmodel.primary_device) model.transformer.ln_f.to(breakmodel.primary_device) if(hasattr(model, 'lm_head')): model.lm_head.to(breakmodel.primary_device) if(hasattr(model.transformer, 'wpe')): model.transformer.wpe.to(breakmodel.primary_device) elif(not hasattr(model.model, "decoder")): model.model.embed_tokens.to(breakmodel.primary_device) model.model.layer_norm.to(breakmodel.primary_device) model.lm_head.to(breakmodel.primary_device) model.model.embed_positions.to(breakmodel.primary_device) else: model.model.decoder.embed_tokens.to(breakmodel.primary_device) if(model.model.decoder.project_in is not None): model.model.decoder.project_in.to(breakmodel.primary_device) if(model.model.decoder.project_out is not None): model.model.decoder.project_out.to(breakmodel.primary_device) model.model.decoder.embed_positions.to(breakmodel.primary_device) gc.collect() GPTNeoModel.forward = breakmodel.new_forward_neo if("GPTJModel" in globals()): GPTJModel.forward = breakmodel.new_forward_neo # type: ignore if("XGLMModel" in globals()): XGLMModel.forward = breakmodel.new_forward_xglm # type: ignore if("OPTDecoder" in globals()): OPTDecoder.forward = breakmodel.new_forward_opt # type: ignore generator = model.generate if(hasattr(model, "transformer")): breakmodel.move_hidden_layers(model.transformer) elif(not hasattr(model.model, "decoder")): breakmodel.move_hidden_layers(model.model, model.model.layers) else: breakmodel.move_hidden_layers(model.model.decoder, model.model.decoder.layers) #==================================================================# # Allow the models to override some settings #==================================================================# def loadmodelsettings(): try: js = json.loads(str(model_config).partition(' ')[2]) except Exception as e: try: try: js = json.load(open(vars.custmodpth + "/config.json", "r")) except Exception as e: js = json.load(open(vars.custmodpth.replace('/', '_') + "/config.json", "r")) except Exception as e: js = {} if vars.model_type == "xglm" or js.get("compat", "j") == "fairseq_lm": vars.newlinemode = "s" # Default to newline mode if using XGLM if vars.model_type == "opt" or vars.model_type == "bloom": vars.newlinemode = "ns" # Handle but don't convert newlines if using Fairseq models that have newlines trained in them vars.modelconfig = js if("badwordsids" in js): vars.badwordsids = js["badwordsids"] if("nobreakmodel" in js): vars.nobreakmodel = js["nobreakmodel"] if("sampler_order" in js): sampler_order = js["sampler_order"] if(len(sampler_order) < 7): sampler_order = [6] + sampler_order vars.sampler_order = sampler_order if("temp" in js): vars.temp = js["temp"] if("top_p" in js): vars.top_p = js["top_p"] if("top_k" in js): vars.top_k = js["top_k"] if("tfs" in js): vars.tfs = js["tfs"] if("typical" in js): vars.typical = js["typical"] if("top_a" in js): vars.top_a = js["top_a"] if("rep_pen" in js): vars.rep_pen = js["rep_pen"] if("rep_pen_slope" in js): vars.rep_pen_slope = js["rep_pen_slope"] if("rep_pen_range" in js): vars.rep_pen_range = js["rep_pen_range"] if("adventure" in js): vars.adventure = js["adventure"] if("chatmode" in js): vars.chatmode = js["chatmode"] if("dynamicscan" in js): vars.dynamicscan = js["dynamicscan"] if("formatoptns" in js): vars.formatoptns = js["formatoptns"] if("welcome" in js): vars.welcome = js["welcome"] if("newlinemode" in js): vars.newlinemode = js["newlinemode"] if("antemplate" in js): vars.setauthornotetemplate = js["antemplate"] if(not vars.gamestarted): vars.authornotetemplate = vars.setauthornotetemplate #==================================================================# # Take settings from vars and write them to client settings file #==================================================================# def savesettings(): # Build json to write js = {} js["apikey"] = vars.apikey js["andepth"] = vars.andepth js["sampler_order"] = vars.sampler_order js["temp"] = vars.temp js["top_p"] = vars.top_p js["top_k"] = vars.top_k js["tfs"] = vars.tfs js["typical"] = vars.typical js["top_a"] = vars.top_a js["rep_pen"] = vars.rep_pen js["rep_pen_slope"] = vars.rep_pen_slope js["rep_pen_range"] = vars.rep_pen_range js["genamt"] = vars.genamt js["max_length"] = vars.max_length js["ikgen"] = vars.ikgen js["formatoptns"] = vars.formatoptns js["numseqs"] = vars.numseqs js["widepth"] = vars.widepth js["useprompt"] = vars.useprompt js["adventure"] = vars.adventure js["chatmode"] = vars.chatmode js["chatname"] = vars.chatname js["dynamicscan"] = vars.dynamicscan js["nopromptgen"] = vars.nopromptgen js["rngpersist"] = vars.rngpersist js["nogenmod"] = vars.nogenmod js["fulldeterminism"] = vars.full_determinism js["autosave"] = vars.autosave js["welcome"] = vars.welcome js["output_streaming"] = vars.output_streaming js["show_probs"] = vars.show_probs js["show_budget"] = vars.show_budget if(vars.seed_specified): js["seed"] = vars.seed else: js["seed"] = None js["newlinemode"] = vars.newlinemode js["antemplate"] = vars.setauthornotetemplate js["userscripts"] = vars.userscripts js["corescript"] = vars.corescript js["softprompt"] = vars.spfilename # Write it if not os.path.exists('settings'): os.mkdir('settings') file = open(get_config_filename(), "w") try: file.write(json.dumps(js, indent=3)) finally: file.close() #==================================================================# # Don't save settings unless 2 seconds have passed without modification #==================================================================# @debounce(2) def settingschanged(): logger.info("Saving settings.") savesettings() #==================================================================# # Read settings from client file JSON and send to vars #==================================================================# def loadsettings(): if(path.exists("defaults/" + getmodelname().replace('/', '_') + ".settings")): # Read file contents into JSON object file = open("defaults/" + getmodelname().replace('/', '_') + ".settings", "r") js = json.load(file) processsettings(js) file.close() if(path.exists(get_config_filename())): # Read file contents into JSON object file = open(get_config_filename(), "r") js = json.load(file) processsettings(js) file.close() def processsettings(js): # Copy file contents to vars if("apikey" in js): # If the model is the HORDE, then previously saved API key in settings # Will always override a new key set. if vars.model != "CLUSTER" or vars.apikey == '': vars.apikey = js["apikey"] if("andepth" in js): vars.andepth = js["andepth"] if("sampler_order" in js): sampler_order = js["sampler_order"] if(len(sampler_order) < 7): sampler_order = [6] + sampler_order vars.sampler_order = sampler_order if("temp" in js): vars.temp = js["temp"] if("top_p" in js): vars.top_p = js["top_p"] if("top_k" in js): vars.top_k = js["top_k"] if("tfs" in js): vars.tfs = js["tfs"] if("typical" in js): vars.typical = js["typical"] if("top_a" in js): vars.top_a = js["top_a"] if("rep_pen" in js): vars.rep_pen = js["rep_pen"] if("rep_pen_slope" in js): vars.rep_pen_slope = js["rep_pen_slope"] if("rep_pen_range" in js): vars.rep_pen_range = js["rep_pen_range"] if("genamt" in js): vars.genamt = js["genamt"] if("max_length" in js): vars.max_length = js["max_length"] if("ikgen" in js): vars.ikgen = js["ikgen"] if("formatoptns" in js): vars.formatoptns = js["formatoptns"] if("numseqs" in js): vars.numseqs = js["numseqs"] if("widepth" in js): vars.widepth = js["widepth"] if("useprompt" in js): vars.useprompt = js["useprompt"] if("adventure" in js): vars.adventure = js["adventure"] if("chatmode" in js): vars.chatmode = js["chatmode"] if("chatname" in js): vars.chatname = js["chatname"] if("dynamicscan" in js): vars.dynamicscan = js["dynamicscan"] if("nopromptgen" in js): vars.nopromptgen = js["nopromptgen"] if("rngpersist" in js): vars.rngpersist = js["rngpersist"] if("nogenmod" in js): vars.nogenmod = js["nogenmod"] if("fulldeterminism" in js): vars.full_determinism = js["fulldeterminism"] if("autosave" in js): vars.autosave = js["autosave"] if("newlinemode" in js): vars.newlinemode = js["newlinemode"] if("welcome" in js): vars.welcome = js["welcome"] if("output_streaming" in js): vars.output_streaming = js["output_streaming"] if("show_probs" in js): vars.show_probs = js["show_probs"] if("show_budget" in js): vars.show_budget = js["show_budget"] if("seed" in js): vars.seed = js["seed"] if(vars.seed is not None): vars.seed_specified = True else: vars.seed_specified = False else: vars.seed_specified = False if("antemplate" in js): vars.setauthornotetemplate = js["antemplate"] if(not vars.gamestarted): vars.authornotetemplate = vars.setauthornotetemplate if("userscripts" in js): vars.userscripts = [] for userscript in js["userscripts"]: if type(userscript) is not str: continue userscript = userscript.strip() if len(userscript) != 0 and all(q not in userscript for q in ("..", ":")) and all(userscript[0] not in q for q in ("/", "\\")) and os.path.exists(fileops.uspath(userscript)): vars.userscripts.append(userscript) if("corescript" in js and type(js["corescript"]) is str and all(q not in js["corescript"] for q in ("..", ":")) and all(js["corescript"][0] not in q for q in ("/", "\\"))): vars.corescript = js["corescript"] else: vars.corescript = "default.lua" #==================================================================# # Load a soft prompt from a file #==================================================================# def check_for_sp_change(): while(True): time.sleep(0.05) if(vars.sp_changed): with app.app_context(): emit('from_server', {'cmd': 'spstatitems', 'data': {vars.spfilename: vars.spmeta} if vars.allowsp and len(vars.spfilename) else {}}, namespace=None, broadcast=True) vars.sp_changed = False if(vars.token_stream_queue.queue): # If emit blocks, waiting for it to complete before clearing could # introduce a race condition that drops tokens. queued_tokens = list(vars.token_stream_queue.queue) vars.token_stream_queue.queue.clear() socketio.emit("from_server", {"cmd": "streamtoken", "data": queued_tokens}, namespace=None, broadcast=True) socketio.start_background_task(check_for_sp_change) def spRequest(filename): if(not vars.allowsp): raise RuntimeError("Soft prompts are not supported by your current model/backend") old_filename = vars.spfilename vars.spfilename = "" settingschanged() if(len(filename) == 0): vars.sp = None vars.sp_length = 0 if(old_filename != filename): vars.sp_changed = True return global np if 'np' not in globals(): import numpy as np z, version, shape, fortran_order, dtype = fileops.checksp(filename, vars.modeldim) if not isinstance(z, zipfile.ZipFile): raise RuntimeError(f"{repr(filename)} is not a valid soft prompt file") with z.open('meta.json') as f: vars.spmeta = json.load(f) z.close() with np.load(fileops.sppath(filename), allow_pickle=False) as f: tensor = f['tensor.npy'] # If the tensor is in bfloat16 format, convert it to float32 if(tensor.dtype == 'V2'): tensor.dtype = np.uint16 tensor = np.uint32(tensor) << 16 tensor.dtype = np.float32 if(tensor.dtype != np.float16): tensor = np.float32(tensor) assert not np.isinf(tensor).any() and not np.isnan(tensor).any() vars.sp_length = tensor.shape[-2] vars.spmeta["n_tokens"] = vars.sp_length if(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")): rows = tensor.shape[0] padding_amount = tpu_mtj_backend.params["seq"] - (tpu_mtj_backend.params["seq"] % -tpu_mtj_backend.params["cores_per_replica"]) - rows tensor = np.pad(tensor, ((0, padding_amount), (0, 0))) tensor = tensor.reshape( tpu_mtj_backend.params["cores_per_replica"], -1, tpu_mtj_backend.params.get("d_embed", tpu_mtj_backend.params["d_model"]), ) vars.sp = tpu_mtj_backend.shard_xmap(np.float32(tensor)) else: vars.sp = torch.from_numpy(tensor) vars.spfilename = filename settingschanged() if(old_filename != filename): vars.sp_changed = True #==================================================================# # Startup #==================================================================# def general_startup(override_args=None): global args # Parsing Parameters parser = argparse.ArgumentParser(description="KoboldAI Server") parser.add_argument("--remote", action='store_true', help="Optimizes KoboldAI for Remote Play") parser.add_argument("--noaimenu", action='store_true', help="Disables the ability to select the AI") parser.add_argument("--ngrok", action='store_true', help="Optimizes KoboldAI for Remote Play using Ngrok") parser.add_argument("--localtunnel", action='store_true', help="Optimizes KoboldAI for Remote Play using Localtunnel") parser.add_argument("--host", action='store_true', help="Optimizes KoboldAI for Remote Play without using a proxy service") parser.add_argument("--port", type=int, help="Specify the port on which the application will be joinable") parser.add_argument("--aria2_port", type=int, help="Specify the port on which aria2's RPC interface will be open if aria2 is installed (defaults to 6799)") parser.add_argument("--model", help="Specify the Model Type to skip the Menu") parser.add_argument("--path", help="Specify the Path for local models (For model NeoCustom or GPT2Custom)") parser.add_argument("--apikey", help="Specify the API key to use for online services") parser.add_argument("--req_model", type=str, action='append', required=False, help="Which models which we allow to generate for us during cluster mode. Can be specified multiple times.") parser.add_argument("--revision", help="Specify the model revision for huggingface models (can be a git branch/tag name or a git commit hash)") parser.add_argument("--cpu", action='store_true', help="By default unattended launches are on the GPU use this option to force CPU usage.") parser.add_argument("--breakmodel", action='store_true', help=argparse.SUPPRESS) parser.add_argument("--breakmodel_layers", type=int, help=argparse.SUPPRESS) parser.add_argument("--breakmodel_gpulayers", type=str, help="If using a model that supports hybrid generation, this is a comma-separated list that specifies how many layers to put on each GPU device. For example to put 8 layers on device 0, 9 layers on device 1 and 11 layers on device 2, use --breakmodel_gpulayers 8,9,11") parser.add_argument("--breakmodel_disklayers", type=int, help="If using a model that supports hybrid generation, this is the number of layers to put in disk cache.") parser.add_argument("--override_delete", action='store_true', help="Deleting stories from inside the browser is disabled if you are using --remote and enabled otherwise. Using this option will instead allow deleting stories if using --remote and prevent deleting stories otherwise.") parser.add_argument("--override_rename", action='store_true', help="Renaming stories from inside the browser is disabled if you are using --remote and enabled otherwise. Using this option will instead allow renaming stories if using --remote and prevent renaming stories otherwise.") parser.add_argument("--configname", help="Force a fixed configuration name to aid with config management.") parser.add_argument("--colab", action='store_true', help="Optimize for Google Colab.") parser.add_argument("--nobreakmodel", action='store_true', help="Disables Breakmodel support completely.") parser.add_argument("--unblock", action='store_true', default=False, help="Unblocks the KoboldAI port to be accessible from other machines without optimizing for remote play (It is recommended to use --host instead)") parser.add_argument("--quiet", action='store_true', default=False, help="If present will suppress any story related text from showing on the console") parser.add_argument("--no_aria2", action='store_true', default=False, help="Prevents KoboldAI from using aria2 to download huggingface models more efficiently, in case aria2 is causing you issues") parser.add_argument("--lowmem", action='store_true', help="Extra Low Memory loading for the GPU, slower but memory does not peak to twice the usage") parser.add_argument("--savemodel", action='store_true', help="Saves the model to the models folder even if --colab is used (Allows you to save models to Google Drive)") parser.add_argument("--customsettings", help="Preloads arguements from json file. You only need to provide the location of the json file. Use customsettings.json template file. It can be renamed if you wish so that you can store multiple configurations. Leave any settings you want as default as null. Any values you wish to set need to be in double quotation marks") parser.add_argument("--no_ui", action='store_true', default=False, help="Disables the GUI and Socket.IO server while leaving the API server running.") parser.add_argument('-v', '--verbosity', action='count', default=0, help="The default logging level is ERROR or higher. This value increases the amount of logging seen in your screen") parser.add_argument('-q', '--quiesce', action='count', default=0, help="The default logging level is ERROR or higher. This value decreases the amount of logging seen in your screen") #args: argparse.Namespace = None if "pytest" in sys.modules and override_args is None: args = parser.parse_args([]) return if override_args is not None: import shlex args = parser.parse_args(shlex.split(override_args)) elif(os.environ.get("KOBOLDAI_ARGS") is not None): import shlex args = parser.parse_args(shlex.split(os.environ["KOBOLDAI_ARGS"])) else: args = parser.parse_args() utils.args = args set_logger_verbosity(args.verbosity) quiesce_logger(args.quiesce) if args.customsettings: f = open (args.customsettings) importedsettings = json.load(f) for items in importedsettings: if importedsettings[items] is not None: setattr(args, items, importedsettings[items]) f.close() if args.no_ui: def new_emit(*args, **kwargs): return old_emit = socketio.emit socketio.emit = new_emit vars.model = args.model; vars.revision = args.revision if args.apikey: vars.apikey = args.apikey if args.req_model: vars.cluster_requested_models = args.req_model if args.colab: args.remote = True; args.override_rename = True; args.override_delete = True; args.nobreakmodel = True; args.quiet = True; args.lowmem = True; args.noaimenu = True; if args.quiet: vars.quiet = True if args.nobreakmodel: vars.nobreakmodel = True; if args.remote: vars.host = True; if args.ngrok: vars.host = True; if args.localtunnel: vars.host = True; if args.host: vars.host = True; if args.cpu: vars.use_colab_tpu = False vars.smandelete = vars.host == args.override_delete vars.smanrename = vars.host == args.override_rename vars.aria2_port = args.aria2_port or 6799 #Now let's look to see if we are going to force a load of a model from a user selected folder if(vars.model == "selectfolder"): print("{0}Please choose the folder where pytorch_model.bin is located:{1}\n".format(colors.CYAN, colors.END)) modpath = fileops.getdirpath(getcwd() + "/models", "Select Model Folder") if(modpath): # Save directory to vars vars.model = "NeoCustom" vars.custmodpth = modpath elif args.model: logger.message(f"Welcome to KoboldAI!") logger.message(f"You have selected the following Model: {vars.model}") if args.path: logger.message(f"You have selected the following path for your Model: {args.path}") vars.custmodpth = args.path; vars.colaburl = args.path + "/request"; # Lets just use the same parameter to keep it simple #==================================================================# # Load Model #==================================================================# def tpumtjgetsofttokens(): soft_tokens = None if(vars.sp is None): global np if 'np' not in globals(): import numpy as np tensor = np.zeros((1, tpu_mtj_backend.params.get("d_embed", tpu_mtj_backend.params["d_model"])), dtype=np.float32) rows = tensor.shape[0] padding_amount = tpu_mtj_backend.params["seq"] - (tpu_mtj_backend.params["seq"] % -tpu_mtj_backend.params["cores_per_replica"]) - rows tensor = np.pad(tensor, ((0, padding_amount), (0, 0))) tensor = tensor.reshape( tpu_mtj_backend.params["cores_per_replica"], -1, tpu_mtj_backend.params.get("d_embed", tpu_mtj_backend.params["d_model"]), ) vars.sp = tpu_mtj_backend.shard_xmap(tensor) soft_tokens = np.arange( tpu_mtj_backend.params["n_vocab"] + tpu_mtj_backend.params["n_vocab_padding"], tpu_mtj_backend.params["n_vocab"] + tpu_mtj_backend.params["n_vocab_padding"] + vars.sp_length, dtype=np.uint32 ) return soft_tokens def get_model_info(model, directory=""): # if the model is in the api list disk_blocks = 0 key = False breakmodel = False gpu = False layer_count = None key_value = "" break_values = [] url = False default_url = None models_on_url = False multi_online_models = False gpu_count = torch.cuda.device_count() gpu_names = [] send_horde_models = False for i in range(gpu_count): gpu_names.append(torch.cuda.get_device_name(i)) if model in ['Colab', 'API']: url = True elif model == 'CLUSTER': models_on_url = True url = True key = True default_url = 'https://horde.koboldai.net' multi_online_models = True if path.exists(get_config_filename(model)): with open(get_config_filename(model), "r") as file: # Check if API key exists js = json.load(file) if("apikey" in js and js["apikey"] != ""): # API key exists, grab it and close the file key_value = js["apikey"] elif 'oaiapikey' in js and js['oaiapikey'] != "": key_value = js["oaiapikey"] if 'url' in js and js['url'] != "": url = js['url'] if key_value != "": send_horde_models = True elif model in [x[1] for x in model_menu['apilist']]: if path.exists(get_config_filename(model)): with open(get_config_filename(model), "r") as file: # Check if API key exists js = json.load(file) if("apikey" in js and js["apikey"] != ""): # API key exists, grab it and close the file key_value = js["apikey"] elif 'oaiapikey' in js and js['oaiapikey'] != "": key_value = js["oaiapikey"] key = True elif model == 'ReadOnly': pass elif not utils.HAS_ACCELERATE and not torch.cuda.is_available(): pass elif args.cpu: pass else: layer_count = get_layer_count(model, directory=directory) if layer_count is None: breakmodel = False gpu = True else: breakmodel = True if model in ["NeoCustom", "GPT2Custom"]: filename = "settings/{}.breakmodel".format(os.path.basename(os.path.normpath(directory))) else: filename = "settings/{}.breakmodel".format(model.replace("/", "_")) if path.exists(filename): with open(filename, "r") as file: data = file.read().split("\n")[:2] if len(data) < 2: data.append("0") break_values, disk_blocks = data break_values = break_values.split(",") else: break_values = [layer_count] break_values += [0] * (gpu_count - len(break_values)) #print("Model_info: {}".format({'cmd': 'selected_model_info', 'key_value': key_value, 'key':key, # 'gpu':gpu, 'layer_count':layer_count, 'breakmodel':breakmodel, # 'break_values': break_values, 'gpu_count': gpu_count, # 'url': url, 'gpu_names': gpu_names})) emit('from_server', {'cmd': 'selected_model_info', 'key_value': key_value, 'key':key, 'gpu':gpu, 'layer_count':layer_count, 'breakmodel':breakmodel, 'disk_break_value': disk_blocks, 'accelerate': utils.HAS_ACCELERATE, 'break_values': break_values, 'gpu_count': gpu_count, 'multi_online_models': multi_online_models, 'url': url, 'default_url': default_url, 'gpu_names': gpu_names, 'models_on_url': models_on_url}, broadcast=True) if send_horde_models: get_cluster_models({'key': key_value, 'url': default_url}) elif key_value != "" and model in [x[1] for x in model_menu['apilist']] and model != 'CLUSTER': get_oai_models(key_value) def get_layer_count(model, directory=""): if(model not in ["InferKit", "Colab", "API", "CLUSTER", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ"]): if(model == "GPT2Custom"): with open(os.path.join(directory, "config.json"), "r") as f: model_config = json.load(f) # Get the model_type from the config or assume a model type if it isn't present else: if(directory): model = directory from transformers import AutoConfig if(os.path.isdir(model.replace('/', '_'))): model_config = AutoConfig.from_pretrained(model.replace('/', '_'), revision=args.revision, cache_dir="cache") elif(os.path.isdir("models/{}".format(model.replace('/', '_')))): model_config = AutoConfig.from_pretrained("models/{}".format(model.replace('/', '_')), revision=args.revision, cache_dir="cache") elif(os.path.isdir(directory)): model_config = AutoConfig.from_pretrained(directory, revision=args.revision, cache_dir="cache") else: model_config = AutoConfig.from_pretrained(model, revision=args.revision, cache_dir="cache") try: if ((utils.HAS_ACCELERATE and model_config.model_type != 'gpt2') or model_config.model_type in ("gpt_neo", "gptj", "xglm", "opt")) and not vars.nobreakmodel: return utils.num_layers(model_config) else: return None except: return None else: return None def get_oai_models(key): vars.oaiapikey = key if vars.model_selected == 'OAI': url = "https://api.openai.com/v1/engines" elif vars.model_selected == 'GooseAI': url = "https://api.goose.ai/v1/engines" else: return # Get list of models from OAI logger.init("OAI Engines", status="Retrieving") req = requests.get( url, headers = { 'Authorization': 'Bearer '+key } ) if(req.status_code == 200): engines = req.json()["data"] try: engines = [[en["id"], "{} ({})".format(en['id'], "Ready" if en["ready"] == True else "Not Ready")] for en in engines] except: logger.error(engines) raise online_model = "" changed=False #Save the key if not path.exists("settings"): # If the client settings file doesn't exist, create it # Write API key to file os.makedirs('settings', exist_ok=True) if path.exists(get_config_filename(vars.model_selected)): with open(get_config_filename(vars.model_selected), "r") as file: js = json.load(file) if 'online_model' in js: online_model = js['online_model'] if "apikey" in js: if js['apikey'] != key: changed=True else: changed=True if changed: js={} with open(get_config_filename(vars.model_selected), "w") as file: js["apikey"] = key file.write(json.dumps(js, indent=3)) logger.init_ok("OAI Engines", status="OK") emit('from_server', {'cmd': 'oai_engines', 'data': engines, 'online_model': online_model}, broadcast=True) else: # Something went wrong, print the message and quit since we can't initialize an engine logger.init_err("OAI Engines", status="Failed") logger.error(req.json()) emit('from_server', {'cmd': 'errmsg', 'data': req.json()}) def get_cluster_models(msg): vars.oaiapikey = msg['key'] vars.apikey = vars.oaiapikey url = msg['url'] # Get list of models from public cluster logger.init("KAI Horde Models", status="Retrieving") try: req = requests.get(f"{url}/api/v2/status/models?type=text") except requests.exceptions.ConnectionError: logger.init_err("KAI Horde Models", status="Failed") logger.error("Provided KoboldAI Horde URL unreachable") emit('from_server', {'cmd': 'errmsg', 'data': "Provided KoboldAI Horde URL unreachable"}) return if(not req.ok): # Something went wrong, print the message and quit since we can't initialize an engine logger.init_err("KAI Horde Models", status="Failed") logger.error(req.json()) emit('from_server', {'cmd': 'errmsg', 'data': req.json()}) return engines = req.json() logger.debug(engines) try: engines = [[en["name"], en["name"]] for en in engines] except: logger.error(engines) raise logger.debug(engines) online_model = "" changed=False #Save the key if not path.exists("settings"): # If the client settings file doesn't exist, create it # Write API key to file os.makedirs('settings', exist_ok=True) if path.exists(get_config_filename(vars.model_selected)): with open(get_config_filename(vars.model_selected), "r") as file: js = json.load(file) if 'online_model' in js: online_model = js['online_model'] if "apikey" in js: if js['apikey'] != vars.oaiapikey: changed=True else: changed=True if changed: js={} with open(get_config_filename(vars.model_selected), "w") as file: js["apikey"] = vars.oaiapikey js["url"] = url file.write(json.dumps(js, indent=3)) logger.init_ok("KAI Horde Models", status="OK") emit('from_server', {'cmd': 'oai_engines', 'data': engines, 'online_model': online_model}, broadcast=True) # Function to patch transformers to use our soft prompt def patch_causallm(model): from torch.nn import Embedding if(getattr(Embedding, "_koboldai_patch_causallm_model", None)): Embedding._koboldai_patch_causallm_model = model return model old_embedding_call = Embedding.__call__ def new_embedding_call(self, input_ids, *args, **kwargs): if(Embedding._koboldai_patch_causallm_model.get_input_embeddings() is not self): return old_embedding_call(self, input_ids, *args, **kwargs) assert input_ids is not None if(vars.sp is not None): shifted_input_ids = input_ids - model.config.vocab_size input_ids.clamp_(max=model.config.vocab_size-1) inputs_embeds = old_embedding_call(self, input_ids, *args, **kwargs) if(vars.sp is not None): vars.sp = vars.sp.to(inputs_embeds.dtype).to(inputs_embeds.device) inputs_embeds = torch.where( (shifted_input_ids >= 0)[..., None], vars.sp[shifted_input_ids.clamp(min=0)], inputs_embeds, ) return inputs_embeds Embedding.__call__ = new_embedding_call Embedding._koboldai_patch_causallm_model = model return model def patch_transformers_download(): global transformers import copy, requests, tqdm, time class Send_to_socketio(object): def write(self, bar): bar = bar.replace("\r", "").replace("\n", "") if bar != "": try: print(bar, end="\r") emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True) eventlet.sleep(seconds=0) except: pass def http_get( url: str, temp_file, proxies=None, resume_size=0, headers=None, file_name=None, ): """ Download remote file. Do not gobble up errors. """ headers = copy.deepcopy(headers) if resume_size > 0: headers["Range"] = f"bytes={resume_size}-" r = requests.get(url, stream=True, proxies=proxies, headers=headers) transformers.utils.hub._raise_for_status(r) content_length = r.headers.get("Content-Length") total = resume_size + int(content_length) if content_length is not None else None # `tqdm` behavior is determined by `utils.logging.is_progress_bar_enabled()` # and can be set using `utils.logging.enable/disable_progress_bar()` if url[-11:] != 'config.json': progress = tqdm.tqdm( unit="B", unit_scale=True, unit_divisor=1024, total=total, initial=resume_size, desc=f"Downloading {file_name}" if file_name is not None else "Downloading", file=Send_to_socketio(), ) for chunk in r.iter_content(chunk_size=1024): if chunk: # filter out keep-alive new chunks if url[-11:] != 'config.json': progress.update(len(chunk)) temp_file.write(chunk) if url[-11:] != 'config.json': progress.close() transformers.utils.hub.http_get = http_get def patch_transformers(): global transformers patch_transformers_download() old_from_pretrained = PreTrainedModel.from_pretrained.__func__ @classmethod def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): vars.fp32_model = False utils.num_shards = None utils.current_shard = 0 utils.from_pretrained_model_name = pretrained_model_name_or_path utils.from_pretrained_index_filename = None utils.from_pretrained_kwargs = kwargs utils.bar = None if not args.no_aria2: utils.aria2_hook(pretrained_model_name_or_path, **kwargs) return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) if(not hasattr(PreTrainedModel, "_kai_patched")): PreTrainedModel.from_pretrained = new_from_pretrained PreTrainedModel._kai_patched = True if(hasattr(modeling_utils, "get_checkpoint_shard_files")): old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): utils.num_shards = utils.get_num_shards(index_filename) utils.from_pretrained_index_filename = index_filename return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs) modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files # Some versions of transformers 4.17.0.dev0 are affected by # https://github.com/huggingface/transformers/issues/15736 # This is a workaround for those versions of transformers. if(transformers_version == "4.17.0.dev0"): try: from transformers.models.xglm.modeling_xglm import XGLMSinusoidalPositionalEmbedding except ImportError: pass else: @torch.no_grad() def new_forward(self, input_ids: torch.Tensor = None, inputs_embeds: torch.Tensor = None, past_key_values_length: int = 0): bsz, seq_len = inputs_embeds.size()[:-1] input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( past_key_values_length + self.padding_idx + 1, past_key_values_length + sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ).unsqueeze(0).expand(input_shape).contiguous() max_pos = self.padding_idx + 1 + seq_len + past_key_values_length if max_pos > self.weights.size(0): self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx) return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach() XGLMSinusoidalPositionalEmbedding.forward = new_forward # Fix a bug in OPTForCausalLM where self.lm_head is the wrong size if(packaging.version.parse("4.19.0.dev0") <= packaging.version.parse(transformers_version) < packaging.version.parse("4.20.0")): try: from transformers import OPTForCausalLM, OPTModel except ImportError: pass else: # This is the same as the original __init__ but with # config.hidden_size # replaced with # config.word_embed_proj_dim def new_init(self, config): super(OPTForCausalLM, self).__init__(config) self.model = OPTModel(config) self.lm_head = torch.nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False) self.post_init() OPTForCausalLM.__init__ = new_init # Patch transformers to use our custom logit warpers from transformers import LogitsProcessorList, LogitsWarper, LogitsProcessor, TopKLogitsWarper, TopPLogitsWarper, TemperatureLogitsWarper, RepetitionPenaltyLogitsProcessor from warpers import AdvancedRepetitionPenaltyLogitsProcessor, TailFreeLogitsWarper, TypicalLogitsWarper, TopALogitsWarper def dynamic_processor_wrap(cls, field_name, var_name, cond=None): old_call = cls.__call__ def new_call(self, *args, **kwargs): if(not isinstance(field_name, str) and isinstance(field_name, Iterable)): conds = [] for f, v in zip(field_name, var_name): conds.append(getattr(vars, v)) setattr(self, f, conds[-1]) else: conds = getattr(vars, var_name) setattr(self, field_name, conds) assert len(args) == 2 if(cond is None or cond(conds)): return old_call(self, *args, **kwargs) return args[1] cls.__call__ = new_call dynamic_processor_wrap(AdvancedRepetitionPenaltyLogitsProcessor, ("penalty", "penalty_slope", "penalty_range"), ("rep_pen", "rep_pen_slope", "rep_pen_range"), cond=lambda x: x[0] != 1.0) dynamic_processor_wrap(TopKLogitsWarper, "top_k", "top_k", cond=lambda x: x > 0) dynamic_processor_wrap(TopALogitsWarper, "top_a", "top_a", cond=lambda x: x > 0.0) dynamic_processor_wrap(TopPLogitsWarper, "top_p", "top_p", cond=lambda x: x < 1.0) dynamic_processor_wrap(TailFreeLogitsWarper, "tfs", "tfs", cond=lambda x: x < 1.0) dynamic_processor_wrap(TypicalLogitsWarper, "typical", "typical", cond=lambda x: x < 1.0) dynamic_processor_wrap(TemperatureLogitsWarper, "temperature", "temp", cond=lambda x: x != 1.0) class LuaLogitsProcessor(LogitsProcessor): def __init__(self): pass def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: assert scores.ndim == 2 assert input_ids.ndim == 2 self.regeneration_required = False self.halt = False if(vars.standalone): return scores scores_shape = scores.shape scores_list = scores.tolist() vars.lua_koboldbridge.logits = vars.lua_state.table() for r, row in enumerate(scores_list): vars.lua_koboldbridge.logits[r+1] = vars.lua_state.table(*row) vars.lua_koboldbridge.vocab_size = scores_shape[-1] execute_genmod() scores = torch.tensor( tuple(tuple(row.values()) for row in vars.lua_koboldbridge.logits.values()), device=scores.device, dtype=scores.dtype, ) assert scores.shape == scores_shape return scores from torch.nn import functional as F def visualize_probabilities(scores: torch.FloatTensor) -> None: assert scores.ndim == 2 if vars.numseqs > 1 or not vars.show_probs: return probs = F.softmax(scores, dim = -1).cpu().numpy()[0] token_prob_info = [] for token_id, score in sorted(enumerate(probs), key=lambda x: x[1], reverse=True)[:8]: token_prob_info.append({ "tokenId": token_id, "decoded": utils.decodenewlines(tokenizer.decode(token_id)), "score": float(score), }) vars.token_stream_queue.probability_buffer = token_prob_info def new_get_logits_processor(*args, **kwargs) -> LogitsProcessorList: processors = new_get_logits_processor.old_get_logits_processor(*args, **kwargs) processors.insert(0, LuaLogitsProcessor()) return processors new_get_logits_processor.old_get_logits_processor = transformers.generation_utils.GenerationMixin._get_logits_processor transformers.generation_utils.GenerationMixin._get_logits_processor = new_get_logits_processor class KoboldLogitsWarperList(LogitsProcessorList): def __init__(self, beams: int = 1, **kwargs): self.__warper_list: List[LogitsWarper] = [] self.__warper_list.append(TopKLogitsWarper(top_k=1, min_tokens_to_keep=1 + (beams > 1))) self.__warper_list.append(TopALogitsWarper(top_a=0.5, min_tokens_to_keep=1 + (beams > 1))) self.__warper_list.append(TopPLogitsWarper(top_p=0.5, min_tokens_to_keep=1 + (beams > 1))) self.__warper_list.append(TailFreeLogitsWarper(tfs=0.5, min_tokens_to_keep=1 + (beams > 1))) self.__warper_list.append(TypicalLogitsWarper(typical=0.5, min_tokens_to_keep=1 + (beams > 1))) self.__warper_list.append(TemperatureLogitsWarper(temperature=0.5)) self.__warper_list.append(AdvancedRepetitionPenaltyLogitsProcessor()) def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, *args, **kwargs): sampler_order = vars.sampler_order[:] if len(sampler_order) < 7: # Add repetition penalty at beginning if it's not present sampler_order = [6] + sampler_order for k in sampler_order: scores = self.__warper_list[k](input_ids, scores, *args, **kwargs) visualize_probabilities(scores) return scores def new_get_logits_warper(beams: int = 1,) -> LogitsProcessorList: return KoboldLogitsWarperList(beams=beams) def new_sample(self, *args, **kwargs): assert kwargs.pop("logits_warper", None) is not None kwargs["logits_warper"] = new_get_logits_warper( beams=1, ) if(vars.newlinemode == "s") or (vars.newlinemode == "ns"): kwargs["eos_token_id"] = -1 kwargs.setdefault("pad_token_id", 2) return new_sample.old_sample(self, *args, **kwargs) new_sample.old_sample = transformers.generation_utils.GenerationMixin.sample transformers.generation_utils.GenerationMixin.sample = new_sample # Allow bad words filter to ban <|endoftext|> token import transformers.generation_logits_process def new_init(self, bad_words_ids: List[List[int]], eos_token_id: int): return new_init.old_init(self, bad_words_ids, -1) new_init.old_init = transformers.generation_logits_process.NoBadWordsLogitsProcessor.__init__ transformers.generation_logits_process.NoBadWordsLogitsProcessor.__init__ = new_init class TokenStreamer(StoppingCriteria): # A StoppingCriteria is used here because it seems to run after # everything has been evaluated score-wise. def __init__(self, tokenizer): self.tokenizer = tokenizer def __call__( self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs, ) -> bool: # Do not intermingle multiple generations' outputs! if vars.numseqs > 1: return False if not (vars.show_probs or vars.output_streaming): return False if vars.chatmode: return False tokenizer_text = utils.decodenewlines(tokenizer.decode(input_ids[0, -1])) vars.token_stream_queue.add_text(tokenizer_text) return False # Sets up dynamic world info scanner class DynamicWorldInfoScanCriteria(StoppingCriteria): def __init__( self, tokenizer, excluded_world_info: List[Set], ): self.regeneration_required = False self.halt = False self.tokenizer = tokenizer self.excluded_world_info = excluded_world_info def __call__( self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs, ) -> bool: vars.generated_tkns += 1 if(not vars.standalone and vars.lua_koboldbridge.generated_cols and vars.generated_tkns != vars.lua_koboldbridge.generated_cols): raise RuntimeError(f"Inconsistency detected between KoboldAI Python and Lua backends ({vars.generated_tkns} != {vars.lua_koboldbridge.generated_cols})") if(vars.abort or vars.generated_tkns >= vars.genamt): self.regeneration_required = False self.halt = False return True if(vars.standalone): return False assert input_ids.ndim == 2 assert len(self.excluded_world_info) == input_ids.shape[0] self.regeneration_required = vars.lua_koboldbridge.regeneration_required self.halt = not vars.lua_koboldbridge.generating vars.lua_koboldbridge.regeneration_required = False for i in range(vars.numseqs): vars.lua_koboldbridge.generated[i+1][vars.generated_tkns] = int(input_ids[i, -1].item()) if(not vars.dynamicscan): return self.regeneration_required or self.halt tail = input_ids[..., -vars.generated_tkns:] for i, t in enumerate(tail): decoded = utils.decodenewlines(tokenizer.decode(t)) _, found = checkworldinfo(decoded, force_use_txt=True, actions=vars._actions) found -= self.excluded_world_info[i] if(len(found) != 0): self.regeneration_required = True break return self.regeneration_required or self.halt old_get_stopping_criteria = transformers.generation_utils.GenerationMixin._get_stopping_criteria def new_get_stopping_criteria(self, *args, **kwargs): stopping_criteria = old_get_stopping_criteria(self, *args, **kwargs) global tokenizer self.kai_scanner = DynamicWorldInfoScanCriteria( tokenizer=tokenizer, excluded_world_info=self.kai_scanner_excluded_world_info, ) token_streamer = TokenStreamer(tokenizer=tokenizer) stopping_criteria.insert(0, self.kai_scanner) stopping_criteria.insert(0, token_streamer) return stopping_criteria transformers.generation_utils.GenerationMixin._get_stopping_criteria = new_get_stopping_criteria def reset_model_settings(): vars.socketio = socketio vars.max_length = 1024 # Maximum number of tokens to submit per action vars.ikmax = 3000 # Maximum number of characters to submit to InferKit vars.genamt = 80 # Amount of text for each action to generate vars.ikgen = 200 # Number of characters for InferKit to generate vars.rep_pen = 1.1 # Default generator repetition_penalty vars.rep_pen_slope = 0.7 # Default generator repetition penalty slope vars.rep_pen_range = 1024 # Default generator repetition penalty range vars.temp = 0.5 # Default generator temperature vars.top_p = 0.9 # Default generator top_p vars.top_k = 0 # Default generator top_k vars.top_a = 0.0 # Default generator top-a vars.tfs = 1.0 # Default generator tfs (tail-free sampling) vars.typical = 1.0 # Default generator typical sampling threshold vars.numseqs = 1 # Number of sequences to ask the generator to create vars.generated_tkns = 0 # If using a backend that supports Lua generation modifiers, how many tokens have already been generated, otherwise 0 vars.badwordsids = [] vars.fp32_model = False # Whether or not the most recently loaded HF model was in fp32 format vars.modeldim = -1 # Embedding dimension of your model (e.g. it's 4096 for GPT-J-6B and 2560 for GPT-Neo-2.7B) vars.sampler_order = [6, 0, 1, 2, 3, 4, 5] vars.newlinemode = "n" vars.revision = None vars.lazy_load = True def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=False, online_model="", use_breakmodel_args=False, breakmodel_args_default_to_cpu=False): global model global generator global torch global model_config global GPT2Tokenizer global tokenizer if(initial_load): use_breakmodel_args = True reset_model_settings() if not utils.HAS_ACCELERATE: disk_layers = None vars.noai = False if not use_breakmodel_args: set_aibusy(True) if vars.model != 'ReadOnly': emit('from_server', {'cmd': 'model_load_status', 'data': "Loading {}".format(vars.model)}, broadcast=True) #Have to add a sleep so the server will send the emit for some reason time.sleep(0.1) if gpu_layers is not None: args.breakmodel_gpulayers = gpu_layers elif use_breakmodel_args: gpu_layers = args.breakmodel_gpulayers if breakmodel_args_default_to_cpu and gpu_layers is None: gpu_layers = args.breakmodel_gpulayers = [] if disk_layers is not None: args.breakmodel_disklayers = int(disk_layers) elif use_breakmodel_args: disk_layers = args.breakmodel_disklayers if breakmodel_args_default_to_cpu and disk_layers is None: disk_layers = args.breakmodel_disklayers = 0 #We need to wipe out the existing model and refresh the cuda cache model = None generator = None model_config = None vars.online_model = '' with torch.no_grad(): with warnings.catch_warnings(): warnings.filterwarnings("ignore", message="torch.distributed.reduce_op is deprecated") for tensor in gc.get_objects(): try: if torch.is_tensor(tensor): tensor.set_(torch.tensor((), device=tensor.device, dtype=tensor.dtype)) except: pass gc.collect() try: torch.cuda.empty_cache() except: pass #Reload our badwords vars.badwordsids = vars.badwordsids_default if online_model == "": vars.configname = getmodelname() #Let's set the GooseAI or OpenAI server URLs if that's applicable else: vars.online_model = online_model # Swap OAI Server if GooseAI was selected if(vars.model == "GooseAI"): vars.oaiengines = "https://api.goose.ai/v1/engines" vars.model = "OAI" vars.configname = f"GooseAI_{online_model.replace('/', '_')}" elif(vars.model == "CLUSTER") and type(online_model) is list: if len(online_model) != 1: vars.configname = vars.model else: vars.configname = f"{vars.model}_{online_model[0].replace('/', '_')}" else: vars.configname = f"{vars.model}_{online_model.replace('/', '_')}" if path.exists(get_config_filename()): changed=False with open(get_config_filename(), "r") as file: # Check if API key exists js = json.load(file) if 'online_model' in js: if js['online_model'] != online_model: changed=True js['online_model'] = online_model else: changed=True js['online_model'] = online_model if changed: with open(get_config_filename(), "w") as file: file.write(json.dumps(js, indent=3)) # Swap OAI Server if GooseAI was selected if(vars.model == "GooseAI"): vars.oaiengines = "https://api.goose.ai/v1/engines" vars.model = "OAI" args.configname = "GooseAI" + "/" + online_model elif vars.model != "CLUSTER": args.configname = vars.model + "/" + online_model vars.oaiurl = vars.oaiengines + "/{0}/completions".format(online_model) # If transformers model was selected & GPU available, ask to use CPU or GPU if(vars.model not in ["InferKit", "Colab", "API", "CLUSTER", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): vars.allowsp = True # Test for GPU support # Make model path the same as the model name to make this consistent with the other loading method if it isn't a known model type # This code is not just a workaround for below, it is also used to make the behavior consistent with other loading methods - Henk717 if(not vars.model in ["NeoCustom", "GPT2Custom"]): vars.custmodpth = vars.model elif(vars.model == "NeoCustom"): vars.model = os.path.basename(os.path.normpath(vars.custmodpth)) # Get the model_type from the config or assume a model type if it isn't present from transformers import AutoConfig if(os.path.isdir(vars.custmodpth.replace('/', '_'))): try: model_config = AutoConfig.from_pretrained(vars.custmodpth.replace('/', '_'), revision=args.revision, cache_dir="cache") vars.model_type = model_config.model_type except ValueError as e: vars.model_type = "not_found" elif(os.path.isdir("models/{}".format(vars.custmodpth.replace('/', '_')))): try: model_config = AutoConfig.from_pretrained("models/{}".format(vars.custmodpth.replace('/', '_')), revision=args.revision, cache_dir="cache") vars.model_type = model_config.model_type except ValueError as e: vars.model_type = "not_found" else: try: model_config = AutoConfig.from_pretrained(vars.custmodpth, revision=args.revision, cache_dir="cache") vars.model_type = model_config.model_type except ValueError as e: vars.model_type = "not_found" if(vars.model_type == "not_found" and vars.model == "NeoCustom"): vars.model_type = "gpt_neo" elif(vars.model_type == "not_found" and vars.model == "GPT2Custom"): vars.model_type = "gpt2" elif(vars.model_type == "not_found"): logger.warning("No model type detected, assuming Neo (If this is a GPT2 model use the other menu option or --model GPT2Custom)") vars.model_type = "gpt_neo" if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "API", "CLUSTER", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): loadmodelsettings() loadsettings() logger.init("GPU support", status="Searching") vars.hascuda = torch.cuda.is_available() and not args.cpu vars.bmsupported = ((utils.HAS_ACCELERATE and vars.model_type != 'gpt2') or vars.model_type in ("gpt_neo", "gptj", "xglm", "opt")) and not vars.nobreakmodel if(args.breakmodel is not None and args.breakmodel): logger.warning("--breakmodel is no longer supported. Breakmodel mode is now automatically enabled when --breakmodel_gpulayers is used (see --help for details).") if(args.breakmodel_layers is not None): logger.warning("--breakmodel_layers is deprecated. Use --breakmodel_gpulayers instead (see --help for details).") if(args.model and vars.bmsupported and not args.breakmodel_gpulayers and not args.breakmodel_layers and (not utils.HAS_ACCELERATE or not args.breakmodel_disklayers)): logger.warning("Model launched without the --breakmodel_gpulayers argument, defaulting to GPU only mode.") vars.bmsupported = False if(not vars.bmsupported and (args.breakmodel_gpulayers is not None or args.breakmodel_layers is not None or args.breakmodel_disklayers is not None)): logger.warning("This model does not support hybrid generation. --breakmodel_gpulayers will be ignored.") if(vars.hascuda): logger.init_ok("GPU support", status="Found") else: logger.init_warn("GPU support", status="Not Found") if args.cpu: vars.usegpu = False gpu_layers = None disk_layers = None vars.breakmodel = False elif vars.hascuda: if(vars.bmsupported): vars.usegpu = False vars.breakmodel = True else: vars.breakmodel = False vars.usegpu = use_gpu # Ask for API key if InferKit was selected if(vars.model == "InferKit"): vars.apikey = vars.oaiapikey # Swap OAI Server if GooseAI was selected if(vars.model == "GooseAI"): vars.oaiengines = "https://api.goose.ai/v1/engines" vars.model = "OAI" vars.configname = "GooseAI" # Ask for API key if OpenAI was selected if(vars.model == "OAI"): if not vars.configname: vars.configname = "OAI" if(vars.model == "ReadOnly"): vars.noai = True # Start transformers and create pipeline if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "API", "CLUSTER", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): if(not vars.noai): logger.init("Transformers", status='Starting') for m in ("GPTJModel", "XGLMModel"): try: globals()[m] = getattr(__import__("transformers"), m) except: pass # Lazy loader import torch_lazy_loader def get_lazy_load_callback(n_layers, convert_to_float16=True): if not vars.lazy_load: return from tqdm.auto import tqdm global breakmodel import breakmodel if utils.HAS_ACCELERATE: import accelerate.utils if args.breakmodel_disklayers is not None: breakmodel.disk_blocks = args.breakmodel_disklayers disk_blocks = breakmodel.disk_blocks gpu_blocks = breakmodel.gpu_blocks ram_blocks = ram_blocks = n_layers - sum(gpu_blocks) cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks)) def lazy_load_callback(model_dict: Dict[str, Union[torch_lazy_loader.LazyTensor, torch.Tensor]], f, **_): if lazy_load_callback.nested: return lazy_load_callback.nested = True device_map: Dict[str, Union[str, int]] = {} @functools.lru_cache(maxsize=None) def get_original_key(key): return max((original_key for original_key in utils.module_names if original_key.endswith(key)), key=len) for key, value in model_dict.items(): original_key = get_original_key(key) if isinstance(value, torch_lazy_loader.LazyTensor) and not any(original_key.startswith(n) for n in utils.layers_module_names): device_map[key] = vars.gpu_device if vars.hascuda and vars.usegpu else "cpu" if not vars.hascuda or not vars.breakmodel else breakmodel.primary_device else: layer = int(max((n for n in utils.layers_module_names if original_key.startswith(n)), key=len).rsplit(".", 1)[1]) device = vars.gpu_device if vars.hascuda and vars.usegpu else "disk" if layer < disk_blocks and layer < ram_blocks else "cpu" if not vars.hascuda or not vars.breakmodel else "shared" if layer < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, layer - ram_blocks) device_map[key] = device if utils.num_shards is None or utils.current_shard == 0: utils.offload_index = {} if utils.HAS_ACCELERATE: if os.path.isdir("accelerate-disk-cache"): # Delete all of the files in the disk cache folder without deleting the folder itself to allow people to create symbolic links for this folder # (the folder doesn't contain any subfolders so os.remove will do just fine) for filename in os.listdir("accelerate-disk-cache"): try: os.remove(os.path.join("accelerate-disk-cache", filename)) except OSError: pass os.makedirs("accelerate-disk-cache", exist_ok=True) if utils.num_shards is not None: num_tensors = len(utils.get_sharded_checkpoint_num_tensors(utils.from_pretrained_model_name, utils.from_pretrained_index_filename, **utils.from_pretrained_kwargs)) else: num_tensors = len(device_map) utils.bar = tqdm(total=num_tensors, desc=f"{colors.PURPLE}INIT{colors.END} | Loading model tensors", file=Send_to_socketio()) with zipfile.ZipFile(f, "r") as z: try: last_storage_key = None zipfolder = os.path.basename(os.path.normpath(f)).split('.')[0] f = None current_offset = 0 able_to_pin_layers = True if utils.num_shards is not None: utils.current_shard += 1 for key in sorted(device_map.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)): storage_key = model_dict[key].key if storage_key != last_storage_key or model_dict[key].seek_offset < current_offset: last_storage_key = storage_key if isinstance(f, zipfile.ZipExtFile): f.close() try: f = z.open(f"archive/data/{storage_key}") except: f = z.open(f"{zipfolder}/data/{storage_key}") current_offset = 0 if current_offset != model_dict[key].seek_offset: f.read(model_dict[key].seek_offset - current_offset) current_offset = model_dict[key].seek_offset device = device_map[key] size = functools.reduce(lambda x, y: x * y, model_dict[key].shape, 1) dtype = model_dict[key].dtype nbytes = size if dtype is torch.bool else size * ((torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits >> 3) #print(f"Transferring <{key}> to {f'({device.upper()})' if isinstance(device, str) else '[device ' + str(device) + ']'} ... ", end="", flush=True) model_dict[key] = model_dict[key].materialize(f, map_location="cpu") if model_dict[key].dtype is torch.float32: vars.fp32_model = True if convert_to_float16 and breakmodel.primary_device != "cpu" and vars.hascuda and (vars.breakmodel or vars.usegpu) and model_dict[key].dtype is torch.float32: model_dict[key] = model_dict[key].to(torch.float16) if breakmodel.primary_device == "cpu" or (not vars.usegpu and not vars.breakmodel and model_dict[key].dtype is torch.float16): model_dict[key] = model_dict[key].to(torch.float32) if device == "shared": model_dict[key] = model_dict[key].to("cpu").detach_() if able_to_pin_layers and utils.HAS_ACCELERATE: try: model_dict[key] = model_dict[key].pin_memory() except: able_to_pin_layers = False elif device == "disk": accelerate.utils.offload_weight(model_dict[key], get_original_key(key), "accelerate-disk-cache", index=utils.offload_index) model_dict[key] = model_dict[key].to("meta") else: model_dict[key] = model_dict[key].to(device) #print("OK", flush=True) current_offset += nbytes utils.bar.update(1) finally: if utils.num_shards is None or utils.current_shard >= utils.num_shards: if utils.offload_index: for name, tensor in utils.named_buffers: dtype = tensor.dtype if convert_to_float16 and breakmodel.primary_device != "cpu" and vars.hascuda and (vars.breakmodel or vars.usegpu): dtype = torch.float16 if breakmodel.primary_device == "cpu" or (not vars.usegpu and not vars.breakmodel): dtype = torch.float32 if name in model_dict and model_dict[name].dtype is not dtype: model_dict[name] = model_dict[name].to(dtype) if tensor.dtype is not dtype: tensor = tensor.to(dtype) if name not in utils.offload_index: accelerate.utils.offload_weight(tensor, name, "accelerate-disk-cache", index=utils.offload_index) accelerate.utils.save_offload_index(utils.offload_index, "accelerate-disk-cache") utils.bar.close() utils.bar = None lazy_load_callback.nested = False if isinstance(f, zipfile.ZipExtFile): f.close() lazy_load_callback.nested = False return lazy_load_callback def maybe_low_cpu_mem_usage() -> Dict[str, Any]: if(packaging.version.parse(transformers_version) < packaging.version.parse("4.11.0")): logger.warning(f"Please upgrade to transformers 4.11.0 for lower RAM usage. You have transformers {transformers_version}.") return {} return {"low_cpu_mem_usage": True} @contextlib.contextmanager def maybe_use_float16(always_use=False): if(always_use or (vars.hascuda and args.lowmem and (vars.usegpu or vars.breakmodel))): original_dtype = torch.get_default_dtype() torch.set_default_dtype(torch.float16) yield True torch.set_default_dtype(original_dtype) else: yield False # If custom GPT2 model was chosen if(vars.model_type == "gpt2"): vars.lazy_load = False if os.path.exists(vars.custmodpth): model_config = open(vars.custmodpth + "/config.json", "r") elif os.path.exists(os.path.join("models/", vars.custmodpth)): config_path = os.path.join("models/", vars.custmodpth) config_path = os.path.join(config_path, "config.json").replace("\\", "//") model_config = open(config_path, "r") #js = json.load(model_config) with(maybe_use_float16()): try: if os.path.exists(vars.custmodpth): model = GPT2LMHeadModel.from_pretrained(vars.custmodpth, revision=args.revision, cache_dir="cache") tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, revision=args.revision, cache_dir="cache") elif os.path.exists(os.path.join("models/", vars.custmodpth)): model = GPT2LMHeadModel.from_pretrained(os.path.join("models/", vars.custmodpth), revision=args.revision, cache_dir="cache") tokenizer = GPT2Tokenizer.from_pretrained(os.path.join("models/", vars.custmodpth), revision=args.revision, cache_dir="cache") else: model = GPT2LMHeadModel.from_pretrained(vars.custmodpth, revision=args.revision, cache_dir="cache") tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, revision=args.revision, cache_dir="cache") except Exception as e: if("out of memory" in traceback.format_exc().lower()): raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.") raise e tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, revision=args.revision, cache_dir="cache") model.save_pretrained("models/{}".format(vars.model.replace('/', '_')), max_shard_size="500MiB") tokenizer.save_pretrained("models/{}".format(vars.model.replace('/', '_'))) vars.modeldim = get_hidden_size_from_model(model) # Is CUDA available? If so, use GPU, otherwise fall back to CPU if(vars.hascuda and vars.usegpu): model = model.half().to(vars.gpu_device) generator = model.generate else: model = model.to('cpu').float() generator = model.generate patch_causallm(model) # Use the Generic implementation else: lowmem = maybe_low_cpu_mem_usage() # We must disable low_cpu_mem_usage (by setting lowmem to {}) if # using a GPT-2 model because GPT-2 is not compatible with this # feature yet if(vars.model_type == "gpt2"): lowmem = {} vars.lazy_load = False # Also, lazy loader doesn't support GPT-2 models # If we're using torch_lazy_loader, we need to get breakmodel config # early so that it knows where to load the individual model tensors if (utils.HAS_ACCELERATE or vars.lazy_load and vars.hascuda and vars.breakmodel) and not vars.nobreakmodel: device_config(model_config) # Download model from Huggingface if it does not exist, otherwise load locally #If we specify a model and it's in the root directory, we need to move it to the models directory (legacy folder structure to new) if os.path.isdir(vars.model.replace('/', '_')): import shutil shutil.move(vars.model.replace('/', '_'), "models/{}".format(vars.model.replace('/', '_'))) if(vars.lazy_load): # If we're using lazy loader, we need to figure out what the model's hidden layers are called with torch_lazy_loader.use_lazy_torch_load(dematerialized_modules=True, use_accelerate_init_empty_weights=True): try: metamodel = AutoModelForCausalLM.from_config(model_config) except Exception as e: metamodel = GPTNeoForCausalLM.from_config(model_config) utils.layers_module_names = utils.get_layers_module_names(metamodel) utils.module_names = list(metamodel.state_dict().keys()) utils.named_buffers = list(metamodel.named_buffers(recurse=True)) with maybe_use_float16(), torch_lazy_loader.use_lazy_torch_load(enable=vars.lazy_load, callback=get_lazy_load_callback(utils.num_layers(model_config)) if vars.lazy_load else None, dematerialized_modules=True): if(vars.lazy_load): # torch_lazy_loader.py and low_cpu_mem_usage can't be used at the same time lowmem = {} if(os.path.isdir(vars.custmodpth)): try: tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=args.revision, cache_dir="cache", use_fast=False) except Exception as e: try: tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=args.revision, cache_dir="cache") except Exception as e: try: tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, revision=args.revision, cache_dir="cache") except Exception as e: tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache") try: model = AutoModelForCausalLM.from_pretrained(vars.custmodpth, revision=args.revision, cache_dir="cache", **lowmem) except Exception as e: if("out of memory" in traceback.format_exc().lower()): raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.") model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, revision=args.revision, cache_dir="cache", **lowmem) elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))): try: tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache", use_fast=False) except Exception as e: try: tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache") except Exception as e: try: tokenizer = GPT2Tokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache") except Exception as e: tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache") try: model = AutoModelForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache", **lowmem) except Exception as e: if("out of memory" in traceback.format_exc().lower()): raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.") model = GPTNeoForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache", **lowmem) else: old_rebuild_tensor = torch._utils._rebuild_tensor def new_rebuild_tensor(storage: Union[torch_lazy_loader.LazyTensor, torch.Storage], storage_offset, shape, stride): if(not isinstance(storage, torch_lazy_loader.LazyTensor)): dtype = storage.dtype else: dtype = storage.storage_type.dtype if(not isinstance(dtype, torch.dtype)): dtype = storage.storage_type(0).dtype if(dtype is torch.float32 and len(shape) >= 2): vars.fp32_model = True return old_rebuild_tensor(storage, storage_offset, shape, stride) torch._utils._rebuild_tensor = new_rebuild_tensor try: tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=args.revision, cache_dir="cache", use_fast=False) except Exception as e: try: tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=args.revision, cache_dir="cache") except Exception as e: try: tokenizer = GPT2Tokenizer.from_pretrained(vars.model, revision=args.revision, cache_dir="cache") except Exception as e: tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache") try: model = AutoModelForCausalLM.from_pretrained(vars.model, revision=args.revision, cache_dir="cache", **lowmem) except Exception as e: if("out of memory" in traceback.format_exc().lower()): raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.") model = GPTNeoForCausalLM.from_pretrained(vars.model, revision=args.revision, cache_dir="cache", **lowmem) torch._utils._rebuild_tensor = old_rebuild_tensor if not args.colab or args.savemodel: import shutil tokenizer.save_pretrained("models/{}".format(vars.model.replace('/', '_'))) if(vars.fp32_model and ("breakmodel" not in globals() or not breakmodel.disk_blocks)): # Use save_pretrained to convert fp32 models to fp16, unless we are using disk cache because save_pretrained is not supported in that case model = model.half() model.save_pretrained("models/{}".format(vars.model.replace('/', '_')), max_shard_size="500MiB") else: # For fp16 models, we can just copy the model files directly import transformers.configuration_utils import transformers.modeling_utils import transformers.file_utils import huggingface_hub legacy = packaging.version.parse(transformers_version) < packaging.version.parse("4.22.0.dev0") # Save the config.json shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(vars.model, transformers.configuration_utils.CONFIG_NAME, revision=args.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(vars.model.replace('/', '_')), transformers.configuration_utils.CONFIG_NAME)) if(utils.num_shards is None): # Save the pytorch_model.bin of an unsharded model shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(vars.model, transformers.modeling_utils.WEIGHTS_NAME, revision=args.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_NAME)) else: with open(utils.from_pretrained_index_filename) as f: map_data = json.load(f) filenames = set(map_data["weight_map"].values()) # Save the pytorch_model.bin.index.json of a sharded model shutil.move(os.path.realpath(utils.from_pretrained_index_filename), os.path.join("models/{}".format(vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_INDEX_NAME)) # Then save the pytorch_model-#####-of-#####.bin files for filename in filenames: shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(vars.model, filename, revision=args.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(vars.model.replace('/', '_')), filename)) shutil.rmtree("cache/") if(vars.badwordsids is vars.badwordsids_default and vars.model_type not in ("gpt2", "gpt_neo", "gptj")): vars.badwordsids = [[v] for k, v in tokenizer.get_vocab().items() if any(c in str(k) for c in "<>[]") if vars.newlinemode != "s" or str(k) != ""] patch_causallm(model) if(vars.hascuda): if(vars.usegpu): vars.modeldim = get_hidden_size_from_model(model) model = model.half().to(vars.gpu_device) generator = model.generate elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel) vars.modeldim = get_hidden_size_from_model(model) if(not vars.lazy_load): device_config(model.config) move_model_to_devices(model) elif(utils.HAS_ACCELERATE and __import__("breakmodel").disk_blocks > 0): move_model_to_devices(model) vars.modeldim = get_hidden_size_from_model(model) generator = model.generate else: model = model.to('cpu').float() vars.modeldim = get_hidden_size_from_model(model) generator = model.generate elif(utils.HAS_ACCELERATE and __import__("breakmodel").disk_blocks > 0): move_model_to_devices(model) vars.modeldim = get_hidden_size_from_model(model) generator = model.generate else: model.to('cpu').float() vars.modeldim = get_hidden_size_from_model(model) generator = model.generate # Suppress Author's Note by flagging square brackets (Old implementation) #vocab = tokenizer.get_vocab() #vocab_keys = vocab.keys() #vars.badwords = gettokenids("[") #for key in vars.badwords: # vars.badwordsids.append([vocab[key]]) logger.info(f"Pipeline created: {vars.model}") else: from transformers import GPT2Tokenizer tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache") else: from transformers import PreTrainedModel from transformers import modeling_utils old_from_pretrained = PreTrainedModel.from_pretrained.__func__ @classmethod def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): vars.fp32_model = False utils.num_shards = None utils.current_shard = 0 utils.from_pretrained_model_name = pretrained_model_name_or_path utils.from_pretrained_index_filename = None utils.from_pretrained_kwargs = kwargs utils.bar = None if not args.no_aria2: utils.aria2_hook(pretrained_model_name_or_path, **kwargs) return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) if(not hasattr(PreTrainedModel, "_kai_patched")): PreTrainedModel.from_pretrained = new_from_pretrained PreTrainedModel._kai_patched = True if(hasattr(modeling_utils, "get_checkpoint_shard_files")): old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): utils.num_shards = utils.get_num_shards(index_filename) utils.from_pretrained_index_filename = index_filename return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs) modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files def tpumtjgenerate_warper_callback(scores) -> "np.array": scores_shape = scores.shape scores_list = scores.tolist() vars.lua_koboldbridge.logits = vars.lua_state.table() for r, row in enumerate(scores_list): vars.lua_koboldbridge.logits[r+1] = vars.lua_state.table(*row) vars.lua_koboldbridge.vocab_size = scores_shape[-1] execute_genmod() scores = np.array( tuple(tuple(row.values()) for row in vars.lua_koboldbridge.logits.values()), dtype=scores.dtype, ) assert scores.shape == scores_shape return scores def tpumtjgenerate_stopping_callback(generated, n_generated, excluded_world_info) -> Tuple[List[set], bool, bool]: vars.generated_tkns += 1 assert len(excluded_world_info) == len(generated) regeneration_required = vars.lua_koboldbridge.regeneration_required halt = vars.abort or not vars.lua_koboldbridge.generating or vars.generated_tkns >= vars.genamt vars.lua_koboldbridge.regeneration_required = False global past for i in range(vars.numseqs): vars.lua_koboldbridge.generated[i+1][vars.generated_tkns] = int(generated[i, tpu_mtj_backend.params["seq"] + n_generated - 1].item()) if(not vars.dynamicscan or halt): return excluded_world_info, regeneration_required, halt for i, t in enumerate(generated): decoded = utils.decodenewlines(tokenizer.decode(past[i])) + utils.decodenewlines(tokenizer.decode(t[tpu_mtj_backend.params["seq"] : tpu_mtj_backend.params["seq"] + n_generated])) _, found = checkworldinfo(decoded, force_use_txt=True, actions=vars._actions) found -= excluded_world_info[i] if(len(found) != 0): regeneration_required = True break return excluded_world_info, regeneration_required, halt def tpumtjgenerate_compiling_callback() -> None: print(colors.GREEN + "TPU backend compilation triggered" + colors.END) vars.compiling = True def tpumtjgenerate_stopped_compiling_callback() -> None: vars.compiling = False def tpumtjgenerate_settings_callback() -> dict: sampler_order = vars.sampler_order[:] if len(sampler_order) < 7: # Add repetition penalty at beginning if it's not present sampler_order = [6] + sampler_order return { "sampler_order": sampler_order, "top_p": float(vars.top_p), "temp": float(vars.temp), "top_k": int(vars.top_k), "tfs": float(vars.tfs), "typical": float(vars.typical), "top_a": float(vars.top_a), "repetition_penalty": float(vars.rep_pen), "rpslope": float(vars.rep_pen_slope), "rprange": int(vars.rep_pen_range), } # If we're running Colab or OAI, we still need a tokenizer. if(vars.model in ("Colab", "API", "CLUSTER")): from transformers import GPT2Tokenizer tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B", revision=args.revision, cache_dir="cache") loadsettings() elif(vars.model == "OAI"): from transformers import GPT2Tokenizer tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache") loadsettings() # Load the TPU backend if requested elif(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")): global tpu_mtj_backend import tpu_mtj_backend if(vars.model == "TPUMeshTransformerGPTNeoX"): vars.badwordsids = vars.badwordsids_neox print("{0}Initializing Mesh Transformer JAX, please wait...{1}".format(colors.PURPLE, colors.END)) if vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") and (not vars.custmodpth or not os.path.isdir(vars.custmodpth)): raise FileNotFoundError(f"The specified model path {repr(vars.custmodpth)} is not the path to a valid folder") import tpu_mtj_backend if(vars.model == "TPUMeshTransformerGPTNeoX"): tpu_mtj_backend.pad_token_id = 2 tpu_mtj_backend.vars = vars tpu_mtj_backend.warper_callback = tpumtjgenerate_warper_callback tpu_mtj_backend.stopping_callback = tpumtjgenerate_stopping_callback tpu_mtj_backend.compiling_callback = tpumtjgenerate_compiling_callback tpu_mtj_backend.stopped_compiling_callback = tpumtjgenerate_stopped_compiling_callback tpu_mtj_backend.settings_callback = tpumtjgenerate_settings_callback vars.allowsp = True loadmodelsettings() loadsettings() tpu_mtj_backend.load_model(vars.custmodpth, hf_checkpoint=vars.model not in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") and vars.use_colab_tpu, **vars.modelconfig) vars.modeldim = int(tpu_mtj_backend.params.get("d_embed", tpu_mtj_backend.params["d_model"])) tokenizer = tpu_mtj_backend.tokenizer if(vars.badwordsids is vars.badwordsids_default and vars.model_type not in ("gpt2", "gpt_neo", "gptj")): vars.badwordsids = [[v] for k, v in tokenizer.get_vocab().items() if any(c in str(k) for c in "<>[]") if vars.newlinemode != "s" or str(k) != ""] else: loadsettings() lua_startup() # Load scripts load_lua_scripts() final_startup() if not initial_load: set_aibusy(False) emit('from_server', {'cmd': 'hide_model_name'}, broadcast=True) time.sleep(0.1) if not vars.gamestarted: setStartState() sendsettings() refresh_settings() # Set up Flask routes @app.route('/') @app.route('/index') def index(): if args.no_ui: return redirect('/api/latest') else: return render_template('index.html', hide_ai_menu=args.noaimenu) @app.route('/api', strict_slashes=False) def api(): return redirect('/api/latest') @app.route('/favicon.ico') def favicon(): return send_from_directory(app.root_path, 'koboldai.ico', mimetype='image/vnd.microsoft.icon') @app.route('/download') def download(): if args.no_ui: raise NotFound() save_format = request.args.get("format", "json").strip().lower() if(save_format == "plaintext"): txt = vars.prompt + "".join(vars.actions.values()) save = Response(txt) filename = path.basename(vars.savedir) if filename[-5:] == ".json": filename = filename[:-5] save.headers.set('Content-Disposition', 'attachment', filename='%s.txt' % filename) return(save) # Build json to write js = {} js["gamestarted"] = vars.gamestarted js["prompt"] = vars.prompt js["memory"] = vars.memory js["authorsnote"] = vars.authornote js["anotetemplate"] = vars.authornotetemplate js["actions"] = tuple(vars.actions.values()) js["actions_metadata"] = vars.actions_metadata js["worldinfo"] = [] # Extract only the important bits of WI for wi in vars.worldinfo: if(wi["constant"] or wi["key"] != ""): js["worldinfo"].append({ "key": wi["key"], "keysecondary": wi["keysecondary"], "content": wi["content"], "comment": wi["comment"], "folder": wi["folder"], "selective": wi["selective"], "constant": wi["constant"] }) save = Response(json.dumps(js, indent=3)) filename = path.basename(vars.savedir) if filename[-5:] == ".json": filename = filename[:-5] save.headers.set('Content-Disposition', 'attachment', filename='%s.json' % filename) return(save) #============================ LUA API =============================# _bridged = {} F = TypeVar("F", bound=Callable) def lua_startup(): global _bridged global F global bridged if(path.exists(get_config_filename())): file = open(get_config_filename(), "r") js = json.load(file) if("userscripts" in js): vars.userscripts = [] for userscript in js["userscripts"]: if type(userscript) is not str: continue userscript = userscript.strip() if len(userscript) != 0 and all(q not in userscript for q in ("..", ":")) and all(userscript[0] not in q for q in ("/", "\\")) and os.path.exists(fileops.uspath(userscript)): vars.userscripts.append(userscript) if("corescript" in js and type(js["corescript"]) is str and all(q not in js["corescript"] for q in ("..", ":")) and all(js["corescript"][0] not in q for q in ("/", "\\"))): vars.corescript = js["corescript"] else: vars.corescript = "default.lua" file.close() #==================================================================# # Lua runtime startup #==================================================================# print("", end="", flush=True) logger.init("LUA bridge", status="Starting") # Set up Lua state vars.lua_state = lupa.LuaRuntime(unpack_returned_tuples=True) # Load bridge.lua bridged = { "corescript_path": "cores", "userscript_path": "userscripts", "config_path": "userscripts", "lib_paths": vars.lua_state.table("lualibs", os.path.join("extern", "lualibs")), "vars": vars, } for kwarg in _bridged: bridged[kwarg] = _bridged[kwarg] try: vars.lua_kobold, vars.lua_koboldcore, vars.lua_koboldbridge = vars.lua_state.globals().dofile("bridge.lua")( vars.lua_state.globals().python, bridged, ) except lupa.LuaError as e: print(colors.RED + "ERROR!" + colors.END) vars.lua_koboldbridge.obliterate_multiverse() logger.error('LUA ERROR: ' + str(e).replace("\033", "")) logger.warning("Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.") exit(1) logger.init_ok("LUA bridge", status="OK") def lua_log_format_name(name): return f"[{name}]" if type(name) is str else "CORE" def bridged_kwarg(name=None): def _bridged_kwarg(f: F): _bridged[name if name is not None else f.__name__[4:] if f.__name__[:4] == "lua_" else f.__name__] = f return f return _bridged_kwarg #==================================================================# # Event triggered when a userscript is loaded #==================================================================# @bridged_kwarg() def load_callback(filename, modulename): print(colors.GREEN + f"Loading Userscript [{modulename}] <{filename}>" + colors.END) #==================================================================# # Load all Lua scripts #==================================================================# def load_lua_scripts(): logger.init("LUA Scripts", status="Starting") filenames = [] modulenames = [] descriptions = [] lst = fileops.getusfiles(long_desc=True) filenames_dict = {ob["filename"]: i for i, ob in enumerate(lst)} for filename in vars.userscripts: if filename in filenames_dict: i = filenames_dict[filename] filenames.append(filename) modulenames.append(lst[i]["modulename"]) descriptions.append(lst[i]["description"]) vars.has_genmod = False try: vars.lua_koboldbridge.obliterate_multiverse() tpool.execute(vars.lua_koboldbridge.load_corescript, vars.corescript) vars.has_genmod = tpool.execute(vars.lua_koboldbridge.load_userscripts, filenames, modulenames, descriptions) vars.lua_running = True except lupa.LuaError as e: try: vars.lua_koboldbridge.obliterate_multiverse() except: pass vars.lua_running = False if(vars.serverstarted): emit('from_server', {'cmd': 'errmsg', 'data': 'Lua script error; please check console.'}, broadcast=True) sendUSStatItems() logger.error('LUA ERROR: ' + str(e).replace("\033", "")) logger.warning("Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.") if(vars.serverstarted): set_aibusy(0) logger.init_ok("LUA Scripts", status="OK") #==================================================================# # Print message that originates from the userscript with the given name #==================================================================# @bridged_kwarg() def lua_print(msg): if(vars.lua_logname != vars.lua_koboldbridge.logging_name): vars.lua_logname = vars.lua_koboldbridge.logging_name print(colors.BLUE + lua_log_format_name(vars.lua_logname) + ":" + colors.END, file=sys.stderr) print(colors.PURPLE + msg.replace("\033", "") + colors.END) #==================================================================# # Print warning that originates from the userscript with the given name #==================================================================# @bridged_kwarg() def lua_warn(msg): if(vars.lua_logname != vars.lua_koboldbridge.logging_name): vars.lua_logname = vars.lua_koboldbridge.logging_name print(colors.BLUE + lua_log_format_name(vars.lua_logname) + ":" + colors.END, file=sys.stderr) print(colors.YELLOW + msg.replace("\033", "") + colors.END) #==================================================================# # Decode tokens into a string using current tokenizer #==================================================================# @bridged_kwarg() def lua_decode(tokens): tokens = list(tokens.values()) assert type(tokens) is list if("tokenizer" not in globals()): from transformers import GPT2Tokenizer global tokenizer tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache") return utils.decodenewlines(tokenizer.decode(tokens)) #==================================================================# # Encode string into list of token IDs using current tokenizer #==================================================================# @bridged_kwarg() def lua_encode(string): assert type(string) is str if("tokenizer" not in globals()): from transformers import GPT2Tokenizer global tokenizer tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache") return tokenizer.encode(utils.encodenewlines(string), max_length=int(4e9), truncation=True) #==================================================================# # Computes context given a submission, Lua array of entry UIDs and a Lua array # of folder UIDs #==================================================================# @bridged_kwarg() def lua_compute_context(submission, entries, folders, kwargs): assert type(submission) is str if(kwargs is None): kwargs = vars.lua_state.table() actions = vars._actions if vars.lua_koboldbridge.userstate == "genmod" else vars.actions allowed_entries = None allowed_folders = None if(entries is not None): allowed_entries = set() i = 1 while(entries[i] is not None): allowed_entries.add(int(entries[i])) i += 1 if(folders is not None): allowed_folders = set() i = 1 while(folders[i] is not None): allowed_folders.add(int(folders[i])) i += 1 winfo, mem, anotetxt, _ = calcsubmitbudgetheader( submission, allowed_entries=allowed_entries, allowed_folders=allowed_folders, force_use_txt=True, scan_story=kwargs["scan_story"] if kwargs["scan_story"] != None else True, ) if kwargs["include_anote"] is not None and not kwargs["include_anote"]: anotetxt = "" txt, _, _ = calcsubmitbudget( len(actions), winfo, mem, anotetxt, actions, ) return utils.decodenewlines(tokenizer.decode(txt)) #==================================================================# # Get property of a world info entry given its UID and property name #==================================================================# @bridged_kwarg() def lua_get_attr(uid, k): assert type(uid) is int and type(k) is str if(uid in vars.worldinfo_u and k in ( "key", "keysecondary", "content", "comment", "folder", "num", "selective", "constant", "uid", )): return vars.worldinfo_u[uid][k] #==================================================================# # Set property of a world info entry given its UID, property name and new value #==================================================================# @bridged_kwarg() def lua_set_attr(uid, k, v): assert type(uid) is int and type(k) is str assert uid in vars.worldinfo_u and k in ( "key", "keysecondary", "content", "comment", "selective", "constant", ) if(type(vars.worldinfo_u[uid][k]) is int and type(v) is float): v = int(v) assert type(vars.worldinfo_u[uid][k]) is type(v) vars.worldinfo_u[uid][k] = v print(colors.GREEN + f"{lua_log_format_name(vars.lua_koboldbridge.logging_name)} set {k} of world info entry {uid} to {v}" + colors.END) #==================================================================# # Get property of a world info folder given its UID and property name #==================================================================# @bridged_kwarg() def lua_folder_get_attr(uid, k): assert type(uid) is int and type(k) is str if(uid in vars.wifolders_d and k in ( "name", )): return vars.wifolders_d[uid][k] #==================================================================# # Set property of a world info folder given its UID, property name and new value #==================================================================# @bridged_kwarg() def lua_folder_set_attr(uid, k, v): assert type(uid) is int and type(k) is str assert uid in vars.wifolders_d and k in ( "name", ) if(type(vars.wifolders_d[uid][k]) is int and type(v) is float): v = int(v) assert type(vars.wifolders_d[uid][k]) is type(v) vars.wifolders_d[uid][k] = v print(colors.GREEN + f"{lua_log_format_name(vars.lua_koboldbridge.logging_name)} set {k} of world info folder {uid} to {v}" + colors.END) #==================================================================# # Get the "Amount to Generate" #==================================================================# @bridged_kwarg() def lua_get_genamt(): return vars.genamt #==================================================================# # Set the "Amount to Generate" #==================================================================# @bridged_kwarg() def lua_set_genamt(genamt): assert vars.lua_koboldbridge.userstate != "genmod" and type(genamt) in (int, float) and genamt >= 0 print(colors.GREEN + f"{lua_log_format_name(vars.lua_koboldbridge.logging_name)} set genamt to {int(genamt)}" + colors.END) vars.genamt = int(genamt) #==================================================================# # Get the "Gens Per Action" #==================================================================# @bridged_kwarg() def lua_get_numseqs(): return vars.numseqs #==================================================================# # Set the "Gens Per Action" #==================================================================# @bridged_kwarg() def lua_set_numseqs(numseqs): assert type(numseqs) in (int, float) and numseqs >= 1 print(colors.GREEN + f"{lua_log_format_name(vars.lua_koboldbridge.logging_name)} set numseqs to {int(numseqs)}" + colors.END) vars.numseqs = int(numseqs) #==================================================================# # Check if a setting exists with the given name #==================================================================# @bridged_kwarg() def lua_has_setting(setting): return setting in ( "anotedepth", "settemp", "settopp", "settopk", "settfs", "settypical", "settopa", "setreppen", "setreppenslope", "setreppenrange", "settknmax", "setwidepth", "setuseprompt", "setadventure", "setchatmode", "setdynamicscan", "setnopromptgen", "autosave", "setrngpersist", "temp", "topp", "top_p", "topk", "top_k", "tfs", "typical", "topa", "reppen", "reppenslope", "reppenrange", "tknmax", "widepth", "useprompt", "chatmode", "chatname", "adventure", "dynamicscan", "nopromptgen", "rngpersist", "frmttriminc", "frmtrmblln", "frmtrmspch", "frmtadsnsp", "frmtsingleline", "triminc", "rmblln", "rmspch", "adsnsp", "singleline", "output_streaming", "show_probs" ) #==================================================================# # Return the setting with the given name if it exists #==================================================================# @bridged_kwarg() def lua_get_setting(setting): if(setting in ("settemp", "temp")): return vars.temp if(setting in ("settopp", "topp", "top_p")): return vars.top_p if(setting in ("settopk", "topk", "top_k")): return vars.top_k if(setting in ("settfs", "tfs")): return vars.tfs if(setting in ("settypical", "typical")): return vars.typical if(setting in ("settopa", "topa")): return vars.top_a if(setting in ("setreppen", "reppen")): return vars.rep_pen if(setting in ("setreppenslope", "reppenslope")): return vars.rep_pen_slope if(setting in ("setreppenrange", "reppenrange")): return vars.rep_pen_range if(setting in ("settknmax", "tknmax")): return vars.max_length if(setting == "anotedepth"): return vars.andepth if(setting in ("setwidepth", "widepth")): return vars.widepth if(setting in ("setuseprompt", "useprompt")): return vars.useprompt if(setting in ("setadventure", "adventure")): return vars.adventure if(setting in ("setchatmode", "chatmode")): return vars.chatmode if(setting in ("setdynamicscan", "dynamicscan")): return vars.dynamicscan if(setting in ("setnopromptgen", "nopromptgen")): return vars.nopromptgen if(setting in ("autosave", "autosave")): return vars.autosave if(setting in ("setrngpersist", "rngpersist")): return vars.rngpersist if(setting in ("frmttriminc", "triminc")): return vars.formatoptns["frmttriminc"] if(setting in ("frmtrmblln", "rmblln")): return vars.formatoptns["frmttrmblln"] if(setting in ("frmtrmspch", "rmspch")): return vars.formatoptns["frmttrmspch"] if(setting in ("frmtadsnsp", "adsnsp")): return vars.formatoptns["frmtadsnsp"] if(setting in ("frmtsingleline", "singleline")): return vars.formatoptns["singleline"] if(setting == "output_streaming"): return vars.output_streaming if(setting == "show_probs"): return vars.show_probs #==================================================================# # Set the setting with the given name if it exists #==================================================================# @bridged_kwarg() def lua_set_setting(setting, v): actual_type = type(lua_get_setting(setting)) assert v is not None and (actual_type is type(v) or (actual_type is int and type(v) is float)) v = actual_type(v) print(colors.GREEN + f"{lua_log_format_name(vars.lua_koboldbridge.logging_name)} set {setting} to {v}" + colors.END) if(setting in ("setadventure", "adventure") and v): vars.actionmode = 1 if(setting in ("settemp", "temp")): vars.temp = v if(setting in ("settopp", "topp")): vars.top_p = v if(setting in ("settopk", "topk")): vars.top_k = v if(setting in ("settfs", "tfs")): vars.tfs = v if(setting in ("settypical", "typical")): vars.typical = v if(setting in ("settopa", "topa")): vars.top_a = v if(setting in ("setreppen", "reppen")): vars.rep_pen = v if(setting in ("setreppenslope", "reppenslope")): vars.rep_pen_slope = v if(setting in ("setreppenrange", "reppenrange")): vars.rep_pen_range = v if(setting in ("settknmax", "tknmax")): vars.max_length = v; return True if(setting == "anotedepth"): vars.andepth = v; return True if(setting in ("setwidepth", "widepth")): vars.widepth = v; return True if(setting in ("setuseprompt", "useprompt")): vars.useprompt = v; return True if(setting in ("setadventure", "adventure")): vars.adventure = v if(setting in ("setdynamicscan", "dynamicscan")): vars.dynamicscan = v if(setting in ("setnopromptgen", "nopromptgen")): vars.nopromptgen = v if(setting in ("autosave", "noautosave")): vars.autosave = v if(setting in ("setrngpersist", "rngpersist")): vars.rngpersist = v if(setting in ("setchatmode", "chatmode")): vars.chatmode = v if(setting in ("frmttriminc", "triminc")): vars.formatoptns["frmttriminc"] = v if(setting in ("frmtrmblln", "rmblln")): vars.formatoptns["frmttrmblln"] = v if(setting in ("frmtrmspch", "rmspch")): vars.formatoptns["frmttrmspch"] = v if(setting in ("frmtadsnsp", "adsnsp")): vars.formatoptns["frmtadsnsp"] = v if(setting in ("frmtsingleline", "singleline")): vars.formatoptns["singleline"] = v if(setting == "output_streaming"): vars.output_streaming = v if(setting == "show_probs"): vars.show_probs = v #==================================================================# # Get contents of memory #==================================================================# @bridged_kwarg() def lua_get_memory(): return vars.memory #==================================================================# # Set contents of memory #==================================================================# @bridged_kwarg() def lua_set_memory(m): assert type(m) is str vars.memory = m #==================================================================# # Get contents of author's note #==================================================================# @bridged_kwarg() def lua_get_authorsnote(): return vars.authornote #==================================================================# # Set contents of author's note #==================================================================# @bridged_kwarg() def lua_set_authorsnote(m): assert type(m) is str vars.authornote = m #==================================================================# # Get contents of author's note template #==================================================================# @bridged_kwarg() def lua_get_authorsnotetemplate(): return vars.authornotetemplate #==================================================================# # Set contents of author's note template #==================================================================# @bridged_kwarg() def lua_set_authorsnotetemplate(m): assert type(m) is str vars.authornotetemplate = m #==================================================================# # Save settings and send them to client #==================================================================# @bridged_kwarg() def lua_resend_settings(): settingschanged() refresh_settings() #==================================================================# # Set story chunk text and delete the chunk if the new chunk is empty #==================================================================# @bridged_kwarg() def lua_set_chunk(k, v): assert type(k) in (int, None) and type(v) is str assert k >= 0 assert k != 0 or len(v) != 0 if(len(v) == 0): print(colors.GREEN + f"{lua_log_format_name(vars.lua_koboldbridge.logging_name)} deleted story chunk {k}" + colors.END) chunk = int(k) if(vars.lua_koboldbridge.userstate == "genmod"): del vars._actions[chunk-1] vars.lua_deleted.add(chunk) if(not hasattr(vars, "_actions") or vars._actions is not vars.actions): #Instead of deleting we'll blank out the text. This way our actions and actions_metadata stay in sync and we can restore the chunk on an undo vars.actions[chunk-1] = "" vars.actions_metadata[chunk-1]['Alternative Text'] = [{"Text": vars.actions_metadata[chunk-1]['Selected Text'], "Pinned": False, "Editted": True}] + vars.actions_metadata[chunk-1]['Alternative Text'] vars.actions_metadata[chunk-1]['Selected Text'] = '' send_debug() else: if(k == 0): print(colors.GREEN + f"{lua_log_format_name(vars.lua_koboldbridge.logging_name)} edited prompt chunk" + colors.END) else: print(colors.GREEN + f"{lua_log_format_name(vars.lua_koboldbridge.logging_name)} edited story chunk {k}" + colors.END) chunk = int(k) if(chunk == 0): if(vars.lua_koboldbridge.userstate == "genmod"): vars._prompt = v vars.lua_edited.add(chunk) vars.prompt = v else: if(vars.lua_koboldbridge.userstate == "genmod"): vars._actions[chunk-1] = v vars.lua_edited.add(chunk) vars.actions[chunk-1] = v vars.actions_metadata[chunk-1]['Alternative Text'] = [{"Text": vars.actions_metadata[chunk-1]['Selected Text'], "Pinned": False, "Editted": True}] + vars.actions_metadata[chunk-1]['Alternative Text'] vars.actions_metadata[chunk-1]['Selected Text'] = v send_debug() #==================================================================# # Get model type as "gpt-2-xl", "gpt-neo-2.7B", etc. #==================================================================# @bridged_kwarg() def lua_get_modeltype(): if(vars.noai): return "readonly" if(vars.model in ("Colab", "API", "CLUSTER", "OAI", "InferKit")): return "api" if(not vars.use_colab_tpu and vars.model not in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") and (vars.model in ("GPT2Custom", "NeoCustom") or vars.model_type in ("gpt2", "gpt_neo", "gptj"))): hidden_size = get_hidden_size_from_model(model) if(vars.model in ("gpt2",) or (vars.model_type == "gpt2" and hidden_size == 768)): return "gpt2" if(vars.model in ("gpt2-medium",) or (vars.model_type == "gpt2" and hidden_size == 1024)): return "gpt2-medium" if(vars.model in ("gpt2-large",) or (vars.model_type == "gpt2" and hidden_size == 1280)): return "gpt2-large" if(vars.model in ("gpt2-xl",) or (vars.model_type == "gpt2" and hidden_size == 1600)): return "gpt2-xl" if(vars.model_type == "gpt_neo" and hidden_size == 768): return "gpt-neo-125M" if(vars.model in ("EleutherAI/gpt-neo-1.3B",) or (vars.model_type == "gpt_neo" and hidden_size == 2048)): return "gpt-neo-1.3B" if(vars.model in ("EleutherAI/gpt-neo-2.7B",) or (vars.model_type == "gpt_neo" and hidden_size == 2560)): return "gpt-neo-2.7B" if(vars.model in ("EleutherAI/gpt-j-6B",) or ((vars.use_colab_tpu or vars.model == "TPUMeshTransformerGPTJ") and tpu_mtj_backend.params["d_model"] == 4096) or (vars.model_type in ("gpt_neo", "gptj") and hidden_size == 4096)): return "gpt-j-6B" return "unknown" #==================================================================# # Get model backend as "transformers" or "mtj" #==================================================================# @bridged_kwarg() def lua_get_modelbackend(): if(vars.noai): return "readonly" if(vars.model in ("Colab", "API", "CLUSTER", "OAI", "InferKit")): return "api" if(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")): return "mtj" return "transformers" #==================================================================# # Check whether model is loaded from a custom path #==================================================================# @bridged_kwarg() def lua_is_custommodel(): return vars.model in ("GPT2Custom", "NeoCustom", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") #==================================================================# # Return the filename (as a string) of the current soft prompt, or # None if no soft prompt is loaded #==================================================================# @bridged_kwarg() def lua_get_spfilename(): return vars.spfilename.strip() or None #==================================================================# # When called with a string as argument, sets the current soft prompt; # when called with None as argument, uses no soft prompt. # Returns True if soft prompt changed, False otherwise. #==================================================================# @bridged_kwarg() def lua_set_spfilename(filename: Union[str, None]): if(filename is None): filename = "" filename = str(filename).strip() changed = lua_get_spfilename() != filename assert all(q not in filename for q in ("/", "\\")) spRequest(filename) return changed #==================================================================# # #==================================================================# def execute_inmod(): setgamesaved(False) vars.lua_logname = ... vars.lua_edited = set() vars.lua_deleted = set() try: tpool.execute(vars.lua_koboldbridge.execute_inmod) except lupa.LuaError as e: vars.lua_koboldbridge.obliterate_multiverse() vars.lua_running = False emit('from_server', {'cmd': 'errmsg', 'data': 'Lua script error; please check console.'}, broadcast=True) sendUSStatItems() logger.error('LUA ERROR: ' + str(e).replace("\033", "")) logger.warning("Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.") set_aibusy(0) def execute_genmod(): vars.lua_koboldbridge.execute_genmod() def execute_outmod(): setgamesaved(False) emit('from_server', {'cmd': 'hidemsg', 'data': ''}, broadcast=True) try: tpool.execute(vars.lua_koboldbridge.execute_outmod) except lupa.LuaError as e: vars.lua_koboldbridge.obliterate_multiverse() vars.lua_running = False emit('from_server', {'cmd': 'errmsg', 'data': 'Lua script error; please check console.'}, broadcast=True) sendUSStatItems() logger.error('LUA ERROR: ' + str(e).replace("\033", "")) logger.warning("Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.") set_aibusy(0) if(vars.lua_koboldbridge.resend_settings_required): vars.lua_koboldbridge.resend_settings_required = False lua_resend_settings() for k in vars.lua_edited: inlineedit(k, vars.actions[k]) for k in vars.lua_deleted: inlinedelete(k) #============================ METHODS =============================# #==================================================================# # Event triggered when browser SocketIO is loaded and connects to server #==================================================================# @socketio.on('connect') def do_connect(): logger.info("Client connected!") emit('from_server', {'cmd': 'setchatname', 'data': vars.chatname}) emit('from_server', {'cmd': 'setanotetemplate', 'data': vars.authornotetemplate}) emit('from_server', {'cmd': 'connected', 'smandelete': vars.smandelete, 'smanrename': vars.smanrename, 'modelname': getmodelname()}) if(vars.host): emit('from_server', {'cmd': 'runs_remotely'}) if(vars.allowsp): emit('from_server', {'cmd': 'allowsp', 'data': vars.allowsp}) sendUSStatItems() emit('from_server', {'cmd': 'spstatitems', 'data': {vars.spfilename: vars.spmeta} if vars.allowsp and len(vars.spfilename) else {}}, broadcast=True) if(not vars.gamestarted): setStartState() sendsettings() refresh_settings() vars.laststory = None emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory}) sendwi() emit('from_server', {'cmd': 'setmemory', 'data': vars.memory}) emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}) vars.mode = "play" else: # Game in session, send current game data and ready state to browser refresh_story() sendsettings() refresh_settings() emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory}) sendwi() emit('from_server', {'cmd': 'setmemory', 'data': vars.memory}) emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}) if(vars.mode == "play"): if(not vars.aibusy): emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}) else: emit('from_server', {'cmd': 'setgamestate', 'data': 'wait'}) elif(vars.mode == "edit"): emit('from_server', {'cmd': 'editmode', 'data': 'true'}) elif(vars.mode == "memory"): emit('from_server', {'cmd': 'memmode', 'data': 'true'}) elif(vars.mode == "wi"): emit('from_server', {'cmd': 'wimode', 'data': 'true'}) emit('from_server', {'cmd': 'gamesaved', 'data': vars.gamesaved}, broadcast=True) #==================================================================# # Event triggered when browser SocketIO sends data to the server #==================================================================# @socketio.on('message') def get_message(msg): if not vars.quiet: logger.debug(f"Data received: {msg}") # Submit action if(msg['cmd'] == 'submit'): if(vars.mode == "play"): if(vars.aibusy): if(msg.get('allowabort', False)): vars.abort = True return vars.abort = False vars.lua_koboldbridge.feedback = None if(vars.chatmode): if(type(msg['chatname']) is not str): raise ValueError("Chatname must be a string") vars.chatname = msg['chatname'] settingschanged() emit('from_server', {'cmd': 'setchatname', 'data': vars.chatname}) vars.recentrng = vars.recentrngm = None actionsubmit(msg['data'], actionmode=msg['actionmode']) elif(vars.mode == "edit"): editsubmit(msg['data']) elif(vars.mode == "memory"): memsubmit(msg['data']) # Retry Action elif(msg['cmd'] == 'retry'): if(vars.aibusy): if(msg.get('allowabort', False)): vars.abort = True return vars.abort = False if(vars.chatmode): if(type(msg['chatname']) is not str): raise ValueError("Chatname must be a string") vars.chatname = msg['chatname'] settingschanged() emit('from_server', {'cmd': 'setchatname', 'data': vars.chatname}) actionretry(msg['data']) # Back/Undo Action elif(msg['cmd'] == 'back'): ignore = actionback() # Forward/Redo Action elif(msg['cmd'] == 'redo'): actionredo() # EditMode Action (old) elif(msg['cmd'] == 'edit'): if(vars.mode == "play"): vars.mode = "edit" emit('from_server', {'cmd': 'editmode', 'data': 'true'}, broadcast=True) elif(vars.mode == "edit"): vars.mode = "play" emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True) # EditLine Action (old) elif(msg['cmd'] == 'editline'): editrequest(int(msg['data'])) # Inline edit elif(msg['cmd'] == 'inlineedit'): inlineedit(msg['chunk'], msg['data']) elif(msg['cmd'] == 'inlinedelete'): inlinedelete(msg['data']) # DeleteLine Action (old) elif(msg['cmd'] == 'delete'): deleterequest() elif(msg['cmd'] == 'memory'): togglememorymode() elif(not vars.host and msg['cmd'] == 'savetofile'): savetofile() elif(not vars.host and msg['cmd'] == 'loadfromfile'): loadfromfile() elif(msg['cmd'] == 'loadfromstring'): loadRequest(json.loads(msg['data']), filename=msg['filename']) elif(not vars.host and msg['cmd'] == 'import'): importRequest() elif(msg['cmd'] == 'newgame'): newGameRequest() elif(msg['cmd'] == 'rndgame'): randomGameRequest(msg['data'], memory=msg['memory']) elif(msg['cmd'] == 'settemp'): vars.temp = float(msg['data']) emit('from_server', {'cmd': 'setlabeltemp', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() elif(msg['cmd'] == 'settopp'): vars.top_p = float(msg['data']) emit('from_server', {'cmd': 'setlabeltopp', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() elif(msg['cmd'] == 'settopk'): vars.top_k = int(msg['data']) emit('from_server', {'cmd': 'setlabeltopk', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() elif(msg['cmd'] == 'settfs'): vars.tfs = float(msg['data']) emit('from_server', {'cmd': 'setlabeltfs', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() elif(msg['cmd'] == 'settypical'): vars.typical = float(msg['data']) emit('from_server', {'cmd': 'setlabeltypical', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() elif(msg['cmd'] == 'settopa'): vars.top_a = float(msg['data']) emit('from_server', {'cmd': 'setlabeltopa', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() elif(msg['cmd'] == 'setreppen'): vars.rep_pen = float(msg['data']) emit('from_server', {'cmd': 'setlabelreppen', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() elif(msg['cmd'] == 'setreppenslope'): vars.rep_pen_slope = float(msg['data']) emit('from_server', {'cmd': 'setlabelreppenslope', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() elif(msg['cmd'] == 'setreppenrange'): vars.rep_pen_range = float(msg['data']) emit('from_server', {'cmd': 'setlabelreppenrange', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() elif(msg['cmd'] == 'setoutput'): vars.genamt = int(msg['data']) emit('from_server', {'cmd': 'setlabeloutput', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() elif(msg['cmd'] == 'settknmax'): vars.max_length = int(msg['data']) emit('from_server', {'cmd': 'setlabeltknmax', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() elif(msg['cmd'] == 'setikgen'): vars.ikgen = int(msg['data']) emit('from_server', {'cmd': 'setlabelikgen', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() # Author's Note field update elif(msg['cmd'] == 'anote'): anotesubmit(msg['data'], template=msg['template']) # Author's Note depth update elif(msg['cmd'] == 'anotedepth'): vars.andepth = int(msg['data']) emit('from_server', {'cmd': 'setlabelanotedepth', 'data': msg['data']}, broadcast=True) settingschanged() refresh_settings() # Format - Trim incomplete sentences elif(msg['cmd'] == 'frmttriminc'): if('frmttriminc' in vars.formatoptns): vars.formatoptns["frmttriminc"] = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'frmtrmblln'): if('frmtrmblln' in vars.formatoptns): vars.formatoptns["frmtrmblln"] = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'frmtrmspch'): if('frmtrmspch' in vars.formatoptns): vars.formatoptns["frmtrmspch"] = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'frmtadsnsp'): if('frmtadsnsp' in vars.formatoptns): vars.formatoptns["frmtadsnsp"] = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'singleline'): if('singleline' in vars.formatoptns): vars.formatoptns["singleline"] = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'importselect'): vars.importnum = int(msg["data"].replace("import", "")) elif(msg['cmd'] == 'importcancel'): emit('from_server', {'cmd': 'popupshow', 'data': False}) vars.importjs = {} elif(msg['cmd'] == 'importaccept'): emit('from_server', {'cmd': 'popupshow', 'data': False}) importgame() elif(msg['cmd'] == 'wi'): togglewimode() elif(msg['cmd'] == 'wiinit'): if(int(msg['data']) < len(vars.worldinfo)): setgamesaved(False) vars.worldinfo[msg['data']]["init"] = True addwiitem(folder_uid=msg['folder']) elif(msg['cmd'] == 'wifolderinit'): addwifolder() elif(msg['cmd'] == 'wimoveitem'): movewiitem(msg['destination'], msg['data']) elif(msg['cmd'] == 'wimovefolder'): movewifolder(msg['destination'], msg['data']) elif(msg['cmd'] == 'widelete'): deletewi(msg['data']) elif(msg['cmd'] == 'wifolderdelete'): deletewifolder(msg['data']) elif(msg['cmd'] == 'wiexpand'): assert 0 <= int(msg['data']) < len(vars.worldinfo) setgamesaved(False) emit('from_server', {'cmd': 'wiexpand', 'data': msg['data']}, broadcast=True) elif(msg['cmd'] == 'wiexpandfolder'): assert 0 <= int(msg['data']) < len(vars.worldinfo) setgamesaved(False) emit('from_server', {'cmd': 'wiexpandfolder', 'data': msg['data']}, broadcast=True) elif(msg['cmd'] == 'wifoldercollapsecontent'): setgamesaved(False) vars.wifolders_d[msg['data']]['collapsed'] = True emit('from_server', {'cmd': 'wifoldercollapsecontent', 'data': msg['data']}, broadcast=True) elif(msg['cmd'] == 'wifolderexpandcontent'): setgamesaved(False) vars.wifolders_d[msg['data']]['collapsed'] = False emit('from_server', {'cmd': 'wifolderexpandcontent', 'data': msg['data']}, broadcast=True) elif(msg['cmd'] == 'wiupdate'): setgamesaved(False) num = int(msg['num']) fields = ("key", "keysecondary", "content", "comment") for field in fields: if(field in msg['data'] and type(msg['data'][field]) is str): vars.worldinfo[num][field] = msg['data'][field] emit('from_server', {'cmd': 'wiupdate', 'num': msg['num'], 'data': {field: vars.worldinfo[num][field] for field in fields}}, broadcast=True) elif(msg['cmd'] == 'wifolderupdate'): setgamesaved(False) uid = int(msg['uid']) fields = ("name", "collapsed") for field in fields: if(field in msg['data'] and type(msg['data'][field]) is (str if field != "collapsed" else bool)): vars.wifolders_d[uid][field] = msg['data'][field] emit('from_server', {'cmd': 'wifolderupdate', 'uid': msg['uid'], 'data': {field: vars.wifolders_d[uid][field] for field in fields}}, broadcast=True) elif(msg['cmd'] == 'wiselon'): setgamesaved(False) vars.worldinfo[msg['data']]["selective"] = True emit('from_server', {'cmd': 'wiselon', 'data': msg['data']}, broadcast=True) elif(msg['cmd'] == 'wiseloff'): setgamesaved(False) vars.worldinfo[msg['data']]["selective"] = False emit('from_server', {'cmd': 'wiseloff', 'data': msg['data']}, broadcast=True) elif(msg['cmd'] == 'wiconstanton'): setgamesaved(False) vars.worldinfo[msg['data']]["constant"] = True emit('from_server', {'cmd': 'wiconstanton', 'data': msg['data']}, broadcast=True) elif(msg['cmd'] == 'wiconstantoff'): setgamesaved(False) vars.worldinfo[msg['data']]["constant"] = False emit('from_server', {'cmd': 'wiconstantoff', 'data': msg['data']}, broadcast=True) elif(msg['cmd'] == 'sendwilist'): commitwi(msg['data']) elif(msg['cmd'] == 'aidgimport'): importAidgRequest(msg['data']) elif(msg['cmd'] == 'saveasrequest'): saveas(msg['data']) elif(msg['cmd'] == 'saverequest'): save() elif(msg['cmd'] == 'loadlistrequest'): getloadlist() elif(msg['cmd'] == 'splistrequest'): getsplist() elif(msg['cmd'] == 'uslistrequest'): unloaded, loaded = getuslist() emit('from_server', {'cmd': 'buildus', 'data': {"unloaded": unloaded, "loaded": loaded}}) elif(msg['cmd'] == 'samplerlistrequest'): emit('from_server', {'cmd': 'buildsamplers', 'data': vars.sampler_order}) elif(msg['cmd'] == 'usloaded'): vars.userscripts = [] for userscript in msg['data']: if type(userscript) is not str: continue userscript = userscript.strip() if len(userscript) != 0 and all(q not in userscript for q in ("..", ":")) and all(userscript[0] not in q for q in ("/", "\\")) and os.path.exists(fileops.uspath(userscript)): vars.userscripts.append(userscript) settingschanged() elif(msg['cmd'] == 'usload'): load_lua_scripts() unloaded, loaded = getuslist() sendUSStatItems() elif(msg['cmd'] == 'samplers'): sampler_order = msg["data"] sampler_order_min_length = 6 sampler_order_max_length = 7 if(not isinstance(sampler_order, list)): raise ValueError(f"Sampler order must be a list, but got a {type(sampler_order)}") if(not (sampler_order_min_length <= len(sampler_order) <= sampler_order_max_length)): raise ValueError(f"Sampler order must be a list of length greater than or equal to {sampler_order_min_length} and less than or equal to {sampler_order_max_length}, but got a list of length {len(sampler_order)}") if(not all(isinstance(e, int) for e in sampler_order)): raise ValueError(f"Sampler order must be a list of ints, but got a list with at least one non-int element") if(min(sampler_order) != 0 or max(sampler_order) != len(sampler_order) - 1 or len(set(sampler_order)) != len(sampler_order)): raise ValueError(f"Sampler order list of length {len(sampler_order)} must be a permutation of the first {len(sampler_order)} nonnegative integers") vars.sampler_order = sampler_order settingschanged() elif(msg['cmd'] == 'list_model'): sendModelSelection(menu=msg['data']) elif(msg['cmd'] == 'load_model'): logger.debug(f"Selected Model: {vars.model_selected}") if not os.path.exists("settings/"): os.mkdir("settings") changed = True if not utils.HAS_ACCELERATE: msg['disk_layers'] = "0" if os.path.exists("settings/" + vars.model_selected.replace('/', '_') + ".breakmodel"): with open("settings/" + vars.model_selected.replace('/', '_') + ".breakmodel", "r") as file: data = file.read().split('\n')[:2] if len(data) < 2: data.append("0") gpu_layers, disk_layers = data if gpu_layers == msg['gpu_layers'] and disk_layers == msg['disk_layers']: changed = False if changed: if vars.model_selected in ["NeoCustom", "GPT2Custom"]: filename = "settings/{}.breakmodel".format(os.path.basename(os.path.normpath(vars.custmodpth))) else: filename = "settings/{}.breakmodel".format(vars.model_selected.replace('/', '_')) f = open(filename, "w") f.write(str(msg['gpu_layers']) + '\n' + str(msg['disk_layers'])) f.close() vars.colaburl = msg['url'] + "/request" vars.model = vars.model_selected if vars.model == "CLUSTER": if type(msg['online_model']) is not list: if msg['online_model'] == '': vars.cluster_requested_models = [] else: vars.cluster_requested_models = [msg['online_model']] else: vars.cluster_requested_models = msg['online_model'] load_model(use_gpu=msg['use_gpu'], gpu_layers=msg['gpu_layers'], disk_layers=msg['disk_layers'], online_model=msg['online_model']) elif(msg['cmd'] == 'show_model'): logger.info(f"Model Name: {getmodelname()}") emit('from_server', {'cmd': 'show_model_name', 'data': getmodelname()}, broadcast=True) elif(msg['cmd'] == 'selectmodel'): # This is run when a model line is selected from the UI (line from the model_menu variable) that is tagged as not a menu # otherwise we should be running the msg['cmd'] == 'list_model' # We have to do a bit of processing though, if we select a custom path, we need to list out the contents of folders # But if we select something else, we need to potentially show model layers for each GPU # We might also need to show key input. All of that happens here # The data variable will contain the model name. But our Custom lines need a bit more processing # If we're on a custom line that we have selected a model for, the path variable will be in msg # so if that's missing we need to run the menu to show the model folders in the models folder if msg['data'] in ('NeoCustom', 'GPT2Custom') and 'path' not in msg and 'path_modelname' not in msg: if 'folder' not in msg or vars.host: folder = "./models" else: folder = msg['folder'] sendModelSelection(menu=msg['data'], folder=folder) elif msg['data'] in ('NeoCustom', 'GPT2Custom') and 'path_modelname' in msg: #Here the user entered custom text in the text box. This could be either a model name or a path. if check_if_dir_is_model(msg['path_modelname']): vars.model_selected = msg['data'] vars.custmodpth = msg['path_modelname'] get_model_info(msg['data'], directory=msg['path']) else: vars.model_selected = msg['path_modelname'] try: get_model_info(vars.model_selected) except: emit('from_server', {'cmd': 'errmsg', 'data': "The model entered doesn't exist."}) elif msg['data'] in ('NeoCustom', 'GPT2Custom'): if check_if_dir_is_model(msg['path']): vars.model_selected = msg['data'] vars.custmodpth = msg['path'] get_model_info(msg['data'], directory=msg['path']) else: if vars.host: sendModelSelection(menu=msg['data'], folder="./models") else: sendModelSelection(menu=msg['data'], folder=msg['path']) else: vars.model_selected = msg['data'] if 'path' in msg: vars.custmodpth = msg['path'] get_model_info(msg['data'], directory=msg['path']) else: get_model_info(vars.model_selected) elif(msg['cmd'] == 'delete_model'): if "{}/models".format(os.getcwd()) in os.path.abspath(msg['data']) or "{}\\models".format(os.getcwd()) in os.path.abspath(msg['data']): if check_if_dir_is_model(msg['data']): logger.warning(f"Someone deleted {msg['data']}") import shutil shutil.rmtree(msg['data']) sendModelSelection(menu=msg['menu']) else: logger.error(f"Someone attempted to delete {msg['data']} but this is not a valid model") else: logger.critical(f"Someone maliciously attempted to delete {msg['data']}. The attempt has been blocked.") elif(msg['cmd'] == 'OAI_Key_Update'): get_oai_models(msg['key']) elif(msg['cmd'] == 'Cluster_Key_Update'): get_cluster_models(msg) elif(msg['cmd'] == 'loadselect'): vars.loadselect = msg["data"] elif(msg['cmd'] == 'spselect'): vars.spselect = msg["data"] elif(msg['cmd'] == 'loadrequest'): loadRequest(fileops.storypath(vars.loadselect)) elif(msg['cmd'] == 'sprequest'): spRequest(vars.spselect) elif(msg['cmd'] == 'deletestory'): deletesave(msg['data']) elif(msg['cmd'] == 'renamestory'): renamesave(msg['data'], msg['newname']) elif(msg['cmd'] == 'clearoverwrite'): vars.svowname = "" vars.saveow = False elif(msg['cmd'] == 'seqsel'): selectsequence(msg['data']) elif(msg['cmd'] == 'seqpin'): pinsequence(msg['data']) elif(msg['cmd'] == 'setnumseq'): vars.numseqs = int(msg['data']) emit('from_server', {'cmd': 'setlabelnumseq', 'data': msg['data']}) settingschanged() refresh_settings() elif(msg['cmd'] == 'setwidepth'): vars.widepth = int(msg['data']) emit('from_server', {'cmd': 'setlabelwidepth', 'data': msg['data']}) settingschanged() refresh_settings() elif(msg['cmd'] == 'setuseprompt'): vars.useprompt = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'setadventure'): vars.adventure = msg['data'] vars.chatmode = False settingschanged() refresh_settings() elif(msg['cmd'] == 'autosave'): vars.autosave = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'setchatmode'): vars.chatmode = msg['data'] vars.adventure = False settingschanged() refresh_settings() elif(msg['cmd'] == 'setdynamicscan'): vars.dynamicscan = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'setnopromptgen'): vars.nopromptgen = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'setrngpersist'): vars.rngpersist = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'setnogenmod'): vars.nogenmod = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'setfulldeterminism'): vars.full_determinism = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'setoutputstreaming'): vars.output_streaming = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'setshowbudget'): vars.show_budget = msg['data'] settingschanged() refresh_settings() elif(msg['cmd'] == 'setshowprobs'): vars.show_probs = msg['data'] settingschanged() refresh_settings() elif(not vars.host and msg['cmd'] == 'importwi'): wiimportrequest() elif(msg['cmd'] == 'debug'): vars.debug = msg['data'] emit('from_server', {'cmd': 'set_debug', 'data': msg['data']}, broadcast=True) if vars.debug: send_debug() elif(msg['cmd'] == 'getfieldbudget'): unencoded = msg["data"]["unencoded"] field = msg["data"]["field"] # Tokenizer may be undefined here when a model has not been chosen. if "tokenizer" not in globals(): # We don't have a tokenizer, just return nulls. emit( 'from_server', {'cmd': 'showfieldbudget', 'data': {"length": None, "max": None, "field": field}}, ) return header_length = len(tokenizer._koboldai_header) max_tokens = vars.max_length - header_length - vars.sp_length - vars.genamt if not unencoded: # Unencoded is empty, just return 0 emit( 'from_server', {'cmd': 'showfieldbudget', 'data': {"length": 0, "max": max_tokens, "field": field}}, broadcast=True ) else: if field == "anoteinput": unencoded = buildauthorsnote(unencoded, msg["data"]["anotetemplate"]) tokens_length = len(tokenizer.encode(unencoded)) emit( 'from_server', {'cmd': 'showfieldbudget', 'data': {"length": tokens_length, "max": max_tokens, "field": field}}, broadcast=True ) #==================================================================# # Send userscripts list to client #==================================================================# def sendUSStatItems(): _, loaded = getuslist() loaded = loaded if vars.lua_running else [] last_userscripts = [e["filename"] for e in loaded] emit('from_server', {'cmd': 'usstatitems', 'data': loaded, 'flash': last_userscripts != vars.last_userscripts}, broadcast=True) vars.last_userscripts = last_userscripts #==================================================================# # KoboldAI Markup Formatting (Mixture of Markdown and sanitized html) #==================================================================# def kml(txt): txt = txt.replace('>', '>') txt = bleach.clean(markdown.markdown(txt), tags = ['p', 'em', 'strong', 'code', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'li', 'ul', 'b', 'i', 'a', 'span', 'button'], styles = ['color', 'font-weight'], attributes=['id', 'class', 'style', 'href']) return txt #==================================================================# # Send start message and tell Javascript to set UI state #==================================================================# def setStartState(): if(vars.welcome): txt = kml(vars.welcome) + "
" else: txt = "Welcome to KoboldAI! You are running "+getmodelname()+".
" if(not vars.noai and not vars.welcome): txt = txt + "Please load a game or enter a prompt below to begin!
" if(vars.noai): txt = txt + "Please load or import a story to read. There is no AI in this mode." emit('from_server', {'cmd': 'updatescreen', 'gamestarted': vars.gamestarted, 'data': txt}, broadcast=True) emit('from_server', {'cmd': 'setgamestate', 'data': 'start'}, broadcast=True) #==================================================================# # Transmit applicable settings to SocketIO to build UI sliders/toggles #==================================================================# def sendsettings(): # Send settings for selected AI type emit('from_server', {'cmd': 'reset_menus'}) if(vars.model != "InferKit"): for set in gensettings.gensettingstf: emit('from_server', {'cmd': 'addsetting', 'data': set}) else: for set in gensettings.gensettingsik: emit('from_server', {'cmd': 'addsetting', 'data': set}) # Send formatting options for frm in gensettings.formatcontrols: emit('from_server', {'cmd': 'addformat', 'data': frm}) # Add format key to vars if it wasn't loaded with client.settings if(not frm["id"] in vars.formatoptns): vars.formatoptns[frm["id"]] = False; #==================================================================# # Set value of gamesaved #==================================================================# def setgamesaved(gamesaved): assert type(gamesaved) is bool if(gamesaved != vars.gamesaved): emit('from_server', {'cmd': 'gamesaved', 'data': gamesaved}, broadcast=True) vars.gamesaved = gamesaved #==================================================================# # Take input text from SocketIO and decide what to do with it #==================================================================# def check_for_backend_compilation(): if(vars.checking): return vars.checking = True for _ in range(31): time.sleep(0.06276680299820175) if(vars.compiling): emit('from_server', {'cmd': 'warnmsg', 'data': 'Compiling TPU backend—this usually takes 1–2 minutes...'}, broadcast=True) break vars.checking = False def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False, disable_recentrng=False, no_generate=False, ignore_aibusy=False): # Ignore new submissions if the AI is currently busy if(not ignore_aibusy and vars.aibusy): return while(True): set_aibusy(1) if(vars.model in ["API","CLUSTER"]): global tokenizer if vars.model == "API": tokenizer_id = requests.get( vars.colaburl[:-8] + "/api/v1/model", ).json()["result"] elif len(vars.cluster_requested_models) >= 1: # If the player has requested one or more models, we use the first one for the tokenizer tokenizer_id = vars.cluster_requested_models[0] # The cluster can return any number of possible models for each gen, but this happens after this step # So at this point, this is unknown else: tokenizer_id = "" if tokenizer_id != vars.api_tokenizer_id: try: if(os.path.isdir(tokenizer_id)): try: tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache") except: tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache", use_fast=False) elif(os.path.isdir("models/{}".format(tokenizer_id.replace('/', '_')))): try: tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=args.revision, cache_dir="cache") except: tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=args.revision, cache_dir="cache", use_fast=False) else: try: tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache") except: tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache", use_fast=False) except: logger.warning(f"Unknown tokenizer {repr(tokenizer_id)}") vars.api_tokenizer_id = tokenizer_id if(disable_recentrng): vars.recentrng = vars.recentrngm = None vars.recentback = False vars.recentedit = False vars.actionmode = actionmode # "Action" mode if(actionmode == 1): data = data.strip().lstrip('>') data = re.sub(r'\n+', ' ', data) if(len(data)): data = f"\n\n> {data}\n" # "Chat" mode if(vars.chatmode and vars.gamestarted): data = re.sub(r'\n+', ' ', data) if(len(data)): data = f"\n{vars.chatname}: {data}\n" # If we're not continuing, store a copy of the raw input if(data != ""): vars.lastact = data if(not vars.gamestarted): vars.submission = data if(not no_generate): execute_inmod() vars.submission = re.sub(r"[^\S\r\n]*([\r\n]*)$", r"\1", vars.submission) # Remove trailing whitespace, excluding newlines data = vars.submission if(not force_submit and len(data.strip()) == 0): assert False # Start the game vars.gamestarted = True if(not no_generate and not vars.noai and vars.lua_koboldbridge.generating and (not vars.nopromptgen or force_prompt_gen)): # Save this first action as the prompt vars.prompt = data # Clear the startup text from game screen emit('from_server', {'cmd': 'updatescreen', 'gamestarted': False, 'data': 'Please wait, generating story...'}, broadcast=True) calcsubmit(data) # Run the first action through the generator if(not no_generate and not vars.abort and vars.lua_koboldbridge.restart_sequence is not None and len(vars.genseqs) == 0): data = "" force_submit = True disable_recentrng = True continue emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True) break else: # Save this first action as the prompt vars.prompt = data if len(data) > 0 else '"' for i in range(vars.numseqs): vars.lua_koboldbridge.outputs[i+1] = "" if(not no_generate): execute_outmod() vars.lua_koboldbridge.regeneration_required = False genout = [] for i in range(vars.numseqs): genout.append({"generated_text": vars.lua_koboldbridge.outputs[i+1]}) assert type(genout[-1]["generated_text"]) is str if(len(genout) == 1): genresult(genout[0]["generated_text"], flash=False) refresh_story() if(len(vars.actions) > 0): emit('from_server', {'cmd': 'texteffect', 'data': vars.actions.get_last_key() + 1}, broadcast=True) if(not vars.abort and vars.lua_koboldbridge.restart_sequence is not None): data = "" force_submit = True disable_recentrng = True continue else: if(not vars.abort and vars.lua_koboldbridge.restart_sequence is not None and vars.lua_koboldbridge.restart_sequence > 0): genresult(genout[vars.lua_koboldbridge.restart_sequence-1]["generated_text"], flash=False) refresh_story() data = "" force_submit = True disable_recentrng = True continue genselect(genout) refresh_story() set_aibusy(0) emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True) break else: # Apply input formatting & scripts before sending to tokenizer if(vars.actionmode == 0): data = applyinputformatting(data) vars.submission = data if(not no_generate): execute_inmod() vars.submission = re.sub(r"[^\S\r\n]*([\r\n]*)$", r"\1", vars.submission) # Remove trailing whitespace, excluding newlines data = vars.submission # Dont append submission if it's a blank/continue action if(data != ""): # Store the result in the Action log if(len(vars.prompt.strip()) == 0): vars.prompt = data else: vars.actions.append(data) # we now need to update the actions_metadata # we'll have two conditions. # 1. This is totally new (user entered) if vars.actions.get_last_key() not in vars.actions_metadata: vars.actions_metadata[vars.actions.get_last_key()] = {"Selected Text": data, "Alternative Text": []} else: # 2. We've selected a chunk of text that is was presented previously try: alternatives = [item['Text'] for item in vars.actions_metadata[len(vars.actions)-1]["Alternative Text"]] except: logger.debug(len(vars.actions)) logger.debug(vars.actions_metadata) raise if data in alternatives: alternatives = [item for item in vars.actions_metadata[vars.actions.get_last_key() ]["Alternative Text"] if item['Text'] != data] vars.actions_metadata[vars.actions.get_last_key()]["Alternative Text"] = alternatives vars.actions_metadata[vars.actions.get_last_key()]["Selected Text"] = data update_story_chunk('last') send_debug() if(not no_generate and not vars.noai and vars.lua_koboldbridge.generating): # Off to the tokenizer! calcsubmit(data) if(not vars.abort and vars.lua_koboldbridge.restart_sequence is not None and len(vars.genseqs) == 0): data = "" force_submit = True disable_recentrng = True continue emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True) break else: if(not no_generate): for i in range(vars.numseqs): vars.lua_koboldbridge.outputs[i+1] = "" execute_outmod() vars.lua_koboldbridge.regeneration_required = False genout = [] for i in range(vars.numseqs): genout.append({"generated_text": vars.lua_koboldbridge.outputs[i+1] if not no_generate else ""}) assert type(genout[-1]["generated_text"]) is str if(len(genout) == 1): genresult(genout[0]["generated_text"]) if(not no_generate and not vars.abort and vars.lua_koboldbridge.restart_sequence is not None): data = "" force_submit = True disable_recentrng = True continue else: if(not no_generate and not vars.abort and vars.lua_koboldbridge.restart_sequence is not None and vars.lua_koboldbridge.restart_sequence > 0): genresult(genout[vars.lua_koboldbridge.restart_sequence-1]["generated_text"]) data = "" force_submit = True disable_recentrng = True continue genselect(genout) set_aibusy(0) emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True) break def apiactionsubmit_generate(txt, minimum, maximum): vars.generated_tkns = 0 if not vars.quiet: logger.debug(f"Prompt Min:{minimum}, Max:{maximum}") logger.prompt(utils.decodenewlines(tokenizer.decode(txt)).encode("unicode_escape").decode("utf-8")) # Clear CUDA cache if using GPU if(vars.hascuda and (vars.usegpu or vars.breakmodel)): gc.collect() torch.cuda.empty_cache() # Submit input text to generator _genout, already_generated = tpool.execute(_generate, txt, minimum, maximum, set()) genout = [applyoutputformatting(utils.decodenewlines(tokenizer.decode(tokens[-already_generated:]))) for tokens in _genout] # Clear CUDA cache again if using GPU if(vars.hascuda and (vars.usegpu or vars.breakmodel)): del _genout gc.collect() torch.cuda.empty_cache() return genout def apiactionsubmit_tpumtjgenerate(txt, minimum, maximum): vars.generated_tkns = 0 if(vars.full_determinism): tpu_mtj_backend.set_rng_seed(vars.seed) if not vars.quiet: logger.debug(f"Prompt Min:{minimum}, Max:{maximum}") logger.prompt(utils.decodenewlines(tokenizer.decode(txt)).encode("unicode_escape").decode("utf-8")) vars._actions = vars.actions vars._prompt = vars.prompt if(vars.dynamicscan): vars._actions = vars._actions.copy() # Submit input text to generator soft_tokens = tpumtjgetsofttokens() genout = tpool.execute( tpu_mtj_backend.infer_static, np.uint32(txt), gen_len = maximum-minimum+1, temp=vars.temp, top_p=vars.top_p, top_k=vars.top_k, tfs=vars.tfs, typical=vars.typical, top_a=vars.top_a, numseqs=vars.numseqs, repetition_penalty=vars.rep_pen, rpslope=vars.rep_pen_slope, rprange=vars.rep_pen_range, soft_embeddings=vars.sp, soft_tokens=soft_tokens, sampler_order=vars.sampler_order, ) genout = [applyoutputformatting(utils.decodenewlines(tokenizer.decode(txt))) for txt in genout] return genout def apiactionsubmit(data, use_memory=False, use_world_info=False, use_story=False, use_authors_note=False): if(vars.model == "Colab"): raise NotImplementedError("API generation is not supported in old Colab API mode.") elif(vars.model == "API"): raise NotImplementedError("API generation is not supported in API mode.") elif(vars.model == "CLUSTER"): raise NotImplementedError("API generation is not supported in API mode.") elif(vars.model == "OAI"): raise NotImplementedError("API generation is not supported in OpenAI/GooseAI mode.") elif(vars.model == "ReadOnly"): raise NotImplementedError("API generation is not supported in read-only mode; please load a model and then try again.") data = applyinputformatting(data) if(vars.memory != "" and vars.memory[-1] != "\n"): mem = vars.memory + "\n" else: mem = vars.memory if(use_authors_note and vars.authornote != ""): anotetxt = ("\n" + vars.authornotetemplate + "\n").replace("<|>", vars.authornote) else: anotetxt = "" MIN_STORY_TOKENS = 8 story_tokens = [] mem_tokens = [] wi_tokens = [] story_budget = lambda: vars.max_length - vars.sp_length - vars.genamt - len(tokenizer._koboldai_header) - len(story_tokens) - len(mem_tokens) - len(wi_tokens) budget = lambda: story_budget() + MIN_STORY_TOKENS if budget() < 0: abort(Response(json.dumps({"detail": { "msg": f"Your Max Tokens setting is too low for your current soft prompt and tokenizer to handle. It needs to be at least {vars.max_length - budget()}.", "type": "token_overflow", }}), mimetype="application/json", status=500)) if use_memory: mem_tokens = tokenizer.encode(utils.encodenewlines(mem))[-budget():] if use_world_info: world_info, _ = checkworldinfo(data, force_use_txt=True, scan_story=use_story) wi_tokens = tokenizer.encode(utils.encodenewlines(world_info))[-budget():] if use_story: if vars.useprompt: story_tokens = tokenizer.encode(utils.encodenewlines(vars.prompt))[-budget():] story_tokens = tokenizer.encode(utils.encodenewlines(data))[-story_budget():] + story_tokens if use_story: for i, action in enumerate(reversed(vars.actions.values())): if story_budget() <= 0: assert story_budget() == 0 break story_tokens = tokenizer.encode(utils.encodenewlines(action))[-story_budget():] + story_tokens if i == vars.andepth - 1: story_tokens = tokenizer.encode(utils.encodenewlines(anotetxt))[-story_budget():] + story_tokens if not vars.useprompt: story_tokens = tokenizer.encode(utils.encodenewlines(vars.prompt))[-budget():] + story_tokens tokens = tokenizer._koboldai_header + mem_tokens + wi_tokens + story_tokens assert story_budget() >= 0 minimum = len(tokens) + 1 maximum = len(tokens) + vars.genamt if(not vars.use_colab_tpu and vars.model not in ["Colab", "API", "CLUSTER", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): genout = apiactionsubmit_generate(tokens, minimum, maximum) elif(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")): genout = apiactionsubmit_tpumtjgenerate(tokens, minimum, maximum) return genout #==================================================================# # #==================================================================# def actionretry(data): if(vars.noai): emit('from_server', {'cmd': 'errmsg', 'data': "Retry function unavailable in Read Only mode."}) return if(vars.recentrng is not None): if(not vars.aibusy): randomGameRequest(vars.recentrng, memory=vars.recentrngm) return if actionback(): actionsubmit("", actionmode=vars.actionmode, force_submit=True) send_debug() elif(not vars.useprompt): emit('from_server', {'cmd': 'errmsg', 'data': "Please enable \"Always Add Prompt\" to retry with your prompt."}) #==================================================================# # #==================================================================# def actionback(): if(vars.aibusy): return # Remove last index of actions and refresh game screen if(len(vars.genseqs) == 0 and len(vars.actions) > 0): # We are going to move the selected text to alternative text in the actions_metadata variable so we can redo this action vars.actions_metadata[vars.actions.get_last_key() ]['Alternative Text'] = [{'Text': vars.actions_metadata[vars.actions.get_last_key() ]['Selected Text'], 'Pinned': False, "Previous Selection": True, "Edited": False}] + vars.actions_metadata[vars.actions.get_last_key() ]['Alternative Text'] vars.actions_metadata[vars.actions.get_last_key() ]['Selected Text'] = "" last_key = vars.actions.get_last_key() vars.actions.pop() vars.recentback = True remove_story_chunk(last_key + 1) #for the redo to not get out of whack, need to reset the max # in the actions sequence vars.actions.set_next_id(last_key) success = True elif(len(vars.genseqs) == 0): emit('from_server', {'cmd': 'errmsg', 'data': "Cannot delete the prompt."}) success = False else: vars.genseqs = [] success = True send_debug() return success def actionredo(): i = 0 #First we need to find the next valid key #We might have deleted text so we don't want to show a redo for that blank chunk restore_id = vars.actions.get_last_key()+1 if restore_id in vars.actions_metadata: ok_to_use = False while not ok_to_use: for item in vars.actions_metadata[restore_id]['Alternative Text']: if item['Previous Selection'] and item['Text'] != "": ok_to_use = True if not ok_to_use: restore_id+=1 if restore_id not in vars.actions_metadata: return else: vars.actions.set_next_id(restore_id) if restore_id in vars.actions_metadata: genout = [{"generated_text": item['Text']} for item in vars.actions_metadata[restore_id]['Alternative Text'] if (item["Previous Selection"]==True)] if len(genout) > 0: genout = genout + [{"generated_text": item['Text']} for item in vars.actions_metadata[restore_id]['Alternative Text'] if (item["Pinned"]==True) and (item["Previous Selection"]==False)] if len(genout) == 1: vars.actions_metadata[restore_id]['Alternative Text'] = [item for item in vars.actions_metadata[restore_id]['Alternative Text'] if (item["Previous Selection"]!=True)] genresult(genout[0]['generated_text'], flash=True, ignore_formatting=True) else: # Store sequences in memory until selection is made vars.genseqs = genout # Send sequences to UI for selection genout = [[item['Text'], "redo"] for item in vars.actions_metadata[restore_id]['Alternative Text'] if (item["Previous Selection"]==True)] emit('from_server', {'cmd': 'genseqs', 'data': genout}, broadcast=True) else: emit('from_server', {'cmd': 'popuperror', 'data': "There's nothing to undo"}, broadcast=True) send_debug() #==================================================================# # #==================================================================# def buildauthorsnote(authorsnote, template): # Build Author's Note if set if authorsnote == "": return "" return ("\n" + template + "\n").replace("<|>", authorsnote) def calcsubmitbudgetheader(txt, **kwargs): # Scan for WorldInfo matches winfo, found_entries = checkworldinfo(txt, **kwargs) # Add a newline to the end of memory if(vars.memory != "" and vars.memory[-1] != "\n"): mem = vars.memory + "\n" else: mem = vars.memory anotetxt = buildauthorsnote(vars.authornote, vars.authornotetemplate) return winfo, mem, anotetxt, found_entries def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions, submission=None, budget_deduction=0): forceanote = False # In case we don't have enough actions to hit A.N. depth anoteadded = False # In case our budget runs out before we hit A.N. depth anotetkns = [] # Placeholder for Author's Note tokens lnanote = 0 # Placeholder for Author's Note length lnsp = vars.sp_length if("tokenizer" not in globals()): from transformers import GPT2Tokenizer global tokenizer tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache") lnheader = len(tokenizer._koboldai_header) # Calculate token budget prompttkns = tokenizer.encode(utils.encodenewlines(vars.comregex_ai.sub('', vars.prompt)), max_length=int(2e9), truncation=True) lnprompt = len(prompttkns) memtokens = tokenizer.encode(utils.encodenewlines(mem), max_length=int(2e9), truncation=True) lnmem = len(memtokens) if(lnmem > vars.max_length - lnheader - lnsp - vars.genamt - budget_deduction): raise OverflowError("The memory in your story is too long. Please either write a shorter memory text or increase the Max Tokens setting. If you are using a soft prompt, additionally consider using a smaller soft prompt.") witokens = tokenizer.encode(utils.encodenewlines(winfo), max_length=int(2e9), truncation=True) lnwi = len(witokens) if(lnmem + lnwi > vars.max_length - lnheader - lnsp - vars.genamt - budget_deduction): raise OverflowError("The current active world info keys take up too many tokens. Please either write shorter world info, decrease World Info Depth or increase the Max Tokens setting. If you are using a soft prompt, additionally consider using a smaller soft prompt.") if(anotetxt != ""): anotetkns = tokenizer.encode(utils.encodenewlines(anotetxt), max_length=int(2e9), truncation=True) lnanote = len(anotetkns) if(lnmem + lnwi + lnanote > vars.max_length - lnheader - lnsp - vars.genamt - budget_deduction): raise OverflowError("The author's note in your story is too long. Please either write a shorter author's note or increase the Max Tokens setting. If you are using a soft prompt, additionally consider using a smaller soft prompt.") if(vars.useprompt): budget = vars.max_length - lnheader - lnsp - lnprompt - lnmem - lnanote - lnwi - vars.genamt - budget_deduction else: budget = vars.max_length - lnheader - lnsp - lnmem - lnanote - lnwi - vars.genamt - budget_deduction lnsubmission = len(tokenizer.encode(utils.encodenewlines(vars.comregex_ai.sub('', submission)), max_length=int(2e9), truncation=True)) if submission is not None else 0 maybe_lnprompt = lnprompt if vars.useprompt and actionlen > 0 else 0 if(lnmem + lnwi + lnanote + maybe_lnprompt + lnsubmission > vars.max_length - lnheader - lnsp - vars.genamt - budget_deduction): raise OverflowError("Your submission is too long. Please either write a shorter submission or increase the Max Tokens setting. If you are using a soft prompt, additionally consider using a smaller soft prompt. If you are using the Always Add Prompt setting, turning it off may help.") assert budget >= 0 if(actionlen == 0): # First/Prompt action tokens = (tokenizer._koboldai_header if vars.model not in ("Colab", "API", "CLUSTER", "OAI") else []) + memtokens + witokens + anotetkns + prompttkns assert len(tokens) <= vars.max_length - lnsp - vars.genamt - budget_deduction ln = len(tokens) + lnsp return tokens, ln+1, ln+vars.genamt else: tokens = [] # Check if we have the action depth to hit our A.N. depth if(anotetxt != "" and actionlen < vars.andepth): forceanote = True # Get most recent action tokens up to our budget n = 0 for key in reversed(actions): chunk = vars.comregex_ai.sub('', actions[key]) assert budget >= 0 if(budget <= 0): break acttkns = tokenizer.encode(utils.encodenewlines(chunk), max_length=int(2e9), truncation=True) tknlen = len(acttkns) if(tknlen < budget): tokens = acttkns + tokens budget -= tknlen else: count = budget * -1 tokens = acttkns[count:] + tokens budget = 0 break # Inject Author's Note if we've reached the desired depth if(n == vars.andepth-1): if(anotetxt != ""): tokens = anotetkns + tokens # A.N. len already taken from bdgt anoteadded = True n += 1 # If we're not using the prompt every time and there's still budget left, # add some prompt. if(not vars.useprompt): if(budget > 0): prompttkns = prompttkns[-budget:] else: prompttkns = [] # Did we get to add the A.N.? If not, do it here if(anotetxt != ""): if((not anoteadded) or forceanote): tokens = (tokenizer._koboldai_header if vars.model not in ("Colab", "API", "CLUSTER", "OAI") else []) + memtokens + witokens + anotetkns + prompttkns + tokens else: tokens = (tokenizer._koboldai_header if vars.model not in ("Colab", "API", "CLUSTER", "OAI") else []) + memtokens + witokens + prompttkns + tokens else: # Prepend Memory, WI, and Prompt before action tokens tokens = (tokenizer._koboldai_header if vars.model not in ("Colab", "API", "CLUSTER", "OAI") else []) + memtokens + witokens + prompttkns + tokens # Send completed bundle to generator assert len(tokens) <= vars.max_length - lnsp - vars.genamt - budget_deduction ln = len(tokens) + lnsp return tokens, ln+1, ln+vars.genamt #==================================================================# # Take submitted text and build the text to be given to generator #==================================================================# def calcsubmit(txt): anotetxt = "" # Placeholder for Author's Note text forceanote = False # In case we don't have enough actions to hit A.N. depth anoteadded = False # In case our budget runs out before we hit A.N. depth actionlen = len(vars.actions) winfo, mem, anotetxt, found_entries = calcsubmitbudgetheader(txt) # For all transformers models if(vars.model != "InferKit"): subtxt, min, max = calcsubmitbudget(actionlen, winfo, mem, anotetxt, vars.actions, submission=txt) if(actionlen == 0): if(not vars.use_colab_tpu and vars.model not in ["Colab", "API", "CLUSTER", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): generate(subtxt, min, max, found_entries=found_entries) elif(vars.model == "Colab"): sendtocolab(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) elif(vars.model == "API"): sendtoapi(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) elif(vars.model == "CLUSTER"): sendtocluster(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) elif(vars.model == "OAI"): oairequest(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) elif(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")): tpumtjgenerate(subtxt, min, max, found_entries=found_entries) else: if(not vars.use_colab_tpu and vars.model not in ["Colab", "API", "CLUSTER", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): generate(subtxt, min, max, found_entries=found_entries) elif(vars.model == "Colab"): sendtocolab(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) elif(vars.model == "API"): sendtoapi(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) elif(vars.model == "CLUSTER"): sendtocluster(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) elif(vars.model == "OAI"): oairequest(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) elif(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")): tpumtjgenerate(subtxt, min, max, found_entries=found_entries) # For InferKit web API else: # Check if we have the action depth to hit our A.N. depth if(anotetxt != "" and actionlen < vars.andepth): forceanote = True if(vars.useprompt): budget = vars.ikmax - len(vars.comregex_ai.sub('', vars.prompt)) - len(anotetxt) - len(mem) - len(winfo) - 1 else: budget = vars.ikmax - len(anotetxt) - len(mem) - len(winfo) - 1 subtxt = "" prompt = vars.comregex_ai.sub('', vars.prompt) n = 0 for key in reversed(vars.actions): chunk = vars.actions[key] if(budget <= 0): break actlen = len(chunk) if(actlen < budget): subtxt = chunk + subtxt budget -= actlen else: count = budget * -1 subtxt = chunk[count:] + subtxt budget = 0 break # If we're not using the prompt every time and there's still budget left, # add some prompt. if(not vars.useprompt): if(budget > 0): prompt = vars.comregex_ai.sub('', vars.prompt)[-budget:] else: prompt = "" # Inject Author's Note if we've reached the desired depth if(n == vars.andepth-1): if(anotetxt != ""): subtxt = anotetxt + subtxt # A.N. len already taken from bdgt anoteadded = True n += 1 # Did we get to add the A.N.? If not, do it here if(anotetxt != ""): if((not anoteadded) or forceanote): subtxt = mem + winfo + anotetxt + prompt + subtxt else: subtxt = mem + winfo + prompt + subtxt else: subtxt = mem + winfo + prompt + subtxt # Send it! ikrequest(subtxt) #==================================================================# # Send text to generator and deal with output #==================================================================# def _generate(txt, minimum, maximum, found_entries): if(vars.full_determinism): torch.manual_seed(vars.seed) gen_in = torch.tensor(txt, dtype=torch.long)[None] if(vars.sp is not None): soft_tokens = torch.arange( model.config.vocab_size, model.config.vocab_size + vars.sp.shape[0], ) gen_in = torch.cat((soft_tokens[None], gen_in), dim=-1) assert gen_in.shape[-1] + vars.genamt <= vars.max_length if(vars.hascuda and vars.usegpu): gen_in = gen_in.to(vars.gpu_device) elif(vars.hascuda and vars.breakmodel): gen_in = gen_in.to(breakmodel.primary_device) else: gen_in = gen_in.to('cpu') model.kai_scanner_excluded_world_info = found_entries vars._actions = vars.actions vars._prompt = vars.prompt if(vars.dynamicscan): vars._actions = vars._actions.copy() with torch.no_grad(): already_generated = 0 numseqs = vars.numseqs while True: genout = generator( gen_in, do_sample=True, max_length=int(2e9), repetition_penalty=1.0, bad_words_ids=vars.badwordsids, use_cache=True, num_return_sequences=numseqs ) already_generated += len(genout[0]) - len(gen_in[0]) assert already_generated <= vars.genamt if(model.kai_scanner.halt or not model.kai_scanner.regeneration_required): break assert genout.ndim >= 2 assert genout.shape[0] == vars.numseqs if(vars.lua_koboldbridge.generated_cols and vars.generated_tkns != vars.lua_koboldbridge.generated_cols): raise RuntimeError("Inconsistency detected between KoboldAI Python and Lua backends") if(already_generated != vars.generated_tkns): raise RuntimeError("WI scanning error") for r in range(vars.numseqs): for c in range(already_generated): assert vars.lua_koboldbridge.generated[r+1][c+1] is not None genout[r][genout.shape[-1] - already_generated + c] = vars.lua_koboldbridge.generated[r+1][c+1] encoded = [] for i in range(vars.numseqs): txt = utils.decodenewlines(tokenizer.decode(genout[i, -already_generated:])) winfo, mem, anotetxt, _found_entries = calcsubmitbudgetheader(txt, force_use_txt=True, actions=vars._actions) found_entries[i].update(_found_entries) txt, _, _ = calcsubmitbudget(len(vars._actions), winfo, mem, anotetxt, vars._actions, submission=txt) encoded.append(torch.tensor(txt, dtype=torch.long, device=genout.device)) max_length = len(max(encoded, key=len)) encoded = torch.stack(tuple(torch.nn.functional.pad(e, (max_length - len(e), 0), value=model.config.pad_token_id or model.config.eos_token_id) for e in encoded)) genout = torch.cat( ( encoded, genout[..., -already_generated:], ), dim=-1 ) if(vars.sp is not None): soft_tokens = torch.arange( model.config.vocab_size, model.config.vocab_size + vars.sp.shape[0], device=genout.device, ) genout = torch.cat((soft_tokens.tile(vars.numseqs, 1), genout), dim=-1) assert genout.shape[-1] + vars.genamt - already_generated <= vars.max_length diff = genout.shape[-1] - gen_in.shape[-1] minimum += diff maximum += diff gen_in = genout numseqs = 1 return genout, already_generated def generate(txt, minimum, maximum, found_entries=None): vars.generated_tkns = 0 if(found_entries is None): found_entries = set() found_entries = tuple(found_entries.copy() for _ in range(vars.numseqs)) if not vars.quiet: logger.debug(f"Prompt Min:{minimum}, Max:{maximum}") logger.prompt(utils.decodenewlines(tokenizer.decode(txt)).encode("unicode_escape").decode("utf-8")) # Store context in memory to use it for comparison with generated content vars.lastctx = utils.decodenewlines(tokenizer.decode(txt)) # Clear CUDA cache if using GPU if(vars.hascuda and (vars.usegpu or vars.breakmodel)): gc.collect() torch.cuda.empty_cache() # Submit input text to generator try: genout, already_generated = tpool.execute(_generate, txt, minimum, maximum, found_entries) except Exception as e: if(issubclass(type(e), lupa.LuaError)): vars.lua_koboldbridge.obliterate_multiverse() vars.lua_running = False emit('from_server', {'cmd': 'errmsg', 'data': 'Lua script error; please check console.'}, broadcast=True) sendUSStatItems() logger.error('LUA ERROR: ' + str(e).replace("\033", "")) logger.warning("Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.") else: emit('from_server', {'cmd': 'errmsg', 'data': 'Error occurred during generator call; please check console.'}, broadcast=True) logger.error(traceback.format_exc().replace("\033", "")) set_aibusy(0) return for i in range(vars.numseqs): vars.lua_koboldbridge.generated[i+1][vars.generated_tkns] = int(genout[i, -1].item()) vars.lua_koboldbridge.outputs[i+1] = utils.decodenewlines(tokenizer.decode(genout[i, -already_generated:])) execute_outmod() if(vars.lua_koboldbridge.regeneration_required): vars.lua_koboldbridge.regeneration_required = False genout = [] for i in range(vars.numseqs): genout.append({"generated_text": vars.lua_koboldbridge.outputs[i+1]}) assert type(genout[-1]["generated_text"]) is str else: genout = [{"generated_text": utils.decodenewlines(tokenizer.decode(tokens[-already_generated:]))} for tokens in genout] if(len(genout) == 1): genresult(genout[0]["generated_text"]) else: if(vars.lua_koboldbridge.restart_sequence is not None and vars.lua_koboldbridge.restart_sequence > 0): genresult(genout[vars.lua_koboldbridge.restart_sequence-1]["generated_text"]) else: genselect(genout) # Clear CUDA cache again if using GPU if(vars.hascuda and (vars.usegpu or vars.breakmodel)): del genout gc.collect() torch.cuda.empty_cache() set_aibusy(0) #==================================================================# # Deal with a single return sequence from generate() #==================================================================# def genresult(genout, flash=True, ignore_formatting=False): if not vars.quiet: logger.generation(genout.encode("unicode_escape").decode("utf-8")) # Format output before continuing if not ignore_formatting: genout = applyoutputformatting(genout) vars.lua_koboldbridge.feedback = genout if(len(genout) == 0): return # Add formatted text to Actions array and refresh the game screen if(len(vars.prompt.strip()) == 0): vars.prompt = genout else: vars.actions.append(genout) if vars.actions.get_last_key() not in vars.actions_metadata: vars.actions_metadata[vars.actions.get_last_key()] = {'Selected Text': genout, 'Alternative Text': []} else: vars.actions_metadata[vars.actions.get_last_key()]['Selected Text'] = genout update_story_chunk('last') if(flash): emit('from_server', {'cmd': 'texteffect', 'data': vars.actions.get_last_key() + 1 if len(vars.actions) else 0}, broadcast=True) send_debug() #==================================================================# # Send generator sequences to the UI for selection #==================================================================# def genselect(genout): i = 0 for result in genout: # Apply output formatting rules to sequences result["generated_text"] = applyoutputformatting(result["generated_text"]) if not vars.quiet: logger.info(f"Generation Result {i}") logger.generation(result["generated_text"].encode("unicode_escape").decode("utf-8")) i += 1 # Add the options to the actions metadata # If we've already generated text for this action but haven't selected one we'll want to kill all non-pinned, non-previous selection, and non-edited options then add the new ones if vars.actions.get_next_id() in vars.actions_metadata: if (vars.actions_metadata[vars.actions.get_next_id()]['Selected Text'] == ""): vars.actions_metadata[vars.actions.get_next_id()]['Alternative Text'] = [{"Text": item['Text'], "Pinned": item['Pinned'], "Previous Selection": item["Previous Selection"], "Edited": item["Edited"]} for item in vars.actions_metadata[vars.actions.get_next_id()]['Alternative Text'] if item['Pinned'] or item["Previous Selection"] or item["Edited"]] + [{"Text": text["generated_text"], "Pinned": False, "Previous Selection": False, "Edited": False} for text in genout] else: vars.actions_metadata[vars.actions.get_next_id()] = {'Selected Text': '', 'Alternative Text': [{"Text": text["generated_text"], "Pinned": False, "Previous Selection": False, "Edited": False} for text in genout]} else: vars.actions_metadata[vars.actions.get_next_id()] = {'Selected Text': '', 'Alternative Text': [{"Text": text["generated_text"], "Pinned": False, "Previous Selection": False, "Edited": False} for text in genout]} genout = [{"generated_text": item['Text']} for item in vars.actions_metadata[vars.actions.get_next_id()]['Alternative Text'] if (item["Previous Selection"]==False) and (item["Edited"]==False)] # Store sequences in memory until selection is made vars.genseqs = genout genout = [[item['Text'], "pinned" if item['Pinned'] else "normal"] for item in vars.actions_metadata[vars.actions.get_next_id()]['Alternative Text'] if (item["Previous Selection"]==False) and (item["Edited"]==False)] # Send sequences to UI for selection emit('from_server', {'cmd': 'genseqs', 'data': genout}, broadcast=True) send_debug() #==================================================================# # Send selected sequence to action log and refresh UI #==================================================================# def selectsequence(n): if(len(vars.genseqs) == 0): return vars.lua_koboldbridge.feedback = vars.genseqs[int(n)]["generated_text"] if(len(vars.lua_koboldbridge.feedback) != 0): vars.actions.append(vars.lua_koboldbridge.feedback) #We'll want to remove the option from the alternative text and put it in selected text vars.actions_metadata[vars.actions.get_last_key() ]['Alternative Text'] = [item for item in vars.actions_metadata[vars.actions.get_last_key()]['Alternative Text'] if item['Text'] != vars.lua_koboldbridge.feedback] vars.actions_metadata[vars.actions.get_last_key() ]['Selected Text'] = vars.lua_koboldbridge.feedback update_story_chunk('last') emit('from_server', {'cmd': 'texteffect', 'data': vars.actions.get_last_key() + 1 if len(vars.actions) else 0}, broadcast=True) emit('from_server', {'cmd': 'hidegenseqs', 'data': ''}, broadcast=True) vars.genseqs = [] if(vars.lua_koboldbridge.restart_sequence is not None): actionsubmit("", actionmode=vars.actionmode, force_submit=True, disable_recentrng=True) send_debug() #==================================================================# # Pin/Unpin the selected sequence #==================================================================# def pinsequence(n): if n.isnumeric(): text = vars.genseqs[int(n)]['generated_text'] if text in [item['Text'] for item in vars.actions_metadata[vars.actions.get_next_id()]['Alternative Text']]: alternatives = vars.actions_metadata[vars.actions.get_next_id()]['Alternative Text'] for i in range(len(alternatives)): if alternatives[i]['Text'] == text: alternatives[i]['Pinned'] = not alternatives[i]['Pinned'] break vars.actions_metadata[vars.actions.get_next_id()]['Alternative Text'] = alternatives send_debug() #==================================================================# # Send transformers-style request to ngrok/colab host #==================================================================# def sendtocolab(txt, min, max): # Log request to console if not vars.quiet: print("{0}Tokens:{1}, Txt:{2}{3}".format(colors.YELLOW, min-1, txt, colors.END)) # Store context in memory to use it for comparison with generated content vars.lastctx = txt # Build request JSON data reqdata = { 'text': txt, 'min': min, 'max': max, 'rep_pen': vars.rep_pen, 'rep_pen_slope': vars.rep_pen_slope, 'rep_pen_range': vars.rep_pen_range, 'temperature': vars.temp, 'top_p': vars.top_p, 'top_k': vars.top_k, 'tfs': vars.tfs, 'typical': vars.typical, 'topa': vars.top_a, 'numseqs': vars.numseqs, 'retfultxt': False } # Create request req = requests.post( vars.colaburl, json = reqdata ) # Deal with the response if(req.status_code == 200): js = req.json()["data"] # Try to be backwards compatible with outdated colab if("text" in js): genout = [getnewcontent(js["text"])] else: genout = js["seqs"] for i in range(vars.numseqs): vars.lua_koboldbridge.outputs[i+1] = genout[i] execute_outmod() if(vars.lua_koboldbridge.regeneration_required): vars.lua_koboldbridge.regeneration_required = False genout = [] for i in range(vars.numseqs): genout.append(vars.lua_koboldbridge.outputs[i+1]) assert type(genout[-1]) is str if(len(genout) == 1): genresult(genout[0]) else: # Convert torch output format to transformers seqs = [] for seq in genout: seqs.append({"generated_text": seq}) if(vars.lua_koboldbridge.restart_sequence is not None and vars.lua_koboldbridge.restart_sequence > 0): genresult(genout[vars.lua_koboldbridge.restart_sequence-1]["generated_text"]) else: genselect(genout) # Format output before continuing #genout = applyoutputformatting(getnewcontent(genout)) # Add formatted text to Actions array and refresh the game screen #vars.actions.append(genout) #refresh_story() #emit('from_server', {'cmd': 'texteffect', 'data': vars.actions.get_last_key() + 1 if len(vars.actions) else 0}) set_aibusy(0) else: errmsg = "Colab API Error: Failed to get a reply from the server. Please check the colab console." print("{0}{1}{2}".format(colors.RED, errmsg, colors.END)) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) #==================================================================# # Send transformers-style request to KoboldAI API #==================================================================# def sendtoapi(txt, min, max): # Log request to console if not vars.quiet: print("{0}Tokens:{1}, Txt:{2}{3}".format(colors.YELLOW, min-1, txt, colors.END)) # Store context in memory to use it for comparison with generated content vars.lastctx = txt # Build request JSON data reqdata = { 'prompt': txt, 'max_length': max - min + 1, 'max_context_length': vars.max_length, 'rep_pen': vars.rep_pen, 'rep_pen_slope': vars.rep_pen_slope, 'rep_pen_range': vars.rep_pen_range, 'temperature': vars.temp, 'top_p': vars.top_p, 'top_k': vars.top_k, 'top_a': vars.top_a, 'tfs': vars.tfs, 'typical': vars.typical, 'n': vars.numseqs, } # Create request while True: req = requests.post( vars.colaburl[:-8] + "/api/v1/generate", json=reqdata, ) if(req.status_code == 503): # Server is currently generating something else so poll until it's our turn time.sleep(1) continue js = req.json() if(req.status_code != 200): errmsg = "KoboldAI API Error: Failed to get a reply from the server. Please check the console." print("{0}{1}{2}".format(colors.RED, json.dumps(js, indent=2), colors.END)) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) return genout = [obj["text"] for obj in js["results"]] for i in range(vars.numseqs): vars.lua_koboldbridge.outputs[i+1] = genout[i] execute_outmod() if(vars.lua_koboldbridge.regeneration_required): vars.lua_koboldbridge.regeneration_required = False genout = [] for i in range(vars.numseqs): genout.append(vars.lua_koboldbridge.outputs[i+1]) assert type(genout[-1]) is str if(len(genout) == 1): genresult(genout[0]) else: adjusted_genout = [] for item in genout: adjusted_genout.append({"generated_text": item}) # Convert torch output format to transformers seqs = [] for seq in adjusted_genout: seqs.append({"generated_text": seq}) if(vars.lua_koboldbridge.restart_sequence is not None and vars.lua_koboldbridge.restart_sequence > 0): genresult(adjusted_genout[vars.lua_koboldbridge.restart_sequence-1]["generated_text"]) else: genselect(adjusted_genout) set_aibusy(0) return #==================================================================# # Send transformers-style request to KoboldAI Cluster #==================================================================# def sendtocluster(txt, min, max): # Log request to console if not vars.quiet: logger.debug(f"Tokens Min:{min-1}") logger.prompt(txt.encode("unicode_escape").decode("utf-8")) # Store context in memory to use it for comparison with generated content vars.lastctx = txt # Build request JSON data reqdata = { 'max_length': max - min + 1, 'max_context_length': vars.max_length, 'rep_pen': vars.rep_pen, 'rep_pen_slope': vars.rep_pen_slope, 'rep_pen_range': vars.rep_pen_range, 'temperature': vars.temp, 'top_p': vars.top_p, 'top_k': vars.top_k, 'top_a': vars.top_a, 'tfs': vars.tfs, 'typical': vars.typical, 'n': vars.numseqs, } cluster_metadata = { 'prompt': txt, 'params': reqdata, 'models': vars.cluster_requested_models, 'trusted_workers': False, } client_agent = "KoboldAI:1.19.3:koboldai.org" cluster_headers = { 'apikey': vars.apikey, "Client-Agent": client_agent } logger.debug(f"Horde Payload: {cluster_metadata}") try: # Create request req = requests.post( vars.colaburl[:-8] + "/api/v2/generate/text/async", json=cluster_metadata, headers=cluster_headers, ) except requests.exceptions.ConnectionError: errmsg = f"Horde unavailable. Please try again later" logger.error(errmsg) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) return if(req.status_code == 503): errmsg = f"KoboldAI API Error: No available KoboldAI servers found in Horde to fulfil this request using the selected models or other properties." logger.error(req.text) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) return if(not req.ok): errmsg = f"KoboldAI API Error: Failed to get a standard reply from the Horde. Please check the console." logger.error(req.text) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) return try: js = req.json() except requests.exceptions.JSONDecodeError: errmsg = f"Unexpected message received from the Horde: '{req.text}'" logger.error(errmsg) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) return request_id = js["id"] logger.debug("Horde Request ID: {}".format(request_id)) cluster_agent_headers = { "Client-Agent": client_agent } finished = False while not finished: try: req = requests.get(vars.colaburl[:-8] + "/api/v2/generate/text/status/" + request_id, headers=cluster_agent_headers) except requests.exceptions.ConnectionError: errmsg = f"Horde unavailable. Please try again later" logger.error(errmsg) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) return if not req.ok: errmsg = f"KoboldAI API Error: Failed to get a standard reply from the Horde. Please check the console." logger.error(req.text) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) return try: req_status = req.json() except requests.exceptions.JSONDecodeError: errmsg = f"Unexpected message received from the KoboldAI Horde: '{req.text}'" logger.error(errmsg) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) return if "done" not in req_status: errmsg = f"Unexpected response received from the KoboldAI Horde: '{js}'" logger.error(errmsg) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) return finished = req_status["done"] if not finished: logger.debug(req_status) time.sleep(1) logger.debug("Last Horde Status Message: {}".format(js)) if req_status["faulted"]: errmsg = "Horde Text generation faulted! Please try again" logger.error(errmsg) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) return generations = req_status['generations'] gen_workers = [(cgen['worker_name'],cgen['worker_id']) for cgen in generations] logger.info(f"Generations by: {gen_workers}") # Just in case we want to announce it to the user if len(generations) == 1: warnmsg = f"Text generated by {[w[0] for w in gen_workers]}" emit('from_server', {'cmd': 'warnmsg', 'data': warnmsg}, broadcast=True) genout = [cgen['text'] for cgen in generations] for i in range(vars.numseqs): vars.lua_koboldbridge.outputs[i+1] = genout[i] execute_outmod() if(vars.lua_koboldbridge.regeneration_required): vars.lua_koboldbridge.regeneration_required = False genout = [] for i in range(vars.numseqs): genout.append(vars.lua_koboldbridge.outputs[i+1]) assert type(genout[-1]) is str if(len(genout) == 1): genresult(genout[0]) else: adjusted_genout = [] for item in genout: adjusted_genout.append({"generated_text": item}) # Convert torch output format to transformers seqs = [] for seq in adjusted_genout: seqs.append({"generated_text": seq}) if(vars.lua_koboldbridge.restart_sequence is not None and vars.lua_koboldbridge.restart_sequence > 0): genresult(adjusted_genout[vars.lua_koboldbridge.restart_sequence-1]["generated_text"]) else: genselect(adjusted_genout) set_aibusy(0) return #==================================================================# # Send text to TPU mesh transformer backend #==================================================================# def tpumtjgenerate(txt, minimum, maximum, found_entries=None): if(vars.full_determinism): tpu_mtj_backend.set_rng_seed(vars.seed) vars.generated_tkns = 0 if(found_entries is None): found_entries = set() found_entries = tuple(found_entries.copy() for _ in range(vars.numseqs)) if not vars.quiet: logger.debug(f"Prompt Min:{minimum}, Max:{maximum}") logger.prompt(utils.decodenewlines(tokenizer.decode(txt)).encode("unicode_escape").decode("utf-8")) vars._actions = vars.actions vars._prompt = vars.prompt if(vars.dynamicscan): vars._actions = vars._actions.copy() # Submit input text to generator try: soft_tokens = tpumtjgetsofttokens() global past socketio.start_background_task(copy_current_request_context(check_for_backend_compilation)) if(vars.dynamicscan or (not vars.nogenmod and vars.has_genmod)): context = np.tile(np.uint32(txt), (vars.numseqs, 1)) past = np.empty((vars.numseqs, 0), dtype=np.uint32) while(True): genout, n_generated, regeneration_required, halt = tpool.execute( tpu_mtj_backend.infer_dynamic, context, gen_len = maximum-minimum+1, numseqs=vars.numseqs, soft_embeddings=vars.sp, soft_tokens=soft_tokens, excluded_world_info=found_entries, ) past = np.pad(past, ((0, 0), (0, n_generated))) for r in range(vars.numseqs): for c in range(vars.lua_koboldbridge.generated_cols): assert vars.lua_koboldbridge.generated[r+1][c+1] is not None past[r, c] = vars.lua_koboldbridge.generated[r+1][c+1] if(vars.abort or halt or not regeneration_required): break print("(regeneration triggered)") encoded = [] for i in range(vars.numseqs): txt = utils.decodenewlines(tokenizer.decode(past[i])) winfo, mem, anotetxt, _found_entries = calcsubmitbudgetheader(txt, force_use_txt=True, actions=vars._actions) found_entries[i].update(_found_entries) txt, _, _ = calcsubmitbudget(len(vars._actions), winfo, mem, anotetxt, vars._actions, submission=txt) encoded.append(np.array(txt, dtype=np.uint32)) max_length = len(max(encoded, key=len)) encoded = np.stack(tuple(np.pad(e, (max_length - len(e), 0), constant_values=tpu_mtj_backend.pad_token_id) for e in encoded)) context = np.concatenate( ( encoded, past, ), axis=-1, ) else: genout = tpool.execute( tpu_mtj_backend.infer_static, np.uint32(txt), gen_len = maximum-minimum+1, temp=vars.temp, top_p=vars.top_p, top_k=vars.top_k, tfs=vars.tfs, typical=vars.typical, top_a=vars.top_a, numseqs=vars.numseqs, repetition_penalty=vars.rep_pen, rpslope=vars.rep_pen_slope, rprange=vars.rep_pen_range, soft_embeddings=vars.sp, soft_tokens=soft_tokens, sampler_order=vars.sampler_order, ) past = genout for i in range(vars.numseqs): vars.lua_koboldbridge.generated[i+1] = vars.lua_state.table(*genout[i].tolist()) vars.lua_koboldbridge.generated_cols = vars.generated_tkns = genout[0].shape[-1] except Exception as e: if(issubclass(type(e), lupa.LuaError)): vars.lua_koboldbridge.obliterate_multiverse() vars.lua_running = False emit('from_server', {'cmd': 'errmsg', 'data': 'Lua script error; please check console.'}, broadcast=True) sendUSStatItems() logger.error('LUA ERROR: ' + str(e).replace("\033", "")) logger.warning("Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.") else: emit('from_server', {'cmd': 'errmsg', 'data': 'Error occurred during generator call; please check console.'}, broadcast=True) print("{0}{1}{2}".format(colors.RED, traceback.format_exc().replace("\033", ""), colors.END), file=sys.stderr) set_aibusy(0) return for i in range(vars.numseqs): vars.lua_koboldbridge.outputs[i+1] = utils.decodenewlines(tokenizer.decode(past[i])) genout = past execute_outmod() if(vars.lua_koboldbridge.regeneration_required): vars.lua_koboldbridge.regeneration_required = False genout = [] for i in range(vars.numseqs): genout.append({"generated_text": vars.lua_koboldbridge.outputs[i+1]}) assert type(genout[-1]["generated_text"]) is str else: genout = [{"generated_text": utils.decodenewlines(tokenizer.decode(txt))} for txt in genout] if(len(genout) == 1): genresult(genout[0]["generated_text"]) else: if(vars.lua_koboldbridge.restart_sequence is not None and vars.lua_koboldbridge.restart_sequence > 0): genresult(genout[vars.lua_koboldbridge.restart_sequence-1]["generated_text"]) else: genselect(genout) set_aibusy(0) #==================================================================# # Replaces returns and newlines with HTML breaks #==================================================================# def formatforhtml(txt): return txt.replace("\\r\\n", "
").replace("\\r", "
").replace("\\n", "
").replace("\r\n", "
").replace('\n', '
').replace('\r', '
').replace('</s>', '
') #==================================================================# # Strips submitted text from the text returned by the AI #==================================================================# def getnewcontent(txt): # If the submitted context was blank, then everything is new if(vars.lastctx == ""): return txt # Tokenize the last context and the generated content ctxtokens = tokenizer.encode(utils.encodenewlines(vars.lastctx), max_length=int(2e9), truncation=True) txttokens = tokenizer.encode(utils.encodenewlines(txt), max_length=int(2e9), truncation=True) dif = (len(txttokens) - len(ctxtokens)) * -1 # Remove the context from the returned text newtokens = txttokens[dif:] return utils.decodenewlines(tokenizer.decode(newtokens)) #==================================================================# # Applies chosen formatting options to text submitted to AI #==================================================================# def applyinputformatting(txt): # Add sentence spacing if(vars.formatoptns["frmtadsnsp"]): txt = utils.addsentencespacing(txt, vars) return txt #==================================================================# # Applies chosen formatting options to text returned from AI #==================================================================# def applyoutputformatting(txt): # Use standard quotes and apostrophes txt = utils.fixquotes(txt) # Adventure mode clipping of all characters after '>' if(vars.adventure): txt = vars.acregex_ai.sub('', txt) # Trim incomplete sentences if(vars.formatoptns["frmttriminc"] and not vars.chatmode): txt = utils.trimincompletesentence(txt) # Replace blank lines if(vars.formatoptns["frmtrmblln"] or vars.chatmode): txt = utils.replaceblanklines(txt) # Remove special characters if(vars.formatoptns["frmtrmspch"]): txt = utils.removespecialchars(txt, vars) # Single Line Mode if(vars.formatoptns["singleline"] or vars.chatmode): txt = utils.singlelineprocessing(txt, vars) return txt #==================================================================# # Sends the current story content to the Game Screen #==================================================================# def refresh_story(): text_parts = ['', vars.comregex_ui.sub(lambda m: '\n'.join('' + l + '' for l in m.group().split('\n')), html.escape(vars.prompt)), ''] for idx in vars.actions: item = vars.actions[idx] idx += 1 item = html.escape(item) item = vars.comregex_ui.sub(lambda m: '\n'.join('' + l + '' for l in m.group().split('\n')), item) # Add special formatting to comments item = vars.acregex_ui.sub('\\1', item) # Add special formatting to adventure actions text_parts.extend(('', item, '')) emit('from_server', {'cmd': 'updatescreen', 'gamestarted': vars.gamestarted, 'data': formatforhtml(''.join(text_parts))}, broadcast=True) #==================================================================# # Signals the Game Screen to update one of the chunks #==================================================================# def update_story_chunk(idx: Union[int, str]): if idx == 'last': if len(vars.actions) <= 1: # In this case, we are better off just refreshing the whole thing as the # prompt might not have been shown yet (with a "Generating story..." # message instead). refresh_story() setgamesaved(False) return idx = (vars.actions.get_last_key() if len(vars.actions) else 0) + 1 if idx == 0: text = vars.prompt else: # Actions are 0 based, but in chunks 0 is the prompt. # So the chunk index is one more than the corresponding action index. if(idx - 1 not in vars.actions): return text = vars.actions[idx - 1] item = html.escape(text) item = vars.comregex_ui.sub(lambda m: '\n'.join('' + l + '' for l in m.group().split('\n')), item) # Add special formatting to comments item = vars.acregex_ui.sub('\\1', item) # Add special formatting to adventure actions chunk_text = f'{formatforhtml(item)}' emit('from_server', {'cmd': 'updatechunk', 'data': {'index': idx, 'html': chunk_text}}, broadcast=True) setgamesaved(False) #If we've set the auto save flag, we'll now save the file if vars.autosave and (".json" in vars.savedir): save() #==================================================================# # Signals the Game Screen to remove one of the chunks #==================================================================# def remove_story_chunk(idx: int): emit('from_server', {'cmd': 'removechunk', 'data': idx}, broadcast=True) setgamesaved(False) #==================================================================# # Sends the current generator settings to the Game Menu #==================================================================# def refresh_settings(): # Suppress toggle change events while loading state emit('from_server', {'cmd': 'allowtoggle', 'data': False}, broadcast=True) if(vars.model != "InferKit"): emit('from_server', {'cmd': 'updatetemp', 'data': vars.temp}, broadcast=True) emit('from_server', {'cmd': 'updatetopp', 'data': vars.top_p}, broadcast=True) emit('from_server', {'cmd': 'updatetopk', 'data': vars.top_k}, broadcast=True) emit('from_server', {'cmd': 'updatetfs', 'data': vars.tfs}, broadcast=True) emit('from_server', {'cmd': 'updatetypical', 'data': vars.typical}, broadcast=True) emit('from_server', {'cmd': 'updatetopa', 'data': vars.top_a}, broadcast=True) emit('from_server', {'cmd': 'updatereppen', 'data': vars.rep_pen}, broadcast=True) emit('from_server', {'cmd': 'updatereppenslope', 'data': vars.rep_pen_slope}, broadcast=True) emit('from_server', {'cmd': 'updatereppenrange', 'data': vars.rep_pen_range}, broadcast=True) emit('from_server', {'cmd': 'updateoutlen', 'data': vars.genamt}, broadcast=True) emit('from_server', {'cmd': 'updatetknmax', 'data': vars.max_length}, broadcast=True) emit('from_server', {'cmd': 'updatenumseq', 'data': vars.numseqs}, broadcast=True) else: emit('from_server', {'cmd': 'updatetemp', 'data': vars.temp}, broadcast=True) emit('from_server', {'cmd': 'updatetopp', 'data': vars.top_p}, broadcast=True) emit('from_server', {'cmd': 'updateikgen', 'data': vars.ikgen}, broadcast=True) emit('from_server', {'cmd': 'updateanotedepth', 'data': vars.andepth}, broadcast=True) emit('from_server', {'cmd': 'updatewidepth', 'data': vars.widepth}, broadcast=True) emit('from_server', {'cmd': 'updateuseprompt', 'data': vars.useprompt}, broadcast=True) emit('from_server', {'cmd': 'updateadventure', 'data': vars.adventure}, broadcast=True) emit('from_server', {'cmd': 'updatechatmode', 'data': vars.chatmode}, broadcast=True) emit('from_server', {'cmd': 'updatedynamicscan', 'data': vars.dynamicscan}, broadcast=True) emit('from_server', {'cmd': 'updateautosave', 'data': vars.autosave}, broadcast=True) emit('from_server', {'cmd': 'updatenopromptgen', 'data': vars.nopromptgen}, broadcast=True) emit('from_server', {'cmd': 'updaterngpersist', 'data': vars.rngpersist}, broadcast=True) emit('from_server', {'cmd': 'updatenogenmod', 'data': vars.nogenmod}, broadcast=True) emit('from_server', {'cmd': 'updatefulldeterminism', 'data': vars.full_determinism}, broadcast=True) emit('from_server', {'cmd': 'updatefrmttriminc', 'data': vars.formatoptns["frmttriminc"]}, broadcast=True) emit('from_server', {'cmd': 'updatefrmtrmblln', 'data': vars.formatoptns["frmtrmblln"]}, broadcast=True) emit('from_server', {'cmd': 'updatefrmtrmspch', 'data': vars.formatoptns["frmtrmspch"]}, broadcast=True) emit('from_server', {'cmd': 'updatefrmtadsnsp', 'data': vars.formatoptns["frmtadsnsp"]}, broadcast=True) emit('from_server', {'cmd': 'updatesingleline', 'data': vars.formatoptns["singleline"]}, broadcast=True) emit('from_server', {'cmd': 'updateoutputstreaming', 'data': vars.output_streaming}, broadcast=True) emit('from_server', {'cmd': 'updateshowbudget', 'data': vars.show_budget}, broadcast=True) emit('from_server', {'cmd': 'updateshowprobs', 'data': vars.show_probs}, broadcast=True) # Allow toggle events again emit('from_server', {'cmd': 'allowtoggle', 'data': True}, broadcast=True) #==================================================================# # Sets the logical and display states for the AI Busy condition #==================================================================# def set_aibusy(state): if(vars.disable_set_aibusy): return if(state): vars.aibusy = True emit('from_server', {'cmd': 'setgamestate', 'data': 'wait'}, broadcast=True) else: vars.aibusy = False emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True) #==================================================================# # #==================================================================# def editrequest(n): if(n == 0): txt = vars.prompt else: txt = vars.actions[n-1] vars.editln = n emit('from_server', {'cmd': 'setinputtext', 'data': txt}, broadcast=True) emit('from_server', {'cmd': 'enablesubmit', 'data': ''}, broadcast=True) #==================================================================# # #==================================================================# def editsubmit(data): vars.recentedit = True if(vars.editln == 0): vars.prompt = data else: vars.actions_metadata[vars.editln-1]['Alternative Text'] = vars.actions_metadata[vars.editln-1]['Alternative Text'] + [{"Text": vars.actions[vars.editln-1], "Pinned": False, "Previous Selection": False, "Edited": True}] vars.actions_metadata[vars.editln-1]['Selected Text'] = data vars.actions[vars.editln-1] = data vars.mode = "play" update_story_chunk(vars.editln) emit('from_server', {'cmd': 'texteffect', 'data': vars.editln}, broadcast=True) emit('from_server', {'cmd': 'editmode', 'data': 'false'}) send_debug() #==================================================================# # #==================================================================# def deleterequest(): vars.recentedit = True # Don't delete prompt if(vars.editln == 0): # Send error message pass else: vars.actions_metadata[vars.editln-1]['Alternative Text'] = [{"Text": vars.actions[vars.editln-1], "Pinned": False, "Previous Selection": True, "Edited": False}] + vars.actions_metadata[vars.editln-1]['Alternative Text'] vars.actions_metadata[vars.editln-1]['Selected Text'] = '' vars.actions[vars.editln-1] = '' vars.mode = "play" remove_story_chunk(vars.editln) emit('from_server', {'cmd': 'editmode', 'data': 'false'}) send_debug() #==================================================================# # #==================================================================# def inlineedit(chunk, data): vars.recentedit = True chunk = int(chunk) if(chunk == 0): if(len(data.strip()) == 0): return vars.prompt = data else: if(chunk-1 in vars.actions): vars.actions_metadata[chunk-1]['Alternative Text'] = vars.actions_metadata[chunk-1]['Alternative Text'] + [{"Text": vars.actions[chunk-1], "Pinned": False, "Previous Selection": False, "Edited": True}] vars.actions_metadata[chunk-1]['Selected Text'] = data vars.actions[chunk-1] = data else: logger.warning(f"Attempted to edit non-existent chunk {chunk}") setgamesaved(False) update_story_chunk(chunk) emit('from_server', {'cmd': 'texteffect', 'data': chunk}, broadcast=True) emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True) send_debug() #==================================================================# # #==================================================================# def inlinedelete(chunk): vars.recentedit = True chunk = int(chunk) # Don't delete prompt if(chunk == 0): # Send error message update_story_chunk(chunk) emit('from_server', {'cmd': 'errmsg', 'data': "Cannot delete the prompt."}) emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True) else: if(chunk-1 in vars.actions): vars.actions_metadata[chunk-1]['Alternative Text'] = [{"Text": vars.actions[chunk-1], "Pinned": False, "Previous Selection": True, "Edited": False}] + vars.actions_metadata[chunk-1]['Alternative Text'] vars.actions_metadata[chunk-1]['Selected Text'] = '' del vars.actions[chunk-1] else: logger.warning(f"Attempted to delete non-existent chunk {chunk}") setgamesaved(False) remove_story_chunk(chunk) emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True) send_debug() #==================================================================# # Toggles the game mode for memory editing and sends UI commands #==================================================================# def togglememorymode(): if(vars.mode == "play"): vars.mode = "memory" emit('from_server', {'cmd': 'memmode', 'data': 'true'}, broadcast=True) emit('from_server', {'cmd': 'setinputtext', 'data': vars.memory}, broadcast=True) emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}, broadcast=True) emit('from_server', {'cmd': 'setanotetemplate', 'data': vars.authornotetemplate}, broadcast=True) elif(vars.mode == "memory"): vars.mode = "play" emit('from_server', {'cmd': 'memmode', 'data': 'false'}, broadcast=True) #==================================================================# # Toggles the game mode for WI editing and sends UI commands #==================================================================# def togglewimode(): if(vars.mode == "play"): vars.mode = "wi" emit('from_server', {'cmd': 'wimode', 'data': 'true'}, broadcast=True) elif(vars.mode == "wi"): # Commit WI fields first requestwi() # Then set UI state back to Play vars.mode = "play" emit('from_server', {'cmd': 'wimode', 'data': 'false'}, broadcast=True) sendwi() #==================================================================# # #==================================================================# def addwiitem(folder_uid=None): assert folder_uid is None or folder_uid in vars.wifolders_d ob = {"key": "", "keysecondary": "", "content": "", "comment": "", "folder": folder_uid, "num": len(vars.worldinfo), "init": False, "selective": False, "constant": False} vars.worldinfo.append(ob) while(True): uid = int.from_bytes(os.urandom(4), "little", signed=True) if(uid not in vars.worldinfo_u): break vars.worldinfo_u[uid] = vars.worldinfo[-1] vars.worldinfo[-1]["uid"] = uid if(folder_uid is not None): vars.wifolders_u[folder_uid].append(vars.worldinfo[-1]) emit('from_server', {'cmd': 'addwiitem', 'data': ob}, broadcast=True) #==================================================================# # Creates a new WI folder with an unused cryptographically secure random UID #==================================================================# def addwifolder(): while(True): uid = int.from_bytes(os.urandom(4), "little", signed=True) if(uid not in vars.wifolders_d): break ob = {"name": "", "collapsed": False} vars.wifolders_d[uid] = ob vars.wifolders_l.append(uid) vars.wifolders_u[uid] = [] emit('from_server', {'cmd': 'addwifolder', 'uid': uid, 'data': ob}, broadcast=True) addwiitem(folder_uid=uid) #==================================================================# # Move the WI entry with UID src so that it immediately precedes # the WI entry with UID dst #==================================================================# def movewiitem(dst, src): setgamesaved(False) if(vars.worldinfo_u[src]["folder"] is not None): for i, e in enumerate(vars.wifolders_u[vars.worldinfo_u[src]["folder"]]): if(e is vars.worldinfo_u[src]): vars.wifolders_u[vars.worldinfo_u[src]["folder"]].pop(i) break if(vars.worldinfo_u[dst]["folder"] is not None): vars.wifolders_u[vars.worldinfo_u[dst]["folder"]].append(vars.worldinfo_u[src]) vars.worldinfo_u[src]["folder"] = vars.worldinfo_u[dst]["folder"] for i, e in enumerate(vars.worldinfo): if(e is vars.worldinfo_u[src]): _src = i elif(e is vars.worldinfo_u[dst]): _dst = i vars.worldinfo.insert(_dst - (_dst >= _src), vars.worldinfo.pop(_src)) sendwi() #==================================================================# # Move the WI folder with UID src so that it immediately precedes # the WI folder with UID dst #==================================================================# def movewifolder(dst, src): setgamesaved(False) vars.wifolders_l.remove(src) if(dst is None): # If dst is None, that means we should move src to be the last folder vars.wifolders_l.append(src) else: vars.wifolders_l.insert(vars.wifolders_l.index(dst), src) sendwi() #==================================================================# # #==================================================================# def sendwi(): # Cache len of WI ln = len(vars.worldinfo) # Clear contents of WI container emit('from_server', {'cmd': 'wistart', 'wifolders_d': vars.wifolders_d, 'wifolders_l': vars.wifolders_l, 'data': ''}, broadcast=True) # Stable-sort WI entries in order of folder stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] # If there are no WI entries, send an empty WI object if(ln == 0): addwiitem() else: # Send contents of WI array last_folder = ... for wi in vars.worldinfo: if(wi["folder"] != last_folder): emit('from_server', {'cmd': 'addwifolder', 'uid': wi["folder"], 'data': vars.wifolders_d[wi["folder"]] if wi["folder"] is not None else None}, broadcast=True) last_folder = wi["folder"] ob = wi emit('from_server', {'cmd': 'addwiitem', 'data': ob}, broadcast=True) emit('from_server', {'cmd': 'wifinish', 'data': ''}, broadcast=True) #==================================================================# # Request current contents of all WI HTML elements #==================================================================# def requestwi(): list = [] for wi in vars.worldinfo: list.append(wi["num"]) emit('from_server', {'cmd': 'requestwiitem', 'data': list}) #==================================================================# # Stable-sort WI items so that items in the same folder are adjacent, # and items in different folders are sorted based on the order of the folders #==================================================================# def stablesortwi(): mapping = {uid: index for index, uid in enumerate(vars.wifolders_l)} vars.worldinfo.sort(key=lambda x: mapping[x["folder"]] if x["folder"] is not None else float("inf")) last_folder = ... last_wi = None for i, wi in enumerate(vars.worldinfo): wi["num"] = i wi["init"] = True if(wi["folder"] != last_folder): if(last_wi is not None and last_folder is not ...): last_wi["init"] = False last_folder = wi["folder"] last_wi = wi if(last_wi is not None): last_wi["init"] = False for folder in vars.wifolders_u: vars.wifolders_u[folder].sort(key=lambda x: x["num"]) #==================================================================# # Extract object from server and send it to WI objects #==================================================================# def commitwi(ar): for ob in ar: ob["uid"] = int(ob["uid"]) vars.worldinfo_u[ob["uid"]]["key"] = ob["key"] vars.worldinfo_u[ob["uid"]]["keysecondary"] = ob["keysecondary"] vars.worldinfo_u[ob["uid"]]["content"] = ob["content"] vars.worldinfo_u[ob["uid"]]["comment"] = ob.get("comment", "") vars.worldinfo_u[ob["uid"]]["folder"] = ob.get("folder", None) vars.worldinfo_u[ob["uid"]]["selective"] = ob["selective"] vars.worldinfo_u[ob["uid"]]["constant"] = ob.get("constant", False) stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] #==================================================================# # #==================================================================# def deletewi(uid): if(uid in vars.worldinfo_u): setgamesaved(False) # Store UID of deletion request vars.deletewi = uid if(vars.deletewi is not None): if(vars.worldinfo_u[vars.deletewi]["folder"] is not None): for i, e in enumerate(vars.wifolders_u[vars.worldinfo_u[vars.deletewi]["folder"]]): if(e is vars.worldinfo_u[vars.deletewi]): vars.wifolders_u[vars.worldinfo_u[vars.deletewi]["folder"]].pop(i) for i, e in enumerate(vars.worldinfo): if(e is vars.worldinfo_u[vars.deletewi]): del vars.worldinfo[i] break del vars.worldinfo_u[vars.deletewi] # Send the new WI array structure sendwi() # And reset deletewi vars.deletewi = None #==================================================================# # #==================================================================# def deletewifolder(uid): uid = int(uid) del vars.wifolders_u[uid] del vars.wifolders_d[uid] del vars.wifolders_l[vars.wifolders_l.index(uid)] setgamesaved(False) # Delete uninitialized entries in the folder we're going to delete vars.worldinfo = [wi for wi in vars.worldinfo if wi["folder"] != uid or wi["init"]] vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] # Move WI entries that are inside of the folder we're going to delete # so that they're outside of all folders for wi in vars.worldinfo: if(wi["folder"] == uid): wi["folder"] = None sendwi() #==================================================================# # Look for WI keys in text to generator #==================================================================# def checkworldinfo(txt, allowed_entries=None, allowed_folders=None, force_use_txt=False, scan_story=True, actions=None): original_txt = txt if(actions is None): actions = vars.actions # Dont go any further if WI is empty if(len(vars.worldinfo) == 0): return "", set() # Cache actions length ln = len(actions) # Don't bother calculating action history if widepth is 0 if(vars.widepth > 0 and scan_story): depth = vars.widepth # If this is not a continue, add 1 to widepth since submitted # text is already in action history @ -1 if(not force_use_txt and (txt != "" and vars.prompt != txt)): txt = "" depth += 1 if(ln > 0): chunks = collections.deque() i = 0 for key in reversed(actions): chunk = actions[key] chunks.appendleft(chunk) i += 1 if(i == depth): break if(ln >= depth): txt = "".join(chunks) elif(ln > 0): txt = vars.comregex_ai.sub('', vars.prompt) + "".join(chunks) elif(ln == 0): txt = vars.comregex_ai.sub('', vars.prompt) if(force_use_txt): txt += original_txt # Scan text for matches on WI keys wimem = "" found_entries = set() for wi in vars.worldinfo: if(allowed_entries is not None and wi["uid"] not in allowed_entries): continue if(allowed_folders is not None and wi["folder"] not in allowed_folders): continue if(wi.get("constant", False)): wimem = wimem + wi["content"] + "\n" found_entries.add(id(wi)) continue if(len(wi["key"].strip()) > 0 and (not wi.get("selective", False) or len(wi.get("keysecondary", "").strip()) > 0)): # Split comma-separated keys keys = wi["key"].split(",") keys_secondary = wi.get("keysecondary", "").split(",") for k in keys: ky = k # Remove leading/trailing spaces if the option is enabled if(vars.wirmvwhtsp): ky = k.strip() if ky.lower() in txt.lower(): if wi.get("selective", False) and len(keys_secondary): found = False for ks in keys_secondary: ksy = ks if(vars.wirmvwhtsp): ksy = ks.strip() if ksy.lower() in txt.lower(): wimem = wimem + wi["content"] + "\n" found_entries.add(id(wi)) found = True break if found: break else: wimem = wimem + wi["content"] + "\n" found_entries.add(id(wi)) break return wimem, found_entries #==================================================================# # Commit changes to Memory storage #==================================================================# def memsubmit(data): emit('from_server', {'cmd': 'setinputtext', 'data': data}, broadcast=True) # Maybe check for length at some point # For now just send it to storage if(data != vars.memory): setgamesaved(False) vars.memory = data vars.mode = "play" emit('from_server', {'cmd': 'memmode', 'data': 'false'}, broadcast=True) # Ask for contents of Author's Note field emit('from_server', {'cmd': 'getanote', 'data': ''}) #==================================================================# # Commit changes to Author's Note #==================================================================# def anotesubmit(data, template=""): assert type(data) is str and type(template) is str # Maybe check for length at some point # For now just send it to storage if(data != vars.authornote): setgamesaved(False) vars.authornote = data if(vars.authornotetemplate != template): vars.setauthornotetemplate = template settingschanged() vars.authornotetemplate = template emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}, broadcast=True) emit('from_server', {'cmd': 'setanotetemplate', 'data': vars.authornotetemplate}, broadcast=True) #==================================================================# # Assembles game data into a request to InferKit API #==================================================================# def ikrequest(txt): # Log request to console if not vars.quiet: print("{0}Len:{1}, Txt:{2}{3}".format(colors.YELLOW, len(txt), txt, colors.END)) # Build request JSON data reqdata = { 'forceNoEnd': True, 'length': vars.ikgen, 'prompt': { 'isContinuation': False, 'text': txt }, 'startFromBeginning': False, 'streamResponse': False, 'temperature': vars.temp, 'topP': vars.top_p } # Create request req = requests.post( vars.url, json = reqdata, headers = { 'Authorization': 'Bearer '+vars.apikey } ) # Deal with the response if(req.status_code == 200): genout = req.json()["data"]["text"] vars.lua_koboldbridge.outputs[1] = genout execute_outmod() if(vars.lua_koboldbridge.regeneration_required): vars.lua_koboldbridge.regeneration_required = False genout = vars.lua_koboldbridge.outputs[1] assert genout is str if not vars.quiet: print("{0}{1}{2}".format(colors.CYAN, genout, colors.END)) vars.actions.append(genout) if vars.actions.get_last_key() in vars.actions_metadata: vars.actions_metadata[vars.actions.get_last_key()] = {"Selected Text": genout, "Alternative Text": []} else: # 2. We've selected a chunk of text that is was presented previously alternatives = [item['Text'] for item in vars.actions_metadata[vars.actions.get_last_key()]["Alternative Text"]] if genout in alternatives: alternatives = [item for item in vars.actions_metadata[vars.actions.get_last_key()]["Alternative Text"] if item['Text'] != genout] vars.actions_metadata[vars.actions.get_last_key()]["Alternative Text"] = alternatives vars.actions_metadata[vars.actions.get_last_key()]["Selected Text"] = genout update_story_chunk('last') emit('from_server', {'cmd': 'texteffect', 'data': vars.actions.get_last_key() + 1 if len(vars.actions) else 0}, broadcast=True) send_debug() set_aibusy(0) else: # Send error message to web client er = req.json() if("error" in er): code = er["error"]["extensions"]["code"] elif("errors" in er): code = er["errors"][0]["extensions"]["code"] errmsg = "InferKit API Error: {0} - {1}".format(req.status_code, code) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) #==================================================================# # Assembles game data into a request to OpenAI API #==================================================================# def oairequest(txt, min, max): # Log request to console if not vars.quiet: print("{0}Len:{1}, Txt:{2}{3}".format(colors.YELLOW, len(txt), txt, colors.END)) # Store context in memory to use it for comparison with generated content vars.lastctx = txt # Build request JSON data # GooseAI is a subntype of OAI. So to check if it's this type, we check the configname as a workaround # as the vars.model will always be OAI if 'GooseAI' in vars.configname: reqdata = { 'prompt': txt, 'max_tokens': vars.genamt, 'temperature': vars.temp, 'top_a': vars.top_a, 'top_p': vars.top_p, 'top_k': vars.top_k, 'tfs': vars.tfs, 'typical_p': vars.typical, 'repetition_penalty': vars.rep_pen, 'repetition_penalty_slope': vars.rep_pen_slope, 'repetition_penalty_range': vars.rep_pen_range, 'n': vars.numseqs, 'stream': False } else: reqdata = { 'prompt': txt, 'max_tokens': vars.genamt, 'temperature': vars.temp, 'top_p': vars.top_p, 'n': vars.numseqs, 'stream': False } req = requests.post( vars.oaiurl, json = reqdata, headers = { 'Authorization': 'Bearer '+vars.oaiapikey, 'Content-Type': 'application/json' } ) # Deal with the response if(req.status_code == 200): outputs = [out["text"] for out in req.json()["choices"]] for idx in range(len(outputs)): vars.lua_koboldbridge.outputs[idx+1] = outputs[idx] execute_outmod() if (vars.lua_koboldbridge.regeneration_required): vars.lua_koboldbridge.regeneration_required = False genout = [] for i in range(len(outputs)): genout.append( {"generated_text": vars.lua_koboldbridge.outputs[i + 1]}) assert type(genout[-1]["generated_text"]) is str else: genout = [ {"generated_text": utils.decodenewlines(txt)} for txt in outputs] if vars.actions.get_last_key() not in vars.actions_metadata: vars.actions_metadata[vars.actions.get_last_key()] = { "Selected Text": genout[0], "Alternative Text": []} else: # 2. We've selected a chunk of text that is was presented previously try: alternatives = [item['Text'] for item in vars.actions_metadata[len(vars.actions)-1]["Alternative Text"]] except: print(len(vars.actions)) print(vars.actions_metadata) raise if genout in alternatives: alternatives = [item for item in vars.actions_metadata[vars.actions.get_last_key() ]["Alternative Text"] if item['Text'] != genout] vars.actions_metadata[vars.actions.get_last_key()]["Alternative Text"] = alternatives vars.actions_metadata[vars.actions.get_last_key()]["Selected Text"] = genout if (len(genout) == 1): genresult(genout[0]["generated_text"]) else: if (vars.lua_koboldbridge.restart_sequence is not None and vars.lua_koboldbridge.restart_sequence > 0): genresult(genout[vars.lua_koboldbridge.restart_sequence - 1][ "generated_text"]) else: genselect(genout) if not vars.quiet: print("{0}{1}{2}".format(colors.CYAN, genout, colors.END)) set_aibusy(0) else: # Send error message to web client er = req.json() if("error" in er): type = er["error"]["type"] message = er["error"]["message"] errmsg = "OpenAI API Error: {0} - {1}".format(type, message) emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) #==================================================================# # Forces UI to Play mode #==================================================================# def exitModes(): if(vars.mode == "edit"): emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True) elif(vars.mode == "memory"): emit('from_server', {'cmd': 'memmode', 'data': 'false'}, broadcast=True) elif(vars.mode == "wi"): emit('from_server', {'cmd': 'wimode', 'data': 'false'}, broadcast=True) vars.mode = "play" #==================================================================# # Launch in-browser save prompt #==================================================================# def saveas(data): name = data['name'] savepins = data['pins'] # Check if filename exists already name = utils.cleanfilename(name) if(not fileops.saveexists(name) or (vars.saveow and vars.svowname == name)): # All clear to save e = saveRequest(fileops.storypath(name), savepins=savepins) vars.saveow = False vars.svowname = "" if(e is None): emit('from_server', {'cmd': 'hidesaveas', 'data': ''}) else: print("{0}{1}{2}".format(colors.RED, str(e), colors.END)) emit('from_server', {'cmd': 'popuperror', 'data': str(e)}) else: # File exists, prompt for overwrite vars.saveow = True vars.svowname = name emit('from_server', {'cmd': 'askforoverwrite', 'data': ''}) #==================================================================# # Launch in-browser story-delete prompt #==================================================================# def deletesave(name): name = utils.cleanfilename(name) e = fileops.deletesave(name) if(e is None): if(vars.smandelete): emit('from_server', {'cmd': 'hidepopupdelete', 'data': ''}) getloadlist() else: emit('from_server', {'cmd': 'popuperror', 'data': "The server denied your request to delete this story"}) else: print("{0}{1}{2}".format(colors.RED, str(e), colors.END)) emit('from_server', {'cmd': 'popuperror', 'data': str(e)}) #==================================================================# # Launch in-browser story-rename prompt #==================================================================# def renamesave(name, newname): # Check if filename exists already name = utils.cleanfilename(name) newname = utils.cleanfilename(newname) if(not fileops.saveexists(newname) or name == newname or (vars.saveow and vars.svowname == newname)): e = fileops.renamesave(name, newname) vars.saveow = False vars.svowname = "" if(e is None): if(vars.smanrename): emit('from_server', {'cmd': 'hidepopuprename', 'data': ''}) getloadlist() else: emit('from_server', {'cmd': 'popuperror', 'data': "The server denied your request to rename this story"}) else: print("{0}{1}{2}".format(colors.RED, str(e), colors.END)) emit('from_server', {'cmd': 'popuperror', 'data': str(e)}) else: # File exists, prompt for overwrite vars.saveow = True vars.svowname = newname emit('from_server', {'cmd': 'askforoverwrite', 'data': ''}) #==================================================================# # Save the currently running story #==================================================================# def save(): # Check if a file is currently open if(".json" in vars.savedir): saveRequest(vars.savedir) else: emit('from_server', {'cmd': 'saveas', 'data': ''}) #==================================================================# # Save the story via file browser #==================================================================# def savetofile(): savpath = fileops.getsavepath(vars.savedir, "Save Story As", [("Json", "*.json")]) saveRequest(savpath) #==================================================================# # Save the story to specified path #==================================================================# def saveRequest(savpath, savepins=True): if(savpath): # Leave Edit/Memory mode before continuing exitModes() # Save path for future saves vars.savedir = savpath txtpath = os.path.splitext(savpath)[0] + ".txt" # Build json to write js = {} js["gamestarted"] = vars.gamestarted js["prompt"] = vars.prompt js["memory"] = vars.memory js["authorsnote"] = vars.authornote js["anotetemplate"] = vars.authornotetemplate js["actions"] = tuple(vars.actions.values()) if savepins: js["actions_metadata"] = vars.actions_metadata js["worldinfo"] = [] js["wifolders_d"] = vars.wifolders_d js["wifolders_l"] = vars.wifolders_l # Extract only the important bits of WI for wi in vars.worldinfo_i: if(True): js["worldinfo"].append({ "key": wi["key"], "keysecondary": wi["keysecondary"], "content": wi["content"], "comment": wi["comment"], "folder": wi["folder"], "selective": wi["selective"], "constant": wi["constant"] }) txt = vars.prompt + "".join(vars.actions.values()) # Write it try: file = open(savpath, "w") except Exception as e: return e try: file.write(json.dumps(js, indent=3)) except Exception as e: file.close() return e file.close() try: file = open(txtpath, "w") except Exception as e: return e try: file.write(txt) except Exception as e: file.close() return e file.close() filename = path.basename(savpath) if(filename.endswith('.json')): filename = filename[:-5] vars.laststory = filename emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory}, broadcast=True) setgamesaved(True) print("{0}Story saved to {1}!{2}".format(colors.GREEN, path.basename(savpath), colors.END)) #==================================================================# # Show list of saved stories #==================================================================# def getloadlist(): emit('from_server', {'cmd': 'buildload', 'data': fileops.getstoryfiles()}) #==================================================================# # Show list of soft prompts #==================================================================# def getsplist(): if(vars.allowsp): emit('from_server', {'cmd': 'buildsp', 'data': fileops.getspfiles(vars.modeldim)}) #==================================================================# # Get list of userscripts #==================================================================# def getuslist(): files = {i: v for i, v in enumerate(fileops.getusfiles())} loaded = [] unloaded = [] userscripts = set(vars.userscripts) for i in range(len(files)): if files[i]["filename"] not in userscripts: unloaded.append(files[i]) files = {files[k]["filename"]: files[k] for k in files} userscripts = set(files.keys()) for filename in vars.userscripts: if filename in userscripts: loaded.append(files[filename]) return unloaded, loaded #==================================================================# # Load a saved story via file browser #==================================================================# def loadfromfile(): loadpath = fileops.getloadpath(vars.savedir, "Select Story File", [("Json", "*.json")]) loadRequest(loadpath) #==================================================================# # Load a stored story from a file #==================================================================# def loadRequest(loadpath, filename=None): if(loadpath): # Leave Edit/Memory mode before continuing exitModes() # Read file contents into JSON object if(isinstance(loadpath, str)): with open(loadpath, "r") as file: js = json.load(file) if(filename is None): filename = path.basename(loadpath) else: js = loadpath if(filename is None): filename = "untitled.json" # Copy file contents to vars vars.gamestarted = js["gamestarted"] vars.prompt = js["prompt"] vars.memory = js["memory"] vars.worldinfo = [] vars.worldinfo = [] vars.worldinfo_u = {} vars.wifolders_d = {int(k): v for k, v in js.get("wifolders_d", {}).items()} vars.wifolders_l = js.get("wifolders_l", []) vars.wifolders_u = {uid: [] for uid in vars.wifolders_d} vars.lastact = "" vars.submission = "" vars.lastctx = "" vars.genseqs = [] del vars.actions vars.actions = structures.KoboldStoryRegister() actions = collections.deque(js["actions"]) if "actions_metadata" in js: if type(js["actions_metadata"]) == dict: temp = js["actions_metadata"] vars.actions_metadata = {} #we need to redo the numbering of the actions_metadata since the actions list doesn't preserve it's number on saving if len(temp) > 0: counter = 0 temp = {int(k):v for k,v in temp.items()} for i in range(max(temp)+1): if i in temp: vars.actions_metadata[counter] = temp[i] counter += 1 del temp else: #fix if we're using the old metadata format vars.actions_metadata = {} i = 0 for text in js['actions']: vars.actions_metadata[i] = {'Selected Text': text, 'Alternative Text': []} i+=1 else: vars.actions_metadata = {} i = 0 for text in js['actions']: vars.actions_metadata[i] = {'Selected Text': text, 'Alternative Text': []} i+=1 footer = "" if(len(vars.prompt.strip()) == 0): while(len(actions)): action = actions.popleft() if(len(action.strip()) != 0): vars.prompt = action break else: vars.gamestarted = False vars.prompt = vars.prompt.lstrip() ln = len(vars.prompt.rstrip()) footer += vars.prompt[ln:] vars.prompt = vars.prompt[:ln] if(vars.gamestarted): for s in actions: if(len(s.strip()) == 0): # If this action only contains whitespace, we merge it with the next action footer += s continue vars.actions.append(footer + s) footer = "" # If there is trailing whitespace at the end of an action, we move that whitespace to the beginning of the next action ln = len(vars.actions[vars.actions.get_last_key()].rstrip()) footer += vars.actions[vars.actions.get_last_key()][ln:] vars.actions[vars.actions.get_last_key()] = vars.actions[vars.actions.get_last_key()][:ln] # Try not to break older save files if("authorsnote" in js): vars.authornote = js["authorsnote"] else: vars.authornote = "" if("anotetemplate" in js): vars.authornotetemplate = js["anotetemplate"] else: vars.authornotetemplate = "[Author's note: <|>]" if("worldinfo" in js): num = 0 for wi in js["worldinfo"]: vars.worldinfo.append({ "key": wi["key"], "keysecondary": wi.get("keysecondary", ""), "content": wi["content"], "comment": wi.get("comment", ""), "folder": wi.get("folder", None), "num": num, "init": True, "selective": wi.get("selective", False), "constant": wi.get("constant", False), "uid": None, }) while(True): uid = int.from_bytes(os.urandom(4), "little", signed=True) if(uid not in vars.worldinfo_u): break vars.worldinfo_u[uid] = vars.worldinfo[-1] vars.worldinfo[-1]["uid"] = uid if(vars.worldinfo[-1]["folder"] is not None): vars.wifolders_u[vars.worldinfo[-1]["folder"]].append(vars.worldinfo[-1]) num += 1 for uid in vars.wifolders_l + [None]: vars.worldinfo.append({"key": "", "keysecondary": "", "content": "", "comment": "", "folder": uid, "num": None, "init": False, "selective": False, "constant": False, "uid": None}) while(True): uid = int.from_bytes(os.urandom(4), "little", signed=True) if(uid not in vars.worldinfo_u): break vars.worldinfo_u[uid] = vars.worldinfo[-1] vars.worldinfo[-1]["uid"] = uid if(vars.worldinfo[-1]["folder"] is not None): vars.wifolders_u[vars.worldinfo[-1]["folder"]].append(vars.worldinfo[-1]) stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] # Save path for save button vars.savedir = loadpath # Clear loadselect var vars.loadselect = "" # Refresh game screen _filename = filename if(filename.endswith('.json')): _filename = filename[:-5] vars.laststory = _filename emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory}, broadcast=True) setgamesaved(True) sendwi() emit('from_server', {'cmd': 'setmemory', 'data': vars.memory}, broadcast=True) emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}, broadcast=True) emit('from_server', {'cmd': 'setanotetemplate', 'data': vars.authornotetemplate}, broadcast=True) refresh_story() emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True) emit('from_server', {'cmd': 'hidegenseqs', 'data': ''}, broadcast=True) print("{0}Story loaded from {1}!{2}".format(colors.GREEN, filename, colors.END)) send_debug() #==================================================================# # Import an AIDungon game exported with Mimi's tool #==================================================================# def importRequest(): importpath = fileops.getloadpath(vars.savedir, "Select AID CAT File", [("Json", "*.json")]) if(importpath): # Leave Edit/Memory mode before continuing exitModes() # Read file contents into JSON object file = open(importpath, "rb") vars.importjs = json.load(file) # If a bundle file is being imported, select just the Adventures object if type(vars.importjs) is dict and "stories" in vars.importjs: vars.importjs = vars.importjs["stories"] # Clear Popup Contents emit('from_server', {'cmd': 'clearpopup', 'data': ''}, broadcast=True) # Initialize vars num = 0 vars.importnum = -1 # Get list of stories for story in vars.importjs: ob = {} ob["num"] = num if(story["title"] != "" and story["title"] != None): ob["title"] = story["title"] else: ob["title"] = "(No Title)" if(story["description"] != "" and story["description"] != None): ob["descr"] = story["description"] else: ob["descr"] = "(No Description)" if("actions" in story): ob["acts"] = len(story["actions"]) elif("actionWindow" in story): ob["acts"] = len(story["actionWindow"]) emit('from_server', {'cmd': 'addimportline', 'data': ob}) num += 1 # Show Popup emit('from_server', {'cmd': 'popupshow', 'data': True}) #==================================================================# # Import an AIDungon game selected in popup #==================================================================# def importgame(): if(vars.importnum >= 0): # Cache reference to selected game ref = vars.importjs[vars.importnum] # Copy game contents to vars vars.gamestarted = True # Support for different versions of export script if("actions" in ref): if(len(ref["actions"]) > 0): vars.prompt = ref["actions"][0]["text"] else: vars.prompt = "" elif("actionWindow" in ref): if(len(ref["actionWindow"]) > 0): vars.prompt = ref["actionWindow"][0]["text"] else: vars.prompt = "" else: vars.prompt = "" vars.memory = ref["memory"] vars.authornote = ref["authorsNote"] if type(ref["authorsNote"]) is str else "" vars.authornotetemplate = "[Author's note: <|>]" vars.actions = structures.KoboldStoryRegister() vars.actions_metadata = {} vars.worldinfo = [] vars.worldinfo_i = [] vars.worldinfo_u = {} vars.wifolders_d = {} vars.wifolders_l = [] vars.wifolders_u = {uid: [] for uid in vars.wifolders_d} vars.lastact = "" vars.submission = "" vars.lastctx = "" # Get all actions except for prompt if("actions" in ref): if(len(ref["actions"]) > 1): for act in ref["actions"][1:]: vars.actions.append(act["text"]) elif("actionWindow" in ref): if(len(ref["actionWindow"]) > 1): for act in ref["actionWindow"][1:]: vars.actions.append(act["text"]) # Get just the important parts of world info if(ref["worldInfo"] != None): if(len(ref["worldInfo"]) > 1): num = 0 for wi in ref["worldInfo"]: vars.worldinfo.append({ "key": wi["keys"], "keysecondary": wi.get("keysecondary", ""), "content": wi["entry"], "comment": wi.get("comment", ""), "folder": wi.get("folder", None), "num": num, "init": True, "selective": wi.get("selective", False), "constant": wi.get("constant", False), "uid": None, }) while(True): uid = int.from_bytes(os.urandom(4), "little", signed=True) if(uid not in vars.worldinfo_u): break vars.worldinfo_u[uid] = vars.worldinfo[-1] vars.worldinfo[-1]["uid"] = uid if(vars.worldinfo[-1]["folder"]) is not None: vars.wifolders_u[vars.worldinfo[-1]["folder"]].append(vars.worldinfo[-1]) num += 1 for uid in vars.wifolders_l + [None]: vars.worldinfo.append({"key": "", "keysecondary": "", "content": "", "comment": "", "folder": uid, "num": None, "init": False, "selective": False, "constant": False, "uid": None}) while(True): uid = int.from_bytes(os.urandom(4), "little", signed=True) if(uid not in vars.worldinfo_u): break vars.worldinfo_u[uid] = vars.worldinfo[-1] vars.worldinfo[-1]["uid"] = uid if(vars.worldinfo[-1]["folder"] is not None): vars.wifolders_u[vars.worldinfo[-1]["folder"]].append(vars.worldinfo[-1]) stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] # Clear import data vars.importjs = {} # Reset current save vars.savedir = getcwd()+"\\stories" # Refresh game screen vars.laststory = None emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory}, broadcast=True) setgamesaved(False) sendwi() emit('from_server', {'cmd': 'setmemory', 'data': vars.memory}, broadcast=True) emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}, broadcast=True) emit('from_server', {'cmd': 'setanotetemplate', 'data': vars.authornotetemplate}, broadcast=True) refresh_story() emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True) emit('from_server', {'cmd': 'hidegenseqs', 'data': ''}, broadcast=True) #==================================================================# # Import an aidg.club prompt and start a new game with it. #==================================================================# def importAidgRequest(id): exitModes() urlformat = "https://aetherroom.club/api/" req = requests.get(urlformat+id) if(req.status_code == 200): js = req.json() # Import game state vars.gamestarted = True vars.prompt = js["promptContent"] vars.memory = js["memory"] vars.authornote = js["authorsNote"] vars.authornotetemplate = "[Author's note: <|>]" vars.actions = structures.KoboldStoryRegister() vars.actions_metadata = {} vars.worldinfo = [] vars.worldinfo_i = [] vars.worldinfo_u = {} vars.wifolders_d = {} vars.wifolders_l = [] vars.wifolders_u = {uid: [] for uid in vars.wifolders_d} vars.lastact = "" vars.submission = "" vars.lastctx = "" if not vars.memory: vars.memory = "" if not vars.authornote: vars.authornote = "" num = 0 for wi in js["worldInfos"]: vars.worldinfo.append({ "key": wi["keys"], "keysecondary": wi.get("keysecondary", ""), "content": wi["entry"], "comment": wi.get("comment", ""), "folder": wi.get("folder", None), "num": num, "init": True, "selective": wi.get("selective", False), "constant": wi.get("constant", False), "uid": None, }) while(True): uid = int.from_bytes(os.urandom(4), "little", signed=True) if(uid not in vars.worldinfo_u): break vars.worldinfo_u[uid] = vars.worldinfo[-1] vars.worldinfo[-1]["uid"] = uid if(vars.worldinfo[-1]["folder"]) is not None: vars.wifolders_u[vars.worldinfo[-1]["folder"]].append(vars.worldinfo[-1]) num += 1 for uid in vars.wifolders_l + [None]: vars.worldinfo.append({"key": "", "keysecondary": "", "content": "", "comment": "", "folder": uid, "num": None, "init": False, "selective": False, "constant": False, "uid": None}) while(True): uid = int.from_bytes(os.urandom(4), "little", signed=True) if(uid not in vars.worldinfo_u): break vars.worldinfo_u[uid] = vars.worldinfo[-1] vars.worldinfo[-1]["uid"] = uid if(vars.worldinfo[-1]["folder"] is not None): vars.wifolders_u[vars.worldinfo[-1]["folder"]].append(vars.worldinfo[-1]) stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] # Reset current save vars.savedir = getcwd()+"\\stories" # Refresh game screen vars.laststory = None emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory}, broadcast=True) setgamesaved(False) sendwi() emit('from_server', {'cmd': 'setmemory', 'data': vars.memory}, broadcast=True) emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}, broadcast=True) emit('from_server', {'cmd': 'setanotetemplate', 'data': vars.authornotetemplate}, broadcast=True) refresh_story() emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True) #==================================================================# # Import World Info JSON file #==================================================================# def wiimportrequest(): importpath = fileops.getloadpath(vars.savedir, "Select World Info File", [("Json", "*.json")]) if(importpath): file = open(importpath, "rb") js = json.load(file) if(len(js) > 0): # If the most recent WI entry is blank, remove it. if(not vars.worldinfo[-1]["init"]): del vars.worldinfo[-1] # Now grab the new stuff num = len(vars.worldinfo) for wi in js: vars.worldinfo.append({ "key": wi["keys"], "keysecondary": wi.get("keysecondary", ""), "content": wi["entry"], "comment": wi.get("comment", ""), "folder": wi.get("folder", None), "num": num, "init": True, "selective": wi.get("selective", False), "constant": wi.get("constant", False), "uid": None, }) while(True): uid = int.from_bytes(os.urandom(4), "little", signed=True) if(uid not in vars.worldinfo_u): break vars.worldinfo_u[uid] = vars.worldinfo[-1] vars.worldinfo[-1]["uid"] = uid if(vars.worldinfo[-1]["folder"]) is not None: vars.wifolders_u[vars.worldinfo[-1]["folder"]].append(vars.worldinfo[-1]) num += 1 for uid in [None]: vars.worldinfo.append({"key": "", "keysecondary": "", "content": "", "comment": "", "folder": uid, "num": None, "init": False, "selective": False, "constant": False, "uid": None}) while(True): uid = int.from_bytes(os.urandom(4), "little", signed=True) if(uid not in vars.worldinfo_u): break vars.worldinfo_u[uid] = vars.worldinfo[-1] vars.worldinfo[-1]["uid"] = uid if(vars.worldinfo[-1]["folder"] is not None): vars.wifolders_u[vars.worldinfo[-1]["folder"]].append(vars.worldinfo[-1]) if not vars.quiet: print("{0}".format(vars.worldinfo[0])) # Refresh game screen setgamesaved(False) sendwi() #==================================================================# # Starts a new story #==================================================================# def newGameRequest(): # Leave Edit/Memory mode before continuing exitModes() # Clear vars values vars.gamestarted = False vars.prompt = "" vars.memory = "" vars.actions = structures.KoboldStoryRegister() vars.actions_metadata = {} vars.authornote = "" vars.authornotetemplate = vars.setauthornotetemplate vars.worldinfo = [] vars.worldinfo_i = [] vars.worldinfo_u = {} vars.wifolders_d = {} vars.wifolders_l = [] vars.lastact = "" vars.submission = "" vars.lastctx = "" # Reset current save vars.savedir = getcwd()+"\\stories" # Refresh game screen vars.laststory = None emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory}, broadcast=True) setgamesaved(True) sendwi() emit('from_server', {'cmd': 'setmemory', 'data': vars.memory}, broadcast=True) emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}, broadcast=True) emit('from_server', {'cmd': 'setanotetemplate', 'data': vars.authornotetemplate}, broadcast=True) setStartState() def randomGameRequest(topic, memory=""): if(vars.noai): newGameRequest() vars.memory = memory emit('from_server', {'cmd': 'setmemory', 'data': vars.memory}, broadcast=True) return vars.recentrng = topic vars.recentrngm = memory newGameRequest() setgamesaved(False) _memory = memory if(len(memory) > 0): _memory = memory.rstrip() + "\n\n" vars.memory = _memory + "You generate the following " + topic + " story concept :" vars.lua_koboldbridge.feedback = None actionsubmit("", force_submit=True, force_prompt_gen=True) vars.memory = memory emit('from_server', {'cmd': 'setmemory', 'data': vars.memory}, broadcast=True) def final_startup(): # Prevent tokenizer from taking extra time the first time it's used def __preempt_tokenizer(): if("tokenizer" not in globals()): return utils.decodenewlines(tokenizer.decode([25678, 559])) tokenizer.encode(utils.encodenewlines("eunoia")) threading.Thread(target=__preempt_tokenizer).start() # Load soft prompt specified by the settings file, if applicable if(path.exists(get_config_filename())): file = open(get_config_filename(), "r") js = json.load(file) if(vars.allowsp and "softprompt" in js and type(js["softprompt"]) is str and all(q not in js["softprompt"] for q in ("..", ":")) and (len(js["softprompt"]) == 0 or all(js["softprompt"][0] not in q for q in ("/", "\\")))): spRequest(js["softprompt"]) else: vars.spfilename = "" file.close() # Precompile TPU backend if required if(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")): soft_tokens = tpumtjgetsofttokens() if(vars.dynamicscan or (not vars.nogenmod and vars.has_genmod)): threading.Thread( target=tpu_mtj_backend.infer_dynamic, args=(np.tile(np.uint32((23403, 727, 20185)), (vars.numseqs, 1)),), kwargs={ "soft_embeddings": vars.sp, "soft_tokens": soft_tokens, "gen_len": 1, "use_callback": False, "numseqs": vars.numseqs, "excluded_world_info": list(set() for _ in range(vars.numseqs)), }, ).start() else: threading.Thread( target=tpu_mtj_backend.infer_static, args=(np.uint32((23403, 727, 20185)),), kwargs={ "soft_embeddings": vars.sp, "soft_tokens": soft_tokens, "gen_len": 1, "numseqs": vars.numseqs, }, ).start() # Set the initial RNG seed if(vars.seed is not None): if(vars.use_colab_tpu): if(vars.seed_specified): __import__("tpu_mtj_backend").set_rng_seed(vars.seed) else: __import__("tpu_mtj_backend").randomize_rng_seed() else: if(vars.seed_specified): __import__("torch").manual_seed(vars.seed) else: __import__("torch").seed() vars.seed = __import__("tpu_mtj_backend").get_rng_seed() if vars.use_colab_tpu else __import__("torch").initial_seed() def send_debug(): if vars.debug: debug_info = "" try: debug_info = "{}Seed: {} ({})\n".format(debug_info, repr(__import__("tpu_mtj_backend").get_rng_seed() if vars.use_colab_tpu else __import__("torch").initial_seed()), "specified by user in settings file" if vars.seed_specified else "randomly generated") except: pass try: debug_info = "{}Newline Mode: {}\n".format(debug_info, vars.newlinemode) except: pass try: debug_info = "{}Action Length: {}\n".format(debug_info, vars.actions.get_last_key()) except: pass try: debug_info = "{}Actions Metadata Length: {}\n".format(debug_info, max(vars.actions_metadata) if len(vars.actions_metadata) > 0 else 0) except: pass try: debug_info = "{}Actions: {}\n".format(debug_info, [k for k in vars.actions]) except: pass try: debug_info = "{}Actions Metadata: {}\n".format(debug_info, [k for k in vars.actions_metadata]) except: pass try: debug_info = "{}Last Action: {}\n".format(debug_info, vars.actions[vars.actions.get_last_key()]) except: pass try: debug_info = "{}Last Metadata: {}\n".format(debug_info, vars.actions_metadata[max(vars.actions_metadata)]) except: pass emit('from_server', {'cmd': 'debug_info', 'data': debug_info}, broadcast=True) #==================================================================# # Load file browser for soft prompts #==================================================================# @socketio.on('show_folder_soft_prompt') def show_folder_soft_prompt(data): file_popup("Load Softprompt", "./softprompts", "", renameable=True, folder_only=False, editable=False, deleteable=True, jailed=True, item_check=None) #==================================================================# # Load file browser for user scripts #==================================================================# @socketio.on('show_folder_usersripts') def show_folder_usersripts(data): file_popup("Load Softprompt", "./userscripts", "", renameable=True, folder_only=False, editable=True, deleteable=True, jailed=True, item_check=None) #==================================================================# # File Popup options #==================================================================# @socketio.on('upload_file') def upload_file(data): print("upload_file {}".format(data['filename'])) print('current_folder' in session) print('popup_jailed_dir' not in session) print(session['popup_jailed_dir']) print(session['current_folder']) if 'current_folder' in session: path = os.path.abspath(os.path.join(session['current_folder'], data['filename']).replace("\\", "/")).replace("\\", "/") print(path) print(os.path.exists(path)) if 'popup_jailed_dir' not in session: print("Someone is trying to upload a file to your server. Blocked.") elif session['popup_jailed_dir'] is None: if os.path.exists(path): print("popup error") emit("error_popup", "The file already exists. Please delete it or rename the file before uploading", room="UI_2"); else: with open(path, "wb") as f: f.write(data['data']) get_files_folders(session['current_folder']) print("saved") elif session['popup_jailed_dir'] in session['current_folder']: if os.path.exists(path): print("popup error") emit("error_popup", "The file already exists. Please delete it or rename the file before uploading", room="UI_2"); else: with open(path, "wb") as f: f.write(data['data']) get_files_folders(session['current_folder']) print("saved") @socketio.on('popup_change_folder') def popup_change_folder(data): print("Doing popup change folder: {}".format(data)) if 'popup_jailed_dir' not in session: print("Someone is trying to get at files in your server. Blocked.") return if session['popup_jailed_dir'] is None: get_files_folders(data) elif session['popup_jailed_dir'] in data: get_files_folders(data) else: print("User is trying to get at files in your server outside the jail. Blocked. Jailed Dir: {} Requested Dir: {}".format(session['popup_jailed_dir'], data)) @socketio.on('popup_rename') def popup_rename(data): if 'popup_renameable' not in session: print("Someone is trying to rename a file in your server. Blocked.") return if not session['popup_renameable']: print("Someone is trying to rename a file in your server. Blocked.") return if session['popup_jailed_dir'] is None: os.rename(data['file'], data['new_name']) get_files_folders(os.path.dirname(data['file'])) elif session['popup_jailed_dir'] in data: os.rename(data['file'], data['new_name']) get_files_folders(os.path.dirname(data['file'])) else: print("User is trying to rename files in your server outside the jail. Blocked. Jailed Dir: {} Requested Dir: {}".format(session['popup_jailed_dir'], data['file'])) @socketio.on('popup_delete') def popup_delete(data): if 'popup_deletable' not in session: print("Someone is trying to delete a file in your server. Blocked.") return if not session['popup_deletable']: print("Someone is trying to delete a file in your server. Blocked.") return if session['popup_jailed_dir'] is None: import shutil if os.path.isdir(data): shutil.rmtree(data) else: os.remove(data) path = os.path.abspath(data).replace("\\", "/") if path[-1] == "/": path = path[:-1] path = "/".join(path.split("/")[:-1]) get_files_folders(path) elif session['popup_jailed_dir'] in data: import shutil if os.path.isdir(data): shutil.rmtree(data) else: os.remove(data) path = os.path.abspath(data).replace("\\", "/") if path[-1] == "/": path = path[:-1] path = "/".join(path.split("/")[:-1]) get_files_folders(path) else: print("User is trying to delete files in your server outside the jail. Blocked. Jailed Dir: {} Requested Dir: {}".format(session['popup_jailed_dir'], data)) @socketio.on('popup_edit') def popup_edit(data): if 'popup_editable' not in session: print("Someone is trying to edit a file in your server. Blocked.") return if not session['popup_editable']: print("Someone is trying to edit a file in your server. Blocked.") return if session['popup_jailed_dir'] is None: emit("popup_edit_file", {"file": data, "text": open(data, 'r', encoding='utf-8').read()}); elif session['popup_jailed_dir'] in data: emit("popup_edit_file", {"file": data, "text": open(data, 'r', encoding='utf-8').read()}); else: print("User is trying to delete files in your server outside the jail. Blocked. Jailed Dir: {} Requested Dir: {}".format(session['popup_jailed_dir'], data)) @socketio.on('popup_change_file') def popup_change_file(data): if 'popup_editable' not in session: print("Someone is trying to edit a file in your server. Blocked.") return if not session['popup_editable']: print("Someone is trying to edit a file in your server. Blocked.") return if session['popup_jailed_dir'] is None: with open(data['file'], 'w') as f: f.write(data['data']) elif session['popup_jailed_dir'] in data['file']: with open(data['file'], 'w') as f: f.write(data['data']) else: print("User is trying to delete files in your server outside the jail. Blocked. Jailed Dir: {} Requested Dir: {}".format(session['popup_jailed_dir'], data)) def file_popup(popup_title, starting_folder, return_event, upload=True, jailed=True, folder_only=True, renameable=False, deleteable=False, editable=False, show_breadcrumbs=True, item_check=None, show_hidden=False): #starting_folder = The folder we're going to get folders and/or items from #return_event = the socketio event that will be emitted when the load button is clicked #jailed = if set to true will look for the session variable jailed_folder and prevent navigation outside of that folder #folder_only = will only show folders, no files #deletable = will show the delete icons/methods. #editable = will show the edit icons/methods #show_breadcrumbs = will show the breadcrumbs at the top of the screen #item_check will call this function to check if the item is valid as a selection if not none. Will pass absolute directory as only argument to function #show_hidden = ... really, you have to ask? if jailed: session['popup_jailed_dir'] = os.path.abspath(starting_folder).replace("\\", "/") else: session['popup_jailed_dir'] = None session['popup_deletable'] = deleteable session['popup_renameable'] = renameable session['popup_editable'] = editable session['popup_show_hidden'] = show_hidden session['popup_item_check'] = item_check session['popup_folder_only'] = folder_only session['popup_show_breadcrumbs'] = show_breadcrumbs session['upload'] = upload socketio.emit("load_popup", {"popup_title": popup_title, "call_back": return_event, "renameable": renameable, "deleteable": deleteable, "editable": editable, 'upload': upload}, broadcast=True) get_files_folders(starting_folder) def get_files_folders(starting_folder): import stat session['current_folder'] = os.path.abspath(starting_folder).replace("\\", "/") item_check = session['popup_item_check'] show_breadcrumbs = session['popup_show_breadcrumbs'] show_hidden = session['popup_show_hidden'] folder_only = session['popup_folder_only'] if starting_folder == 'This PC': breadcrumbs = [['This PC', 'This PC']] items = [["{}:/".format(chr(i)), "{}:\\".format(chr(i))] for i in range(65, 91) if os.path.exists("{}:".format(chr(i)))] else: path = os.path.abspath(starting_folder).replace("\\", "/") if path[-1] == "/": path = path[:-1] breadcrumbs = [] for i in range(len(path.split("/"))): breadcrumbs.append(["/".join(path.split("/")[:i+1]), path.split("/")[i]]) if len(breadcrumbs) == 1: breadcrumbs = [["{}:/".format(chr(i)), "{}:\\".format(chr(i))] for i in range(65, 91) if os.path.exists("{}:".format(chr(i)))] else: if len([["{}:/".format(chr(i)), "{}:\\".format(chr(i))] for i in range(65, 91) if os.path.exists("{}:".format(chr(i)))]) > 0: breadcrumbs.insert(0, ['This PC', 'This PC']) #if we're jailed, remove the stuff before the jail from the breadcrumbs if session['popup_jailed_dir'] is not None: breadcrumbs = breadcrumbs[len(session['popup_jailed_dir'].split("/")):] folders = [] files = [] base_path = os.path.abspath(starting_folder).replace("\\", "/") for item in os.listdir(base_path): item_full_path = os.path.join(base_path, item).replace("\\", "/") if hasattr(os.stat(item_full_path), "st_file_attributes"): hidden = bool(os.stat(item_full_path).st_file_attributes & stat.FILE_ATTRIBUTE_HIDDEN) else: hidden = item[0] == "." if item_check is None: valid_selection = True else: valid_selection = item_check(item_full_path) if (show_hidden and hidden) or not hidden: if os.path.isdir(os.path.join(base_path, item)): folders.append([True, item_full_path, item, valid_selection]) else: files.append([False, item_full_path, item, valid_selection]) items = folders if not folder_only: items += files socketio.emit("popup_items", items, broadcast=True, include_self=True) if show_breadcrumbs: socketio.emit("popup_breadcrumbs", breadcrumbs, broadcast=True) class EmptySchema(KoboldSchema): pass class BasicTextResultInnerSchema(KoboldSchema): text: str = fields.String(required=True) class BasicTextResultSchema(KoboldSchema): result: BasicTextResultInnerSchema = fields.Nested(BasicTextResultInnerSchema) class BasicResultInnerSchema(KoboldSchema): result: str = fields.String(required=True) class BasicResultSchema(KoboldSchema): result: BasicResultInnerSchema = fields.Nested(BasicResultInnerSchema, required=True) class BasicResultsSchema(KoboldSchema): results: BasicResultInnerSchema = fields.List(fields.Nested(BasicResultInnerSchema), required=True) class BasicStringSchema(KoboldSchema): value: str = fields.String(required=True) class BasicBooleanSchema(KoboldSchema): value: bool = fields.Boolean(required=True) class BasicUIDSchema(KoboldSchema): uid: str = fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info entry/folder."}) class BasicErrorSchema(KoboldSchema): msg: str = fields.String(required=True) type: str = fields.String(required=True) class StoryEmptyErrorSchema(KoboldSchema): detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True) class StoryTooShortErrorSchema(KoboldSchema): detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True) class OutOfMemoryErrorSchema(KoboldSchema): detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True) class NotFoundErrorSchema(KoboldSchema): detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True) api_out_of_memory_response = """507: description: Out of memory content: application/json: schema: OutOfMemoryErrorSchema examples: gpu.cuda: value: detail: msg: "KoboldAI ran out of memory: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 4.00 GiB total capacity; 2.97 GiB already allocated; 0 bytes free; 2.99 GiB reserved in total by PyTorch)" type: out_of_memory.gpu.cuda gpu.hip: value: detail: msg: "KoboldAI ran out of memory: HIP out of memory. Tried to allocate 20.00 MiB (GPU 0; 4.00 GiB total capacity; 2.97 GiB already allocated; 0 bytes free; 2.99 GiB reserved in total by PyTorch)" type: out_of_memory.gpu.hip tpu.hbm: value: detail: msg: "KoboldAI ran out of memory: Compilation failed: Compilation failure: Ran out of memory in memory space hbm. Used 8.83G of 8.00G hbm. Exceeded hbm capacity by 848.88M." type: out_of_memory.tpu.hbm cpu.default_cpu_allocator: value: detail: msg: "KoboldAI ran out of memory: DefaultCPUAllocator: not enough memory: you tried to allocate 209715200 bytes." type: out_of_memory.cpu.default_cpu_allocator unknown.unknown: value: detail: msg: "KoboldAI ran out of memory." type: out_of_memory.unknown.unknown""" class ValidationErrorSchema(KoboldSchema): detail: Dict[str, List[str]] = fields.Dict(keys=fields.String(), values=fields.List(fields.String(), validate=validate.Length(min=1)), required=True) api_validation_error_response = """422: description: Validation error content: application/json: schema: ValidationErrorSchema""" class ServerBusyErrorSchema(KoboldSchema): detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True) api_server_busy_response = """503: description: Server is busy content: application/json: schema: ServerBusyErrorSchema example: detail: msg: Server is busy; please try again later. type: service_unavailable""" class NotImplementedErrorSchema(KoboldSchema): detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True) api_not_implemented_response = """501: description: Not implemented content: application/json: schema: NotImplementedErrorSchema example: detail: msg: API generation is not supported in read-only mode; please load a model and then try again. type: not_implemented""" class SamplerSettingsSchema(KoboldSchema): rep_pen: Optional[float] = fields.Float(validate=validate.Range(min=1), metadata={"description": "Base repetition penalty value."}) rep_pen_range: Optional[int] = fields.Integer(validate=validate.Range(min=0), metadata={"description": "Repetition penalty range."}) rep_pen_slope: Optional[float] = fields.Float(validate=validate.Range(min=0), metadata={"description": "Repetition penalty slope."}) top_k: Optional[int] = fields.Integer(validate=validate.Range(min=0), metadata={"description": "Top-k sampling value."}) top_a: Optional[float] = fields.Float(validate=validate.Range(min=0), metadata={"description": "Top-a sampling value."}) top_p: Optional[float] = fields.Float(validate=validate.Range(min=0, max=1), metadata={"description": "Top-p sampling value."}) tfs: Optional[float] = fields.Float(validate=validate.Range(min=0, max=1), metadata={"description": "Tail free sampling value."}) typical: Optional[float] = fields.Float(validate=validate.Range(min=0, max=1), metadata={"description": "Typical sampling value."}) temperature: Optional[float] = fields.Float(validate=validate.Range(min=0, min_inclusive=False), metadata={"description": "Temperature value."}) def soft_prompt_validator(soft_prompt: str): if len(soft_prompt.strip()) == 0: return if not vars.allowsp: raise ValidationError("Cannot use soft prompts with current backend.") if any(q in soft_prompt for q in ("/", "\\")): return z, _, _, _, _ = fileops.checksp(soft_prompt.strip(), vars.modeldim) if isinstance(z, int): raise ValidationError("Must be a valid soft prompt name.") z.close() return True def story_load_validator(name: str): if any(q in name for q in ("/", "\\")): return if len(name.strip()) == 0 or not os.path.isfile(fileops.storypath(name)): raise ValidationError("Must be a valid story name.") return True def permutation_validator(lst: list): if any(not isinstance(e, int) for e in lst): return if min(lst) != 0 or max(lst) != len(lst) - 1 or len(set(lst)) != len(lst): raise ValidationError("Must be a permutation of the first N non-negative integers, where N is the length of this array") return True class GenerationInputSchema(SamplerSettingsSchema): prompt: str = fields.String(required=True, metadata={"description": "This is the submission."}) use_memory: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the memory from the KoboldAI GUI when generating text."}) use_story: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the story from the KoboldAI GUI when generating text."}) use_authors_note: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the author's note from the KoboldAI GUI when generating text. This has no effect unless `use_story` is also enabled."}) use_world_info: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the world info from the KoboldAI GUI when generating text."}) use_userscripts: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the userscripts from the KoboldAI GUI when generating text."}) soft_prompt: Optional[str] = fields.String(metadata={"description": "Soft prompt to use when generating. If set to the empty string or any other string containing no non-whitespace characters, uses no soft prompt."}, validate=[soft_prompt_validator, validate.Regexp(r"^[^/\\]*$")]) max_length: int = fields.Integer(validate=validate.Range(min=1, max=512), metadata={"description": "Number of tokens to generate."}) max_context_length: int = fields.Integer(validate=validate.Range(min=512, max=2048), metadata={"description": "Maximum number of tokens to send to the model."}) n: int = fields.Integer(validate=validate.Range(min=1, max=5), metadata={"description": "Number of outputs to generate."}) disable_output_formatting: bool = fields.Boolean(load_default=True, metadata={"description": "When enabled, all output formatting options default to `false` instead of the value in the KoboldAI GUI."}) frmttriminc: Optional[bool] = fields.Boolean(metadata={"description": "Output formatting option. When enabled, removes some characters from the end of the output such that the output doesn't end in the middle of a sentence. If the output is less than one sentence long, does nothing.\n\nIf `disable_output_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."}) frmtrmblln: Optional[bool] = fields.Boolean(metadata={"description": "Output formatting option. When enabled, replaces all occurrences of two or more consecutive newlines in the output with one newline.\n\nIf `disable_output_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."}) frmtrmspch: Optional[bool] = fields.Boolean(metadata={"description": "Output formatting option. When enabled, removes `#/@%{}+=~|\^<>` from the output.\n\nIf `disable_output_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."}) singleline: Optional[bool] = fields.Boolean(metadata={"description": "Output formatting option. When enabled, removes everything after the first line of the output, including the newline.\n\nIf `disable_output_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."}) disable_input_formatting: bool = fields.Boolean(load_default=True, metadata={"description": "When enabled, all input formatting options default to `false` instead of the value in the KoboldAI GUI"}) frmtadsnsp: Optional[bool] = fields.Boolean(metadata={"description": "Input formatting option. When enabled, adds a leading space to your input if there is no trailing whitespace at the end of the previous action.\n\nIf `disable_input_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."}) quiet: Optional[bool] = fields.Boolean(metadata={"description": "When enabled, Generated output will not be displayed in the console."}) sampler_order: Optional[List[int]] = fields.List(fields.Integer(), validate=[validate.Length(min=6), permutation_validator], metadata={"description": "Sampler order to be used. If N is the length of this array, then N must be greater than or equal to 6 and the array must be a permutation of the first N non-negative integers."}) sampler_seed: Optional[int] = fields.Integer(validate=validate.Range(min=0, max=2**64 - 1), metadata={"description": "RNG seed to use for sampling. If not specified, the global RNG will be used."}) sampler_full_determinism: Optional[bool] = fields.Boolean(metadata={"description": "If enabled, the generated text will always be the same as long as you use the same RNG seed, input and settings. If disabled, only the *sequence* of generated texts that you get when repeatedly generating text will be the same given the same RNG seed, input and settings."}) class GenerationResultSchema(KoboldSchema): text: str = fields.String(required=True, metadata={"description": "Generated output as plain text."}) class GenerationOutputSchema(KoboldSchema): results: List[GenerationResultSchema] = fields.List(fields.Nested(GenerationResultSchema), required=True, metadata={"description": "Array of generated outputs."}) class StoryNumsChunkSchema(KoboldSchema): num: int = fields.Integer(required=True, metadata={"description": "Guaranteed to not equal the `num` of any other active story chunk. Equals 0 iff this is the first action of the story (the prompt)."}) class StoryChunkSchema(StoryNumsChunkSchema, KoboldSchema): text: str = fields.String(required=True, metadata={"description": "The text inside this story chunk."}) class StorySchema(KoboldSchema): results: List[StoryChunkSchema] = fields.List(fields.Nested(StoryChunkSchema), required=True, metadata={"description": "Array of story actions. The array is sorted such that actions closer to the end of this array are closer to the end of the story."}) class BasicBooleanSchema(KoboldSchema): result: bool = fields.Boolean(required=True) class StoryNumsSchema(KoboldSchema): results: List[int] = fields.List(fields.Integer(), required=True, metadata={"description": "Array of story action nums. The array is sorted such that actions closer to the end of this array are closer to the end of the story."}) class StoryChunkResultSchema(KoboldSchema): result: StoryChunkSchema = fields.Nested(StoryChunkSchema, required=True) class StoryChunkNumSchema(KoboldSchema): value: int = fields.Integer(required=True) class StoryChunkTextSchema(KoboldSchema): value: str = fields.String(required=True) class StoryChunkSetTextSchema(KoboldSchema): value: str = fields.String(required=True, validate=validate.Regexp(r"^(.|\n)*\S$")) class StoryLoadSchema(KoboldSchema): name: str = fields.String(required=True, validate=[story_load_validator, validate.Regexp(r"^[^/\\]*$")]) class StorySaveSchema(KoboldSchema): name: str = fields.String(required=True, validate=validate.Regexp(r"^(?=.*\S)(?!.*[/\\]).*$")) class WorldInfoEntrySchema(KoboldSchema): uid: int = fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info entry."}) content: str = fields.String(required=True, metadata={"description": "The \"What To Remember\" for this entry."}) key: str = fields.String(required=True, metadata={"description": "Comma-separated list of keys, or of primary keys if selective mode is enabled."}) keysecondary: str = fields.String(metadata={"description": "Comma-separated list of secondary keys if selective mode is enabled."}) selective: bool = fields.Boolean(required=True, metadata={"description": "Whether or not selective mode is enabled for this world info entry."}) constant: bool = fields.Boolean(required=True, metadata={"description": "Whether or not constant mode is enabled for this world info entry."}) comment: bool = fields.String(required=True, metadata={"description": "The comment/description/title for this world info entry."}) class WorldInfoEntryResultSchema(KoboldSchema): result: WorldInfoEntrySchema = fields.Nested(WorldInfoEntrySchema, required=True) class WorldInfoFolderBasicSchema(KoboldSchema): uid: int = fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info folder."}) name: str = fields.String(required=True, metadata={"description": "Name of this world info folder."}) class WorldInfoFolderSchema(WorldInfoFolderBasicSchema): entries: List[WorldInfoEntrySchema] = fields.List(fields.Nested(WorldInfoEntrySchema), required=True) class WorldInfoFolderUIDsSchema(KoboldSchema): uid: int = fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info folder."}) entries: List[int] = fields.List(fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info entry."}), required=True) class WorldInfoEntriesSchema(KoboldSchema): entries: List[WorldInfoEntrySchema] = fields.List(fields.Nested(WorldInfoEntrySchema), required=True) class WorldInfoFoldersSchema(KoboldSchema): folders: List[WorldInfoFolderBasicSchema] = fields.List(fields.Nested(WorldInfoFolderBasicSchema), required=True) class WorldInfoSchema(WorldInfoEntriesSchema): folders: List[WorldInfoFolderSchema] = fields.List(fields.Nested(WorldInfoFolderSchema), required=True) class WorldInfoEntriesUIDsSchema(KoboldSchema): entries: List[int] = fields.List(fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info entry."}), required=True) class WorldInfoFoldersUIDsSchema(KoboldSchema): folders: List[int] = fields.List(fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info folder."}), required=True) class WorldInfoUIDsSchema(WorldInfoEntriesUIDsSchema): folders: List[WorldInfoFolderSchema] = fields.List(fields.Nested(WorldInfoFolderUIDsSchema), required=True) class ModelSelectionSchema(KoboldSchema): model: str = fields.String(required=True, validate=validate.Regexp(r"^(?!\s*NeoCustom)(?!\s*GPT2Custom)(?!\s*TPUMeshTransformerGPTJ)(?!\s*TPUMeshTransformerGPTNeoX)(?!\s*GooseAI)(?!\s*OAI)(?!\s*InferKit)(?!\s*Colab)(?!\s*API).*$"), metadata={"description": 'Hugging Face model ID, the path to a model folder (relative to the "models" folder in the KoboldAI root folder) or "ReadOnly" for no model'}) def _generate_text(body: GenerationInputSchema): if vars.aibusy or vars.genseqs: abort(Response(json.dumps({"detail": { "msg": "Server is busy; please try again later.", "type": "service_unavailable", }}), mimetype="application/json", status=503)) if vars.use_colab_tpu: import tpu_mtj_backend if hasattr(body, "sampler_seed"): # If a seed was specified, we need to save the global RNG state so we # can restore it later old_seed = vars.seed old_rng_state = tpu_mtj_backend.get_rng_state() if vars.use_colab_tpu else torch.get_rng_state() vars.seed = body.sampler_seed # We should try to use a previously saved RNG state with the same seed if body.sampler_seed in vars.rng_states: if vars.use_colab_tpu: tpu_mtj_backend.set_rng_state(vars.rng_states[body.sampler_seed]) else: torch.set_rng_state(vars.rng_states[body.sampler_seed]) else: if vars.use_colab_tpu: tpu_mtj_backend.set_rng_state(tpu_mtj_backend.new_rng_state(body.sampler_seed)) else: torch.manual_seed(body.sampler_seed) vars.rng_states[body.sampler_seed] = tpu_mtj_backend.get_rng_state() if vars.use_colab_tpu else torch.get_rng_state() if hasattr(body, "sampler_order"): if len(body.sampler_order) < 7: body.sampler_order = [6] + body.sampler_order # This maps each property of the setting to use when sending the generate idempotently # To the object which typically contains it's value # This allows to set the property only for the API generation, and then revert the setting # To what it was before. mapping = { "disable_input_formatting": ("vars", "disable_input_formatting", None), "disable_output_formatting": ("vars", "disable_output_formatting", None), "rep_pen": ("vars", "rep_pen", None), "rep_pen_range": ("vars", "rep_pen_range", None), "rep_pen_slope": ("vars", "rep_pen_slope", None), "top_k": ("vars", "top_k", None), "top_a": ("vars", "top_a", None), "top_p": ("vars", "top_p", None), "tfs": ("vars", "tfs", None), "typical": ("vars", "typical", None), "temperature": ("vars", "temp", None), "frmtadsnsp": ("vars.formatoptns", "@frmtadsnsp", "input"), "frmttriminc": ("vars.formatoptns", "@frmttriminc", "output"), "frmtrmblln": ("vars.formatoptns", "@frmtrmblln", "output"), "frmtrmspch": ("vars.formatoptns", "@frmtrmspch", "output"), "singleline": ("vars.formatoptns", "@singleline", "output"), "max_length": ("vars", "genamt", None), "max_context_length": ("vars", "max_length", None), "n": ("vars", "numseqs", None), "quiet": ("vars", "quiet", None), "sampler_order": ("vars", "sampler_order", None), "sampler_full_determinism": ("vars", "full_determinism", None), } saved_settings = {} set_aibusy(1) disable_set_aibusy = vars.disable_set_aibusy vars.disable_set_aibusy = True _standalone = vars.standalone vars.standalone = True show_probs = vars.show_probs vars.show_probs = False output_streaming = vars.output_streaming vars.output_streaming = False for key, entry in mapping.items(): obj = {"vars": vars, "vars.formatoptns": vars.formatoptns}[entry[0]] if entry[2] == "input" and vars.disable_input_formatting and not hasattr(body, key): setattr(body, key, False) if entry[2] == "output" and vars.disable_output_formatting and not hasattr(body, key): setattr(body, key, False) if getattr(body, key, None) is not None: if entry[1].startswith("@"): saved_settings[key] = obj[entry[1][1:]] obj[entry[1][1:]] = getattr(body, key) else: saved_settings[key] = getattr(obj, entry[1]) setattr(obj, entry[1], getattr(body, key)) try: if vars.allowsp and getattr(body, "soft_prompt", None) is not None: if any(q in body.soft_prompt for q in ("/", "\\")): raise RuntimeError old_spfilename = vars.spfilename spRequest(body.soft_prompt.strip()) genout = apiactionsubmit(body.prompt, use_memory=body.use_memory, use_story=body.use_story, use_world_info=body.use_world_info, use_authors_note=body.use_authors_note) output = {"results": [{"text": txt} for txt in genout]} finally: for key in saved_settings: entry = mapping[key] obj = {"vars": vars, "vars.formatoptns": vars.formatoptns}[entry[0]] if getattr(body, key, None) is not None: if entry[1].startswith("@"): if obj[entry[1][1:]] == getattr(body, key): obj[entry[1][1:]] = saved_settings[key] else: if getattr(obj, entry[1]) == getattr(body, key): setattr(obj, entry[1], saved_settings[key]) vars.disable_set_aibusy = disable_set_aibusy vars.standalone = _standalone vars.show_probs = show_probs vars.output_streaming = output_streaming if vars.allowsp and getattr(body, "soft_prompt", None) is not None: spRequest(old_spfilename) if hasattr(body, "sampler_seed"): vars.seed = old_seed if vars.use_colab_tpu: tpu_mtj_backend.set_rng_state(old_rng_state) else: torch.set_rng_state(old_rng_state) set_aibusy(0) return output @api_v1.get("/info/version") @api_schema_wrap def get_version(): """--- get: summary: Current API version tags: - info description: |-2 Returns the version of the API that you are currently using. responses: 200: description: Successful request content: application/json: schema: BasicResultSchema example: result: 1.0.0 """ return {"result": api_version} @api_v1.get("/info/version/latest") @api_schema_wrap def get_version_latest(): """--- get: summary: Latest API version tags: - info description: |-2 Returns the latest API version available. responses: 200: description: Successful request content: application/json: schema: BasicResultSchema example: result: 1.0.0 """ return {"result": api_versions[-1]} @api_v1.get("/info/version/list") @api_schema_wrap def get_version_list(): """--- get: summary: List API versions tags: - info description: |-2 Returns a list of available API versions sorted in ascending order. responses: 200: description: Successful request content: application/json: schema: BasicResultsSchema example: results: - 1.0.0 """ return {"results": api_versions} @api_v1.post("/generate") @api_schema_wrap def post_generate(body: GenerationInputSchema): """--- post: summary: Generate text tags: - generate description: |-2 Generates text given a submission, sampler settings, soft prompt and number of return sequences. By default, the story, userscripts, memory, author's note and world info are disabled. Unless otherwise specified, optional values default to the values in the KoboldAI GUI. requestBody: required: true content: application/json: schema: GenerationInputSchema example: prompt: |-2 Niko the kobold stalked carefully down the alley, his small scaly figure obscured by a dusky cloak that fluttered lightly in the cold winter breeze. top_p: 0.9 temperature: 0.5 responses: 200: description: Successful request content: application/json: schema: GenerationOutputSchema example: results: - text: |-2 Holding up his tail to keep it from dragging in the dirty snow that covered the cobblestone, he waited patiently for the butcher to turn his attention from his stall so that he could pilfer his next meal: a tender-looking chicken. {api_validation_error_response} {api_not_implemented_response} {api_server_busy_response} {api_out_of_memory_response} """ return _generate_text(body) @api_v1.get("/model") @api_schema_wrap def get_model(): """--- get: summary: Retrieve the current model string description: |-2 Gets the current model string, which is shown in the title of the KoboldAI GUI in parentheses, e.g. "KoboldAI Client (KoboldAI/fairseq-dense-13B-Nerys-v2)". tags: - model responses: 200: description: Successful request content: application/json: schema: BasicResultSchema example: result: KoboldAI/fairseq-dense-13B-Nerys-v2 """ return {"result": vars.model} @api_v1.put("/model") @api_schema_wrap def put_model(body: ModelSelectionSchema): """--- put: summary: Load a model description: |-2 Loads a model given its Hugging Face model ID, the path to a model folder (relative to the "models" folder in the KoboldAI root folder) or "ReadOnly" for no model. tags: - model requestBody: required: true content: application/json: schema: ModelSelectionSchema example: model: ReadOnly responses: 200: description: Successful request content: application/json: schema: EmptySchema {api_validation_error_response} {api_server_busy_response} """ if vars.aibusy or vars.genseqs: abort(Response(json.dumps({"detail": { "msg": "Server is busy; please try again later.", "type": "service_unavailable", }}), mimetype="application/json", status=503)) set_aibusy(1) old_model = vars.model vars.model = body.model.strip() try: load_model(use_breakmodel_args=True, breakmodel_args_default_to_cpu=True) except Exception as e: vars.model = old_model raise e set_aibusy(0) return {} def prompt_validator(prompt: str): if len(prompt.strip()) == 0: raise ValidationError("String does not match expected pattern.") class SubmissionInputSchema(KoboldSchema): prompt: str = fields.String(required=True, validate=prompt_validator, metadata={"pattern": r"^[\S\s]*\S[\S\s]*$", "description": "This is the submission."}) disable_input_formatting: bool = fields.Boolean(load_default=True, metadata={"description": "When enabled, disables all input formatting options, overriding their individual enabled/disabled states."}) frmtadsnsp: Optional[bool] = fields.Boolean(metadata={"description": "Input formatting option. When enabled, adds a leading space to your input if there is no trailing whitespace at the end of the previous action."}) @api_v1.post("/story/end") @api_schema_wrap def post_story_end(body: SubmissionInputSchema): """--- post: summary: Add an action to the end of the story tags: - story description: |-2 Inserts a single action at the end of the story in the KoboldAI GUI without generating text. requestBody: required: true content: application/json: schema: SubmissionInputSchema example: prompt: |-2 This is some text to put at the end of the story. responses: 200: description: Successful request content: application/json: schema: EmptySchema {api_validation_error_response} {api_server_busy_response} """ if vars.aibusy or vars.genseqs: abort(Response(json.dumps({"detail": { "msg": "Server is busy; please try again later.", "type": "service_unavailable", }}), mimetype="application/json", status=503)) set_aibusy(1) disable_set_aibusy = vars.disable_set_aibusy vars.disable_set_aibusy = True _standalone = vars.standalone vars.standalone = True numseqs = vars.numseqs vars.numseqs = 1 try: actionsubmit(body.prompt, force_submit=True, no_generate=True, ignore_aibusy=True) finally: vars.disable_set_aibusy = disable_set_aibusy vars.standalone = _standalone vars.numseqs = numseqs set_aibusy(0) return {} @api_v1.get("/story/end") @api_schema_wrap def get_story_end(): """--- get: summary: Retrieve the last action of the story tags: - story description: |-2 Returns the last action of the story in the KoboldAI GUI. responses: 200: description: Successful request content: application/json: schema: StoryChunkResultSchema 510: description: Story is empty content: application/json: schema: StoryEmptyErrorSchema example: detail: msg: Could not retrieve the last action of the story because the story is empty. type: story_empty """ if not vars.gamestarted: abort(Response(json.dumps({"detail": { "msg": "Could not retrieve the last action of the story because the story is empty.", "type": "story_empty", }}), mimetype="application/json", status=510)) if len(vars.actions) == 0: return {"result": {"text": vars.prompt, "num": 0}} return {"result": {"text": vars.actions[vars.actions.get_last_key()], "num": vars.actions.get_last_key() + 1}} @api_v1.get("/story/end/num") @api_schema_wrap def get_story_end_num(): """--- get: summary: Retrieve the num of the last action of the story tags: - story description: |-2 Returns the `num` of the last action of the story in the KoboldAI GUI. responses: 200: description: Successful request content: application/json: schema: StoryChunkNumSchema 510: description: Story is empty content: application/json: schema: StoryEmptyErrorSchema example: detail: msg: Could not retrieve the last action of the story because the story is empty. type: story_empty """ if not vars.gamestarted: abort(Response(json.dumps({"detail": { "msg": "Could not retrieve the last action of the story because the story is empty.", "type": "story_empty", }}), mimetype="application/json", status=510)) if len(vars.actions) == 0: return {"result": {"text": 0}} return {"result": {"text": vars.actions.get_last_key() + 1}} @api_v1.get("/story/end/text") @api_schema_wrap def get_story_end_text(): """--- get: summary: Retrieve the text of the last action of the story tags: - story description: |-2 Returns the text of the last action of the story in the KoboldAI GUI. responses: 200: description: Successful request content: application/json: schema: StoryChunkTextSchema 510: description: Story is empty content: application/json: schema: StoryEmptyErrorSchema example: detail: msg: Could not retrieve the last action of the story because the story is empty. type: story_empty """ if not vars.gamestarted: abort(Response(json.dumps({"detail": { "msg": "Could not retrieve the last action of the story because the story is empty.", "type": "story_empty", }}), mimetype="application/json", status=510)) if len(vars.actions) == 0: return {"result": {"text": vars.prompt}} return {"result": {"text": vars.actions[vars.actions.get_last_key()]}} @api_v1.put("/story/end/text") @api_schema_wrap def put_story_end_text(body: StoryChunkSetTextSchema): """--- put: summary: Set the text of the last action of the story tags: - story description: |-2 Sets the text of the last action of the story in the KoboldAI GUI to the desired value. requestBody: required: true content: application/json: schema: StoryChunkSetTextSchema example: value: string responses: 200: description: Successful request content: application/json: schema: EmptySchema 510: description: Story is empty content: application/json: schema: StoryEmptyErrorSchema example: detail: msg: Could not retrieve the last action of the story because the story is empty. type: story_empty {api_validation_error_response} """ if not vars.gamestarted: abort(Response(json.dumps({"detail": { "msg": "Could not retrieve the last action of the story because the story is empty.", "type": "story_empty", }}), mimetype="application/json", status=510)) value = body.value.rstrip() if len(vars.actions) == 0: inlineedit(0, value) else: inlineedit(vars.actions.get_last_key() + 1, value) return {} @api_v1.post("/story/end/delete") @api_schema_wrap def post_story_end_delete(body: EmptySchema): """--- post: summary: Remove the last action of the story tags: - story description: |-2 Removes the last action of the story in the KoboldAI GUI. requestBody: required: true content: application/json: schema: EmptySchema responses: 200: description: Successful request content: application/json: schema: EmptySchema 510: description: Story too short content: application/json: schema: StoryTooShortErrorSchema example: detail: msg: Could not delete the last action of the story because the number of actions in the story is less than or equal to 1. type: story_too_short {api_validation_error_response} {api_server_busy_response} """ if vars.aibusy or vars.genseqs: abort(Response(json.dumps({"detail": { "msg": "Server is busy; please try again later.", "type": "service_unavailable", }}), mimetype="application/json", status=503)) if not vars.gamestarted or not len(vars.actions): abort(Response(json.dumps({"detail": { "msg": "Could not delete the last action of the story because the number of actions in the story is less than or equal to 1.", "type": "story_too_short", }}), mimetype="application/json", status=510)) actionback() return {} @api_v1.get("/story") @api_schema_wrap def get_story(): """--- get: summary: Retrieve the entire story tags: - story description: |-2 Returns the entire story currently shown in the KoboldAI GUI. responses: 200: description: Successful request content: application/json: schema: StorySchema """ chunks = [] if vars.gamestarted: chunks.append({"num": 0, "text": vars.prompt}) for num, action in vars.actions.items(): chunks.append({"num": num + 1, "text": action}) return {"results": chunks} @api_v1.get("/story/nums") @api_schema_wrap def get_story_nums(): """--- get: summary: Retrieve a list of the nums of the chunks in the current story tags: - story description: |-2 Returns the `num`s of the story chunks currently shown in the KoboldAI GUI. responses: 200: description: Successful request content: application/json: schema: StorySchema """ chunks = [] if vars.gamestarted: chunks.append(0) for num in vars.actions.keys(): chunks.append(num + 1) return {"results": chunks} @api_v1.get("/story/nums/") @api_schema_wrap def get_story_nums_num(num: int): """--- get: summary: Determine whether or not there is a story chunk with the given num tags: - story parameters: - name: num in: path description: |-2 `num` of the desired story chunk. schema: type: integer responses: 200: description: Successful request content: application/json: schema: BasicBooleanSchema """ if num == 0: return {"result": vars.gamestarted} return {"result": num - 1 in vars.actions} @api_v1.get("/story/") @api_schema_wrap def get_story_num(num: int): """--- get: summary: Retrieve a story chunk tags: - story description: |-2 Returns information about a story chunk given its `num`. parameters: - name: num in: path description: |-2 `num` of the desired story chunk. schema: type: integer responses: 200: description: Successful request content: application/json: schema: StoryChunkResultSchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No chunk with the given num exists. type: key_error """ if num == 0: if not vars.gamestarted: abort(Response(json.dumps({"detail": { "msg": "No chunk with the given num exists.", "type": "key_error", }}), mimetype="application/json", status=404)) return {"result": {"text": vars.prompt, "num": num}} if num - 1 not in vars.actions: abort(Response(json.dumps({"detail": { "msg": "No chunk with the given num exists.", "type": "key_error", }}), mimetype="application/json", status=404)) return {"result": {"text": vars.actions[num - 1], "num": num}} @api_v1.get("/story//text") @api_schema_wrap def get_story_num_text(num: int): """--- get: summary: Retrieve the text of a story chunk tags: - story description: |-2 Returns the text inside a story chunk given its `num`. parameters: - name: num in: path description: |-2 `num` of the desired story chunk. schema: type: integer responses: 200: description: Successful request content: application/json: schema: StoryChunkTextSchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No chunk with the given num exists. type: key_error """ if num == 0: if not vars.gamestarted: abort(Response(json.dumps({"detail": { "msg": "No chunk with the given num exists.", "type": "key_error", }}), mimetype="application/json", status=404)) return {"value": vars.prompt} if num - 1 not in vars.actions: abort(Response(json.dumps({"detail": { "msg": "No chunk with the given num exists.", "type": "key_error", }}), mimetype="application/json", status=404)) return {"value": vars.actions[num - 1]} @api_v1.put("/story//text") @api_schema_wrap def put_story_num_text(body: StoryChunkSetTextSchema, num: int): """--- put: summary: Set the text of a story chunk tags: - story description: |-2 Sets the text inside a story chunk given its `num`. parameters: - name: num in: path description: |-2 `num` of the desired story chunk. schema: type: integer requestBody: required: true content: application/json: schema: StoryChunkSetTextSchema example: value: string responses: 200: description: Successful request content: application/json: schema: EmptySchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No chunk with the given num exists. type: key_error {api_validation_error_response} """ if num == 0: if not vars.gamestarted: abort(Response(json.dumps({"detail": { "msg": "No chunk with the given num exists.", "type": "key_error", }}), mimetype="application/json", status=404)) inlineedit(0, body.value.rstrip()) return {} if num - 1 not in vars.actions: abort(Response(json.dumps({"detail": { "msg": "No chunk with the given num exists.", "type": "key_error", }}), mimetype="application/json", status=404)) inlineedit(num, body.value.rstrip()) return {} @api_v1.delete("/story/") @api_schema_wrap def post_story_num_delete(num: int): """--- delete: summary: Remove a story chunk tags: - story description: |-2 Removes a story chunk from the story in the KoboldAI GUI given its `num`. Cannot be used to delete the first action (the prompt). parameters: - name: num in: path description: |-2 `num` of the desired story chunk. Must be larger than or equal to 1. schema: type: integer minimum: 1 responses: 200: description: Successful request content: application/json: schema: EmptySchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No chunk with the given num exists. type: key_error {api_server_busy_response} """ if num < 1: abort(Response(json.dumps({"detail": { "num": ["Must be greater than or equal to 1."], }}), mimetype="application/json", status=422)) if num - 1 not in vars.actions: abort(Response(json.dumps({"detail": { "msg": "No chunk with the given num exists.", "type": "key_error", }}), mimetype="application/json", status=404)) if vars.aibusy or vars.genseqs: abort(Response(json.dumps({"detail": { "msg": "Server is busy; please try again later.", "type": "service_unavailable", }}), mimetype="application/json", status=503)) inlinedelete(num) return {} @api_v1.delete("/story") @api_schema_wrap def delete_story(): """--- delete: summary: Clear the story tags: - story description: |-2 Starts a new blank story. responses: 200: description: Successful request content: application/json: schema: EmptySchema {api_server_busy_response} """ if vars.aibusy or vars.genseqs: abort(Response(json.dumps({"detail": { "msg": "Server is busy; please try again later.", "type": "service_unavailable", }}), mimetype="application/json", status=503)) newGameRequest() return {} @api_v1.put("/story/load") @api_schema_wrap def put_story_load(body: StoryLoadSchema): """--- put: summary: Load a story tags: - story description: |-2 Loads a story given its filename (without the .json). requestBody: required: true content: application/json: schema: StoryLoadSchema example: name: string responses: 200: description: Successful request content: application/json: schema: EmptySchema {api_validation_error_response} {api_server_busy_response} """ if vars.aibusy or vars.genseqs: abort(Response(json.dumps({"detail": { "msg": "Server is busy; please try again later.", "type": "service_unavailable", }}), mimetype="application/json", status=503)) loadRequest(fileops.storypath(body.name.strip())) return {} @api_v1.put("/story/save") @api_schema_wrap def put_story_save(body: StorySaveSchema): """--- put: summary: Save the current story tags: - story description: |-2 Saves the current story given its destination filename (without the .json). requestBody: required: true content: application/json: schema: StorySaveSchema example: name: string responses: 200: description: Successful request content: application/json: schema: EmptySchema {api_validation_error_response} """ saveRequest(fileops.storypath(body.name.strip())) return {} @api_v1.get("/world_info") @api_schema_wrap def get_world_info(): """--- get: summary: Retrieve all world info entries tags: - world_info description: |-2 Returns all world info entries currently shown in the KoboldAI GUI. The `folders` are sorted in the same order as they are in the GUI and the `entries` within the folders and within the parent `result` object are all sorted in the same order as they are in their respective parts of the GUI. responses: 200: description: Successful request content: application/json: schema: WorldInfoSchema """ folders = [] entries = [] ln = len(vars.worldinfo) stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] folder: Optional[list] = None if ln: last_folder = ... for wi in vars.worldinfo_i: if wi["folder"] != last_folder: folder = [] if wi["folder"] is not None: folders.append({"uid": wi["folder"], "name": vars.wifolders_d[wi["folder"]]["name"], "entries": folder}) last_folder = wi["folder"] (folder if wi["folder"] is not None else entries).append({k: v for k, v in wi.items() if k not in ("init", "folder", "num") and (wi["selective"] or k != "keysecondary")}) return {"folders": folders, "entries": entries} @api_v1.get("/world_info/uids") @api_schema_wrap def get_world_info_uids(): """--- get: summary: Retrieve the UIDs of all world info entries tags: - world_info description: |-2 Returns in a similar format as GET /world_info except only the `uid`s are returned. responses: 200: description: Successful request content: application/json: schema: WorldInfoUIDsSchema """ folders = [] entries = [] ln = len(vars.worldinfo) stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] folder: Optional[list] = None if ln: last_folder = ... for wi in vars.worldinfo_i: if wi["folder"] != last_folder: folder = [] if wi["folder"] is not None: folders.append({"uid": wi["folder"], "entries": folder}) last_folder = wi["folder"] (folder if wi["folder"] is not None else entries).append(wi["uid"]) return {"folders": folders, "entries": entries} @api_v1.get("/world_info/uids/") @api_schema_wrap def get_world_info_uids_uid(uid: int): """--- get: summary: Determine whether or not there is a world info entry with the given UID tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: BasicBooleanSchema """ return {"result": uid in vars.worldinfo_u and vars.worldinfo_u[uid]["init"]} @api_v1.get("/world_info/folders") @api_schema_wrap def get_world_info_folders(): """--- get: summary: Retrieve all world info folders tags: - world_info description: |-2 Returns details about all world info folders currently shown in the KoboldAI GUI. The `folders` are sorted in the same order as they are in the GUI. responses: 200: description: Successful request content: application/json: schema: WorldInfoFoldersSchema """ stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] return {"folders": [{"uid": folder, **{k: v for k, v in vars.wifolders_d[folder].items() if k != "collapsed"}} for folder in vars.wifolders_l]} @api_v1.get("/world_info/folders/uids") @api_schema_wrap def get_world_info_folders_uids(): """--- get: summary: Retrieve the UIDs all world info folders tags: - world_info description: |-2 Returns the `uid`s of all world info folders currently shown in the KoboldAI GUI. The `folders` are sorted in the same order as they are in the GUI. responses: 200: description: Successful request content: application/json: schema: WorldInfoFoldersUIDsSchema """ stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] return {"folders": vars.wifolders_l} @api_v1.get("/world_info/folders/none") @api_schema_wrap def get_world_info_folders_none(): """--- get: summary: Retrieve all world info entries not in a folder tags: - world_info description: |-2 Returns all world info entries that are not in a world info folder. The `entries` are sorted in the same order as they are in the KoboldAI GUI. responses: 200: description: Successful request content: application/json: schema: WorldInfoEntriesSchema """ entries = [] stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] for wi in reversed(vars.worldinfo_i): if wi["folder"] is not None: break entries.append({k: v for k, v in wi.items() if k not in ("init", "folder", "num") and (wi["selective"] or k != "keysecondary")}) return {"entries": list(reversed(entries))} @api_v1.get("/world_info/folders/none/uids") @api_schema_wrap def get_world_info_folders_none_uids(): """--- get: summary: Retrieve the UIDs of all world info entries not in a folder tags: - world_info description: |-2 Returns the `uid`s of all world info entries that are not in a world info folder. The `entries` are sorted in the same order as they are in the KoboldAI GUI. responses: 200: description: Successful request content: application/json: schema: WorldInfoEntriesUIDsSchema """ entries = [] stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] for wi in reversed(vars.worldinfo_i): if wi["folder"] is not None: break entries.append(wi["uid"]) return {"entries": list(reversed(entries))} @api_v1.get("/world_info/folders/none/uids/") @api_schema_wrap def get_world_info_folders_none_uids_uid(uid: int): """--- get: summary: Determine whether or not there is a world info entry with the given UID that is not in a world info folder tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: BasicBooleanSchema """ return {"result": uid in vars.worldinfo_u and vars.worldinfo_u[uid]["folder"] is None and vars.worldinfo_u[uid]["init"]} @api_v1.get("/world_info/folders/") @api_schema_wrap def get_world_info_folders_uid(uid: int): """--- get: summary: Retrieve all world info entries in the given folder tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info folder. schema: type: integer minimum: -2147483648 maximum: 2147483647 description: |-2 Returns all world info entries that are in the world info folder with the given `uid`. The `entries` are sorted in the same order as they are in the KoboldAI GUI. responses: 200: description: Successful request content: application/json: schema: WorldInfoEntriesSchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info folder with the given uid exists. type: key_error """ if uid not in vars.wifolders_d: abort(Response(json.dumps({"detail": { "msg": "No world info folder with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) entries = [] stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] for wi in vars.wifolders_u[uid]: if wi["init"]: entries.append({k: v for k, v in wi.items() if k not in ("init", "folder", "num") and (wi["selective"] or k != "keysecondary")}) return {"entries": entries} @api_v1.get("/world_info/folders//uids") @api_schema_wrap def get_world_info_folders_uid_uids(uid: int): """--- get: summary: Retrieve the UIDs of all world info entries in the given folder tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info folder. schema: type: integer minimum: -2147483648 maximum: 2147483647 description: |-2 Returns the `uid`s of all world info entries that are in the world info folder with the given `uid`. The `entries` are sorted in the same order as they are in the KoboldAI GUI. responses: 200: description: Successful request content: application/json: schema: WorldInfoEntriesUIDsSchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info folder with the given uid exists. type: key_error """ if uid not in vars.wifolders_d: abort(Response(json.dumps({"detail": { "msg": "No world info folder with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) entries = [] stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] for wi in vars.wifolders_u[uid]: if wi["init"]: entries.append(wi["uid"]) return {"entries": entries} @api_v1.get("/world_info/folders//uids/") @api_schema_wrap def get_world_info_folders_folder_uid_uids_entry_uid(folder_uid: int, entry_uid: int): """--- get: summary: Determine whether or not there is a world info entry with the given UID in the world info folder with the given UID tags: - world_info parameters: - name: folder_uid in: path description: |-2 `uid` of the desired world info folder. schema: type: integer minimum: -2147483648 maximum: 2147483647 - name: entry_uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: BasicBooleanSchema """ return {"result": entry_uid in vars.worldinfo_u and vars.worldinfo_u[entry_uid]["folder"] == folder_uid and vars.worldinfo_u[entry_uid]["init"]} @api_v1.get("/world_info/folders//name") @api_schema_wrap def get_world_info_folders_uid_name(uid: int): """--- get: summary: Retrieve the name of the world info folder with the given UID tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info folder. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: BasicStringSchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info folder with the given uid exists. type: key_error """ if uid not in vars.wifolders_d: abort(Response(json.dumps({"detail": { "msg": "No world info folder with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) return {"value": vars.wifolders_d[uid]["name"]} @api_v1.put("/world_info/folders//name") @api_schema_wrap def put_world_info_folders_uid_name(body: BasicStringSchema, uid: int): """--- put: summary: Set the name of the world info folder with the given UID to the specified value tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info folder. schema: type: integer minimum: -2147483648 maximum: 2147483647 requestBody: required: true content: application/json: schema: BasicStringSchema example: value: string responses: 200: description: Successful request content: application/json: schema: EmptySchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info folder with the given uid exists. type: key_error {api_validation_error_response} """ if uid not in vars.wifolders_d: abort(Response(json.dumps({"detail": { "msg": "No world info folder with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) vars.wifolders_d[uid]["name"] = body.value setgamesaved(False) return {} @api_v1.get("/world_info/") @api_schema_wrap def get_world_info_uid(uid: int): """--- get: summary: Retrieve information about the world info entry with the given UID tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: WorldInfoEntrySchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) wi = vars.worldinfo_u[uid] return {k: v for k, v in wi.items() if k not in ("init", "folder", "num") and (wi["selective"] or k != "keysecondary")} @api_v1.get("/world_info//comment") @api_schema_wrap def get_world_info_uid_comment(uid: int): """--- get: summary: Retrieve the comment of the world info entry with the given UID tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: BasicStringSchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) return {"value": vars.worldinfo_u[uid]["comment"]} @api_v1.put("/world_info//comment") @api_schema_wrap def put_world_info_uid_comment(body: BasicStringSchema, uid: int): """--- put: summary: Set the comment of the world info entry with the given UID to the specified value tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 requestBody: required: true content: application/json: schema: BasicStringSchema example: value: string responses: 200: description: Successful request content: application/json: schema: EmptySchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error {api_validation_error_response} """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) vars.worldinfo_u[uid]["comment"] = body.value setgamesaved(False) return {} @api_v1.get("/world_info//content") @api_schema_wrap def get_world_info_uid_content(uid: int): """--- get: summary: Retrieve the content of the world info entry with the given UID tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: BasicStringSchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) return {"value": vars.worldinfo_u[uid]["content"]} @api_v1.put("/world_info//content") @api_schema_wrap def put_world_info_uid_content(body: BasicStringSchema, uid: int): """--- put: summary: Set the content of the world info entry with the given UID to the specified value tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 requestBody: required: true content: application/json: schema: BasicStringSchema example: value: string responses: 200: description: Successful request content: application/json: schema: EmptySchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error {api_validation_error_response} """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) vars.worldinfo_u[uid]["content"] = body.value setgamesaved(False) return {} @api_v1.get("/world_info//key") @api_schema_wrap def get_world_info_uid_key(uid: int): """--- get: summary: Retrieve the keys or primary keys of the world info entry with the given UID tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: BasicStringSchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) return {"value": vars.worldinfo_u[uid]["key"]} @api_v1.put("/world_info//key") @api_schema_wrap def put_world_info_uid_key(body: BasicStringSchema, uid: int): """--- put: summary: Set the keys or primary keys of the world info entry with the given UID to the specified value tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 requestBody: required: true content: application/json: schema: BasicStringSchema example: value: string responses: 200: description: Successful request content: application/json: schema: EmptySchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error {api_validation_error_response} """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) vars.worldinfo_u[uid]["key"] = body.value setgamesaved(False) return {} @api_v1.get("/world_info//keysecondary") @api_schema_wrap def get_world_info_uid_keysecondary(uid: int): """--- get: summary: Retrieve the secondary keys of the world info entry with the given UID tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: BasicStringSchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) return {"value": vars.worldinfo_u[uid]["keysecondary"]} @api_v1.put("/world_info//keysecondary") @api_schema_wrap def put_world_info_uid_keysecondary(body: BasicStringSchema, uid: int): """--- put: summary: Set the secondary keys of the world info entry with the given UID to the specified value tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 requestBody: required: true content: application/json: schema: BasicStringSchema example: value: string responses: 200: description: Successful request content: application/json: schema: EmptySchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error {api_validation_error_response} """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) vars.worldinfo_u[uid]["keysecondary"] = body.value setgamesaved(False) return {} @api_v1.get("/world_info//selective") @api_schema_wrap def get_world_info_uid_selective(uid: int): """--- get: summary: Retrieve the selective mode state of the world info entry with the given UID tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: BasicBooleanSchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) return {"value": vars.worldinfo_u[uid]["selective"]} @api_v1.put("/world_info//selective") @api_schema_wrap def put_world_info_uid_selective(body: BasicBooleanSchema, uid: int): """--- put: summary: Set the selective mode state of the world info entry with the given UID to the specified value tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 requestBody: required: true content: application/json: schema: BasicBooleanSchema example: value: true responses: 200: description: Successful request content: application/json: schema: EmptySchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error {api_validation_error_response} """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) vars.worldinfo_u[uid]["selective"] = body.value setgamesaved(False) return {} @api_v1.get("/world_info//constant") @api_schema_wrap def get_world_info_uid_constant(uid: int): """--- get: summary: Retrieve the constant mode state of the world info entry with the given UID tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: BasicBooleanSchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) return {"value": vars.worldinfo_u[uid]["constant"]} @api_v1.put("/world_info//constant") @api_schema_wrap def put_world_info_uid_constant(body: BasicBooleanSchema, uid: int): """--- put: summary: Set the constant mode state of the world info entry with the given UID to the specified value tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 requestBody: required: true content: application/json: schema: BasicBooleanSchema example: value: true responses: 200: description: Successful request content: application/json: schema: EmptySchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error {api_validation_error_response} """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) vars.worldinfo_u[uid]["constant"] = body.value setgamesaved(False) return {} @api_v1.post("/world_info/folders/none") @api_schema_wrap def post_world_info_folders_none(body: EmptySchema): """--- post: summary: Create a new world info entry outside of a world info folder, at the end of the world info tags: - world_info requestBody: required: true content: application/json: schema: EmptySchema responses: 200: description: Successful request content: application/json: schema: BasicUIDSchema {api_validation_error_response} """ stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] setgamesaved(False) emit('from_server', {'cmd': 'wiexpand', 'data': vars.worldinfo[-1]["num"]}, broadcast=True) vars.worldinfo[-1]["init"] = True addwiitem(folder_uid=None) return {"uid": vars.worldinfo[-2]["uid"]} @api_v1.post("/world_info/folders/") @api_schema_wrap def post_world_info_folders_uid(body: EmptySchema, uid: int): """--- post: summary: Create a new world info entry at the end of the world info folder with the given UID tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info folder. schema: type: integer minimum: -2147483648 maximum: 2147483647 requestBody: required: true content: application/json: schema: EmptySchema responses: 200: description: Successful request content: application/json: schema: BasicUIDSchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info folder with the given uid exists. type: key_error {api_validation_error_response} """ if uid not in vars.wifolders_d: abort(Response(json.dumps({"detail": { "msg": "No world info folder with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) stablesortwi() vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] setgamesaved(False) emit('from_server', {'cmd': 'wiexpand', 'data': vars.wifolders_u[uid][-1]["num"]}, broadcast=True) vars.wifolders_u[uid][-1]["init"] = True addwiitem(folder_uid=uid) return {"uid": vars.wifolders_u[uid][-2]["uid"]} @api_v1.delete("/world_info/") @api_schema_wrap def delete_world_info_uid(uid: int): """--- delete: summary: Delete the world info entry with the given UID tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info entry. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: EmptySchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info entry with the given uid exists. type: key_error """ if uid not in vars.worldinfo_u: abort(Response(json.dumps({"detail": { "msg": "No world info entry with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) deletewi(uid) return {} @api_v1.post("/world_info/folders") @api_schema_wrap def post_world_info_folders(body: EmptySchema): """--- post: summary: Create a new world info folder at the end of the world info tags: - world_info requestBody: required: true content: application/json: schema: EmptySchema responses: 200: description: Successful request content: application/json: schema: BasicUIDSchema {api_validation_error_response} """ addwifolder() return {"uid": vars.wifolders_l[-1]} @api_v1.delete("/world_info/folders/") @api_schema_wrap def delete_world_info_folders_uid(uid: int): """--- delete: summary: Delete the world info folder with the given UID tags: - world_info parameters: - name: uid in: path description: |-2 `uid` of the desired world info folder. schema: type: integer minimum: -2147483648 maximum: 2147483647 responses: 200: description: Successful request content: application/json: schema: EmptySchema 404: description: Not found content: application/json: schema: NotFoundErrorSchema example: detail: msg: No world info folders with the given uid exists. type: key_error """ if uid not in vars.wifolders_d: abort(Response(json.dumps({"detail": { "msg": "No world info folder with the given uid exists.", "type": "key_error", }}), mimetype="application/json", status=404)) deletewifolder(uid) return {} def _make_f_get(obj, _var_name, _name, _schema, _example_yaml_value): def f_get(): """--- get: summary: Retrieve the current {} setting value tags: - config responses: 200: description: Successful request content: application/json: schema: {} example: value: {} """ _obj = {"vars": vars, "vars.formatoptns": vars.formatoptns}[obj] if _var_name.startswith("@"): return {"value": _obj[_var_name[1:]]} else: return {"value": getattr(_obj, _var_name)} f_get.__doc__ = f_get.__doc__.format(_name, _schema, _example_yaml_value) return f_get def _make_f_put(schema_class: Type[KoboldSchema], obj, _var_name, _name, _schema, _example_yaml_value): def f_put(body: schema_class): """--- put: summary: Set {} setting to specified value tags: - config requestBody: required: true content: application/json: schema: {} example: value: {} responses: 200: description: Successful request content: application/json: schema: EmptySchema {api_validation_error_response} """ _obj = {"vars": vars, "vars.formatoptns": vars.formatoptns}[obj] if _var_name.startswith("@"): _obj[_var_name[1:]] = body.value else: setattr(_obj, _var_name, body.value) settingschanged() refresh_settings() return {} f_put.__doc__ = f_put.__doc__.format(_name, _schema, _example_yaml_value, api_validation_error_response=api_validation_error_response) return f_put def create_config_endpoint(method="GET", schema="MemorySchema"): _name = globals()[schema].KoboldMeta.name _var_name = globals()[schema].KoboldMeta.var_name _route_name = globals()[schema].KoboldMeta.route_name _obj = globals()[schema].KoboldMeta.obj _example_yaml_value = globals()[schema].KoboldMeta.example_yaml_value _schema = schema f = _make_f_get(_obj, _var_name, _name, _schema, _example_yaml_value) if method == "GET" else _make_f_put(globals()[schema], _obj, _var_name, _name, _schema, _example_yaml_value) f.__name__ = f"{method.lower()}_config_{_name}" f = api_schema_wrap(f) for api in (api_v1,): f = api.route(f"/config/{_route_name}", methods=[method])(f) class SoftPromptSettingSchema(KoboldSchema): value: str = fields.String(required=True, validate=[soft_prompt_validator, validate.Regexp(r"^[^/\\]*$")], metadata={"description": "Soft prompt name, or a string containing only whitespace for no soft prompt. If using the GET method and no soft prompt is loaded, this will always be the empty string."}) @api_v1.get("/config/soft_prompt") @api_schema_wrap def get_config_soft_prompt(): """--- get: summary: Retrieve the current soft prompt name tags: - config responses: 200: description: Successful request content: application/json: schema: SoftPromptSettingSchema example: value: "" """ return {"value": vars.spfilename.strip()} class SoftPromptsListSchema(KoboldSchema): values: List[SoftPromptSettingSchema] = fields.List(fields.Nested(SoftPromptSettingSchema), required=True, metadata={"description": "Array of available softprompts."}) @api_v1.get("/config/soft_prompts_list") @api_schema_wrap def get_config_soft_prompts_list(): """--- get: summary: Retrieve all available softprompt filenames tags: - config responses: 200: description: Successful request content: application/json: schema: SoftPromptsListSchema example: values: [] """ splist = [] for sp in fileops.getspfiles(vars.modeldim): splist.append({"value":sp["filename"]}) return {"values": splist} @api_v1.put("/config/soft_prompt") @api_schema_wrap def put_config_soft_prompt(body: SoftPromptSettingSchema): """--- put: summary: Set soft prompt by name tags: - config requestBody: required: true content: application/json: schema: SoftPromptSettingSchema example: value: "" responses: 200: description: Successful request content: application/json: schema: EmptySchema {api_validation_error_response} """ if vars.allowsp: spRequest(body.value) settingschanged() return {} class SamplerSeedSettingSchema(KoboldSchema): value: int = fields.Integer(validate=validate.Range(min=0, max=2**64 - 1), required=True) @api_v1.get("/config/sampler_seed") @api_schema_wrap def get_config_sampler_seed(): """--- get: summary: Retrieve the current global sampler seed value tags: - config responses: 200: description: Successful request content: application/json: schema: SamplerSeedSettingSchema example: value: 3475097509890965500 """ return {"value": __import__("tpu_mtj_backend").get_rng_seed() if vars.use_colab_tpu else __import__("torch").initial_seed()} @api_v1.put("/config/sampler_seed") @api_schema_wrap def put_config_sampler_seed(body: SamplerSeedSettingSchema): """--- put: summary: Set the global sampler seed value tags: - config requestBody: required: true content: application/json: schema: SamplerSeedSettingSchema example: value: 3475097509890965500 responses: 200: description: Successful request content: application/json: schema: EmptySchema {api_validation_error_response} """ if vars.use_colab_tpu: import tpu_mtj_backend tpu_mtj_backend.set_rng_seed(body.value) else: import torch torch.manual_seed(body.value) vars.seed = body.value return {} config_endpoint_schemas: List[Type[KoboldSchema]] = [] def config_endpoint_schema(c: Type[KoboldSchema]): config_endpoint_schemas.append(c) return c @config_endpoint_schema class MemorySettingSchema(KoboldSchema): value = fields.String(required=True) class KoboldMeta: route_name = "memory" obj = "vars" var_name = "memory" name = "memory" example_yaml_value = "Memory" @config_endpoint_schema class AuthorsNoteSettingSchema(KoboldSchema): value = fields.String(required=True) class KoboldMeta: route_name = "authors_note" obj = "vars" var_name = "authornote" name = "author's note" example_yaml_value = "''" @config_endpoint_schema class AuthorsNoteTemplateSettingSchema(KoboldSchema): value = fields.String(required=True) class KoboldMeta: route_name = "authors_note_template" obj = "vars" var_name = "authornotetemplate" name = "author's note template" example_yaml_value = "\"[Author's note: <|>]\"" @config_endpoint_schema class TopKSamplingSettingSchema(KoboldSchema): value = fields.Integer(validate=validate.Range(min=0), required=True) class KoboldMeta: route_name = "top_k" obj = "vars" var_name = "top_k" name = "top-k sampling" example_yaml_value = "0" @config_endpoint_schema class TopASamplingSettingSchema(KoboldSchema): value = fields.Float(validate=validate.Range(min=0), required=True) class KoboldMeta: route_name = "top_a" obj = "vars" var_name = "top_a" name = "top-a sampling" example_yaml_value = "0.0" @config_endpoint_schema class TopPSamplingSettingSchema(KoboldSchema): value = fields.Float(validate=validate.Range(min=0, max=1), required=True) class KoboldMeta: route_name = "top_p" obj = "vars" var_name = "top_p" name = "top-p sampling" example_yaml_value = "0.9" @config_endpoint_schema class TailFreeSamplingSettingSchema(KoboldSchema): value = fields.Float(validate=validate.Range(min=0, max=1), required=True) class KoboldMeta: route_name = "tfs" obj = "vars" var_name = "tfs" name = "tail free sampling" example_yaml_value = "1.0" @config_endpoint_schema class TypicalSamplingSettingSchema(KoboldSchema): value = fields.Float(validate=validate.Range(min=0, max=1), required=True) class KoboldMeta: route_name = "typical" obj = "vars" var_name = "typical" name = "typical sampling" example_yaml_value = "1.0" @config_endpoint_schema class TemperatureSamplingSettingSchema(KoboldSchema): value = fields.Float(validate=validate.Range(min=0, min_inclusive=False), required=True) class KoboldMeta: route_name = "temperature" obj = "vars" var_name = "temp" name = "temperature" example_yaml_value = "0.5" @config_endpoint_schema class GensPerActionSettingSchema(KoboldSchema): value = fields.Integer(validate=validate.Range(min=0, max=5), required=True) class KoboldMeta: route_name = "n" obj = "vars" var_name = "numseqs" name = "Gens Per Action" example_yaml_value = "1" @config_endpoint_schema class MaxLengthSettingSchema(KoboldSchema): value = fields.Integer(validate=validate.Range(min=1, max=512), required=True) class KoboldMeta: route_name = "max_length" obj = "vars" var_name = "genamt" name = "max length" example_yaml_value = "80" @config_endpoint_schema class WorldInfoDepthSettingSchema(KoboldSchema): value = fields.Integer(validate=validate.Range(min=1, max=5), required=True) class KoboldMeta: route_name = "world_info_depth" obj = "vars" var_name = "widepth" name = "world info depth" example_yaml_value = "3" @config_endpoint_schema class AuthorsNoteDepthSettingSchema(KoboldSchema): value = fields.Integer(validate=validate.Range(min=1, max=5), required=True) class KoboldMeta: route_name = "authors_note_depth" obj = "vars" var_name = "andepth" name = "author's note depth" example_yaml_value = "3" @config_endpoint_schema class MaxContextLengthSettingSchema(KoboldSchema): value = fields.Integer(validate=validate.Range(min=512, max=2048), required=True) class KoboldMeta: route_name = "max_context_length" obj = "vars" var_name = "max_length" name = "max context length" example_yaml_value = "2048" @config_endpoint_schema class TrimIncompleteSentencesSettingsSchema(KoboldSchema): value = fields.Boolean(required=True) class KoboldMeta: route_name = "frmttriminc" obj = "vars.formatoptns" var_name = "@frmttriminc" name = "trim incomplete sentences (output formatting)" example_yaml_value = "false" @config_endpoint_schema class RemoveBlankLinesSettingsSchema(KoboldSchema): value = fields.Boolean(required=True) class KoboldMeta: route_name = "frmtrmblln" obj = "vars.formatoptns" var_name = "@frmtrmblln" name = "remove blank lines (output formatting)" example_yaml_value = "false" @config_endpoint_schema class RemoveSpecialCharactersSettingsSchema(KoboldSchema): value = fields.Boolean(required=True) class KoboldMeta: route_name = "frmtrmspch" obj = "vars.formatoptns" var_name = "@frmtrmspch" name = "remove special characters (output formatting)" example_yaml_value = "false" @config_endpoint_schema class SingleLineSettingsSchema(KoboldSchema): value = fields.Boolean(required=True) class KoboldMeta: route_name = "singleline" obj = "vars.formatoptns" var_name = "@singleline" name = "single line (output formatting)" example_yaml_value = "false" @config_endpoint_schema class AddSentenceSpacingSettingsSchema(KoboldSchema): value = fields.Boolean(required=True) class KoboldMeta: route_name = "frmtadsnsp" obj = "vars.formatoptns" var_name = "@frmtadsnsp" name = "add sentence spacing (input formatting)" example_yaml_value = "false" @config_endpoint_schema class SamplerOrderSettingSchema(KoboldSchema): value = fields.List(fields.Integer(), validate=[validate.Length(min=6), permutation_validator], required=True) class KoboldMeta: route_name = "sampler_order" obj = "vars" var_name = "sampler_order" name = "sampler order" example_yaml_value = "[6, 0, 1, 2, 3, 4, 5]" @config_endpoint_schema class SamplerFullDeterminismSettingSchema(KoboldSchema): value = fields.Boolean(required=True) class KoboldMeta: route_name = "sampler_full_determinism" obj = "vars" var_name = "full_determinism" name = "sampler full determinism" example_yaml_value = "false" for schema in config_endpoint_schemas: create_config_endpoint(schema=schema.__name__, method="GET") create_config_endpoint(schema=schema.__name__, method="PUT") #==================================================================# # Final startup commands to launch Flask app #==================================================================# if __name__ == "__main__": general_startup() # Start flask & SocketIO logger.init("Flask", status="Starting") Session(app) logger.init_ok("Flask", status="OK") logger.init("Webserver", status="Starting") patch_transformers() #show_select_model_list() if vars.model == "" or vars.model is None: vars.model = "ReadOnly" load_model(initial_load=True) # Start Flask/SocketIO (Blocking, so this must be last method!) port = args.port if "port" in args and args.port is not None else 5000 #socketio.run(app, host='0.0.0.0', port=port) if(vars.host): if(args.localtunnel): import subprocess, shutil localtunnel = subprocess.Popen([shutil.which('lt'), '-p', str(port), 'http'], stdout=subprocess.PIPE) attempts = 0 while attempts < 10: try: cloudflare = str(localtunnel.stdout.readline()) cloudflare = (re.search("(?Phttps?:\/\/[^\s]+loca.lt)", cloudflare).group("url")) break except: attempts += 1 time.sleep(3) continue if attempts == 10: print("LocalTunnel could not be created, falling back to cloudflare...") from flask_cloudflared import _run_cloudflared cloudflare = _run_cloudflared(port) elif(args.ngrok): from flask_ngrok import _run_ngrok cloudflare = _run_ngrok() elif(args.remote): from flask_cloudflared import _run_cloudflared cloudflare = _run_cloudflared(port) if(args.localtunnel or args.ngrok or args.remote): with open('cloudflare.log', 'w') as cloudflarelog: cloudflarelog.write("KoboldAI has finished loading and is available at the following link : " + cloudflare) logger.init_ok("Webserver", status="OK") logger.message(f"KoboldAI has finished loading and is available at the following link: {cloudflare}") else: logger.init_ok("Webserver", status="OK") logger.message(f"Webserver has started, you can now connect to this machine at port: {port}") vars.serverstarted = True socketio.run(app, host='0.0.0.0', port=port) else: if args.unblock: if not args.no_ui: try: import webbrowser webbrowser.open_new('http://localhost:{0}'.format(port)) except: pass logger.init_ok("Webserver", status="OK") logger.message(f"Webserver started! You may now connect with a browser at http://127.0.0.1:{port}") vars.serverstarted = True socketio.run(app, port=port, host='0.0.0.0') else: if not args.no_ui: try: import webbrowser webbrowser.open_new('http://localhost:{0}'.format(port)) except: pass logger.init_ok("Webserver", status="OK") logger.message(f"Webserver started! You may now connect with a browser at http://127.0.0.1:{port}") vars.serverstarted = True socketio.run(app, port=port) logger.init("Webserver", status="Closed") else: general_startup() # Start flask & SocketIO logger.init("Flask", status="Starting") Session(app) logger.init_ok("Flask", status="OK") patch_transformers() #show_select_model_list() if vars.model == "" or vars.model is None: vars.model = "ReadOnly" load_model(initial_load=True) print("{0}\nServer started in WSGI mode!{1}".format(colors.GREEN, colors.END), flush=True)