#!/usr/bin/python3
#==================================================================#
# KoboldAI
# Version: 1.18.1
# By: KoboldAIDev and 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
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
from collections.abc import Iterable
from typing import Any, Callable, TypeVar, Tuple, Union, Dict, Set, List
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, GPT2TokenizerFast, 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):
print(f"Please install lupa==1.10. You have lupa {lupa.__version__}.", file=sys.stderr)
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 Fairseq Dense", "fsdlist", "", True],
["Untuned XGLM", "xglmlist", "", True],
["Untuned GPT2", "gpt2list", "", True],
["Online Services", "apilist", "", True],
["Read Only (No AI)", "ReadOnly", "", False]
],
'adventurelist': [
["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", "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 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", "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],
["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': [
["Shinen FSD 13B (NSFW)", "KoboldAI/fairseq-dense-13B-Shinen", "32GB", False],
["Shinen FSD 6.7B (NSFW)", "KoboldAI/fairseq-dense-6.7B-Shinen", "16GB", False],
["Lit 6B (NSFW)", "hakurei/lit-6B", "16GB", False],
["Shinen 6B (NSFW)", "KoboldAI/GPT-J-6B-Shinen", "16GB", 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-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],
["GPT-Neo 125M", "EleutherAI/gpt-neo-125M", "2GB", 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],
],
'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],
["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 = "" # Model ID string chosen at startup
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 = False # 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
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()
chatmode = False
chatname = "You"
adventure = False
actionmode = 1
dynamicscan = False
host = False
flaskwebgui = 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
output_streaming = True
token_stream_queue = TokenStreamQueue() # Queue for the token streaming
show_probs = False # Whether or not to show token probabilities
utils.vars = vars
class Send_to_socketio(object):
def write(self, bar):
print(bar, end="")
time.sleep(0.01)
try:
emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True)
except:
pass
# Set logging level to reduce chatter from Flask
import logging
log = logging.getLogger('werkzeug')
log.setLevel(logging.ERROR)
# Start flask & SocketIO
print("{0}Initializing Flask... {1}".format(colors.PURPLE, colors.END), end="")
from flask import Flask, render_template, Response, request, copy_current_request_context, send_from_directory, session
from flask_socketio import SocketIO, emit
from flask_session import Session
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
Session(app)
socketio = SocketIO(app, async_method="eventlet")
print("{0}OK!{1}".format(colors.GREEN, colors.END))
#==================================================================#
# 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):
if os.path.exists(path):
try:
from transformers import AutoConfig
model_config = AutoConfig.from_pretrained(path)
except:
return False
return True
else:
return False
#==================================================================#
# 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(args.configname):
modelname = args.configname
return modelname
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
#==================================================================#
# 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
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:
print("WARNING: --breakmodel_gpulayers is malformatted. Please use the --help option to see correct usage of --breakmodel_gpulayers. Defaulting to all layers on device 0.", file=sys.stderr)
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):
print("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}")
print(colors.PURPLE + "\nFinal device configuration:")
device_list(n_layers)
# 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):
print("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 </s> newline mode if using XGLM
if vars.model_type == "opt" or vars.model_type == "bloom":
vars.newlinemode = "ns" # Handle </s> 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):
vars.sampler_order = js["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
if(vars.seed_specified):
js["seed"] = vars.seed
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("settings/" + getmodelname().replace('/', '_') + ".settings", "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():
print("{0}Saving settings!{1}".format(colors.GREEN, colors.END))
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("settings/" + getmodelname().replace('/', '_') + ".settings")):
# Read file contents into JSON object
file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "r")
js = json.load(file)
processsettings(js)
file.close()
def processsettings(js):
# Copy file contents to vars
if("apikey" in js):
vars.apikey = js["apikey"]
if("andepth" in js):
vars.andepth = js["andepth"]
if("sampler_order" in js):
vars.sampler_order = js["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("seed" in js):
vars.seed = js["seed"]
vars.seed_specified = True
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("--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 --beakmodel_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")
#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()
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()
vars.model = args.model;
vars.revision = args.revision
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:
print("Welcome to KoboldAI!\nYou have selected the following Model:", vars.model)
if args.path:
print("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
gpu_count = torch.cuda.device_count()
gpu_names = []
for i in range(gpu_count):
gpu_names.append(torch.cuda.get_device_name(i))
if model in [x[1] for x in model_menu['apilist']]:
if path.exists("settings/{}.settings".format(model)):
with open("settings/{}.settings".format(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 model == 'Colab':
url = True
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
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,
'url': url, 'gpu_names': gpu_names}, broadcast=True)
if key_value != "":
get_oai_models(key_value)
def get_layer_count(model, directory=""):
if(model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ"]):
if(vars.model == "GPT2Custom"):
model_config = open(vars.custmodpth + "/config.json", "r")
# Get the model_type from the config or assume a model type if it isn't present
else:
from transformers import AutoConfig
if directory == "":
model_config = AutoConfig.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
elif(os.path.isdir(vars.custmodpth.replace('/', '_'))):
model_config = AutoConfig.from_pretrained(vars.custmodpth.replace('/', '_'), revision=vars.revision, cache_dir="cache")
elif(os.path.isdir(directory)):
model_config = AutoConfig.from_pretrained(directory, revision=vars.revision, cache_dir="cache")
else:
model_config = AutoConfig.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
return utils.num_layers(model_config)
else:
return None
def get_oai_models(key):
vars.oaiapikey = key
if vars.model == 'OAI':
url = "https://api.openai.com/v1/engines"
elif vars.model == 'GooseAI':
url = "https://api.goose.ai/v1/engines"
else:
return
# Get list of models from OAI
print("{0}Retrieving engine list...{1}".format(colors.PURPLE, colors.END), end="")
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:
print(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("settings/{}.settings".format(vars.model)):
with open("settings/{}.settings".format(vars.model), "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
if changed:
with open("settings/{}.settings".format(vars.model), "w") as file:
js["apikey"] = key
file.write(json.dumps(js, indent=3))
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
print("{0}ERROR!{1}".format(colors.RED, colors.END))
print(req.json())
emit('from_server', {'cmd': 'errmsg', 'data': req.json()})
# 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: transformers.utils.hub.BinaryIO,
proxies=None,
resume_size=0,
headers: transformers.utils.hub.Optional[transformers.utils.hub.Dict[str, str]] = None,
file_name: transformers.utils.hub.Optional[str] = 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)
PreTrainedModel.from_pretrained = new_from_pretrained
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)
RepetitionPenaltyLogitsProcessor.__init__ = AdvancedRepetitionPenaltyLogitsProcessor.__init__
RepetitionPenaltyLogitsProcessor.__call__ = AdvancedRepetitionPenaltyLogitsProcessor.__call__
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
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
class ProbabilityVisualizerLogitsProcessor(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
if vars.numseqs > 1 or not vars.show_probs:
return scores
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
return scores
def new_get_logits_processor(*args, **kwargs) -> LogitsProcessorList:
processors = new_get_logits_processor.old_get_logits_processor(*args, **kwargs)
processors.insert(0, LuaLogitsProcessor())
processors.append(ProbabilityVisualizerLogitsProcessor())
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))
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, *args, **kwargs):
for k in vars.sampler_order:
scores = self.__warper_list[k](input_ids, scores, *args, **kwargs)
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
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(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
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 = [0, 1, 2, 3, 4, 5]
vars.newlinemode = "n"
vars.revision = None
def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=False, online_model=""):
global model
global generator
global torch
global model_config
global GPT2TokenizerFast
global tokenizer
reset_model_settings()
if not utils.HAS_ACCELERATE:
disk_layers = None
vars.noai = False
if not initial_load:
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 initial_load:
gpu_layers = args.breakmodel_gpulayers
if disk_layers is not None:
args.breakmodel_disklayers = int(disk_layers)
elif initial_load:
disk_layers = args.breakmodel_disklayers
#We need to wipe out the existing model and refresh the cuda cache
model = None
generator = None
model_config = None
for tensor in gc.get_objects():
try:
if torch.is_tensor(tensor):
with torch.no_grad():
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
#Let's set the GooseAI or OpenAI server URLs if that's applicable
if online_model != "":
if path.exists("settings/{}.settings".format(vars.model)):
changed=False
with open("settings/{}.settings".format(vars.model), "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("settings/{}.settings".format(vars.model), "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
else:
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", "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=vars.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=vars.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=vars.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"):
print("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", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
loadmodelsettings()
loadsettings()
print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="")
vars.hascuda = torch.cuda.is_available()
vars.bmsupported = (utils.HAS_ACCELERATE or vars.model_type in ("gpt_neo", "gptj", "xglm", "opt")) and not vars.nobreakmodel
if(args.breakmodel is not None and args.breakmodel):
print("WARNING: --breakmodel is no longer supported. Breakmodel mode is now automatically enabled when --breakmodel_gpulayers is used (see --help for details).", file=sys.stderr)
if(args.breakmodel_layers is not None):
print("WARNING: --breakmodel_layers is deprecated. Use --breakmodel_gpulayers instead (see --help for details).", file=sys.stderr)
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)):
print("WARNING: Model launched without the --breakmodel_gpulayers argument, defaulting to GPU only mode.", file=sys.stderr)
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)):
print("WARNING: This model does not support hybrid generation. --breakmodel_gpulayers will be ignored.", file=sys.stderr)
if(vars.hascuda):
print("{0}FOUND!{1}".format(colors.GREEN, colors.END))
else:
print("{0}NOT FOUND!{1}".format(colors.YELLOW, colors.END))
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 = True
# 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"
args.configname = "GooseAI"
# Ask for API key if OpenAI was selected
if(vars.model == "OAI"):
if not args.configname:
args.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", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
if(not vars.noai):
print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END))
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)
print(flush=True)
utils.bar = tqdm(total=num_tensors, desc="Loading model tensors", file=Send_to_socketio())
with zipfile.ZipFile(f, "r") as z:
try:
last_storage_key = None
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()
f = z.open(f"archive/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:
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 get_hidden_size_from_model(model):
try:
return int(model.model.decoder.project_in.in_features)
except:
try:
return int(model.model.decoder.embed_tokens.out_features)
except:
try:
return int(model.transformer.hidden_size)
except:
try:
return int(model.transformer.embed_dim)
except:
return int(model.lm_head.in_features)
def maybe_low_cpu_mem_usage() -> Dict[str, Any]:
if(packaging.version.parse(transformers_version) < packaging.version.parse("4.11.0")):
print(f"\nWARNING: Please upgrade to transformers 4.11.0 for lower RAM usage. You have transformers {transformers_version}.", file=sys.stderr)
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 == "GPT2Custom"):
vars.lazy_load = False
model_config = open(vars.custmodpth + "/config.json", "r")
js = json.load(model_config)
with(maybe_use_float16()):
try:
model = GPT2LMHeadModel.from_pretrained(vars.custmodpth, revision=vars.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 = GPT2TokenizerFast.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
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):
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('/', '_')))
print("\n", flush=True)
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=vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained(vars.custmodpth, revision=vars.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=vars.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=vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.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=vars.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=vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained(vars.model, revision=vars.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=vars.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): # Use save_pretrained to convert fp32 models to fp16
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
# Save the config.json
shutil.move(transformers.file_utils.get_from_cache(transformers.file_utils.hf_bucket_url(vars.model, transformers.configuration_utils.CONFIG_NAME, revision=vars.revision), cache_dir="cache", local_files_only=True), 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(transformers.file_utils.get_from_cache(transformers.file_utils.hf_bucket_url(vars.model, transformers.modeling_utils.WEIGHTS_NAME, revision=vars.revision), cache_dir="cache", local_files_only=True), 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(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(transformers.file_utils.get_from_cache(transformers.file_utils.hf_bucket_url(vars.model, filename, revision=vars.revision), cache_dir="cache", local_files_only=True), 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) != "</s>"]
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]])
print("{0}OK! {1} pipeline created!{2}".format(colors.GREEN, vars.model, colors.END))
else:
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.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)
PreTrainedModel.from_pretrained = new_from_pretrained
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:
return {
"sampler_order": vars.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 == "Colab"):
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("EleutherAI/gpt-neo-2.7B", revision=vars.revision, cache_dir="cache")
loadsettings()
elif(vars.model == "OAI"):
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.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) != "</s>"]
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 'new_ui' in request.args:
return render_template('index_new.html', hide_ai_menu=args.noaimenu)
else:
return render_template('index.html', hide_ai_menu=args.noaimenu, flaskwebgui=vars.flaskwebgui)
@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():
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("settings/" + getmodelname().replace('/', '_') + ".settings")):
file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "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)
print(colors.PURPLE + "Initializing Lua Bridge... " + colors.END, end="", flush=True)
# 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()
print("{0}{1}{2}".format(colors.RED, "***LUA ERROR***: ", colors.END), end="", file=sys.stderr)
print("{0}{1}{2}".format(colors.RED, str(e).replace("\033", ""), colors.END), file=sys.stderr)
exit(1)
print(colors.GREEN + "OK!" + colors.END)
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():
print(colors.GREEN + "Loading Core Script" + colors.END)
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()
print("{0}{1}{2}".format(colors.RED, "***LUA ERROR***: ", colors.END), end="", file=sys.stderr)
print("{0}{1}{2}".format(colors.RED, str(e).replace("\033", ""), colors.END), file=sys.stderr)
print("{0}{1}{2}".format(colors.YELLOW, "Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.", colors.END), file=sys.stderr)
if(vars.serverstarted):
set_aibusy(0)
#==================================================================#
# 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 GPT2TokenizerFast
global tokenizer
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.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 GPT2TokenizerFast
global tokenizer
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.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,
)
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", "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", "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()
print("{0}{1}{2}".format(colors.RED, "***LUA ERROR***: ", colors.END), end="", file=sys.stderr)
print("{0}{1}{2}".format(colors.RED, str(e).replace("\033", ""), colors.END), file=sys.stderr)
print("{0}{1}{2}".format(colors.YELLOW, "Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.", colors.END), file=sys.stderr)
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()
print("{0}{1}{2}".format(colors.RED, "***LUA ERROR***: ", colors.END), end="", file=sys.stderr)
print("{0}{1}{2}".format(colors.RED, str(e).replace("\033", ""), colors.END), file=sys.stderr)
print("{0}{1}{2}".format(colors.YELLOW, "Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.", colors.END), file=sys.stderr)
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():
print("{0}Client connected!{1}".format(colors.GREEN, colors.END))
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.flaskwebgui):
emit('from_server', {'cmd': 'flaskwebgui'})
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:
print("{0}Data received:{1}{2}".format(colors.GREEN, msg, colors.END))
# 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"]
if(not isinstance(sampler_order, list)):
raise ValueError(f"Sampler order must be a list, but got a {type(sampler_order)}")
if(len(sampler_order) != len(vars.sampler_order)):
raise ValueError(f"Sampler order must be a list of length {len(vars.sampler_order)}, 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")
vars.sampler_order = sampler_order
settingschanged()
elif(msg['cmd'] == 'list_model'):
sendModelSelection(menu=msg['data'])
elif(msg['cmd'] == 'load_model'):
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.replace('/', '_') + ".breakmodel"):
with open("settings/" + vars.model.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 in ["NeoCustom", "GPT2Custom"]:
filename = "settings/{}.breakmodel".format(os.path.basename(os.path.normpath(vars.custmodpth)))
else:
filename = "settings/{}.breakmodel".format(vars.model.replace('/', '_'))
f = open(filename, "w")
f.write(str(msg['gpu_layers']) + '\n' + str(msg['disk_layers']))
f.close()
vars.colaburl = msg['url'] + "/request"
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'):
print("Model Name: {}".format(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 = msg['data']
vars.custmodpth = msg['path_modelname']
get_model_info(msg['data'], directory=msg['path'])
else:
vars.model = msg['path_modelname']
try:
get_model_info(vars.model)
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 = 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 = msg['data']
if 'path' in msg:
vars.custmodpth = msg['path']
get_model_info(msg['data'], directory=msg['path'])
else:
get_model_info(vars.model)
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']):
print(colors.YELLOW + "WARNING: Someone deleted " + msg['data'])
import shutil
shutil.rmtree(msg['data'])
sendModelSelection(menu=msg['menu'])
else:
print(colors.RED + "ERROR: Someone attempted to delete " + msg['data'] + " but this is not a valid model")
else:
print(colors.RED + "WARNING!!: 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'] == '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'] == '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()
#==================================================================#
# 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) + "<br/>"
else:
txt = "<span>Welcome to <span class=\"color_cyan\">KoboldAI</span>! You are running <span class=\"color_green\">"+getmodelname()+"</span>.<br/>"
if(not vars.noai and not vars.welcome):
txt = txt + "Please load a game or enter a prompt below to begin!</span>"
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):
# Ignore new submissions if the AI is currently busy
if(vars.aibusy):
return
while(True):
set_aibusy(1)
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
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 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 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] = ""
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
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:
print(len(vars.actions))
print(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 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:
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]})
assert type(genout[-1]["generated_text"]) is str
if(len(genout) == 1):
genresult(genout[0]["generated_text"])
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"])
data = ""
force_submit = True
disable_recentrng = True
continue
genselect(genout)
set_aibusy(0)
emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True)
break
#==================================================================#
#
#==================================================================#
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 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
# Build Author's Note if set
if(vars.authornote != ""):
anotetxt = ("\n" + vars.authornotetemplate + "\n").replace("<|>", vars.authornote)
else:
anotetxt = ""
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 GPT2TokenizerFast
global tokenizer
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.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 + 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 + memtokens + witokens + anotetkns + prompttkns + tokens
else:
tokens = tokenizer._koboldai_header + memtokens + witokens + prompttkns + tokens
else:
# Prepend Memory, WI, and Prompt before action tokens
tokens = tokenizer._koboldai_header + 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", "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 == "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", "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 == "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=1.1,
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:
print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, minimum, maximum, utils.decodenewlines(tokenizer.decode(txt)), colors.END))
# 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()
print("{0}{1}{2}".format(colors.RED, "***LUA ERROR***: ", colors.END), end="", file=sys.stderr)
print("{0}{1}{2}".format(colors.RED, str(e).replace("\033", ""), colors.END), file=sys.stderr)
print("{0}{1}{2}".format(colors.YELLOW, "Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.", colors.END), file=sys.stderr)
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.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:
print("{0}{1}{2}".format(colors.CYAN, genout, colors.END))
# 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:
print("{0}[Result {1}]\n{2}{3}".format(colors.CYAN, i, result["generated_text"], colors.END))
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 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:
print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, minimum, maximum, utils.decodenewlines(tokenizer.decode(txt)), colors.END))
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()
print("{0}{1}{2}".format(colors.RED, "***LUA ERROR***: ", colors.END), end="", file=sys.stderr)
print("{0}{1}{2}".format(colors.RED, str(e).replace("\033", ""), colors.END), file=sys.stderr)
print("{0}{1}{2}".format(colors.YELLOW, "Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.", colors.END), file=sys.stderr)
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", "<br/>").replace("\\r", "<br/>").replace("\\n", "<br/>").replace("\r\n", "<br/>").replace('\n', '<br/>').replace('\r', '<br/>').replace('</s>', '<br/>')
#==================================================================#
# 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 = ['<chunk n="0" id="n0" tabindex="-1">', vars.comregex_ui.sub(lambda m: '\n'.join('<comment>' + l + '</comment>' for l in m.group().split('\n')), html.escape(vars.prompt)), '</chunk>']
for idx in vars.actions:
item = vars.actions[idx]
idx += 1
item = html.escape(item)
item = vars.comregex_ui.sub(lambda m: '\n'.join('<comment>' + l + '</comment>' for l in m.group().split('\n')), item) # Add special formatting to comments
item = vars.acregex_ui.sub('<action>\\1</action>', item) # Add special formatting to adventure actions
text_parts.extend(('<chunk n="', str(idx), '" id="n', str(idx), '" tabindex="-1">', item, '</chunk>'))
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('<comment>' + l + '</comment>' for l in m.group().split('\n')), item) # Add special formatting to comments
item = vars.acregex_ui.sub('<action>\\1</action>', item) # Add special formatting to adventure actions
chunk_text = f'<chunk n="{idx}" id="n{idx}" tabindex="-1">{formatforhtml(item)}</chunk>'
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': '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(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:
print(f"WARNING: 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'] = ''
vars.actions[chunk-1] = ''
else:
print(f"WARNING: 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 in txt:
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 in txt:
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
if 'GooseAI' in args.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("settings/" + getmodelname().replace('/', '_') + ".settings")):
file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "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)
#==================================================================#
# Final startup commands to launch Flask app
#==================================================================#
print("", end="", flush=True)
if __name__ == "__main__":
print("{0}\nStarting webserver...{1}".format(colors.GREEN, colors.END), flush=True)
general_startup()
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("(?P<url>https?:\/\/[^\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)
print(format(colors.GREEN) + "KoboldAI has finished loading and is available at the following link : " + cloudflare + format(colors.END))
else:
print("{0}Webserver has started, you can now connect to this machine at port {1}{2}"
.format(colors.GREEN, port, colors.END))
vars.serverstarted = True
socketio.run(app, host='0.0.0.0', port=port)
else:
if args.unblock:
import webbrowser
webbrowser.open_new('http://localhost:{0}'.format(port))
print("{0}Server started!\nYou may now connect with a browser at http://127.0.0.1:{1}/{2}"
.format(colors.GREEN, port, colors.END))
vars.serverstarted = True
socketio.run(app, port=port, host='0.0.0.0')
else:
try:
from flaskwebgui import FlaskUI
vars.serverstarted = True
vars.flaskwebgui = True
FlaskUI(app, socketio=socketio, start_server="flask-socketio", maximized=True, close_server_on_exit=True).run()
except:
pass
import webbrowser
webbrowser.open_new('http://localhost:{0}'.format(port))
print("{0}Server started!\nYou may now connect with a browser at http://127.0.0.1:{1}/{2}"
.format(colors.GREEN, port, colors.END))
vars.serverstarted = True
socketio.run(app, port=port)
else:
general_startup()
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)