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KoboldAI-Client/aiserver.py
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#!/usr/bin/python3
#==================================================================#
# KoboldAI
# Version: 1.18.1
# By: KoboldAIDev and the KoboldAI Community
#==================================================================#
# External packages
import eventlet
from eventlet import tpool
eventlet.monkey_patch(all=True, thread=False, os=False)
#eventlet.monkey_patch(os=True, select=True, socket=True, thread=True, time=True, psycopg=True)
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'
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 collections import OrderedDict
from typing import Any, Callable, Optional, 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 koboldai_settings
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 (koboldai_vars.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 GPT-Neo/J", "gptneolist", "", True],
["Untuned Fairseq Dense", "fsdlist", "", True],
["Untuned OPT", "optlist", "", True],
["Untuned XGLM", "xglmlist", "", True],
["Untuned GPT2", "gpt2list", "", True],
["Online Services", "apilist", "", True],
["Read Only (No AI)", "ReadOnly", "", False]
],
'adventurelist': [
["Nerys FSD 13B (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys", "32GB", False],
["Skein 6B", "KoboldAI/GPT-J-6B-Skein", "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 (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys", "32GB", False],
["Janeway FSD 13B", "KoboldAI/fairseq-dense-13B-Janeway", "32GB", 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 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 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, room="UI_1")
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, has_request_context
from flask_socketio import SocketIO, emit, join_room, leave_room
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", manage_session=False)
#socketio = SocketIO(app, async_method="eventlet", logger=True, engineio_logger=True, manage_session=False)
koboldai_vars = koboldai_settings.koboldai_vars(session, socketio)
utils.koboldai_vars = koboldai_vars
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 koboldai_vars.host:
breadcrumbs = []
menu_list = [[folder, menu, "", False] for folder in paths]
menu_list_ui_2 = [[folder[0], folder[1], "", False] for folder in paths]
menu_list.append(["Return to Main Menu", "mainmenu", "", True])
menu_list_ui_2.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, room="UI_1")
emit('show_model_menu', {'data': menu_list_ui_2, 'menu': menu, 'breadcrumbs': breadcrumbs, "showdelete": showdelete}, broadcast=False, room="UI_2")
else:
emit('from_server', {'cmd': 'show_model_menu', 'data': model_menu[menu], 'menu': menu, 'breadcrumbs': [], "showdelete": False}, broadcast=True, room="UI_1")
emit('show_model_menu', {'data': model_menu[menu], 'menu': menu, 'breadcrumbs': [], "showdelete": False}, broadcast=False, room="UI_2")
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
koboldai_vars.model = ''
while(koboldai_vars.model == ''):
modelsel = input("Model #> ")
if(modelsel.isnumeric() and int(modelsel) > 0 and int(modelsel) <= len(modellist)):
koboldai_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(koboldai_vars.model))
except Exception as e:
if(koboldai_vars.model == "Return"):
getModelSelection(mainmenu)
# If custom model was selected, get the filesystem location and store it
if(koboldai_vars.model == "NeoCustom" or koboldai_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
koboldai_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(koboldai_vars.model in ("NeoCustom", "GPT2Custom", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")):
modelname = os.path.basename(os.path.normpath(koboldai_vars.custmodpth))
return modelname
else:
modelname = koboldai_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.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))):
koboldai_vars.breakmodel = False
koboldai_vars.usegpu = True
koboldai_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"
koboldai_vars.breakmodel = False
koboldai_vars.usegpu = False
return
def move_model_to_devices(model):
global generator
if(not utils.HAS_ACCELERATE and not koboldai_vars.breakmodel):
if(koboldai_vars.usegpu):
model = model.half().to(koboldai_vars.gpu_device)
else:
model = model.to('cpu').float()
generator = model.generate
return
import breakmodel
if(utils.HAS_ACCELERATE):
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(koboldai_vars.custmodpth + "/config.json", "r"))
except Exception as e:
js = json.load(open(koboldai_vars.custmodpth.replace('/', '_') + "/config.json", "r"))
except Exception as e:
js = {}
if koboldai_vars.model_type == "xglm" or js.get("compat", "j") == "fairseq_lm":
koboldai_vars.newlinemode = "s" # Default to </s> newline mode if using XGLM
if koboldai_vars.model_type == "opt" or koboldai_vars.model_type == "bloom":
koboldai_vars.newlinemode = "ns" # Handle </s> but don't convert newlines if using Fairseq models that have newlines trained in them
koboldai_vars.modelconfig = js
if("badwordsids" in js):
koboldai_vars.badwordsids = js["badwordsids"]
if("nobreakmodel" in js):
koboldai_vars.nobreakmodel = js["nobreakmodel"]
if("sampler_order" in js):
koboldai_vars.sampler_order = js["sampler_order"]
koboldai_vars.default_preset['sampler_order'] = js["sampler_order"]
if("temp" in js):
koboldai_vars.temp = js["temp"]
koboldai_vars.default_preset['temp'] = js["temp"]
if("top_p" in js):
koboldai_vars.top_p = js["top_p"]
koboldai_vars.default_preset['top_p'] = js["top_p"]
if("top_k" in js):
koboldai_vars.top_k = js["top_k"]
koboldai_vars.default_preset['top_k'] = js["top_k"]
if("tfs" in js):
koboldai_vars.tfs = js["tfs"]
koboldai_vars.default_preset['tfs'] = js["tfs"]
if("typical" in js):
koboldai_vars.typical = js["typical"]
koboldai_vars.default_preset['typical'] = js["typical"]
if("top_a" in js):
koboldai_vars.top_a = js["top_a"]
koboldai_vars.default_preset['top_a'] = js["top_a"]
if("rep_pen" in js):
koboldai_vars.rep_pen = js["rep_pen"]
koboldai_vars.default_preset['rep_pen'] = js["rep_pen"]
if("rep_pen_slope" in js):
koboldai_vars.rep_pen_slope = js["rep_pen_slope"]
koboldai_vars.default_preset['rep_pen_slope'] = js["rep_pen_slope"]
if("rep_pen_range" in js):
koboldai_vars.rep_pen_range = js["rep_pen_range"]
koboldai_vars.default_preset['rep_pen_range'] = js["rep_pen_range"]
if("adventure" in js):
koboldai_vars.adventure = js["adventure"]
if("chatmode" in js):
koboldai_vars.chatmode = js["chatmode"]
if("dynamicscan" in js):
koboldai_vars.dynamicscan = js["dynamicscan"]
if("formatoptns" in js):
koboldai_vars.formatoptns = js["formatoptns"]
if("welcome" in js):
koboldai_vars.welcome = js["welcome"]
if("newlinemode" in js):
koboldai_vars.newlinemode = js["newlinemode"]
if("antemplate" in js):
koboldai_vars.setauthornotetemplate = js["antemplate"]
if(not koboldai_vars.gamestarted):
koboldai_vars.authornotetemplate = koboldai_vars.setauthornotetemplate
#==================================================================#
# Take settings from vars and write them to client settings file
#==================================================================#
def savesettings():
# Build json to write
js = {}
js["apikey"] = koboldai_vars.apikey
js["andepth"] = koboldai_vars.andepth
js["sampler_order"] = koboldai_vars.sampler_order
js["temp"] = koboldai_vars.temp
js["top_p"] = koboldai_vars.top_p
js["top_k"] = koboldai_vars.top_k
js["tfs"] = koboldai_vars.tfs
js["typical"] = koboldai_vars.typical
js["top_a"] = koboldai_vars.top_a
js["rep_pen"] = koboldai_vars.rep_pen
js["rep_pen_slope"] = koboldai_vars.rep_pen_slope
js["rep_pen_range"] = koboldai_vars.rep_pen_range
js["genamt"] = koboldai_vars.genamt
js["max_length"] = koboldai_vars.max_length
js["ikgen"] = koboldai_vars.ikgen
js["formatoptns"] = koboldai_vars.formatoptns
js["numseqs"] = koboldai_vars.numseqs
js["widepth"] = koboldai_vars.widepth
js["useprompt"] = koboldai_vars.useprompt
js["adventure"] = koboldai_vars.adventure
js["chatmode"] = koboldai_vars.chatmode
js["chatname"] = koboldai_vars.chatname
js["dynamicscan"] = koboldai_vars.dynamicscan
js["nopromptgen"] = koboldai_vars.nopromptgen
js["rngpersist"] = koboldai_vars.rngpersist
js["nogenmod"] = koboldai_vars.nogenmod
js["autosave"] = koboldai_vars.autosave
js["welcome"] = koboldai_vars.welcome
js["newlinemode"] = koboldai_vars.newlinemode
js["output_streaming"] = koboldai_vars.output_streaming
js["antemplate"] = koboldai_vars.setauthornotetemplate
js["userscripts"] = koboldai_vars.userscripts
js["corescript"] = koboldai_vars.corescript
js["softprompt"] = koboldai_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):
koboldai_vars.apikey = js["apikey"]
if("andepth" in js):
koboldai_vars.andepth = js["andepth"]
if("sampler_order" in js):
koboldai_vars.sampler_order = js["sampler_order"]
if("temp" in js):
koboldai_vars.temp = js["temp"]
if("top_p" in js):
koboldai_vars.top_p = js["top_p"]
if("top_k" in js):
koboldai_vars.top_k = js["top_k"]
if("tfs" in js):
koboldai_vars.tfs = js["tfs"]
if("typical" in js):
koboldai_vars.typical = js["typical"]
if("top_a" in js):
koboldai_vars.top_a = js["top_a"]
if("rep_pen" in js):
koboldai_vars.rep_pen = js["rep_pen"]
if("rep_pen_slope" in js):
koboldai_vars.rep_pen_slope = js["rep_pen_slope"]
if("rep_pen_range" in js):
koboldai_vars.rep_pen_range = js["rep_pen_range"]
if("genamt" in js):
koboldai_vars.genamt = js["genamt"]
if("max_length" in js):
koboldai_vars.max_length = js["max_length"]
if("ikgen" in js):
koboldai_vars.ikgen = js["ikgen"]
if("formatoptns" in js):
koboldai_vars.formatoptns = js["formatoptns"]
if("numseqs" in js):
koboldai_vars.numseqs = js["numseqs"]
if("widepth" in js):
koboldai_vars.widepth = js["widepth"]
if("useprompt" in js):
koboldai_vars.useprompt = js["useprompt"]
if("adventure" in js):
koboldai_vars.adventure = js["adventure"]
if("chatmode" in js):
koboldai_vars.chatmode = js["chatmode"]
if("chatname" in js):
koboldai_vars.chatname = js["chatname"]
if("dynamicscan" in js):
koboldai_vars.dynamicscan = js["dynamicscan"]
if("nopromptgen" in js):
koboldai_vars.nopromptgen = js["nopromptgen"]
if("rngpersist" in js):
koboldai_vars.rngpersist = js["rngpersist"]
if("nogenmod" in js):
koboldai_vars.nogenmod = js["nogenmod"]
if("autosave" in js):
koboldai_vars.autosave = js["autosave"]
if("newlinemode" in js):
koboldai_vars.newlinemode = js["newlinemode"]
if("welcome" in js):
koboldai_vars.welcome = js["welcome"]
if("output_streaming" in js):
koboldai_vars.autosave = js["output_streaming"]
if("antemplate" in js):
koboldai_vars.setauthornotetemplate = js["antemplate"]
if(not koboldai_vars.gamestarted):
koboldai_vars.authornotetemplate = koboldai_vars.setauthornotetemplate
if("userscripts" in js):
koboldai_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)):
koboldai_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 ("/", "\\"))):
koboldai_vars.corescript = js["corescript"]
else:
koboldai_vars.corescript = "default.lua"
#==================================================================#
# Load a soft prompt from a file
#==================================================================#
def check_for_sp_change():
while(True):
time.sleep(0.1)
if(koboldai_vars.sp_changed):
with app.app_context():
emit('from_server', {'cmd': 'spstatitems', 'data': {koboldai_vars.spfilename: koboldai_vars.spmeta} if koboldai_vars.allowsp and len(koboldai_vars.spfilename) else {}}, namespace=None, broadcast=True, room="UI_1")
koboldai_vars.sp_changed = False
socketio.start_background_task(check_for_sp_change)
def spRequest(filename):
if(not koboldai_vars.allowsp):
raise RuntimeError("Soft prompts are not supported by your current model/backend")
old_filename = koboldai_vars.spfilename
koboldai_vars.spfilename = ""
settingschanged()
if(len(filename) == 0):
koboldai_vars.sp = None
koboldai_vars.sp_length = 0
if(old_filename != filename):
koboldai_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, koboldai_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:
koboldai_vars.spmeta = json.load(f)
koboldai_vars.spname = koboldai_vars.spmeta['name']
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()
koboldai_vars.sp_length = tensor.shape[-2]
koboldai_vars.spmeta["n_tokens"] = koboldai_vars.sp_length
if(koboldai_vars.use_colab_tpu or koboldai_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"]),
)
koboldai_vars.sp = tpu_mtj_backend.shard_xmap(np.float32(tensor))
else:
koboldai_vars.sp = torch.from_numpy(tensor)
koboldai_vars.spfilename = filename
settingschanged()
if(old_filename != filename):
koboldai_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)")
#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()
temp = [x for x in vars(args)]
for arg in temp:
if arg == "path":
if "model_path" in os.environ:
setattr(args, arg, os.environ["model_path"])
else:
if arg in os.environ:
if isinstance(getattr(args, arg), bool):
if os.environ[arg].lower() == "true":
setattr(args, arg, True)
else:
setattr(args, arg, False)
else:
setattr(args, arg, os.environ[arg])
koboldai_vars.model = args.model;
koboldai_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:
koboldai_vars.quiet = True
if args.nobreakmodel:
koboldai_vars.nobreakmodel = True;
if args.remote:
koboldai_vars.host = True;
if args.ngrok:
koboldai_vars.host = True;
if args.localtunnel:
koboldai_vars.host = True;
if args.host:
koboldai_vars.host = True;
if args.cpu:
koboldai_vars.use_colab_tpu = False
koboldai_vars.smandelete = koboldai_vars.host == args.override_delete
koboldai_vars.smanrename = koboldai_vars.host == args.override_rename
koboldai_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(koboldai_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
koboldai_vars.model = "NeoCustom"
koboldai_vars.custmodpth = modpath
elif args.model:
print("Welcome to KoboldAI!\nYou have selected the following Model:", koboldai_vars.model)
if args.path:
print("You have selected the following path for your Model :", args.path)
koboldai_vars.custmodpth = args.path;
koboldai_vars.colaburl = args.path + "/request"; # Lets just use the same parameter to keep it simple
#==================================================================#
# Load Model
#==================================================================#
def tpumtjgetsofttokens():
soft_tokens = None
if(koboldai_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"]),
)
koboldai_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"] + koboldai_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"]
if model in ('GooseAI', 'OAI'):
get_oai_models({'model': model, 'key': key_value})
key = True
elif model == 'ReadOnly':
pass
elif model == 'Colab':
url = True
elif not utils.HAS_ACCELERATE and not torch.cuda.is_available():
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 = 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 = [int(x) for x in break_values]
break_values += [0] * (gpu_count - len(break_values))
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, room="UI_1")
emit('selected_model_info', {'key_value': key_value, 'key':key,
'gpu':gpu, 'layer_count':layer_count, 'breakmodel':breakmodel,
'disk_break_value': disk_blocks, 'disk_break': utils.HAS_ACCELERATE,
'break_values': break_values, 'gpu_count': gpu_count,
'url': url, 'gpu_names': gpu_names}, broadcast=False, room="UI_2")
def get_layer_count(model, directory=""):
if(model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ"]):
if(koboldai_vars.model == "GPT2Custom"):
model_config = open(directory + "/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(model, cache_dir="cache")
elif os.path.isdir(directory):
model_config = AutoConfig.from_pretrained(directory, cache_dir="cache")
else:
assert "Selected Model directory doesn't exist"
return utils.num_layers(model_config)
else:
return None
@socketio.on('OAI_Key_Update')
def get_oai_models(data):
key = data['key']
model = data['model']
koboldai_vars.oaiapikey = key
if model == 'OAI':
url = "https://api.openai.com/v1/engines"
elif 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(model)):
with open("settings/{}.settings".format(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(model), "w") as file:
js["apikey"] = key
file.write(json.dumps(js, indent=3), room="UI_1")
emit('from_server', {'cmd': 'oai_engines', 'data': engines, 'online_model': online_model}, broadcast=True, room="UI_1")
emit('oai_engines', {'data': engines, 'online_model': online_model}, broadcast=False, room="UI_2")
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), room="UI_1")
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(koboldai_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(koboldai_vars.sp is not None):
koboldai_vars.sp = koboldai_vars.sp.to(inputs_embeds.dtype).to(inputs_embeds.device)
inputs_embeds = torch.where(
(shifted_input_ids >= 0)[..., None],
koboldai_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(" ", "&nbsp;")}, broadcast=True, room="UI_1")
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()`
koboldai_vars.total_download_chunks = total
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
progress.update(len(chunk))
koboldai_vars.downloaded_chunks += len(chunk)
temp_file.write(chunk)
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):
koboldai_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(koboldai_vars, v))
setattr(self, f, conds[-1])
else:
conds = getattr(koboldai_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 PhraseBiasLogitsProcessor(LogitsProcessor):
def __init__(self):
pass
def _rindex(self, lst: List, target) -> Optional[int]:
for index, item in enumerate(reversed(lst)):
if item == target:
return len(lst) - index - 1
return None
def _find_intersection(self, big: List, small: List) -> int:
# Find the intersection of the end of "big" and the beginning of
# "small". A headache to think about, personally. Returns the index
# into "small" where the two stop intersecting.
start = self._rindex(big, small[0])
# No progress into the token sequence, bias the first one.
if not start:
return 0
for i in range(len(small)):
try:
big_i = big[start + i]
except IndexError:
return i
# It's completed :^)
return 0
def _get_biased_tokens(self, input_ids: List) -> Dict:
# TODO: Different "bias slopes"?
ret = {}
for phrase, _bias in koboldai_vars.biases.items():
bias_score, completion_threshold = _bias
# TODO: Cache these tokens, invalidate when model or bias is
# changed.
token_seq = tokenizer.encode(phrase)
bias_index = self._find_intersection(input_ids, token_seq)
# Ensure completion after completion_threshold tokens
if bias_index + 1 > completion_threshold:
bias_score = 999
token_to_bias = token_seq[bias_index]
ret[token_to_bias] = bias_score
return ret
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
assert scores.ndim == 2
assert input_ids.ndim == 2
scores_shape = scores.shape
for batch in range(scores_shape[0]):
for token, bias in self._get_biased_tokens(input_ids[batch]).items():
scores[batch][token] += bias
return scores
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()
koboldai_vars.lua_koboldbridge.logits = koboldai_vars.lua_state.table()
for r, row in enumerate(scores_list):
koboldai_vars.lua_koboldbridge.logits[r+1] = koboldai_vars.lua_state.table(*row)
koboldai_vars.lua_koboldbridge.vocab_size = scores_shape[-1]
execute_genmod()
scores = torch.tensor(
tuple(tuple(row.values()) for row in koboldai_vars.lua_koboldbridge.logits.values()),
device=scores.device,
dtype=scores.dtype,
)
assert scores.shape == scores_shape
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.insert(0, PhraseBiasLogitsProcessor())
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 koboldai_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(koboldai_vars.newlinemode == "s") or (koboldai_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:
if (not koboldai_vars.output_streaming):
return False
#for batch, ids in enumerate(input_ids):
#tokenizer_text = utils.decodenewlines(tokenizer.decode(ids[-1]))
#koboldai_vars.actions.stream_token(tokenizer_text, batch=batch)
koboldai_vars.actions.stream_tokens([utils.decodenewlines(tokenizer.decode(x[-1])) for x in input_ids])
#if len(input_ids) > 1:
# koboldai_vars.actions.clear_unused_options()
# koboldai_vars.actions.append_options([utils.decodenewlines(tokenizer.decode(x[-1])) for x in input_ids])
#else:
# koboldai_vars.actions[koboldai_vars.actions.action_count+1] = utils.decodenewlines(tokenizer.decode(input_ids[0, -1]))
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:
koboldai_vars.generated_tkns += 1
if(koboldai_vars.lua_koboldbridge.generated_cols and koboldai_vars.generated_tkns != koboldai_vars.lua_koboldbridge.generated_cols):
raise RuntimeError(f"Inconsistency detected between KoboldAI Python and Lua backends ({koboldai_vars.generated_tkns} != {koboldai_vars.lua_koboldbridge.generated_cols})")
if(koboldai_vars.abort or koboldai_vars.generated_tkns >= koboldai_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 = koboldai_vars.lua_koboldbridge.regeneration_required
self.halt = not koboldai_vars.lua_koboldbridge.generating
koboldai_vars.lua_koboldbridge.regeneration_required = False
for i in range(koboldai_vars.numseqs):
koboldai_vars.lua_koboldbridge.generated[i+1][koboldai_vars.generated_tkns] = int(input_ids[i, -1].item())
if(not koboldai_vars.dynamicscan):
return self.regeneration_required or self.halt
tail = input_ids[..., -koboldai_vars.generated_tkns:]
for i, t in enumerate(tail):
decoded = utils.decodenewlines(tokenizer.decode(t))
_, found = checkworldinfo(decoded, force_use_txt=True, actions=koboldai_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,
)
stopping_criteria.insert(0, self.kai_scanner)
token_streamer = TokenStreamer(tokenizer=tokenizer)
stopping_criteria.insert(0, token_streamer)
return stopping_criteria
transformers.generation_utils.GenerationMixin._get_stopping_criteria = new_get_stopping_criteria
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
if not utils.HAS_ACCELERATE:
disk_layers = None
koboldai_vars.reset_model()
koboldai_vars.noai = False
if not initial_load:
set_aibusy(True)
if koboldai_vars.model != 'ReadOnly':
emit('from_server', {'cmd': 'model_load_status', 'data': "Loading {}".format(koboldai_vars.model)}, broadcast=True, room="UI_1")
#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
if disk_layers is not None:
args.breakmodel_disklayers = int(disk_layers)
#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:
with torch.no_grad():
torch.cuda.empty_cache()
except:
pass
#Reload our badwords
koboldai_vars.badwordsids = koboldai_settings.badwordsids_default
#Let's set the GooseAI or OpenAI server URLs if that's applicable
if online_model != "":
if path.exists("settings/{}.settings".format(koboldai_vars.model)):
changed=False
with open("settings/{}.settings".format(koboldai_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(koboldai_vars.model), "w") as file:
file.write(json.dumps(js, indent=3))
# Swap OAI Server if GooseAI was selected
if(koboldai_vars.model == "GooseAI"):
koboldai_vars.oaiengines = "https://api.goose.ai/v1/engines"
koboldai_vars.model = "OAI"
args.configname = "GooseAI" + "/" + online_model
else:
args.configname = koboldai_vars.model + "/" + online_model
koboldai_vars.oaiurl = koboldai_vars.oaiengines + "/{0}/completions".format(online_model)
# If transformers model was selected & GPU available, ask to use CPU or GPU
if(koboldai_vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
koboldai_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 koboldai_vars.model in ["NeoCustom", "GPT2Custom"]):
koboldai_vars.custmodpth = koboldai_vars.model
elif(koboldai_vars.model == "NeoCustom"):
koboldai_vars.model = os.path.basename(os.path.normpath(koboldai_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(koboldai_vars.custmodpth.replace('/', '_'))):
try:
model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth.replace('/', '_'), revision=koboldai_vars.revision, cache_dir="cache")
koboldai_vars.model_type = model_config.model_type
except ValueError as e:
koboldai_vars.model_type = "not_found"
elif(os.path.isdir("models/{}".format(koboldai_vars.custmodpth.replace('/', '_')))):
try:
model_config = AutoConfig.from_pretrained("models/{}".format(koboldai_vars.custmodpth.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
koboldai_vars.model_type = model_config.model_type
except ValueError as e:
koboldai_vars.model_type = "not_found"
else:
try:
model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
koboldai_vars.model_type = model_config.model_type
except ValueError as e:
koboldai_vars.model_type = "not_found"
if(koboldai_vars.model_type == "not_found" and koboldai_vars.model == "NeoCustom"):
koboldai_vars.model_type = "gpt_neo"
elif(koboldai_vars.model_type == "not_found" and koboldai_vars.model == "GPT2Custom"):
koboldai_vars.model_type = "gpt2"
elif(koboldai_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)")
koboldai_vars.model_type = "gpt_neo"
if(not koboldai_vars.use_colab_tpu and koboldai_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="")
koboldai_vars.hascuda = torch.cuda.is_available()
koboldai_vars.bmsupported = (utils.HAS_ACCELERATE or koboldai_vars.model_type in ("gpt_neo", "gptj", "xglm", "opt")) and not koboldai_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 koboldai_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)
koboldai_vars.bmsupported = False
if(not koboldai_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(koboldai_vars.hascuda):
print("{0}FOUND!{1}".format(colors.GREEN, colors.END))
else:
print("{0}NOT FOUND!{1}".format(colors.YELLOW, colors.END))
if args.model:
if(koboldai_vars.hascuda):
genselected = True
koboldai_vars.usegpu = True
koboldai_vars.breakmodel = utils.HAS_ACCELERATE
if(koboldai_vars.bmsupported):
koboldai_vars.usegpu = False
koboldai_vars.breakmodel = True
if(args.cpu):
koboldai_vars.usegpu = False
koboldai_vars.breakmodel = utils.HAS_ACCELERATE
elif(koboldai_vars.hascuda):
if(koboldai_vars.bmsupported):
genselected = True
koboldai_vars.usegpu = False
koboldai_vars.breakmodel = True
else:
genselected = False
else:
genselected = False
if(koboldai_vars.hascuda):
if(use_gpu):
if(koboldai_vars.bmsupported):
koboldai_vars.breakmodel = True
koboldai_vars.usegpu = False
genselected = True
else:
koboldai_vars.breakmodel = False
koboldai_vars.usegpu = True
genselected = True
else:
koboldai_vars.breakmodel = utils.HAS_ACCELERATE
koboldai_vars.usegpu = False
genselected = True
# Ask for API key if InferKit was selected
if(koboldai_vars.model == "InferKit"):
koboldai_vars.apikey = koboldai_vars.oaiapikey
# Swap OAI Server if GooseAI was selected
if(koboldai_vars.model == "GooseAI"):
koboldai_vars.oaiengines = "https://api.goose.ai/v1/engines"
koboldai_vars.model = "OAI"
args.configname = "GooseAI"
# Ask for API key if OpenAI was selected
if(koboldai_vars.model == "OAI"):
if not args.configname:
args.configname = "OAI"
if(koboldai_vars.model == "ReadOnly"):
koboldai_vars.noai = True
# Start transformers and create pipeline
if(not koboldai_vars.use_colab_tpu and koboldai_vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
if(not koboldai_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 koboldai_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] = koboldai_vars.gpu_device if koboldai_vars.hascuda and koboldai_vars.usegpu else "cpu" if not koboldai_vars.hascuda or not koboldai_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 = koboldai_vars.gpu_device if koboldai_vars.hascuda and koboldai_vars.usegpu else "disk" if layer < disk_blocks and layer < ram_blocks else "cpu" if not koboldai_vars.hascuda or not koboldai_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)
koboldai_vars.total_layers = num_tensors
koboldai_vars.loaded_layers = 0
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:
koboldai_vars.fp32_model = True
if convert_to_float16 and breakmodel.primary_device != "cpu" and koboldai_vars.hascuda and (koboldai_vars.breakmodel or koboldai_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 koboldai_vars.usegpu and not koboldai_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)
koboldai_vars.loaded_layers += 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 (koboldai_vars.hascuda and args.lowmem and (koboldai_vars.usegpu or koboldai_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(koboldai_vars.model == "GPT2Custom"):
koboldai_vars.lazy_load = False
model_config = open(koboldai_vars.custmodpth + "/config.json", "r")
js = json.load(model_config)
with(maybe_use_float16()):
try:
model = GPT2LMHeadModel.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_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(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
koboldai_vars.modeldim = get_hidden_size_from_model(model)
# Is CUDA available? If so, use GPU, otherwise fall back to CPU
if(koboldai_vars.hascuda and koboldai_vars.usegpu):
model = model.half().to(koboldai_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(koboldai_vars.model_type == "gpt2"):
lowmem = {}
koboldai_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 koboldai_vars.lazy_load and koboldai_vars.hascuda and koboldai_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(koboldai_vars.model.replace('/', '_')):
import shutil
shutil.move(koboldai_vars.model.replace('/', '_'), "models/{}".format(koboldai_vars.model.replace('/', '_')))
print("\n", flush=True)
if(koboldai_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=koboldai_vars.lazy_load, callback=get_lazy_load_callback(utils.num_layers(model_config)) if koboldai_vars.lazy_load else None, dematerialized_modules=True):
if(koboldai_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(koboldai_vars.custmodpth)):
try:
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_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(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
elif(os.path.isdir("models/{}".format(koboldai_vars.model.replace('/', '_')))):
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_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(koboldai_vars.model.replace('/', '_')), revision=koboldai_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):
koboldai_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(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
pass
try:
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained(koboldai_vars.model, revision=koboldai_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(koboldai_vars.model, revision=koboldai_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(koboldai_vars.model.replace('/', '_')))
if(koboldai_vars.fp32_model): # Use save_pretrained to convert fp32 models to fp16
model = model.half()
model.save_pretrained("models/{}".format(koboldai_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(koboldai_vars.model, transformers.configuration_utils.CONFIG_NAME, revision=koboldai_vars.revision), cache_dir="cache", local_files_only=True), os.path.join("models/{}".format(koboldai_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(koboldai_vars.model, transformers.modeling_utils.WEIGHTS_NAME, revision=koboldai_vars.revision), cache_dir="cache", local_files_only=True), os.path.join("models/{}".format(koboldai_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(koboldai_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(koboldai_vars.model, filename, revision=koboldai_vars.revision), cache_dir="cache", local_files_only=True), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), filename))
shutil.rmtree("cache/")
if(koboldai_vars.badwordsids is koboldai_settings.badwordsids_default and koboldai_vars.model_type not in ("gpt2", "gpt_neo", "gptj")):
koboldai_vars.badwordsids = [[v] for k, v in tokenizer.get_vocab().items() if any(c in str(k) for c in "<>[]") if koboldai_vars.newlinemode != "s" or str(k) != "</s>"]
patch_causallm(model)
if(koboldai_vars.hascuda):
if(koboldai_vars.usegpu):
koboldai_vars.modeldim = get_hidden_size_from_model(model)
model = model.half().to(koboldai_vars.gpu_device)
generator = model.generate
elif(koboldai_vars.breakmodel): # Use both RAM and VRAM (breakmodel)
koboldai_vars.modeldim = get_hidden_size_from_model(model)
if(not koboldai_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)
koboldai_vars.modeldim = get_hidden_size_from_model(model)
generator = model.generate
else:
model = model.to('cpu').float()
koboldai_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)
koboldai_vars.modeldim = get_hidden_size_from_model(model)
generator = model.generate
else:
model.to('cpu').float()
koboldai_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:
# koboldai_vars.badwordsids.append([vocab[key]])
print("{0}OK! {1} pipeline created!{2}".format(colors.GREEN, koboldai_vars.model, colors.END))
else:
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=koboldai_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):
koboldai_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()
koboldai_vars.lua_koboldbridge.logits = koboldai_vars.lua_state.table()
for r, row in enumerate(scores_list):
koboldai_vars.lua_koboldbridge.logits[r+1] = koboldai_vars.lua_state.table(*row)
koboldai_vars.lua_koboldbridge.vocab_size = scores_shape[-1]
execute_genmod()
scores = np.array(
tuple(tuple(row.values()) for row in koboldai_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]:
koboldai_vars.generated_tkns += 1
assert len(excluded_world_info) == len(generated)
regeneration_required = koboldai_vars.lua_koboldbridge.regeneration_required
halt = koboldai_vars.abort or not koboldai_vars.lua_koboldbridge.generating or koboldai_vars.generated_tkns >= koboldai_vars.genamt
koboldai_vars.lua_koboldbridge.regeneration_required = False
global past
for i in range(koboldai_vars.numseqs):
koboldai_vars.lua_koboldbridge.generated[i+1][koboldai_vars.generated_tkns] = int(generated[i, tpu_mtj_backend.params["seq"] + n_generated - 1].item())
if(not koboldai_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=koboldai_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)
koboldai_vars.compiling = True
def tpumtjgenerate_stopped_compiling_callback() -> None:
koboldai_vars.compiling = False
def tpumtjgenerate_settings_callback() -> dict:
return {
"sampler_order": koboldai_vars.sampler_order,
"top_p": float(koboldai_vars.top_p),
"temp": float(koboldai_vars.temp),
"top_k": int(koboldai_vars.top_k),
"tfs": float(koboldai_vars.tfs),
"typical": float(koboldai_vars.typical),
"top_a": float(koboldai_vars.top_a),
"repetition_penalty": float(koboldai_vars.rep_pen),
"rpslope": float(koboldai_vars.rep_pen_slope),
"rprange": int(koboldai_vars.rep_pen_range),
}
# If we're running Colab or OAI, we still need a tokenizer.
if(koboldai_vars.model == "Colab"):
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("EleutherAI/gpt-neo-2.7B", revision=koboldai_vars.revision, cache_dir="cache")
loadsettings()
elif(koboldai_vars.model == "OAI"):
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
loadsettings()
# Load the TPU backend if requested
elif(koboldai_vars.use_colab_tpu or koboldai_vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")):
global tpu_mtj_backend
import tpu_mtj_backend
if(koboldai_vars.model == "TPUMeshTransformerGPTNeoX"):
koboldai_vars.badwordsids = koboldai_settings.badwordsids_neox
print("{0}Initializing Mesh Transformer JAX, please wait...{1}".format(colors.PURPLE, colors.END))
if koboldai_vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") and (not koboldai_vars.custmodpth or not os.path.isdir(koboldai_vars.custmodpth)):
raise FileNotFoundError(f"The specified model path {repr(koboldai_vars.custmodpth)} is not the path to a valid folder")
import tpu_mtj_backend
if(koboldai_vars.model == "TPUMeshTransformerGPTNeoX"):
tpu_mtj_backend.pad_token_id = 2
tpu_mtj_backend.koboldai_vars = koboldai_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
koboldai_vars.allowsp = True
loadmodelsettings()
loadsettings()
tpu_mtj_backend.load_model(koboldai_vars.custmodpth, hf_checkpoint=koboldai_vars.model not in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") and koboldai_vars.use_colab_tpu, **koboldai_vars.modelconfig)
koboldai_vars.modeldim = int(tpu_mtj_backend.params.get("d_embed", tpu_mtj_backend.params["d_model"]))
tokenizer = tpu_mtj_backend.tokenizer
if(koboldai_vars.badwordsids is koboldai_settings.badwordsids_default and koboldai_vars.model_type not in ("gpt2", "gpt_neo", "gptj")):
koboldai_vars.badwordsids = [[v] for k, v in tokenizer.get_vocab().items() if any(c in str(k) for c in "<>[]") if koboldai_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, room="UI_1")
time.sleep(0.1)
if not koboldai_vars.gamestarted:
setStartState()
sendsettings()
refresh_settings()
#Saving the tokenizer to the KoboldStoryRegister class so we can do token counting on the story data
if 'tokenizer' in [x for x in globals()]:
koboldai_vars.tokenizer = tokenizer
#Let's load the presets
with open('official.presets') as f:
presets = json.load(f)
koboldai_vars.uid_presets = {x['uid']: x for x in presets}
#We want our data to be a 2 deep dict. Top level is "Recommended", "Same Class", "Model 1", "Model 2", etc
#Next layer is "Official", "Custom"
#Then the preset name
to_use = OrderedDict()
to_use["Recommended"] = {"Official": [], "Custom": []}
to_use["Same Class"] = {"Official": [], "Custom": []}
to_use["Other"] = {"Official": [], "Custom": []}
used_ids = []
#Build recommended first:
for preset in presets:
if preset['Model Type'] == koboldai_vars.model and preset['uid'] not in used_ids:
if preset['Model Category'] == 'Custom':
to_use['Recommended']['Custom'].append(preset)
else:
to_use['Recommended']['Official'].append(preset)
used_ids.append(preset['uid'])
#Build Same Class
for preset in presets:
if preset['Model Size'] in koboldai_vars.model.replace("6.7B", "6B") and preset['uid'] not in used_ids:
if preset['Model Category'] == 'Custom':
to_use['Same Class']['Custom'].append(preset)
else:
to_use['Same Class']['Official'].append(preset)
used_ids.append(preset['uid'])
#Build the rest of the stuff
for preset in presets:
if preset['uid'] not in used_ids:
used_ids.append(preset['uid'])
if preset['Model Category'] == 'Custom':
to_use["Other"]['Custom'].append(preset)
else:
to_use["Other"]['Official'].append(preset)
koboldai_vars.presets = to_use
# Set up Flask routes
@app.route('/')
@app.route('/index')
def index():
if 'story' in session:
if session['story'] not in koboldai_vars.story_list():
session['story'] = 'default'
return render_template('index.html', hide_ai_menu=args.noaimenu, flaskwebgui=koboldai_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 = koboldai_vars.prompt + "".join(koboldai_vars.actions.values())
save = Response(txt)
filename = path.basename(koboldai_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"] = koboldai_vars.gamestarted
js["prompt"] = koboldai_vars.prompt
js["memory"] = koboldai_vars.memory
js["authorsnote"] = koboldai_vars.authornote
js["anotetemplate"] = koboldai_vars.authornotetemplate
js["actions"] = koboldai_vars.actions.to_json()
js["worldinfo"] = []
# Extract only the important bits of WI
for wi in koboldai_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(koboldai_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):
koboldai_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)):
koboldai_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 ("/", "\\"))):
koboldai_vars.corescript = js["corescript"]
else:
koboldai_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
koboldai_vars.lua_state = lupa.LuaRuntime(unpack_returned_tuples=True)
# Load bridge.lua
bridged = {
"corescript_path": "cores",
"userscript_path": "userscripts",
"config_path": "userscripts",
"lib_paths": koboldai_vars.lua_state.table("lualibs", os.path.join("extern", "lualibs")),
"koboldai_vars": koboldai_vars
}
for kwarg in _bridged:
bridged[kwarg] = _bridged[kwarg]
try:
koboldai_vars.lua_kobold, koboldai_vars.lua_koboldcore, koboldai_vars.lua_koboldbridge = koboldai_vars.lua_state.globals().dofile("bridge.lua")(
koboldai_vars.lua_state.globals().python,
bridged,
)
except lupa.LuaError as e:
print(colors.RED + "ERROR!" + colors.END)
koboldai_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)
socketio.emit("error", str(e), broadcast=True, room="UI_2")
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 koboldai_vars.userscripts:
if filename in filenames_dict:
i = filenames_dict[filename]
filenames.append(filename)
modulenames.append(lst[i]["modulename"])
descriptions.append(lst[i]["description"])
koboldai_vars.has_genmod = False
try:
koboldai_vars.lua_koboldbridge.obliterate_multiverse()
tpool.execute(koboldai_vars.lua_koboldbridge.load_corescript, koboldai_vars.corescript)
koboldai_vars.has_genmod = tpool.execute(koboldai_vars.lua_koboldbridge.load_userscripts, filenames, modulenames, descriptions)
koboldai_vars.lua_running = True
except lupa.LuaError as e:
try:
koboldai_vars.lua_koboldbridge.obliterate_multiverse()
except:
pass
koboldai_vars.lua_running = False
if(koboldai_vars.serverstarted):
emit('from_server', {'cmd': 'errmsg', 'data': 'Lua script error; please check console.'}, broadcast=True, room="UI_1")
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)
socketio.emit("error", str(e), broadcast=True, room="UI_2")
if(koboldai_vars.serverstarted):
set_aibusy(0)
#==================================================================#
# Print message that originates from the userscript with the given name
#==================================================================#
@bridged_kwarg()
def lua_print(msg):
if(koboldai_vars.lua_logname != koboldai_vars.lua_koboldbridge.logging_name):
koboldai_vars.lua_logname = koboldai_vars.lua_koboldbridge.logging_name
print(colors.BLUE + lua_log_format_name(koboldai_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(koboldai_vars.lua_logname != koboldai_vars.lua_koboldbridge.logging_name):
koboldai_vars.lua_logname = koboldai_vars.lua_koboldbridge.logging_name
print(colors.BLUE + lua_log_format_name(koboldai_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=koboldai_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=koboldai_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 = koboldai_vars.lua_state.table()
actions = koboldai_vars._actions if koboldai_vars.lua_koboldbridge.userstate == "genmod" else koboldai_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 koboldai_vars.worldinfo_u and k in (
"key",
"keysecondary",
"content",
"comment",
"folder",
"num",
"selective",
"constant",
"uid",
)):
return koboldai_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 koboldai_vars.worldinfo_u and k in (
"key",
"keysecondary",
"content",
"comment",
"selective",
"constant",
)
if(type(koboldai_vars.worldinfo_u[uid][k]) is int and type(v) is float):
v = int(v)
assert type(koboldai_vars.worldinfo_u[uid][k]) is type(v)
koboldai_vars.worldinfo_u[uid][k] = v
print(colors.GREEN + f"{lua_log_format_name(koboldai_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 koboldai_vars.wifolders_d and k in (
"name",
)):
return koboldai_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 koboldai_vars.wifolders_d and k in (
"name",
)
if(type(koboldai_vars.wifolders_d[uid][k]) is int and type(v) is float):
v = int(v)
assert type(koboldai_vars.wifolders_d[uid][k]) is type(v)
koboldai_vars.wifolders_d[uid][k] = v
print(colors.GREEN + f"{lua_log_format_name(koboldai_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 koboldai_vars.genamt
#==================================================================#
# Set the "Amount to Generate"
#==================================================================#
@bridged_kwarg()
def lua_set_genamt(genamt):
assert koboldai_vars.lua_koboldbridge.userstate != "genmod" and type(genamt) in (int, float) and genamt >= 0
print(colors.GREEN + f"{lua_log_format_name(koboldai_vars.lua_koboldbridge.logging_name)} set genamt to {int(genamt)}" + colors.END)
koboldai_vars.genamt = int(genamt)
#==================================================================#
# Get the "Gens Per Action"
#==================================================================#
@bridged_kwarg()
def lua_get_numseqs():
return koboldai_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(koboldai_vars.lua_koboldbridge.logging_name)} set numseqs to {int(numseqs)}" + colors.END)
koboldai_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",
)
#==================================================================#
# Return the setting with the given name if it exists
#==================================================================#
@bridged_kwarg()
def lua_get_setting(setting):
if(setting in ("settemp", "temp")): return koboldai_vars.temp
if(setting in ("settopp", "topp", "top_p")): return koboldai_vars.top_p
if(setting in ("settopk", "topk", "top_k")): return koboldai_vars.top_k
if(setting in ("settfs", "tfs")): return koboldai_vars.tfs
if(setting in ("settypical", "typical")): return koboldai_vars.typical
if(setting in ("settopa", "topa")): return koboldai_vars.top_a
if(setting in ("setreppen", "reppen")): return koboldai_vars.rep_pen
if(setting in ("setreppenslope", "reppenslope")): return koboldai_vars.rep_pen_slope
if(setting in ("setreppenrange", "reppenrange")): return koboldai_vars.rep_pen_range
if(setting in ("settknmax", "tknmax")): return koboldai_vars.max_length
if(setting == "anotedepth"): return koboldai_vars.andepth
if(setting in ("setwidepth", "widepth")): return koboldai_vars.widepth
if(setting in ("setuseprompt", "useprompt")): return koboldai_vars.useprompt
if(setting in ("setadventure", "adventure")): return koboldai_vars.adventure
if(setting in ("setchatmode", "chatmode")): return koboldai_vars.chatmode
if(setting in ("setdynamicscan", "dynamicscan")): return koboldai_vars.dynamicscan
if(setting in ("setnopromptgen", "nopromptgen")): return koboldai_vars.nopromptgen
if(setting in ("autosave", "autosave")): return koboldai_vars.autosave
if(setting in ("setrngpersist", "rngpersist")): return koboldai_vars.rngpersist
if(setting in ("frmttriminc", "triminc")): return koboldai_vars.formatoptns["frmttriminc"]
if(setting in ("frmtrmblln", "rmblln")): return koboldai_vars.formatoptns["frmttrmblln"]
if(setting in ("frmtrmspch", "rmspch")): return koboldai_vars.formatoptns["frmttrmspch"]
if(setting in ("frmtadsnsp", "adsnsp")): return koboldai_vars.formatoptns["frmtadsnsp"]
if(setting in ("frmtsingleline", "singleline")): return koboldai_vars.formatoptns["singleline"]
if(setting == "outputstreaming"): koboldai_vars.output_streaming = v
#==================================================================#
# 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(koboldai_vars.lua_koboldbridge.logging_name)} set {setting} to {v}" + colors.END)
if(setting in ("setadventure", "adventure") and v):
koboldai_vars.actionmode = 1
if(setting in ("settemp", "temp")): koboldai_vars.temp = v
if(setting in ("settopp", "topp")): koboldai_vars.top_p = v
if(setting in ("settopk", "topk")): koboldai_vars.top_k = v
if(setting in ("settfs", "tfs")): koboldai_vars.tfs = v
if(setting in ("settypical", "typical")): koboldai_vars.typical = v
if(setting in ("settopa", "topa")): koboldai_vars.top_a = v
if(setting in ("setreppen", "reppen")): koboldai_vars.rep_pen = v
if(setting in ("setreppenslope", "reppenslope")): koboldai_vars.rep_pen_slope = v
if(setting in ("setreppenrange", "reppenrange")): koboldai_vars.rep_pen_range = v
if(setting in ("settknmax", "tknmax")): koboldai_vars.max_length = v; return True
if(setting == "anotedepth"): koboldai_vars.andepth = v; return True
if(setting in ("setwidepth", "widepth")): koboldai_vars.widepth = v; return True
if(setting in ("setuseprompt", "useprompt")): koboldai_vars.useprompt = v; return True
if(setting in ("setadventure", "adventure")): koboldai_vars.adventure = v
if(setting in ("setdynamicscan", "dynamicscan")): koboldai_vars.dynamicscan = v
if(setting in ("setnopromptgen", "nopromptgen")): koboldai_vars.nopromptgen = v
if(setting in ("autosave", "noautosave")): koboldai_vars.autosave = v
if(setting in ("setrngpersist", "rngpersist")): koboldai_vars.rngpersist = v
if(setting in ("setchatmode", "chatmode")): koboldai_vars.chatmode = v
if(setting in ("frmttriminc", "triminc")): koboldai_vars.formatoptns["frmttriminc"] = v
if(setting in ("frmtrmblln", "rmblln")): koboldai_vars.formatoptns["frmttrmblln"] = v
if(setting in ("frmtrmspch", "rmspch")): koboldai_vars.formatoptns["frmttrmspch"] = v
if(setting in ("frmtadsnsp", "adsnsp")): koboldai_vars.formatoptns["frmtadsnsp"] = v
if(setting in ("frmtsingleline", "singleline")): koboldai_vars.formatoptns["singleline"] = v
#==================================================================#
# Get contents of memory
#==================================================================#
@bridged_kwarg()
def lua_get_memory():
return koboldai_vars.memory
#==================================================================#
# Set contents of memory
#==================================================================#
@bridged_kwarg()
def lua_set_memory(m):
assert type(m) is str
koboldai_vars.memory = m
#==================================================================#
# Get contents of author's note
#==================================================================#
@bridged_kwarg()
def lua_get_authorsnote():
return koboldai_vars.authornote
#==================================================================#
# Set contents of author's note
#==================================================================#
@bridged_kwarg()
def lua_set_authorsnote(m):
assert type(m) is str
koboldai_vars.authornote = m
#==================================================================#
# Get contents of author's note template
#==================================================================#
@bridged_kwarg()
def lua_get_authorsnotetemplate():
return koboldai_vars.authornotetemplate
#==================================================================#
# Set contents of author's note template
#==================================================================#
@bridged_kwarg()
def lua_set_authorsnotetemplate(m):
assert type(m) is str
koboldai_vars.authornotetemplate = m
#==================================================================#
# Save settings and send them to client
#==================================================================#
@bridged_kwarg()
def lua_resend_settings():
print("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(koboldai_vars.lua_koboldbridge.logging_name)} deleted story chunk {k}" + colors.END)
chunk = int(k)
if(koboldai_vars.lua_koboldbridge.userstate == "genmod"):
del koboldai_vars._actions[chunk-1]
koboldai_vars.lua_deleted.add(chunk)
if(not hasattr(koboldai_vars, "_actions") or koboldai_vars._actions is not koboldai_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
koboldai_vars.actions[chunk-1] = ""
send_debug()
else:
if(k == 0):
print(colors.GREEN + f"{lua_log_format_name(koboldai_vars.lua_koboldbridge.logging_name)} edited prompt chunk" + colors.END)
else:
print(colors.GREEN + f"{lua_log_format_name(koboldai_vars.lua_koboldbridge.logging_name)} edited story chunk {k}" + colors.END)
chunk = int(k)
if(chunk == 0):
if(koboldai_vars.lua_koboldbridge.userstate == "genmod"):
koboldai_vars._prompt = v
koboldai_vars.lua_edited.add(chunk)
koboldai_vars.prompt = v
else:
if(koboldai_vars.lua_koboldbridge.userstate == "genmod"):
koboldai_vars._actions[chunk-1] = v
koboldai_vars.lua_edited.add(chunk)
koboldai_vars.actions[chunk-1] = v
send_debug()
#==================================================================#
# Get model type as "gpt-2-xl", "gpt-neo-2.7B", etc.
#==================================================================#
@bridged_kwarg()
def lua_get_modeltype():
if(koboldai_vars.noai):
return "readonly"
if(koboldai_vars.model in ("Colab", "OAI", "InferKit")):
return "api"
if(not koboldai_vars.use_colab_tpu and koboldai_vars.model not in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") and (koboldai_vars.model in ("GPT2Custom", "NeoCustom") or koboldai_vars.model_type in ("gpt2", "gpt_neo", "gptj"))):
hidden_size = get_hidden_size_from_model(model)
if(koboldai_vars.model in ("gpt2",) or (koboldai_vars.model_type == "gpt2" and hidden_size == 768)):
return "gpt2"
if(koboldai_vars.model in ("gpt2-medium",) or (koboldai_vars.model_type == "gpt2" and hidden_size == 1024)):
return "gpt2-medium"
if(koboldai_vars.model in ("gpt2-large",) or (koboldai_vars.model_type == "gpt2" and hidden_size == 1280)):
return "gpt2-large"
if(koboldai_vars.model in ("gpt2-xl",) or (koboldai_vars.model_type == "gpt2" and hidden_size == 1600)):
return "gpt2-xl"
if(koboldai_vars.model_type == "gpt_neo" and hidden_size == 768):
return "gpt-neo-125M"
if(koboldai_vars.model in ("EleutherAI/gpt-neo-1.3B",) or (koboldai_vars.model_type == "gpt_neo" and hidden_size == 2048)):
return "gpt-neo-1.3B"
if(koboldai_vars.model in ("EleutherAI/gpt-neo-2.7B",) or (koboldai_vars.model_type == "gpt_neo" and hidden_size == 2560)):
return "gpt-neo-2.7B"
if(koboldai_vars.model in ("EleutherAI/gpt-j-6B",) or ((koboldai_vars.use_colab_tpu or koboldai_vars.model == "TPUMeshTransformerGPTJ") and tpu_mtj_backend.params["d_model"] == 4096) or (koboldai_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(koboldai_vars.noai):
return "readonly"
if(koboldai_vars.model in ("Colab", "OAI", "InferKit")):
return "api"
if(koboldai_vars.use_colab_tpu or koboldai_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 koboldai_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 koboldai_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("softprompts/"+filename)
return changed
#==================================================================#
#
#==================================================================#
def execute_inmod():
setgamesaved(False)
koboldai_vars.lua_logname = ...
koboldai_vars.lua_edited = set()
koboldai_vars.lua_deleted = set()
try:
tpool.execute(koboldai_vars.lua_koboldbridge.execute_inmod)
except lupa.LuaError as e:
koboldai_vars.lua_koboldbridge.obliterate_multiverse()
koboldai_vars.lua_running = False
emit('from_server', {'cmd': 'errmsg', 'data': 'Lua script error; please check console.'}, broadcast=True, room="UI_1")
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)
socketio.emit("error", str(e), broadcast=True, room="UI_2")
set_aibusy(0)
def execute_genmod():
koboldai_vars.lua_koboldbridge.execute_genmod()
def execute_outmod():
setgamesaved(False)
emit('from_server', {'cmd': 'hidemsg', 'data': ''}, broadcast=True, room="UI_1")
try:
tpool.execute(koboldai_vars.lua_koboldbridge.execute_outmod)
except lupa.LuaError as e:
koboldai_vars.lua_koboldbridge.obliterate_multiverse()
koboldai_vars.lua_running = False
emit('from_server', {'cmd': 'errmsg', 'data': 'Lua script error; please check console.'}, broadcast=True, room="UI_1")
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)
socketio.emit("error", str(e), broadcast=True, room="UI_2")
set_aibusy(0)
if(koboldai_vars.lua_koboldbridge.resend_settings_required):
koboldai_vars.lua_koboldbridge.resend_settings_required = False
lua_resend_settings()
for k in koboldai_vars.lua_edited:
inlineedit(k, koboldai_vars.actions[k])
for k in koboldai_vars.lua_deleted:
inlinedelete(k)
#============================ METHODS =============================#
#==================================================================#
# Event triggered when browser SocketIO is loaded and connects to server
#==================================================================#
@socketio.on('connect')
def do_connect():
if request.args.get("rely") == "true":
return
join_room("UI_{}".format(request.args.get('ui')))
print("Joining Room UI_{}".format(request.args.get('ui')))
if request.args.get("ui") == "2":
ui2_connect()
return
print("{0}Client connected!{1}".format(colors.GREEN, colors.END))
emit('from_server', {'cmd': 'setchatname', 'data': koboldai_vars.chatname}, room="UI_1")
emit('from_server', {'cmd': 'setanotetemplate', 'data': koboldai_vars.authornotetemplate}, room="UI_1")
emit('from_server', {'cmd': 'connected', 'smandelete': koboldai_vars.smandelete, 'smanrename': koboldai_vars.smanrename, 'modelname': getmodelname()}, room="UI_1")
if(koboldai_vars.host):
emit('from_server', {'cmd': 'runs_remotely'}, room="UI_1")
if(koboldai_vars.flaskwebgui):
emit('from_server', {'cmd': 'flaskwebgui'}, room="UI_1")
if(koboldai_vars.allowsp):
emit('from_server', {'cmd': 'allowsp', 'data': koboldai_vars.allowsp}, room="UI_1")
sendUSStatItems()
emit('from_server', {'cmd': 'spstatitems', 'data': {koboldai_vars.spfilename: koboldai_vars.spmeta} if koboldai_vars.allowsp and len(koboldai_vars.spfilename) else {}}, broadcast=True, room="UI_1")
if(not koboldai_vars.gamestarted):
setStartState()
sendsettings()
refresh_settings()
koboldai_vars.laststory = None
emit('from_server', {'cmd': 'setstoryname', 'data': koboldai_vars.laststory}, room="UI_1")
sendwi()
emit('from_server', {'cmd': 'setmemory', 'data': koboldai_vars.memory}, room="UI_1")
emit('from_server', {'cmd': 'setanote', 'data': koboldai_vars.authornote}, room="UI_1")
koboldai_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': koboldai_vars.laststory}, room="UI_1")
sendwi()
emit('from_server', {'cmd': 'setmemory', 'data': koboldai_vars.memory}, room="UI_1")
emit('from_server', {'cmd': 'setanote', 'data': koboldai_vars.authornote}, room="UI_1")
if(koboldai_vars.mode == "play"):
if(not koboldai_vars.aibusy):
emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, room="UI_1")
else:
emit('from_server', {'cmd': 'setgamestate', 'data': 'wait'}, room="UI_1")
elif(koboldai_vars.mode == "edit"):
emit('from_server', {'cmd': 'editmode', 'data': 'true'}, room="UI_1")
elif(koboldai_vars.mode == "memory"):
emit('from_server', {'cmd': 'memmode', 'data': 'true'}, room="UI_1")
elif(koboldai_vars.mode == "wi"):
emit('from_server', {'cmd': 'wimode', 'data': 'true'}, room="UI_1")
emit('from_server', {'cmd': 'gamesaved', 'data': koboldai_vars.gamesaved}, broadcast=True, room="UI_1")
#==================================================================#
# Event triggered when browser SocketIO sends data to the server
#==================================================================#
@socketio.on('message')
def get_message(msg):
if not koboldai_vars.quiet:
print("{0}Data received:{1}{2}".format(colors.GREEN, msg, colors.END))
# Submit action
if(msg['cmd'] == 'submit'):
if(koboldai_vars.mode == "play"):
if(koboldai_vars.aibusy):
if(msg.get('allowabort', False)):
koboldai_vars.abort = True
return
koboldai_vars.abort = False
koboldai_vars.lua_koboldbridge.feedback = None
if(koboldai_vars.chatmode):
if(type(msg['chatname']) is not str):
raise ValueError("Chatname must be a string")
koboldai_vars.chatname = msg['chatname']
settingschanged()
emit('from_server', {'cmd': 'setchatname', 'data': koboldai_vars.chatname}, room="UI_1")
koboldai_vars.recentrng = koboldai_vars.recentrngm = None
actionsubmit(msg['data'], actionmode=msg['actionmode'])
elif(koboldai_vars.mode == "edit"):
editsubmit(msg['data'])
elif(koboldai_vars.mode == "memory"):
memsubmit(msg['data'])
# Retry Action
elif(msg['cmd'] == 'retry'):
if(koboldai_vars.aibusy):
if(msg.get('allowabort', False)):
koboldai_vars.abort = True
return
koboldai_vars.abort = False
if(koboldai_vars.chatmode):
if(type(msg['chatname']) is not str):
raise ValueError("Chatname must be a string")
koboldai_vars.chatname = msg['chatname']
settingschanged()
emit('from_server', {'cmd': 'setchatname', 'data': koboldai_vars.chatname}, room="UI_1")
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(koboldai_vars.mode == "play"):
koboldai_vars.mode = "edit"
emit('from_server', {'cmd': 'editmode', 'data': 'true'}, broadcast=True, room="UI_1")
elif(koboldai_vars.mode == "edit"):
koboldai_vars.mode = "play"
emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True, room="UI_1")
# 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 koboldai_vars.host and msg['cmd'] == 'savetofile'):
savetofile()
elif(not koboldai_vars.host and msg['cmd'] == 'loadfromfile'):
loadfromfile()
elif(msg['cmd'] == 'loadfromstring'):
loadRequest(json.loads(msg['data']), filename=msg['filename'])
elif(not koboldai_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'):
koboldai_vars.temp = float(msg['data'])
emit('from_server', {'cmd': 'setlabeltemp', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'settopp'):
koboldai_vars.top_p = float(msg['data'])
emit('from_server', {'cmd': 'setlabeltopp', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'settopk'):
koboldai_vars.top_k = int(msg['data'])
emit('from_server', {'cmd': 'setlabeltopk', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'settfs'):
koboldai_vars.tfs = float(msg['data'])
emit('from_server', {'cmd': 'setlabeltfs', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'settypical'):
koboldai_vars.typical = float(msg['data'])
emit('from_server', {'cmd': 'setlabeltypical', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'settopa'):
koboldai_vars.top_a = float(msg['data'])
emit('from_server', {'cmd': 'setlabeltopa', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setreppen'):
koboldai_vars.rep_pen = float(msg['data'])
emit('from_server', {'cmd': 'setlabelreppen', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setreppenslope'):
koboldai_vars.rep_pen_slope = float(msg['data'])
emit('from_server', {'cmd': 'setlabelreppenslope', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setreppenrange'):
koboldai_vars.rep_pen_range = float(msg['data'])
emit('from_server', {'cmd': 'setlabelreppenrange', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setoutput'):
koboldai_vars.genamt = int(msg['data'])
emit('from_server', {'cmd': 'setlabeloutput', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'settknmax'):
koboldai_vars.max_length = int(msg['data'])
emit('from_server', {'cmd': 'setlabeltknmax', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setikgen'):
koboldai_vars.ikgen = int(msg['data'])
emit('from_server', {'cmd': 'setlabelikgen', 'data': msg['data']}, broadcast=True, room="UI_1")
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'):
koboldai_vars.andepth = int(msg['data'])
emit('from_server', {'cmd': 'setlabelanotedepth', 'data': msg['data']}, broadcast=True, room="UI_1")
settingschanged()
refresh_settings()
# Format - Trim incomplete sentences
elif(msg['cmd'] == 'frmttriminc'):
if('frmttriminc' in koboldai_vars.formatoptns):
koboldai_vars.formatoptns["frmttriminc"] = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'frmtrmblln'):
if('frmtrmblln' in koboldai_vars.formatoptns):
koboldai_vars.formatoptns["frmtrmblln"] = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'frmtrmspch'):
if('frmtrmspch' in koboldai_vars.formatoptns):
koboldai_vars.formatoptns["frmtrmspch"] = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'frmtadsnsp'):
if('frmtadsnsp' in koboldai_vars.formatoptns):
koboldai_vars.formatoptns["frmtadsnsp"] = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'singleline'):
if('singleline' in koboldai_vars.formatoptns):
koboldai_vars.formatoptns["singleline"] = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'importselect'):
koboldai_vars.importnum = int(msg["data"].replace("import", ""))
elif(msg['cmd'] == 'importcancel'):
emit('from_server', {'cmd': 'popupshow', 'data': False}, room="UI_1")
koboldai_vars.importjs = {}
elif(msg['cmd'] == 'importaccept'):
emit('from_server', {'cmd': 'popupshow', 'data': False}, room="UI_1")
importgame()
elif(msg['cmd'] == 'wi'):
togglewimode()
elif(msg['cmd'] == 'wiinit'):
if(int(msg['data']) < len(koboldai_vars.worldinfo)):
setgamesaved(False)
koboldai_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(koboldai_vars.worldinfo)
setgamesaved(False)
emit('from_server', {'cmd': 'wiexpand', 'data': msg['data']}, broadcast=True, room="UI_1")
elif(msg['cmd'] == 'wiexpandfolder'):
assert 0 <= int(msg['data']) < len(koboldai_vars.worldinfo)
setgamesaved(False)
emit('from_server', {'cmd': 'wiexpandfolder', 'data': msg['data']}, broadcast=True, room="UI_1")
elif(msg['cmd'] == 'wifoldercollapsecontent'):
setgamesaved(False)
koboldai_vars.wifolders_d[msg['data']]['collapsed'] = True
emit('from_server', {'cmd': 'wifoldercollapsecontent', 'data': msg['data']}, broadcast=True, room="UI_1")
elif(msg['cmd'] == 'wifolderexpandcontent'):
setgamesaved(False)
koboldai_vars.wifolders_d[msg['data']]['collapsed'] = False
emit('from_server', {'cmd': 'wifolderexpandcontent', 'data': msg['data']}, broadcast=True, room="UI_1")
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):
koboldai_vars.worldinfo[num][field] = msg['data'][field]
emit('from_server', {'cmd': 'wiupdate', 'num': msg['num'], 'data': {field: koboldai_vars.worldinfo[num][field] for field in fields}}, broadcast=True, room="UI_1")
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)):
koboldai_vars.wifolders_d[uid][field] = msg['data'][field]
emit('from_server', {'cmd': 'wifolderupdate', 'uid': msg['uid'], 'data': {field: koboldai_vars.wifolders_d[uid][field] for field in fields}}, broadcast=True, room="UI_1")
elif(msg['cmd'] == 'wiselon'):
setgamesaved(False)
koboldai_vars.worldinfo[msg['data']]["selective"] = True
emit('from_server', {'cmd': 'wiselon', 'data': msg['data']}, broadcast=True, room="UI_1")
elif(msg['cmd'] == 'wiseloff'):
setgamesaved(False)
koboldai_vars.worldinfo[msg['data']]["selective"] = False
emit('from_server', {'cmd': 'wiseloff', 'data': msg['data']}, broadcast=True, room="UI_1")
elif(msg['cmd'] == 'wiconstanton'):
setgamesaved(False)
koboldai_vars.worldinfo[msg['data']]["constant"] = True
emit('from_server', {'cmd': 'wiconstanton', 'data': msg['data']}, broadcast=True, room="UI_1")
elif(msg['cmd'] == 'wiconstantoff'):
setgamesaved(False)
koboldai_vars.worldinfo[msg['data']]["constant"] = False
emit('from_server', {'cmd': 'wiconstantoff', 'data': msg['data']}, broadcast=True, room="UI_1")
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}}, room="UI_1")
elif(msg['cmd'] == 'samplerlistrequest'):
emit('from_server', {'cmd': 'buildsamplers', 'data': koboldai_vars.sampler_order}, room="UI_1")
elif(msg['cmd'] == 'usloaded'):
koboldai_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)):
koboldai_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(koboldai_vars.sampler_order)):
raise ValueError(f"Sampler order must be a list of length {len(koboldai_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")
koboldai_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/" + koboldai_vars.model.replace('/', '_') + ".breakmodel"):
with open("settings/" + koboldai_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:
f = open("settings/" + koboldai_vars.model.replace('/', '_') + ".breakmodel", "w")
f.write(msg['gpu_layers'] + '\n' + msg['disk_layers'])
f.close()
koboldai_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, room="UI_1")
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 koboldai_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']):
koboldai_vars.model = msg['data']
koboldai_vars.custmodpth = msg['path_modelname']
get_model_info(msg['data'], directory=msg['path'])
else:
koboldai_vars.model = msg['path_modelname']
try:
get_model_info(koboldai_vars.model)
except:
emit('from_server', {'cmd': 'errmsg', 'data': "The model entered doesn't exist."}, room="UI_1")
elif msg['data'] in ('NeoCustom', 'GPT2Custom'):
if check_if_dir_is_model(msg['path']):
koboldai_vars.model = msg['data']
koboldai_vars.custmodpth = msg['path']
get_model_info(msg['data'], directory=msg['path'])
else:
if koboldai_vars.host:
sendModelSelection(menu=msg['data'], folder="./models")
else:
sendModelSelection(menu=msg['data'], folder=msg['path'])
else:
koboldai_vars.model = msg['data']
if 'path' in msg:
koboldai_vars.custmodpth = msg['path']
get_model_info(msg['data'], directory=msg['path'])
else:
get_model_info(koboldai_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({'model': koboldai_vars.model, 'key': msg['key']})
elif(msg['cmd'] == 'loadselect'):
koboldai_vars.loadselect = msg["data"]
elif(msg['cmd'] == 'spselect'):
koboldai_vars.spselect = msg["data"]
elif(msg['cmd'] == 'loadrequest'):
loadRequest(fileops.storypath(koboldai_vars.loadselect))
elif(msg['cmd'] == 'sprequest'):
spRequest("softprompts/"+koboldai_vars.spselect)
elif(msg['cmd'] == 'deletestory'):
deletesave(msg['data'])
elif(msg['cmd'] == 'renamestory'):
renamesave(msg['data'], msg['newname'])
elif(msg['cmd'] == 'clearoverwrite'):
koboldai_vars.svowname = ""
koboldai_vars.saveow = False
elif(msg['cmd'] == 'seqsel'):
selectsequence(msg['data'])
elif(msg['cmd'] == 'seqpin'):
pinsequence(msg['data'])
elif(msg['cmd'] == 'setnumseq'):
koboldai_vars.numseqs = int(msg['data'])
emit('from_server', {'cmd': 'setlabelnumseq', 'data': msg['data']}, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setwidepth'):
koboldai_vars.widepth = int(msg['data'])
emit('from_server', {'cmd': 'setlabelwidepth', 'data': msg['data']}, room="UI_1")
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setuseprompt'):
koboldai_vars.useprompt = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setadventure'):
koboldai_vars.adventure = msg['data']
koboldai_vars.chatmode = False
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'autosave'):
koboldai_vars.autosave = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setchatmode'):
koboldai_vars.chatmode = msg['data']
koboldai_vars.adventure = False
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setdynamicscan'):
koboldai_vars.dynamicscan = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setnopromptgen'):
koboldai_vars.nopromptgen = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setrngpersist'):
koboldai_vars.rngpersist = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setnogenmod'):
koboldai_vars.nogenmod = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setoutputstreaming'):
koboldai_vars.output_streaming = msg['data']
settingschanged()
refresh_settings()
elif(not koboldai_vars.host and msg['cmd'] == 'importwi'):
wiimportrequest()
elif(msg['cmd'] == 'debug'):
koboldai_vars.debug = msg['data']
emit('from_server', {'cmd': 'set_debug', 'data': msg['data']}, broadcast=True, room="UI_1")
if koboldai_vars.debug:
send_debug()
#==================================================================#
# Send userscripts list to client
#==================================================================#
def sendUSStatItems():
_, loaded = getuslist()
loaded = loaded if koboldai_vars.lua_running else []
last_userscripts = [e["filename"] for e in loaded]
emit('from_server', {'cmd': 'usstatitems', 'data': loaded, 'flash': last_userscripts != koboldai_vars.last_userscripts}, broadcast=True, room="UI_1")
koboldai_vars.last_userscripts = last_userscripts
#==================================================================#
# KoboldAI Markup Formatting (Mixture of Markdown and sanitized html)
#==================================================================#
def kml(txt):
txt = txt.replace('>', '&gt;')
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(koboldai_vars.welcome):
txt = kml(koboldai_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 koboldai_vars.noai and not koboldai_vars.welcome):
txt = txt + "Please load a game or enter a prompt below to begin!</span>"
if(koboldai_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': koboldai_vars.gamestarted, 'data': txt}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setgamestate', 'data': 'start'}, broadcast=True, room="UI_1")
#==================================================================#
# Transmit applicable settings to SocketIO to build UI sliders/toggles
#==================================================================#
def sendsettings():
# Send settings for selected AI type
emit('from_server', {'cmd': 'reset_menus'}, room="UI_1")
if(koboldai_vars.model != "InferKit"):
for set in gensettings.gensettingstf:
emit('from_server', {'cmd': 'addsetting', 'data': set}, room="UI_1")
else:
for set in gensettings.gensettingsik:
emit('from_server', {'cmd': 'addsetting', 'data': set}, room="UI_1")
# Send formatting options
for frm in gensettings.formatcontrols:
emit('from_server', {'cmd': 'addformat', 'data': frm}, room="UI_1")
# Add format key to vars if it wasn't loaded with client.settings
if(not frm["id"] in koboldai_vars.formatoptns):
koboldai_vars.formatoptns[frm["id"]] = False;
#==================================================================#
# Set value of gamesaved
#==================================================================#
def setgamesaved(gamesaved):
assert type(gamesaved) is bool
if(gamesaved != koboldai_vars.gamesaved):
emit('from_server', {'cmd': 'gamesaved', 'data': gamesaved}, broadcast=True, room="UI_1")
koboldai_vars.gamesaved = gamesaved
#==================================================================#
# Take input text from SocketIO and decide what to do with it
#==================================================================#
def check_for_backend_compilation():
if(koboldai_vars.checking):
return
koboldai_vars.checking = True
for _ in range(31):
time.sleep(0.06276680299820175)
if(koboldai_vars.compiling):
emit('from_server', {'cmd': 'warnmsg', 'data': 'Compiling TPU backend&mdash;this usually takes 1&ndash;2 minutes...'}, broadcast=True, room="UI_1")
break
koboldai_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(koboldai_vars.aibusy):
return
while(True):
set_aibusy(1)
if(disable_recentrng):
koboldai_vars.recentrng = koboldai_vars.recentrngm = None
koboldai_vars.recentback = False
koboldai_vars.recentedit = False
koboldai_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(koboldai_vars.chatmode and koboldai_vars.gamestarted):
data = re.sub(r'\n+', ' ', data)
if(len(data)):
data = f"\n{koboldai_vars.chatname}: {data}\n"
# If we're not continuing, store a copy of the raw input
if(data != ""):
koboldai_vars.lastact = data
if(not koboldai_vars.gamestarted):
koboldai_vars.submission = data
execute_inmod()
data = koboldai_vars.submission
if(not force_submit and len(data.strip()) == 0):
assert False
# Start the game
koboldai_vars.gamestarted = True
if(not koboldai_vars.noai and koboldai_vars.lua_koboldbridge.generating and (not koboldai_vars.nopromptgen or force_prompt_gen)):
# Save this first action as the prompt
koboldai_vars.prompt = data
# Clear the startup text from game screen
emit('from_server', {'cmd': 'updatescreen', 'gamestarted': False, 'data': 'Please wait, generating story...'}, broadcast=True, room="UI_1")
calcsubmit(data) # Run the first action through the generator
if(not koboldai_vars.abort and koboldai_vars.lua_koboldbridge.restart_sequence is not None and len(koboldai_vars.genseqs) == 0):
data = ""
force_submit = True
disable_recentrng = True
continue
emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True, room="UI_1")
break
else:
# Save this first action as the prompt
koboldai_vars.prompt = data if len(data) > 0 else '"'
for i in range(koboldai_vars.numseqs):
koboldai_vars.lua_koboldbridge.outputs[i+1] = ""
execute_outmod()
koboldai_vars.lua_koboldbridge.regeneration_required = False
genout = []
for i in range(koboldai_vars.numseqs):
genout.append({"generated_text": koboldai_vars.lua_koboldbridge.outputs[i+1]})
assert type(genout[-1]["generated_text"]) is str
koboldai_vars.actions.clear_unused_options()
koboldai_vars.actions.append_options([x["generated_text"] for x in genout])
genout = [{"generated_text": x['text']} for x in koboldai_vars.actions.get_current_options()]
if(len(genout) == 1):
genresult(genout[0]["generated_text"], flash=False)
refresh_story()
if(len(koboldai_vars.actions) > 0):
emit('from_server', {'cmd': 'texteffect', 'data': koboldai_vars.actions.get_last_key() + 1}, broadcast=True, room="UI_1")
if(not koboldai_vars.abort and koboldai_vars.lua_koboldbridge.restart_sequence is not None):
data = ""
force_submit = True
disable_recentrng = True
continue
else:
if(not koboldai_vars.abort and koboldai_vars.lua_koboldbridge.restart_sequence is not None and koboldai_vars.lua_koboldbridge.restart_sequence > 0):
genresult(genout[koboldai_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, room="UI_1")
break
else:
# Apply input formatting & scripts before sending to tokenizer
if(koboldai_vars.actionmode == 0):
data = applyinputformatting(data)
koboldai_vars.submission = data
execute_inmod()
data = koboldai_vars.submission
# Dont append submission if it's a blank/continue action
if(data != ""):
# Store the result in the Action log
if(len(koboldai_vars.prompt.strip()) == 0):
koboldai_vars.prompt = data
else:
koboldai_vars.actions.append(data)
update_story_chunk('last')
send_debug()
if(not koboldai_vars.noai and koboldai_vars.lua_koboldbridge.generating):
# Off to the tokenizer!
calcsubmit(data)
if(not koboldai_vars.abort and koboldai_vars.lua_koboldbridge.restart_sequence is not None and len(koboldai_vars.genseqs) == 0):
data = ""
force_submit = True
disable_recentrng = True
continue
emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True, room="UI_1")
break
else:
for i in range(koboldai_vars.numseqs):
koboldai_vars.lua_koboldbridge.outputs[i+1] = ""
execute_outmod()
koboldai_vars.lua_koboldbridge.regeneration_required = False
genout = []
for i in range(koboldai_vars.numseqs):
genout.append({"generated_text": koboldai_vars.lua_koboldbridge.outputs[i+1]})
assert type(genout[-1]["generated_text"]) is str
koboldai_vars.actions.clear_unused_options()
koboldai_vars.actions.append_options([x["generated_text"] for x in genout])
genout = [{"generated_text": x['text']} for x in koboldai_vars.actions.get_current_options()]
if(len(genout) == 1):
genresult(genout[0]["generated_text"])
if(not koboldai_vars.abort and koboldai_vars.lua_koboldbridge.restart_sequence is not None):
data = ""
force_submit = True
disable_recentrng = True
continue
else:
if(not koboldai_vars.abort and koboldai_vars.lua_koboldbridge.restart_sequence is not None and koboldai_vars.lua_koboldbridge.restart_sequence > 0):
genresult(genout[koboldai_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, room="UI_1")
break
#==================================================================#
#
#==================================================================#
def actionretry(data):
if(koboldai_vars.noai):
emit('from_server', {'cmd': 'errmsg', 'data': "Retry function unavailable in Read Only mode."}, room="UI_1")
return
if(koboldai_vars.recentrng is not None):
if(not koboldai_vars.aibusy):
randomGameRequest(koboldai_vars.recentrng, memory=koboldai_vars.recentrngm)
return
if actionback():
actionsubmit("", actionmode=koboldai_vars.actionmode, force_submit=True)
send_debug()
elif(not koboldai_vars.useprompt):
emit('from_server', {'cmd': 'errmsg', 'data': "Please enable \"Always Add Prompt\" to retry with your prompt."}, room="UI_1")
#==================================================================#
#
#==================================================================#
def actionback():
if(koboldai_vars.aibusy):
return
# Remove last index of actions and refresh game screen
if(len(koboldai_vars.genseqs) == 0 and len(koboldai_vars.actions) > 0):
last_key = koboldai_vars.actions.get_last_key()
koboldai_vars.actions.pop()
koboldai_vars.recentback = True
remove_story_chunk(last_key + 1)
success = True
elif(len(koboldai_vars.genseqs) == 0):
emit('from_server', {'cmd': 'errmsg', 'data': "Cannot delete the prompt."}, room="UI_1")
success = False
else:
koboldai_vars.genseqs = []
success = True
send_debug()
return success
def actionredo():
genout = [[x['text'], "redo" if x['Previous Selection'] else "pinned" if x['Pinned'] else "normal"] for x in koboldai_vars.actions.get_redo_options()]
if len(genout) == 0:
emit('from_server', {'cmd': 'popuperror', 'data': "There's nothing to redo"}, broadcast=True, room="UI_1")
elif len(genout) == 1:
genresult(genout[0][0], flash=True, ignore_formatting=True)
else:
koboldai_vars.genseqs = [{"generated_text": x[0]} for x in genout]
emit('from_server', {'cmd': 'genseqs', 'data': genout}, broadcast=True, room="UI_1")
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(koboldai_vars.memory != "" and koboldai_vars.memory[-1] != "\n"):
mem = koboldai_vars.memory + "\n"
else:
mem = koboldai_vars.memory
# Build Author's Note if set
if(koboldai_vars.authornote != ""):
anotetxt = ("\n" + koboldai_vars.authornotetemplate + "\n").replace("<|>", koboldai_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 = koboldai_vars.sp_length
if("tokenizer" not in globals()):
from transformers import GPT2TokenizerFast
global tokenizer
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
lnheader = len(tokenizer._koboldai_header)
# Calculate token budget
prompttkns = tokenizer.encode(utils.encodenewlines(koboldai_vars.comregex_ai.sub('', koboldai_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 > koboldai_vars.max_length - lnheader - lnsp - koboldai_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 > koboldai_vars.max_length - lnheader - lnsp - koboldai_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 > koboldai_vars.max_length - lnheader - lnsp - koboldai_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(koboldai_vars.useprompt):
budget = koboldai_vars.max_length - lnsp - lnprompt - lnmem - lnanote - lnwi - koboldai_vars.genamt - budget_deduction
else:
budget = koboldai_vars.max_length - lnsp - lnmem - lnanote - lnwi - koboldai_vars.genamt - budget_deduction
lnsubmission = len(tokenizer.encode(utils.encodenewlines(koboldai_vars.comregex_ai.sub('', submission)), max_length=int(2e9), truncation=True)) if submission is not None else 0
maybe_lnprompt = lnprompt if koboldai_vars.useprompt and actionlen > 0 else 0
if(lnmem + lnwi + lnanote + maybe_lnprompt + lnsubmission > koboldai_vars.max_length - lnheader - lnsp - koboldai_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) <= koboldai_vars.max_length - lnsp - koboldai_vars.genamt - budget_deduction
ln = len(tokens) + lnsp
return tokens, ln+1, ln+koboldai_vars.genamt
else:
tokens = []
# Check if we have the action depth to hit our A.N. depth
if(anotetxt != "" and actionlen < koboldai_vars.andepth):
forceanote = True
# Get most recent action tokens up to our budget
n = 0
for key in reversed(actions):
chunk = koboldai_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 == koboldai_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 koboldai_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) <= koboldai_vars.max_length - lnsp - koboldai_vars.genamt - budget_deduction
ln = len(tokens) + lnsp
return tokens, ln+1, ln+koboldai_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(koboldai_vars.actions)
winfo, mem, anotetxt, found_entries = calcsubmitbudgetheader(txt)
# For all transformers models
if(koboldai_vars.model != "InferKit"):
subtxt, min, max = calcsubmitbudget(actionlen, winfo, mem, anotetxt, koboldai_vars.actions, submission=txt)
if(actionlen == 0):
if(not koboldai_vars.use_colab_tpu and koboldai_vars.model not in ["Colab", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
generate(subtxt, min, max, found_entries=found_entries)
elif(koboldai_vars.model == "Colab"):
sendtocolab(utils.decodenewlines(tokenizer.decode(subtxt)), min, max)
elif(koboldai_vars.model == "OAI"):
oairequest(utils.decodenewlines(tokenizer.decode(subtxt)), min, max)
elif(koboldai_vars.use_colab_tpu or koboldai_vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")):
tpumtjgenerate(subtxt, min, max, found_entries=found_entries)
else:
if(not koboldai_vars.use_colab_tpu and koboldai_vars.model not in ["Colab", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
generate(subtxt, min, max, found_entries=found_entries)
elif(koboldai_vars.model == "Colab"):
sendtocolab(utils.decodenewlines(tokenizer.decode(subtxt)), min, max)
elif(koboldai_vars.model == "OAI"):
oairequest(utils.decodenewlines(tokenizer.decode(subtxt)), min, max)
elif(koboldai_vars.use_colab_tpu or koboldai_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 < koboldai_vars.andepth):
forceanote = True
if(koboldai_vars.useprompt):
budget = koboldai_vars.ikmax - len(koboldai_vars.comregex_ai.sub('', koboldai_vars.prompt)) - len(anotetxt) - len(mem) - len(winfo) - 1
else:
budget = koboldai_vars.ikmax - len(anotetxt) - len(mem) - len(winfo) - 1
subtxt = ""
prompt = koboldai_vars.comregex_ai.sub('', koboldai_vars.prompt)
n = 0
for key in reversed(koboldai_vars.actions):
chunk = koboldai_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 koboldai_vars.useprompt):
if(budget > 0):
prompt = koboldai_vars.comregex_ai.sub('', koboldai_vars.prompt)[-budget:]
else:
prompt = ""
# Inject Author's Note if we've reached the desired depth
if(n == koboldai_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):
gen_in = torch.tensor(txt, dtype=torch.long)[None]
if(koboldai_vars.sp is not None):
soft_tokens = torch.arange(
model.config.vocab_size,
model.config.vocab_size + koboldai_vars.sp.shape[0],
)
gen_in = torch.cat((soft_tokens[None], gen_in), dim=-1)
assert gen_in.shape[-1] + koboldai_vars.genamt <= koboldai_vars.max_length
if(koboldai_vars.hascuda and koboldai_vars.usegpu):
gen_in = gen_in.to(koboldai_vars.gpu_device)
elif(koboldai_vars.hascuda and koboldai_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
koboldai_vars._actions = koboldai_vars.actions
koboldai_vars._prompt = koboldai_vars.prompt
if(koboldai_vars.dynamicscan):
koboldai_vars._actions = koboldai_vars._actions.copy()
with torch.no_grad():
already_generated = 0
numseqs = koboldai_vars.numseqs
while True:
genout = generator(
gen_in,
do_sample=True,
max_length=int(2e9),
repetition_penalty=1.1,
bad_words_ids=koboldai_vars.badwordsids,
use_cache=True,
num_return_sequences=numseqs
)
already_generated += len(genout[0]) - len(gen_in[0])
assert already_generated <= koboldai_vars.genamt
if(model.kai_scanner.halt or not model.kai_scanner.regeneration_required):
break
assert genout.ndim >= 2
assert genout.shape[0] == koboldai_vars.numseqs
if(koboldai_vars.lua_koboldbridge.generated_cols and koboldai_vars.generated_tkns != koboldai_vars.lua_koboldbridge.generated_cols):
raise RuntimeError("Inconsistency detected between KoboldAI Python and Lua backends")
if(already_generated != koboldai_vars.generated_tkns):
raise RuntimeError("WI scanning error")
for r in range(koboldai_vars.numseqs):
for c in range(already_generated):
assert koboldai_vars.lua_koboldbridge.generated[r+1][c+1] is not None
genout[r][genout.shape[-1] - already_generated + c] = koboldai_vars.lua_koboldbridge.generated[r+1][c+1]
encoded = []
for i in range(koboldai_vars.numseqs):
txt = utils.decodenewlines(tokenizer.decode(genout[i, -already_generated:]))
winfo, mem, anotetxt, _found_entries = calcsubmitbudgetheader(txt, force_use_txt=True, actions=koboldai_vars._actions)
found_entries[i].update(_found_entries)
txt, _, _ = calcsubmitbudget(len(koboldai_vars._actions), winfo, mem, anotetxt, koboldai_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(koboldai_vars.sp is not None):
soft_tokens = torch.arange(
model.config.vocab_size,
model.config.vocab_size + koboldai_vars.sp.shape[0],
device=genout.device,
)
genout = torch.cat((soft_tokens.tile(koboldai_vars.numseqs, 1), genout), dim=-1)
assert genout.shape[-1] + koboldai_vars.genamt - already_generated <= koboldai_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):
koboldai_vars.generated_tkns = 0
if(found_entries is None):
found_entries = set()
found_entries = tuple(found_entries.copy() for _ in range(koboldai_vars.numseqs))
if not koboldai_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
koboldai_vars.lastctx = utils.decodenewlines(tokenizer.decode(txt))
# Clear CUDA cache if using GPU
if(koboldai_vars.hascuda and (koboldai_vars.usegpu or koboldai_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)):
koboldai_vars.lua_koboldbridge.obliterate_multiverse()
koboldai_vars.lua_running = False
emit('from_server', {'cmd': 'errmsg', 'data': 'Lua script error; please check console.'}, broadcast=True, room="UI_1")
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)
socketio.emit("error", str(e), broadcast=True, room="UI_2")
else:
emit('from_server', {'cmd': 'errmsg', 'data': 'Error occurred during generator call; please check console.'}, broadcast=True, room="UI_1")
print("{0}{1}{2}".format(colors.RED, traceback.format_exc().replace("\033", ""), colors.END), file=sys.stderr)
socketio.emit("error", str(e), broadcast=True, room="UI_2")
set_aibusy(0)
return
for i in range(koboldai_vars.numseqs):
koboldai_vars.lua_koboldbridge.generated[i+1][koboldai_vars.generated_tkns] = int(genout[i, -1].item())
koboldai_vars.lua_koboldbridge.outputs[i+1] = utils.decodenewlines(tokenizer.decode(genout[i, -already_generated:]))
execute_outmod()
if(koboldai_vars.lua_koboldbridge.regeneration_required):
koboldai_vars.lua_koboldbridge.regeneration_required = False
genout = []
for i in range(koboldai_vars.numseqs):
genout.append({"generated_text": koboldai_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]
koboldai_vars.actions.clear_unused_options()
koboldai_vars.actions.append_options([x["generated_text"] for x in genout])
genout = [{"generated_text": x['text']} for x in koboldai_vars.actions.get_current_options()]
if(len(genout) == 1):
genresult(genout[0]["generated_text"])
else:
if(koboldai_vars.lua_koboldbridge.restart_sequence is not None and koboldai_vars.lua_koboldbridge.restart_sequence > 0):
genresult(genout[koboldai_vars.lua_koboldbridge.restart_sequence-1]["generated_text"])
else:
genselect(genout)
# Clear CUDA cache again if using GPU
if(koboldai_vars.hascuda and (koboldai_vars.usegpu or koboldai_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 koboldai_vars.quiet:
print("{0}{1}{2}".format(colors.CYAN, genout, colors.END))
# Format output before continuing
if not ignore_formatting:
genout = applyoutputformatting(genout)
koboldai_vars.lua_koboldbridge.feedback = genout
if(len(genout) == 0):
return
# Add formatted text to Actions array and refresh the game screen
if(len(koboldai_vars.prompt.strip()) == 0):
koboldai_vars.prompt = genout
else:
koboldai_vars.actions.append(genout)
update_story_chunk('last')
if(flash):
emit('from_server', {'cmd': 'texteffect', 'data': koboldai_vars.actions.get_last_key() + 1 if len(koboldai_vars.actions) else 0}, broadcast=True, room="UI_1")
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 koboldai_vars.quiet:
print("{0}[Result {1}]\n{2}{3}".format(colors.CYAN, i, result["generated_text"], colors.END))
i += 1
# Store sequences in memory until selection is made
koboldai_vars.genseqs = genout
genout = koboldai_vars.actions.get_current_options_no_edits(ui=1)
# Send sequences to UI for selection
emit('from_server', {'cmd': 'genseqs', 'data': genout}, broadcast=True, room="UI_1")
send_debug()
#==================================================================#
# Send selected sequence to action log and refresh UI
#==================================================================#
def selectsequence(n):
if(len(koboldai_vars.genseqs) == 0):
return
koboldai_vars.lua_koboldbridge.feedback = koboldai_vars.genseqs[int(n)]["generated_text"]
if(len(koboldai_vars.lua_koboldbridge.feedback) != 0):
koboldai_vars.actions.append(koboldai_vars.lua_koboldbridge.feedback)
update_story_chunk('last')
emit('from_server', {'cmd': 'texteffect', 'data': koboldai_vars.actions.get_last_key() + 1 if len(koboldai_vars.actions) else 0}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'hidegenseqs', 'data': ''}, broadcast=True, room="UI_1")
koboldai_vars.genseqs = []
if(koboldai_vars.lua_koboldbridge.restart_sequence is not None):
actionsubmit("", actionmode=koboldai_vars.actionmode, force_submit=True, disable_recentrng=True)
send_debug()
#==================================================================#
# Pin/Unpin the selected sequence
#==================================================================#
def pinsequence(n):
if n.isnumeric():
koboldai_vars.actions.toggle_pin(koboldai_vars.actions.get_last_key()+1, int(n))
text = koboldai_vars.genseqs[int(n)]['generated_text']
send_debug()
#==================================================================#
# Send transformers-style request to ngrok/colab host
#==================================================================#
def sendtocolab(txt, min, max):
# Log request to console
if not koboldai_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
koboldai_vars.lastctx = txt
# Build request JSON data
reqdata = {
'text': txt,
'min': min,
'max': max,
'rep_pen': koboldai_vars.rep_pen,
'rep_pen_slope': koboldai_vars.rep_pen_slope,
'rep_pen_range': koboldai_vars.rep_pen_range,
'temperature': koboldai_vars.temp,
'top_p': koboldai_vars.top_p,
'top_k': koboldai_vars.top_k,
'tfs': koboldai_vars.tfs,
'typical': koboldai_vars.typical,
'topa': koboldai_vars.top_a,
'numseqs': koboldai_vars.numseqs,
'retfultxt': False
}
# Create request
req = requests.post(
koboldai_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(koboldai_vars.numseqs):
koboldai_vars.lua_koboldbridge.outputs[i+1] = genout[i]
execute_outmod()
if(koboldai_vars.lua_koboldbridge.regeneration_required):
koboldai_vars.lua_koboldbridge.regeneration_required = False
genout = []
for i in range(koboldai_vars.numseqs):
genout.append(koboldai_vars.lua_koboldbridge.outputs[i+1])
assert type(genout[-1]) is str
koboldai_vars.actions.clear_unused_options()
koboldai_vars.actions.append_options([x["generated_text"] for x in genout])
genout = [{"generated_text": x['text']} for x in koboldai_vars.actions.get_current_options()]
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(koboldai_vars.lua_koboldbridge.restart_sequence is not None and koboldai_vars.lua_koboldbridge.restart_sequence > 0):
genresult(genout[koboldai_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
#koboldai_vars.actions.append(genout)
#refresh_story()
#emit('from_server', {'cmd': 'texteffect', 'data': koboldai_vars.actions.get_last_key() + 1 if len(koboldai_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, room="UI_1")
set_aibusy(0)
#==================================================================#
# Send text to TPU mesh transformer backend
#==================================================================#
def tpumtjgenerate(txt, minimum, maximum, found_entries=None):
koboldai_vars.generated_tkns = 0
if(found_entries is None):
found_entries = set()
found_entries = tuple(found_entries.copy() for _ in range(koboldai_vars.numseqs))
if not koboldai_vars.quiet:
print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, minimum, maximum, utils.decodenewlines(tokenizer.decode(txt)), colors.END))
koboldai_vars._actions = koboldai_vars.actions
koboldai_vars._prompt = koboldai_vars.prompt
if(koboldai_vars.dynamicscan):
koboldai_vars._actions = koboldai_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(koboldai_vars.dynamicscan or (not koboldai_vars.nogenmod and koboldai_vars.has_genmod)):
context = np.tile(np.uint32(txt), (koboldai_vars.numseqs, 1))
past = np.empty((koboldai_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=koboldai_vars.numseqs,
soft_embeddings=koboldai_vars.sp,
soft_tokens=soft_tokens,
excluded_world_info=found_entries,
)
past = np.pad(past, ((0, 0), (0, n_generated)))
for r in range(koboldai_vars.numseqs):
for c in range(koboldai_vars.lua_koboldbridge.generated_cols):
assert koboldai_vars.lua_koboldbridge.generated[r+1][c+1] is not None
past[r, c] = koboldai_vars.lua_koboldbridge.generated[r+1][c+1]
if(koboldai_vars.abort or halt or not regeneration_required):
break
print("(regeneration triggered)")
encoded = []
for i in range(koboldai_vars.numseqs):
txt = utils.decodenewlines(tokenizer.decode(past[i]))
winfo, mem, anotetxt, _found_entries = calcsubmitbudgetheader(txt, force_use_txt=True, actions=koboldai_vars._actions)
found_entries[i].update(_found_entries)
txt, _, _ = calcsubmitbudget(len(koboldai_vars._actions), winfo, mem, anotetxt, koboldai_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=koboldai_vars.temp,
top_p=koboldai_vars.top_p,
top_k=koboldai_vars.top_k,
tfs=koboldai_vars.tfs,
typical=koboldai_vars.typical,
top_a=koboldai_vars.top_a,
numseqs=koboldai_vars.numseqs,
repetition_penalty=koboldai_vars.rep_pen,
rpslope=koboldai_vars.rep_pen_slope,
rprange=koboldai_vars.rep_pen_range,
soft_embeddings=koboldai_vars.sp,
soft_tokens=soft_tokens,
sampler_order=koboldai_vars.sampler_order,
)
past = genout
for i in range(koboldai_vars.numseqs):
koboldai_vars.lua_koboldbridge.generated[i+1] = koboldai_vars.lua_state.table(*genout[i].tolist())
koboldai_vars.lua_koboldbridge.generated_cols = koboldai_vars.generated_tkns = genout[0].shape[-1]
except Exception as e:
if(issubclass(type(e), lupa.LuaError)):
koboldai_vars.lua_koboldbridge.obliterate_multiverse()
koboldai_vars.lua_running = False
emit('from_server', {'cmd': 'errmsg', 'data': 'Lua script error; please check console.'}, broadcast=True, room="UI_1")
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)
socketio.emit("error", str(e), broadcast=True, room="UI_2")
else:
emit('from_server', {'cmd': 'errmsg', 'data': 'Error occurred during generator call; please check console.'}, broadcast=True, room="UI_1")
print("{0}{1}{2}".format(colors.RED, traceback.format_exc().replace("\033", ""), colors.END), file=sys.stderr)
socketio.emit("error", str(e), broadcast=True, room="UI_2")
set_aibusy(0)
return
for i in range(koboldai_vars.numseqs):
koboldai_vars.lua_koboldbridge.outputs[i+1] = utils.decodenewlines(tokenizer.decode(past[i]))
genout = past
execute_outmod()
if(koboldai_vars.lua_koboldbridge.regeneration_required):
koboldai_vars.lua_koboldbridge.regeneration_required = False
genout = []
for i in range(koboldai_vars.numseqs):
genout.append({"generated_text": koboldai_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]
koboldai_vars.actions.clear_unused_options()
koboldai_vars.actions.append_options([x["generated_text"] for x in genout])
genout = [{"generated_text": x['text']} for x in koboldai_vars.actions.get_current_options()]
if(len(koboldai_vars.actions.get_current_options()) == 1):
genresult(koboldai_vars.actions.get_current_options()[0]['text'])
else:
if(koboldai_vars.lua_koboldbridge.restart_sequence is not None and koboldai_vars.lua_koboldbridge.restart_sequence > 0):
genresult(genout[koboldai_vars.lua_koboldbridge.restart_sequence-1]["generated_text"])
else:
genselect([{"generated_text": x['text']} for x in koboldai_vars.actions.get_current_options()])
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('&lt;/s&gt;', '<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(koboldai_vars.lastctx == ""):
return txt
# Tokenize the last context and the generated content
ctxtokens = tokenizer.encode(utils.encodenewlines(koboldai_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(koboldai_vars.formatoptns["frmtadsnsp"]):
txt = utils.addsentencespacing(txt, koboldai_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(koboldai_vars.adventure):
txt = koboldai_vars.acregex_ai.sub('', txt)
# Trim incomplete sentences
if(koboldai_vars.formatoptns["frmttriminc"] and not koboldai_vars.chatmode):
txt = utils.trimincompletesentence(txt)
# Replace blank lines
if(koboldai_vars.formatoptns["frmtrmblln"] or koboldai_vars.chatmode):
txt = utils.replaceblanklines(txt)
# Remove special characters
if(koboldai_vars.formatoptns["frmtrmspch"]):
txt = utils.removespecialchars(txt, koboldai_vars)
# Single Line Mode
if(koboldai_vars.formatoptns["singleline"] or koboldai_vars.chatmode):
txt = utils.singlelineprocessing(txt, koboldai_vars)
return txt
#==================================================================#
# Sends the current story content to the Game Screen
#==================================================================#
def refresh_story():
text_parts = ['<chunk n="0" id="n0" tabindex="-1">', koboldai_vars.comregex_ui.sub(lambda m: '\n'.join('<comment>' + l + '</comment>' for l in m.group().split('\n')), html.escape(koboldai_vars.prompt)), '</chunk>']
for idx in koboldai_vars.actions:
item = koboldai_vars.actions[idx]
idx += 1
item = html.escape(item)
item = koboldai_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 = koboldai_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': koboldai_vars.gamestarted, 'data': formatforhtml(''.join(text_parts))}, broadcast=True, room="UI_1")
#==================================================================#
# Signals the Game Screen to update one of the chunks
#==================================================================#
def update_story_chunk(idx: Union[int, str]):
if idx == 'last':
if len(koboldai_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 = (koboldai_vars.actions.get_last_key() if len(koboldai_vars.actions) else 0) + 1
if idx == 0:
text = koboldai_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 koboldai_vars.actions):
return
text = koboldai_vars.actions[idx - 1]
item = html.escape(text)
item = koboldai_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 = koboldai_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, room="UI_1")
setgamesaved(False)
#If we've set the auto save flag, we'll now save the file
if koboldai_vars.autosave and (".json" in koboldai_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, room="UI_1")
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, room="UI_1")
if(koboldai_vars.model != "InferKit"):
emit('from_server', {'cmd': 'updatetemp', 'data': koboldai_vars.temp}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatetopp', 'data': koboldai_vars.top_p}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatetopk', 'data': koboldai_vars.top_k}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatetfs', 'data': koboldai_vars.tfs}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatetypical', 'data': koboldai_vars.typical}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatetopa', 'data': koboldai_vars.top_a}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatereppen', 'data': koboldai_vars.rep_pen}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatereppenslope', 'data': koboldai_vars.rep_pen_slope}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatereppenrange', 'data': koboldai_vars.rep_pen_range}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updateoutlen', 'data': koboldai_vars.genamt}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatetknmax', 'data': koboldai_vars.max_length}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatenumseq', 'data': koboldai_vars.numseqs}, broadcast=True, room="UI_1")
else:
emit('from_server', {'cmd': 'updatetemp', 'data': koboldai_vars.temp}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatetopp', 'data': koboldai_vars.top_p}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updateikgen', 'data': koboldai_vars.ikgen}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updateanotedepth', 'data': koboldai_vars.andepth}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatewidepth', 'data': koboldai_vars.widepth}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updateuseprompt', 'data': koboldai_vars.useprompt}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updateadventure', 'data': koboldai_vars.adventure}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatechatmode', 'data': koboldai_vars.chatmode}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatedynamicscan', 'data': koboldai_vars.dynamicscan}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updateautosave', 'data': koboldai_vars.autosave}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatenopromptgen', 'data': koboldai_vars.nopromptgen}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updaterngpersist', 'data': koboldai_vars.rngpersist}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatenogenmod', 'data': koboldai_vars.nogenmod}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatefrmttriminc', 'data': koboldai_vars.formatoptns["frmttriminc"]}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatefrmtrmblln', 'data': koboldai_vars.formatoptns["frmtrmblln"]}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatefrmtrmspch', 'data': koboldai_vars.formatoptns["frmtrmspch"]}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatefrmtadsnsp', 'data': koboldai_vars.formatoptns["frmtadsnsp"]}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatesingleline', 'data': koboldai_vars.formatoptns["singleline"]}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updateoutputstreaming', 'data': koboldai_vars.output_streaming}, broadcast=True, room="UI_1")
# Allow toggle events again
emit('from_server', {'cmd': 'allowtoggle', 'data': True}, broadcast=True, room="UI_1")
#==================================================================#
# Sets the logical and display states for the AI Busy condition
#==================================================================#
def set_aibusy(state):
if(state):
koboldai_vars.aibusy = True
emit('from_server', {'cmd': 'setgamestate', 'data': 'wait'}, broadcast=True, room="UI_1")
else:
koboldai_vars.aibusy = False
emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True, room="UI_1")
#==================================================================#
#
#==================================================================#
def editrequest(n):
if(n == 0):
txt = koboldai_vars.prompt
else:
txt = koboldai_vars.actions[n-1]
koboldai_vars.editln = n
emit('from_server', {'cmd': 'setinputtext', 'data': txt}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'enablesubmit', 'data': ''}, broadcast=True, room="UI_1")
#==================================================================#
#
#==================================================================#
def editsubmit(data):
koboldai_vars.recentedit = True
if(koboldai_vars.editln == 0):
koboldai_vars.prompt = data
else:
koboldai_vars.actions[koboldai_vars.editln-1] = data
koboldai_vars.mode = "play"
update_story_chunk(koboldai_vars.editln)
emit('from_server', {'cmd': 'texteffect', 'data': koboldai_vars.editln}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'editmode', 'data': 'false'}, room="UI_1")
send_debug()
#==================================================================#
#
#==================================================================#
def deleterequest():
koboldai_vars.recentedit = True
# Don't delete prompt
if(koboldai_vars.editln == 0):
# Send error message
pass
else:
koboldai_vars.actions.delete_action(koboldai_vars.editln-1)
koboldai_vars.mode = "play"
remove_story_chunk(koboldai_vars.editln)
emit('from_server', {'cmd': 'editmode', 'data': 'false'}, room="UI_1")
send_debug()
#==================================================================#
#
#==================================================================#
def inlineedit(chunk, data):
koboldai_vars.recentedit = True
chunk = int(chunk)
if(chunk == 0):
if(len(data.strip()) == 0):
return
koboldai_vars.prompt = data
else:
if(chunk-1 in koboldai_vars.actions):
koboldai_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, room="UI_1")
emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True, room="UI_1")
send_debug()
#==================================================================#
#
#==================================================================#
def inlinedelete(chunk):
koboldai_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."}, room="UI_1")
emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True, room="UI_1")
else:
if(chunk-1 in koboldai_vars.actions):
koboldai_vars.actions.delete_action(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, room="UI_1")
send_debug()
#==================================================================#
# Toggles the game mode for memory editing and sends UI commands
#==================================================================#
def togglememorymode():
if(koboldai_vars.mode == "play"):
koboldai_vars.mode = "memory"
emit('from_server', {'cmd': 'memmode', 'data': 'true'}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setinputtext', 'data': koboldai_vars.memory}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setanote', 'data': koboldai_vars.authornote}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setanotetemplate', 'data': koboldai_vars.authornotetemplate}, broadcast=True, room="UI_1")
elif(koboldai_vars.mode == "memory"):
koboldai_vars.mode = "play"
emit('from_server', {'cmd': 'memmode', 'data': 'false'}, broadcast=True, room="UI_1")
#==================================================================#
# Toggles the game mode for WI editing and sends UI commands
#==================================================================#
def togglewimode():
if(koboldai_vars.mode == "play"):
koboldai_vars.mode = "wi"
emit('from_server', {'cmd': 'wimode', 'data': 'true'}, broadcast=True, room="UI_1")
elif(koboldai_vars.mode == "wi"):
# Commit WI fields first
requestwi()
# Then set UI state back to Play
koboldai_vars.mode = "play"
emit('from_server', {'cmd': 'wimode', 'data': 'false'}, broadcast=True, room="UI_1")
sendwi()
#==================================================================#
#
#==================================================================#
def addwiitem(folder_uid=None):
assert folder_uid is None or folder_uid in koboldai_vars.wifolders_d
ob = {"key": "", "keysecondary": "", "content": "", "comment": "", "folder": folder_uid, "num": len(koboldai_vars.worldinfo), "init": False, "selective": False, "constant": False}
koboldai_vars.worldinfo.append(ob)
while(True):
uid = int.from_bytes(os.urandom(4), "little", signed=True)
if(uid not in koboldai_vars.worldinfo_u):
break
koboldai_vars.worldinfo_u[uid] = koboldai_vars.worldinfo[-1]
koboldai_vars.worldinfo[-1]["uid"] = uid
if(folder_uid is not None):
koboldai_vars.wifolders_u[folder_uid].append(koboldai_vars.worldinfo[-1])
emit('from_server', {'cmd': 'addwiitem', 'data': ob}, broadcast=True, room="UI_1")
#==================================================================#
# 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 koboldai_vars.wifolders_d):
break
ob = {"name": "", "collapsed": False}
koboldai_vars.wifolders_d[uid] = ob
koboldai_vars.wifolders_l.append(uid)
koboldai_vars.wifolders_u[uid] = []
emit('from_server', {'cmd': 'addwifolder', 'uid': uid, 'data': ob}, broadcast=True, room="UI_1")
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(koboldai_vars.worldinfo_u[src]["folder"] is not None):
for i, e in enumerate(koboldai_vars.wifolders_u[koboldai_vars.worldinfo_u[src]["folder"]]):
if(e is koboldai_vars.worldinfo_u[src]):
koboldai_vars.wifolders_u[koboldai_vars.worldinfo_u[src]["folder"]].pop(i)
break
if(koboldai_vars.worldinfo_u[dst]["folder"] is not None):
koboldai_vars.wifolders_u[koboldai_vars.worldinfo_u[dst]["folder"]].append(koboldai_vars.worldinfo_u[src])
koboldai_vars.worldinfo_u[src]["folder"] = koboldai_vars.worldinfo_u[dst]["folder"]
for i, e in enumerate(koboldai_vars.worldinfo):
if(e is koboldai_vars.worldinfo_u[src]):
_src = i
elif(e is koboldai_vars.worldinfo_u[dst]):
_dst = i
koboldai_vars.worldinfo.insert(_dst - (_dst >= _src), koboldai_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)
koboldai_vars.wifolders_l.remove(src)
if(dst is None):
# If dst is None, that means we should move src to be the last folder
koboldai_vars.wifolders_l.append(src)
else:
koboldai_vars.wifolders_l.insert(koboldai_vars.wifolders_l.index(dst), src)
sendwi()
#==================================================================#
#
#==================================================================#
def sendwi():
# Cache len of WI
ln = len(koboldai_vars.worldinfo)
# Clear contents of WI container
emit('from_server', {'cmd': 'wistart', 'wifolders_d': koboldai_vars.wifolders_d, 'wifolders_l': koboldai_vars.wifolders_l, 'data': ''}, broadcast=True, room="UI_1")
# Stable-sort WI entries in order of folder
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_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 koboldai_vars.worldinfo:
if(wi["folder"] != last_folder):
emit('from_server', {'cmd': 'addwifolder', 'uid': wi["folder"], 'data': koboldai_vars.wifolders_d[wi["folder"]] if wi["folder"] is not None else None}, broadcast=True, room="UI_1")
last_folder = wi["folder"]
ob = wi
emit('from_server', {'cmd': 'addwiitem', 'data': ob}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'wifinish', 'data': ''}, broadcast=True, room="UI_1")
#==================================================================#
# Request current contents of all WI HTML elements
#==================================================================#
def requestwi():
list = []
for wi in koboldai_vars.worldinfo:
list.append(wi["num"])
emit('from_server', {'cmd': 'requestwiitem', 'data': list}, room="UI_1")
#==================================================================#
# 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(koboldai_vars.wifolders_l)}
koboldai_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(koboldai_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 koboldai_vars.wifolders_u:
koboldai_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"])
koboldai_vars.worldinfo_u[ob["uid"]]["key"] = ob["key"]
koboldai_vars.worldinfo_u[ob["uid"]]["keysecondary"] = ob["keysecondary"]
koboldai_vars.worldinfo_u[ob["uid"]]["content"] = ob["content"]
koboldai_vars.worldinfo_u[ob["uid"]]["comment"] = ob.get("comment", "")
koboldai_vars.worldinfo_u[ob["uid"]]["folder"] = ob.get("folder", None)
koboldai_vars.worldinfo_u[ob["uid"]]["selective"] = ob["selective"]
koboldai_vars.worldinfo_u[ob["uid"]]["constant"] = ob.get("constant", False)
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_vars.worldinfo if wi["init"]]
#==================================================================#
#
#==================================================================#
def deletewi(uid):
if(uid in koboldai_vars.worldinfo_u):
setgamesaved(False)
# Store UID of deletion request
koboldai_vars.deletewi = uid
if(koboldai_vars.deletewi is not None):
if(koboldai_vars.worldinfo_u[koboldai_vars.deletewi]["folder"] is not None):
for i, e in enumerate(koboldai_vars.wifolders_u[koboldai_vars.worldinfo_u[koboldai_vars.deletewi]["folder"]]):
if(e is koboldai_vars.worldinfo_u[koboldai_vars.deletewi]):
koboldai_vars.wifolders_u[koboldai_vars.worldinfo_u[koboldai_vars.deletewi]["folder"]].pop(i)
for i, e in enumerate(koboldai_vars.worldinfo):
if(e is koboldai_vars.worldinfo_u[koboldai_vars.deletewi]):
del koboldai_vars.worldinfo[i]
break
del koboldai_vars.worldinfo_u[koboldai_vars.deletewi]
# Send the new WI array structure
sendwi()
# And reset deletewi
koboldai_vars.deletewi = None
#==================================================================#
#
#==================================================================#
def deletewifolder(uid):
uid = int(uid)
del koboldai_vars.wifolders_u[uid]
del koboldai_vars.wifolders_d[uid]
del koboldai_vars.wifolders_l[koboldai_vars.wifolders_l.index(uid)]
setgamesaved(False)
# Delete uninitialized entries in the folder we're going to delete
koboldai_vars.worldinfo = [wi for wi in koboldai_vars.worldinfo if wi["folder"] != uid or wi["init"]]
koboldai_vars.worldinfo_i = [wi for wi in koboldai_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 koboldai_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 = koboldai_vars.actions
# Dont go any further if WI is empty
if(len(koboldai_vars.worldinfo) == 0):
return "", set()
# Cache actions length
ln = len(actions)
# Don't bother calculating action history if widepth is 0
if(koboldai_vars.widepth > 0 and scan_story):
depth = koboldai_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 koboldai_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 = koboldai_vars.comregex_ai.sub('', koboldai_vars.prompt) + "".join(chunks)
elif(ln == 0):
txt = koboldai_vars.comregex_ai.sub('', koboldai_vars.prompt)
if(force_use_txt):
txt += original_txt
# Scan text for matches on WI keys
wimem = ""
found_entries = set()
for wi in koboldai_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(koboldai_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(koboldai_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, room="UI_1")
# Maybe check for length at some point
# For now just send it to storage
if(data != koboldai_vars.memory):
setgamesaved(False)
koboldai_vars.memory = data
koboldai_vars.mode = "play"
emit('from_server', {'cmd': 'memmode', 'data': 'false'}, broadcast=True, room="UI_1")
# Ask for contents of Author's Note field
emit('from_server', {'cmd': 'getanote', 'data': ''}, room="UI_1")
#==================================================================#
# 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 != koboldai_vars.authornote):
setgamesaved(False)
koboldai_vars.authornote = data
if(koboldai_vars.authornotetemplate != template):
koboldai_vars.setauthornotetemplate = template
print("anotesubmit")
settingschanged()
koboldai_vars.authornotetemplate = template
emit('from_server', {'cmd': 'setanote', 'data': koboldai_vars.authornote}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setanotetemplate', 'data': koboldai_vars.authornotetemplate}, broadcast=True, room="UI_1")
#==================================================================#
# Assembles game data into a request to InferKit API
#==================================================================#
def ikrequest(txt):
# Log request to console
if not koboldai_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': koboldai_vars.ikgen,
'prompt': {
'isContinuation': False,
'text': txt
},
'startFromBeginning': False,
'streamResponse': False,
'temperature': koboldai_vars.temp,
'topP': koboldai_vars.top_p
}
# Create request
req = requests.post(
koboldai_vars.url,
json = reqdata,
headers = {
'Authorization': 'Bearer '+koboldai_vars.apikey
}
)
# Deal with the response
if(req.status_code == 200):
genout = req.json()["data"]["text"]
koboldai_vars.lua_koboldbridge.outputs[1] = genout
execute_outmod()
if(koboldai_vars.lua_koboldbridge.regeneration_required):
koboldai_vars.lua_koboldbridge.regeneration_required = False
genout = koboldai_vars.lua_koboldbridge.outputs[1]
assert genout is str
if not koboldai_vars.quiet:
print("{0}{1}{2}".format(colors.CYAN, genout, colors.END))
koboldai_vars.actions.append(genout)
update_story_chunk('last')
emit('from_server', {'cmd': 'texteffect', 'data': koboldai_vars.actions.get_last_key() + 1 if len(koboldai_vars.actions) else 0}, broadcast=True, room="UI_1")
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, room="UI_1")
set_aibusy(0)
#==================================================================#
# Assembles game data into a request to OpenAI API
#==================================================================#
def oairequest(txt, min, max):
# Log request to console
if not koboldai_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
koboldai_vars.lastctx = txt
# Build request JSON data
if 'GooseAI' in args.configname:
reqdata = {
'prompt': txt,
'max_tokens': koboldai_vars.genamt,
'temperature': koboldai_vars.temp,
'top_a': koboldai_vars.top_a,
'top_p': koboldai_vars.top_p,
'top_k': koboldai_vars.top_k,
'tfs': koboldai_vars.tfs,
'typical_p': koboldai_vars.typical,
'repetition_penalty': koboldai_vars.rep_pen,
'repetition_penalty_slope': koboldai_vars.rep_pen_slope,
'repetition_penalty_range': koboldai_vars.rep_pen_range,
'n': koboldai_vars.numseqs,
'stream': False
}
else:
reqdata = {
'prompt': txt,
'max_tokens': koboldai_vars.genamt,
'temperature': koboldai_vars.temp,
'top_p': koboldai_vars.top_p,
'n': koboldai_vars.numseqs,
'stream': False
}
req = requests.post(
koboldai_vars.oaiurl,
json = reqdata,
headers = {
'Authorization': 'Bearer '+koboldai_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)):
koboldai_vars.lua_koboldbridge.outputs[idx+1] = outputs[idx]
execute_outmod()
if (koboldai_vars.lua_koboldbridge.regeneration_required):
koboldai_vars.lua_koboldbridge.regeneration_required = False
genout = []
for i in range(len(outputs)):
genout.append(
{"generated_text": koboldai_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]
koboldai_vars.actions.clear_unused_options()
koboldai_vars.actions.append_options([x["generated_text"] for x in genout])
genout = [{"generated_text": x['text']} for x in koboldai_vars.actions.get_current_options()]
if (len(genout) == 1):
genresult(genout[0]["generated_text"])
else:
if (koboldai_vars.lua_koboldbridge.restart_sequence is not None and
koboldai_vars.lua_koboldbridge.restart_sequence > 0):
genresult(genout[koboldai_vars.lua_koboldbridge.restart_sequence - 1][
"generated_text"])
else:
genselect(genout)
if not koboldai_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, room="UI_1")
set_aibusy(0)
#==================================================================#
# Forces UI to Play mode
#==================================================================#
def exitModes():
if(koboldai_vars.mode == "edit"):
emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True, room="UI_1")
elif(koboldai_vars.mode == "memory"):
emit('from_server', {'cmd': 'memmode', 'data': 'false'}, broadcast=True, room="UI_1")
elif(koboldai_vars.mode == "wi"):
emit('from_server', {'cmd': 'wimode', 'data': 'false'}, broadcast=True, room="UI_1")
koboldai_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 (koboldai_vars.saveow and koboldai_vars.svowname == name)):
# All clear to save
e = saveRequest(fileops.storypath(name), savepins=savepins)
koboldai_vars.saveow = False
koboldai_vars.svowname = ""
if(e is None):
emit('from_server', {'cmd': 'hidesaveas', 'data': ''}, room="UI_1")
else:
print("{0}{1}{2}".format(colors.RED, str(e), colors.END))
emit('from_server', {'cmd': 'popuperror', 'data': str(e)}, room="UI_1")
else:
# File exists, prompt for overwrite
koboldai_vars.saveow = True
koboldai_vars.svowname = name
emit('from_server', {'cmd': 'askforoverwrite', 'data': ''}, room="UI_1")
#==================================================================#
# Launch in-browser story-delete prompt
#==================================================================#
def deletesave(name):
name = utils.cleanfilename(name)
e = fileops.deletesave(name)
if(e is None):
if(koboldai_vars.smandelete):
emit('from_server', {'cmd': 'hidepopupdelete', 'data': ''}, room="UI_1")
getloadlist()
else:
emit('from_server', {'cmd': 'popuperror', 'data': "The server denied your request to delete this story"}, room="UI_1")
else:
print("{0}{1}{2}".format(colors.RED, str(e), colors.END))
emit('from_server', {'cmd': 'popuperror', 'data': str(e)}, room="UI_1")
#==================================================================#
# 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 (koboldai_vars.saveow and koboldai_vars.svowname == newname)):
e = fileops.renamesave(name, newname)
koboldai_vars.saveow = False
koboldai_vars.svowname = ""
if(e is None):
if(koboldai_vars.smanrename):
emit('from_server', {'cmd': 'hidepopuprename', 'data': ''}, room="UI_1")
getloadlist()
else:
emit('from_server', {'cmd': 'popuperror', 'data': "The server denied your request to rename this story"}, room="UI_1")
else:
print("{0}{1}{2}".format(colors.RED, str(e), colors.END))
emit('from_server', {'cmd': 'popuperror', 'data': str(e)}, room="UI_1")
else:
# File exists, prompt for overwrite
koboldai_vars.saveow = True
koboldai_vars.svowname = newname
emit('from_server', {'cmd': 'askforoverwrite', 'data': ''}, room="UI_1")
#==================================================================#
# Save the currently running story
#==================================================================#
def save():
# Check if a file is currently open
if(".json" in koboldai_vars.savedir):
saveRequest(koboldai_vars.savedir)
else:
emit('from_server', {'cmd': 'saveas', 'data': ''}, room="UI_1")
#==================================================================#
# Save the story via file browser
#==================================================================#
def savetofile():
savpath = fileops.getsavepath(koboldai_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
koboldai_vars.savedir = savpath
txtpath = os.path.splitext(savpath)[0] + ".txt"
# Build json to write
js = {}
js["gamestarted"] = koboldai_vars.gamestarted
js["prompt"] = koboldai_vars.prompt
js["memory"] = koboldai_vars.memory
js["authorsnote"] = koboldai_vars.authornote
js["anotetemplate"] = koboldai_vars.authornotetemplate
js["actions"] = tuple(koboldai_vars.actions.values())
if savepins:
js["actions_metadata"] = koboldai_vars.actions.options(ui_version=1)
js["worldinfo"] = []
js["wifolders_d"] = koboldai_vars.wifolders_d
js["wifolders_l"] = koboldai_vars.wifolders_l
# Extract only the important bits of WI
for wi in koboldai_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 = koboldai_vars.prompt + "".join(koboldai_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]
koboldai_vars.laststory = filename
emit('from_server', {'cmd': 'setstoryname', 'data': koboldai_vars.laststory}, broadcast=True, room="UI_1")
setgamesaved(True)
print("{0}Story saved to {1}!{2}".format(colors.GREEN, path.basename(savpath), colors.END))
#==================================================================#
# Show list of saved stories
#==================================================================#
def getloadlist(data=None):
emit('from_server', {'cmd': 'buildload', 'data': fileops.getstoryfiles()}, room="UI_1")
#==================================================================#
# Show list of soft prompts
#==================================================================#
def getsplist():
if(koboldai_vars.allowsp):
emit('from_server', {'cmd': 'buildsp', 'data': fileops.getspfiles(koboldai_vars.modeldim)}, room="UI_1")
#==================================================================#
# Get list of userscripts
#==================================================================#
def getuslist():
files = {i: v for i, v in enumerate(fileops.getusfiles())}
loaded = []
unloaded = []
userscripts = set(koboldai_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 koboldai_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(koboldai_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"
js['v1_loadpath'] = loadpath
js['v1_filename'] = filename
loadJSON(js)
def loadJSON(json_text_or_dict):
if isinstance(json_text_or_dict, str):
json_data = json.loads(json_text_or_dict)
else:
json_data = json_text_or_dict
if "file_version" in json_data:
if json_data['file_version'] == 2:
load_story_v2(json_data)
else:
load_story_v1(json_data)
else:
load_story_v1(json_data)
def load_story_v1(js):
loadpath = js['v1_loadpath']
filename = js['v1_filename']
_filename = filename
if(filename.endswith('.json')):
_filename = filename[:-5]
session['story'] = _filename
#create the story
#koboldai_vars.create_story(session['story'])
koboldai_vars.create_story('default')
koboldai_vars.laststory = _filename
#set the story_name
koboldai_vars.story_name = _filename
# Copy file contents to vars
koboldai_vars.gamestarted = js["gamestarted"]
koboldai_vars.prompt = js["prompt"]
koboldai_vars.memory = js["memory"]
koboldai_vars.worldinfo_v2.reset()
koboldai_vars.worldinfo = []
koboldai_vars.worldinfo_i = []
koboldai_vars.worldinfo_u = {}
koboldai_vars.wifolders_d = {int(k): v for k, v in js.get("wifolders_d", {}).items()}
koboldai_vars.wifolders_l = js.get("wifolders_l", [])
koboldai_vars.wifolders_u = {uid: [] for uid in koboldai_vars.wifolders_d}
koboldai_vars.lastact = ""
koboldai_vars.submission = ""
koboldai_vars.lastctx = ""
koboldai_vars.genseqs = []
actions = collections.deque(js["actions"])
if(len(koboldai_vars.prompt.strip()) == 0):
while(len(actions)):
action = actions.popleft()
if(len(action.strip()) != 0):
koboldai_vars.prompt = action
break
else:
koboldai_vars.gamestarted = False
if(koboldai_vars.gamestarted):
for s in actions:
koboldai_vars.actions.append(s)
if "actions_metadata" in js:
if type(js["actions_metadata"]) == dict:
for key in js["actions_metadata"]:
if js["actions_metadata"][key]["Alternative Text"] != []:
data = js["actions_metadata"][key]["Alternative Text"]
data["text"] = data.pop("Text")
koboldai_vars.actions.set_options(self, data, key)
# Try not to break older save files
if("authorsnote" in js):
koboldai_vars.authornote = js["authorsnote"]
else:
koboldai_vars.authornote = ""
if("anotetemplate" in js):
koboldai_vars.authornotetemplate = js["anotetemplate"]
else:
koboldai_vars.authornotetemplate = "[Author's note: <|>]"
if("worldinfo" in js):
num = 0
for wi in js["worldinfo"]:
koboldai_vars.worldinfo_v2.add_item([x.strip() for x in wi["key"].split(",")][0], wi["key"], wi.get("keysecondary", ""),
wi.get("folder", "root"), wi.get("constant", False),
wi["content"], wi.get("comment", ""))
# Save path for save button
koboldai_vars.savedir = loadpath
# Clear loadselect var
koboldai_vars.loadselect = ""
# Refresh game screen
emit('from_server', {'cmd': 'setstoryname', 'data': koboldai_vars.laststory}, broadcast=True, room="UI_1")
setgamesaved(True)
sendwi()
emit('from_server', {'cmd': 'setmemory', 'data': koboldai_vars.memory}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setanote', 'data': koboldai_vars.authornote}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setanotetemplate', 'data': koboldai_vars.authornotetemplate}, broadcast=True, room="UI_1")
refresh_story()
emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'hidegenseqs', 'data': ''}, broadcast=True, room="UI_1")
print("{0}Story loaded from {1}!{2}".format(colors.GREEN, filename, colors.END))
send_debug()
def load_story_v2(js):
session['story'] = js['story_name']
koboldai_vars.load_story(session['story'], js)
#==================================================================#
# Import an AIDungon game exported with Mimi's tool
#==================================================================#
def importRequest():
importpath = fileops.getloadpath(koboldai_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")
koboldai_vars.importjs = json.load(file)
# If a bundle file is being imported, select just the Adventures object
if type(koboldai_vars.importjs) is dict and "stories" in koboldai_vars.importjs:
koboldai_vars.importjs = koboldai_vars.importjs["stories"]
# Clear Popup Contents
emit('from_server', {'cmd': 'clearpopup', 'data': ''}, broadcast=True, room="UI_1")
# Initialize vars
num = 0
koboldai_vars.importnum = -1
# Get list of stories
for story in koboldai_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}, room="UI_1")
num += 1
# Show Popup
emit('from_server', {'cmd': 'popupshow', 'data': True}, room="UI_1")
#==================================================================#
# Import an AIDungon game selected in popup
#==================================================================#
def importgame():
if(koboldai_vars.importnum >= 0):
# Cache reference to selected game
ref = koboldai_vars.importjs[koboldai_vars.importnum]
# Copy game contents to vars
koboldai_vars.gamestarted = True
# Support for different versions of export script
if("actions" in ref):
if(len(ref["actions"]) > 0):
koboldai_vars.prompt = ref["actions"][0]["text"]
else:
koboldai_vars.prompt = ""
elif("actionWindow" in ref):
if(len(ref["actionWindow"]) > 0):
koboldai_vars.prompt = ref["actionWindow"][0]["text"]
else:
koboldai_vars.prompt = ""
else:
koboldai_vars.prompt = ""
koboldai_vars.memory = ref["memory"]
koboldai_vars.authornote = ref["authorsNote"] if type(ref["authorsNote"]) is str else ""
koboldai_vars.authornotetemplate = "[Author's note: <|>]"
koboldai_vars.actions.reset()
koboldai_vars.actions_metadata = {}
koboldai_vars.worldinfo = []
koboldai_vars.worldinfo_i = []
koboldai_vars.worldinfo_u = {}
koboldai_vars.wifolders_d = {}
koboldai_vars.wifolders_l = []
koboldai_vars.wifolders_u = {uid: [] for uid in koboldai_vars.wifolders_d}
koboldai_vars.lastact = ""
koboldai_vars.submission = ""
koboldai_vars.lastctx = ""
# Get all actions except for prompt
if("actions" in ref):
if(len(ref["actions"]) > 1):
for act in ref["actions"][1:]:
koboldai_vars.actions.append(act["text"])
elif("actionWindow" in ref):
if(len(ref["actionWindow"]) > 1):
for act in ref["actionWindow"][1:]:
koboldai_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"]:
koboldai_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 koboldai_vars.worldinfo_u):
break
koboldai_vars.worldinfo_u[uid] = koboldai_vars.worldinfo[-1]
koboldai_vars.worldinfo[-1]["uid"] = uid
if(koboldai_vars.worldinfo[-1]["folder"]) is not None:
koboldai_vars.wifolders_u[koboldai_vars.worldinfo[-1]["folder"]].append(koboldai_vars.worldinfo[-1])
num += 1
for uid in koboldai_vars.wifolders_l + [None]:
koboldai_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 koboldai_vars.worldinfo_u):
break
koboldai_vars.worldinfo_u[uid] = koboldai_vars.worldinfo[-1]
koboldai_vars.worldinfo[-1]["uid"] = uid
if(koboldai_vars.worldinfo[-1]["folder"] is not None):
koboldai_vars.wifolders_u[koboldai_vars.worldinfo[-1]["folder"]].append(koboldai_vars.worldinfo[-1])
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_vars.worldinfo if wi["init"]]
# Clear import data
koboldai_vars.importjs = {}
# Reset current save
koboldai_vars.savedir = getcwd()+"\\stories"
# Refresh game screen
koboldai_vars.laststory = None
emit('from_server', {'cmd': 'setstoryname', 'data': koboldai_vars.laststory}, broadcast=True, room="UI_1")
setgamesaved(False)
sendwi()
emit('from_server', {'cmd': 'setmemory', 'data': koboldai_vars.memory}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setanote', 'data': koboldai_vars.authornote}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setanotetemplate', 'data': koboldai_vars.authornotetemplate}, broadcast=True, room="UI_1")
refresh_story()
emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'hidegenseqs', 'data': ''}, broadcast=True, room="UI_1")
#==================================================================#
# 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
koboldai_vars.create_story("")
koboldai_vars.gamestarted = True
koboldai_vars.prompt = js["promptContent"]
koboldai_vars.memory = js["memory"]
koboldai_vars.authornote = js["authorsNote"]
if not koboldai_vars.memory:
koboldai_vars.memory = ""
if not koboldai_vars.authornote:
koboldai_vars.authornote = ""
num = 0
for wi in js["worldInfos"]:
koboldai_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,
})
koboldai_vars.worldinfo_v2.add_item([x.strip() for x in wi["keys"].split(",")][0], wi["keys"], wi.get("keysecondary", ""),
wi.get("folder", "root"), wi.get("constant", False),
wi["entry"], wi.get("comment", ""))
# Reset current save
koboldai_vars.savedir = getcwd()+"\\stories"
# Refresh game screen
koboldai_vars.laststory = None
emit('from_server', {'cmd': 'setstoryname', 'data': koboldai_vars.laststory}, broadcast=True, room="UI_1")
setgamesaved(False)
sendwi()
emit('from_server', {'cmd': 'setmemory', 'data': koboldai_vars.memory}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setanote', 'data': koboldai_vars.authornote}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setanotetemplate', 'data': koboldai_vars.authornotetemplate}, broadcast=True, room="UI_1")
refresh_story()
emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True, room="UI_1")
#==================================================================#
# Import World Info JSON file
#==================================================================#
def wiimportrequest():
importpath = fileops.getloadpath(koboldai_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 koboldai_vars.worldinfo[-1]["init"]):
del koboldai_vars.worldinfo[-1]
# Now grab the new stuff
num = len(koboldai_vars.worldinfo)
for wi in js:
koboldai_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 koboldai_vars.worldinfo_u):
break
koboldai_vars.worldinfo_u[uid] = koboldai_vars.worldinfo[-1]
koboldai_vars.worldinfo[-1]["uid"] = uid
if(koboldai_vars.worldinfo[-1]["folder"]) is not None:
koboldai_vars.wifolders_u[koboldai_vars.worldinfo[-1]["folder"]].append(koboldai_vars.worldinfo[-1])
num += 1
for uid in [None]:
koboldai_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 koboldai_vars.worldinfo_u):
break
koboldai_vars.worldinfo_u[uid] = koboldai_vars.worldinfo[-1]
koboldai_vars.worldinfo[-1]["uid"] = uid
if(koboldai_vars.worldinfo[-1]["folder"] is not None):
koboldai_vars.wifolders_u[koboldai_vars.worldinfo[-1]["folder"]].append(koboldai_vars.worldinfo[-1])
if not koboldai_vars.quiet:
print("{0}".format(koboldai_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
koboldai_vars.gamestarted = False
koboldai_vars.prompt = ""
koboldai_vars.memory = ""
koboldai_vars.actions.reset()
koboldai_vars.actions_metadata = {}
koboldai_vars.authornote = ""
koboldai_vars.authornotetemplate = koboldai_vars.setauthornotetemplate
koboldai_vars.worldinfo = []
koboldai_vars.worldinfo_i = []
koboldai_vars.worldinfo_u = {}
koboldai_vars.wifolders_d = {}
koboldai_vars.wifolders_l = []
koboldai_vars.lastact = ""
koboldai_vars.submission = ""
koboldai_vars.lastctx = ""
# Reset current save
koboldai_vars.savedir = getcwd()+"\\stories"
# Refresh game screen
koboldai_vars.laststory = None
emit('from_server', {'cmd': 'setstoryname', 'data': koboldai_vars.laststory}, broadcast=True, room="UI_1")
setgamesaved(True)
sendwi()
emit('from_server', {'cmd': 'setmemory', 'data': koboldai_vars.memory}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setanote', 'data': koboldai_vars.authornote}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'setanotetemplate', 'data': koboldai_vars.authornotetemplate}, broadcast=True, room="UI_1")
setStartState()
def randomGameRequest(topic, memory=""):
if(koboldai_vars.noai):
newGameRequest()
koboldai_vars.memory = memory
emit('from_server', {'cmd': 'setmemory', 'data': koboldai_vars.memory}, broadcast=True, room="UI_1")
return
koboldai_vars.recentrng = topic
koboldai_vars.recentrngm = memory
newGameRequest()
setgamesaved(False)
_memory = memory
if(len(memory) > 0):
_memory = memory.rstrip() + "\n\n"
koboldai_vars.memory = _memory + "You generate the following " + topic + " story concept :"
koboldai_vars.lua_koboldbridge.feedback = None
actionsubmit("", force_submit=True, force_prompt_gen=True)
koboldai_vars.memory = memory
emit('from_server', {'cmd': 'setmemory', 'data': koboldai_vars.memory}, broadcast=True, room="UI_1")
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(koboldai_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 and all(js["softprompt"][0] not in q for q in ("/", "\\")))):
spRequest("softprompts/"+js["softprompt"])
else:
koboldai_vars.spfilename = ""
file.close()
# Precompile TPU backend if required
if(koboldai_vars.use_colab_tpu or koboldai_vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")):
soft_tokens = tpumtjgetsofttokens()
if(koboldai_vars.dynamicscan or (not koboldai_vars.nogenmod and koboldai_vars.has_genmod)):
threading.Thread(
target=tpu_mtj_backend.infer_dynamic,
args=(np.tile(np.uint32((23403, 727, 20185)), (koboldai_vars.numseqs, 1)),),
kwargs={
"soft_embeddings": koboldai_vars.sp,
"soft_tokens": soft_tokens,
"gen_len": 1,
"use_callback": False,
"numseqs": koboldai_vars.numseqs,
"excluded_world_info": list(set() for _ in range(koboldai_vars.numseqs)),
},
).start()
else:
threading.Thread(
target=tpu_mtj_backend.infer_static,
args=(np.uint32((23403, 727, 20185)),),
kwargs={
"soft_embeddings": koboldai_vars.sp,
"soft_tokens": soft_tokens,
"gen_len": 1,
"numseqs": koboldai_vars.numseqs,
},
).start()
def send_debug():
if koboldai_vars.debug:
debug_info = ""
try:
debug_info = "{}Newline Mode: {}\n".format(debug_info, koboldai_vars.newlinemode)
except:
pass
try:
debug_info = "{}Action Length: {}\n".format(debug_info, koboldai_vars.actions.get_last_key())
except:
pass
try:
debug_info = "{}Actions Metadata Length: {}\n".format(debug_info, max(koboldai_vars.actions_metadata) if len(koboldai_vars.actions_metadata) > 0 else 0)
except:
pass
try:
debug_info = "{}Actions: {}\n".format(debug_info, [k for k in koboldai_vars.actions])
except:
pass
try:
debug_info = "{}Actions Metadata: {}\n".format(debug_info, [k for k in koboldai_vars.actions_metadata])
except:
pass
try:
debug_info = "{}Last Action: {}\n".format(debug_info, koboldai_vars.actions[koboldai_vars.actions.get_last_key()])
except:
pass
try:
debug_info = "{}Last Metadata: {}\n".format(debug_info, koboldai_vars.actions_metadata[max(koboldai_vars.actions_metadata)])
except:
pass
emit('from_server', {'cmd': 'debug_info', 'data': debug_info}, broadcast=True, room="UI_1")
#==================================================================#
# UI V2 CODE
#==================================================================#
@app.route('/new_ui')
def new_ui_index():
if 'story' in session:
if session['story'] not in koboldai_vars.story_list():
session['story'] = 'default'
return render_template('index_new.html', settings=gensettings.gensettingstf if koboldai_vars.model != "InferKit" else gensettings.gensettingsik )
def ui2_connect():
#Send all variables to client
koboldai_vars.send_to_ui()
pass
#==================================================================#
# File Popup options
#==================================================================#
@socketio.on('upload_file')
def upload_file(data):
print("upload_file {}".format(data['filename']))
if 'current_folder' in session:
path = os.path.abspath(os.path.join(session['current_folder'], data['filename']).replace("\\", "/")).replace("\\", "/")
print("Want to save to {}".format(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):
emit("error_popup", "The file already exists. Please delete it or rename the file before uploading", broadcast=False, room="UI_2");
else:
with open(path, "wb") as f:
f.write(data['data'])
get_files_folders(session['current_folder'])
elif session['popup_jailed_dir'] in session['current_folder']:
if os.path.exists(path):
emit("error_popup", "The file already exists. Please delete it or rename the file before uploading", broadcast=False, room="UI_2");
else:
with open(path, "wb") as f:
f.write(data['data'])
get_files_folders(session['current_folder'])
@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,
valid_only=False, hide_extention=False, extra_parameter_function=None,
column_names=['File Name'], show_filename=True,
column_widths=["100%"]):
#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?
#valid_only = only show valid files
#hide_extention = hide extensions
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['extra_parameter_function'] = extra_parameter_function
session['column_names'] = column_names
session['popup_folder_only'] = folder_only
session['popup_show_breadcrumbs'] = show_breadcrumbs
session['upload'] = upload
session['valid_only'] = valid_only
session['hide_extention'] = hide_extention
session['show_filename'] = show_filename
session['column_widths'] = column_widths
socketio.emit("load_popup", {"popup_title": popup_title, "call_back": return_event, "renameable": renameable, "deleteable": deleteable, "editable": editable, 'upload': upload}, broadcast=False, room="UI_2")
socketio.emit("load_popup", {"popup_title": popup_title, "call_back": return_event, "renameable": renameable, "deleteable": deleteable, "editable": editable, 'upload': upload}, broadcast=True, room="UI_1")
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']
extra_parameter_function = session['extra_parameter_function']
show_breadcrumbs = session['popup_show_breadcrumbs']
show_hidden = session['popup_show_hidden']
folder_only = session['popup_folder_only']
valid_only = session['valid_only']
column_names = session['column_names']
hide_extention = session['hide_extention']
show_filename = session['show_filename']
column_widths = session['column_widths']
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 extra_parameter_function is None:
extra_parameters = []
else:
extra_parameters = extra_parameter_function(item_full_path, item, valid_selection)
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, extra_parameters])
else:
if hide_extention:
item = ".".join(item.split(".")[:-1])
if valid_only:
if valid_selection:
files.append([False, item_full_path, item, valid_selection, extra_parameters])
else:
files.append([False, item_full_path, item, valid_selection, extra_parameters])
items = folders
if not folder_only:
items += files
#items is a list of [Folder True/False, full path, file/folder name, validity of item to load, [list of extra columns]]
socketio.emit("popup_items", {"items": items, "column_names": column_names, "show_filename": show_filename, "column_widths": column_widths}, broadcast=False, include_self=True, room="UI_2")
socketio.emit("popup_items", items, broadcast=True, include_self=True, room="UI_1")
if show_breadcrumbs:
socketio.emit("popup_breadcrumbs", breadcrumbs, broadcast=False, room="UI_2")
socketio.emit("popup_breadcrumbs", breadcrumbs, broadcast=True, room="UI_1")
#==================================================================#
# Event triggered when browser SocketIO detects a variable change
#==================================================================#
@socketio.on('var_change')
def UI_2_var_change(data):
if 'value' not in data:
return
classname = data['ID'].split("_")[0]
name = data['ID'][len(classname)+1:]
classname += "_settings"
#Need to fix the data type of value to match the module
if type(getattr(koboldai_vars, name)) == int:
value = int(data['value'])
elif type(getattr(koboldai_vars, name)) == float:
value = float(data['value'])
elif type(getattr(koboldai_vars, name)) == bool:
value = bool(data['value'])
elif type(getattr(koboldai_vars, name)) == str:
value = str(data['value'])
elif type(getattr(koboldai_vars, name)) == list:
value = list(data['value'])
else:
print("Unknown Type {} = {}".format(name, type(getattr(koboldai_vars, name))))
#print("Setting {} to {} as type {}".format(name, value, type(value)))
setattr(koboldai_vars, name, value)
#Now let's save except for story changes
if classname != "story_settings":
with open("settings/{}.v2_settings".format(classname), "w") as settings_file:
settings_file.write(getattr(koboldai_vars, "_{}".format(classname)).to_json())
return {'id': data['ID'], 'status': "Saved"}
#==================================================================#
# Saving Story
#==================================================================#
@socketio.on('save_story')
def UI_2_save_story(data):
if data is None:
#We need to check to see if there is a file already and if it's not the same story so we can ask the client if this is OK
save_name = koboldai_vars.story_name if koboldai_vars.story_name != "" else "untitled"
same_story = True
if os.path.exists("stories/{}_v2.json".format(save_name)):
with open("stories/{}_v2.json".format(save_name), "r") as settings_file:
json_data = json.load(settings_file)
if 'story_id' in json_data:
same_story = json_data['story_id'] == koboldai_vars.story_id
else:
same_story = False
if same_story:
koboldai_vars.save_story()
return "OK"
else:
return "overwrite?"
else:
#We have an ack that it's OK to save over the file if one exists
koboldai_vars.save_story()
#==================================================================#
# Save story to json
#==================================================================#
@app.route("/json")
def UI_2_save_to_json():
return Response(
koboldai_vars.to_json('story_settings'),
mimetype="application/json",
headers={"Content-disposition":
"attachment; filename={}_v2.json".format(koboldai_vars.story_name)})
#==================================================================#
# Event triggered when Selected Text is edited
#==================================================================#
@socketio.on('Set Selected Text')
def UI_2_Set_Selected_Text(data):
print("Updating Selected Text: {}".format(data))
koboldai_vars.actions[int(data['id'])] = data['text']
#==================================================================#
# Event triggered when Option is Selected
#==================================================================#
@socketio.on('Use Option Text')
def UI_2_Set_Selected_Text(data):
print("Using Option Text: {}".format(data))
if koboldai_vars.prompt == "":
print(koboldai_vars.actions.get_current_options())
koboldai_vars.prompt = koboldai_vars.actions.get_current_options()[int(data['option'])]['text']
koboldai_vars.actions.clear_unused_options()
else:
koboldai_vars.actions.use_option(int(data['option']), action_step=int(data['chunk']))
#==================================================================#
# Event triggered when user clicks the submit button
#==================================================================#
@socketio.on('submit')
def UI_2_submit(data):
print(data)
if not koboldai_vars.noai and data['theme'] != "":
memory = koboldai_vars.memory
koboldai_vars.memory = "{}\n\nYou generate the following {} story concept :".format(koboldai_vars.memory, data['theme'])
koboldai_vars.lua_koboldbridge.feedback = None
actionsubmit("", force_submit=True, force_prompt_gen=True)
koboldai_vars.memory = memory
else:
koboldai_vars.actions.clear_unused_options()
koboldai_vars.lua_koboldbridge.feedback = None
koboldai_vars.recentrng = koboldai_vars.recentrngm = None
actionsubmit(data['data'], actionmode=koboldai_vars.actionmode)
#==================================================================#
# Event triggered when user clicks the submit button
#==================================================================#
@socketio.on('abort')
def UI_2_abort(data):
koboldai_vars.abort = True
#==================================================================#
# Event triggered when user clicks the pin button
#==================================================================#
@socketio.on('Pinning')
def UI_2_Pinning(data):
koboldai_vars.actions.toggle_pin(int(data['chunk']), int(data['option']))
#==================================================================#
# Event triggered when user clicks the back button
#==================================================================#
@socketio.on('back')
def UI_2_back(data):
print("back")
ignore = koboldai_vars.actions.pop()
#==================================================================#
# Event triggered when user clicks the redo button
#==================================================================#
@socketio.on('redo')
def UI_2_redo(data):
if len(koboldai_vars.actions.get_current_options()) == 1:
koboldai_vars.actions.use_option(0)
#==================================================================#
# Event triggered when user clicks the retry button
#==================================================================#
@socketio.on('retry')
def UI_2_retry(data):
if koboldai_vars.numseqs == 1:
UI_2_back(None)
koboldai_vars.actions.clear_unused_options()
koboldai_vars.lua_koboldbridge.feedback = None
koboldai_vars.recentrng = koboldai_vars.recentrngm = None
actionsubmit("", actionmode=koboldai_vars.actionmode)
#==================================================================#
# Event triggered when user clicks the load model button
#==================================================================#
@socketio.on('load_model_button')
def UI_2_load_model_button(data):
sendModelSelection()
#==================================================================#
# Event triggered when user clicks the a model
#==================================================================#
@socketio.on('select_model')
def UI_2_load_model_button(data):
print(data)
#We've selected a menu
if data['model'] in model_menu:
sendModelSelection(menu=data['model'])
#We've selected a custom line
elif data['menu'] in ("NeoCustom", "GPT2Custom"):
get_model_info(data['menu'], directory=data['display_name'])
#We've selected a custom menu
elif data['model'] in ("NeoCustom", "GPT2Custom"):
sendModelSelection(menu=data['model'], folder="./models")
else:
#We now have some model we want to potentially load.
#First we need to send the client the model parameters (layers, etc)
get_model_info(data['model'])
#==================================================================#
# Event triggered when user loads a model
#==================================================================#
@socketio.on('load_model')
def UI_2_load_model(data):
print(data)
if not os.path.exists("settings/"):
os.mkdir("settings")
changed = True
if not utils.HAS_ACCELERATE:
data['disk_layers'] = "0"
if os.path.exists("settings/" + data['model'].replace('/', '_') + ".breakmodel"):
with open("settings/" + data['model'].replace('/', '_') + ".breakmodel", "r") as file:
file_data = file.read().split('\n')[:2]
if len(file_data) < 2:
file_data.append("0")
gpu_layers, disk_layers = file_data
if gpu_layers == data['gpu_layers'] and disk_layers == data['disk_layers']:
changed = False
if changed:
f = open("settings/" + data['model'].replace('/', '_') + ".breakmodel", "w")
f.write(data['gpu_layers'] + '\n' + data['disk_layers'])
f.close()
koboldai_vars.colaburl = data['url'] + "/request"
koboldai_vars.model = data['model']
koboldai_vars.custmodpth = data['path']
print("loading Model")
load_model(use_gpu=data['use_gpu'], gpu_layers=data['gpu_layers'], disk_layers=data['disk_layers'], online_model=data['online_model'])
#==================================================================#
# Event triggered when load story is clicked
#==================================================================#
@socketio.on('load_story_list')
def UI_2_load_story_list(data):
file_popup("Select Story to Load", "./stories", "load_story", upload=True, jailed=True, folder_only=False, renameable=True,
deleteable=True, show_breadcrumbs=True, item_check=valid_story,
valid_only=True, hide_extention=True, extra_parameter_function=get_story_length,
column_names=['Story Name', 'Action Count'],
column_widths=['auto', '100px'])
def get_story_length(item_full_path, item, valid_selection):
if not valid_selection:
return [""]
with open(item_full_path, "r") as f:
js = json.load(f)
if 'file_version' not in js:
return [len(js['actions'])]
if js['file_version'] == 1:
return [len(js['actions'])]
return [0 if js['actions']['action_count'] == -1 else js['actions']['action_count'] ]
def valid_story(file):
if file.endswith(".json"):
with open(file, "r") as f:
try:
js = json.load(f)
except:
pass
return False
return 'actions' in js
#==================================================================#
# Event triggered on load story
#==================================================================#
@socketio.on('load_story')
def UI_2_load_story(file):
print("loading {}".format(file))
loadRequest(file)
#==================================================================#
# Event triggered on load story
#==================================================================#
@socketio.on('new_story')
def UI_2_new_story(data):
koboldai_vars.create_story("")
#==================================================================#
# Event triggered when user moves world info
#==================================================================#
@socketio.on('move_wi')
def UI_2_move_wi(data):
print(data)
if data['folder'] is None:
koboldai_vars.worldinfo_v2.reorder(int(data['dragged_id']), int(data['drop_id']))
else:
koboldai_vars.worldinfo_v2.add_item_to_folder(int(data['dragged_id']), data['folder'], before=int(data['drop_id']))
#==================================================================#
# Event triggered when user moves world info
#==================================================================#
@socketio.on('wi_set_folder')
def UI_2_wi_set_folder(data):
print(data)
koboldai_vars.worldinfo_v2.add_item_to_folder(int(data['dragged_id']), data['folder'])
#==================================================================#
# Event triggered when user renames world info folder
#==================================================================#
@socketio.on('Rename_World_Info_Folder')
def UI_2_Rename_World_Info_Folder(data):
print("Rename_World_Info_Folder")
print(data)
koboldai_vars.worldinfo_v2.rename_folder(data['old_folder'], data['new_folder'])
#==================================================================#
# Event triggered when user edits world info item
#==================================================================#
@socketio.on('edit_world_info')
def UI_2_edit_world_info(data):
print("Rename_World_Info_Folder")
print(data)
if data['uid'] == -1:
koboldai_vars.worldinfo_v2.add_item(data['title'], data['key'],
data['keysecondary'], data['folder'],
data['constant'], data['content'],
data['comment'])
emit("delete_new_world_info_entry", {})
else:
koboldai_vars.worldinfo_v2.edit_item(data['uid'], data['title'], data['key'],
data['keysecondary'], data['folder'],
data['constant'], data['content'],
data['comment'])
#==================================================================#
# Event triggered when user edits world info item
#==================================================================#
@socketio.on('create_world_info_folder')
def UI_2_create_world_info_folder(data):
koboldai_vars.worldinfo_v2.add_folder("New Folder")
#==================================================================#
# Event triggered when user edits world info item
#==================================================================#
@socketio.on('delete_world_info')
def UI_2_delete_world_info(uid):
koboldai_vars.worldinfo_v2.delete(int(uid))
#==================================================================#
# Event triggered when user edits phrase biases
#==================================================================#
@socketio.on('phrase_bias_update')
def UI_2_phrase_bias_update(biases):
print(biases)
koboldai_vars.biases = biases
#==================================================================#
# Event triggered to rely a message
#==================================================================#
@socketio.on('relay')
def UI_2_relay(data):
socketio.emit(data[0], data[1], **data[2])
#==================================================================#
# Event triggered when program errors out
#==================================================================#
def my_except_hook(exctype, value, traceback):
print("sending error to clients")
socketio.emit("error", "{}: {}".format(exctype, value), broadcast=True, room="UI_2")
sys.__excepthook__(exctype, value, traceback)
sys.excepthook = my_except_hook
#==================================================================#
# Event triggered when Softprompt is clicked
#==================================================================#
@socketio.on('load_softprompt_list')
def UI_2_load_softprompt_list(data):
if not koboldai_vars.allowsp:
socketio.emit("error", "Soft prompts are not supported by your current model/backend", broadcast=True, room="UI_2")
assert koboldai_vars.allowsp, "Soft prompts are not supported by your current model/backend"
file_popup("Select Softprompt to Load", "./softprompts", "load_softprompt", upload=True, jailed=True, folder_only=False, renameable=True,
deleteable=True, show_breadcrumbs=True, item_check=valid_softprompt,
valid_only=True, hide_extention=True, extra_parameter_function=get_softprompt_desc,
column_names=['Softprompt Name', 'Softprompt Description'],
show_filename=False,
column_widths=['150px', 'auto'])
def valid_softprompt(file):
z, version, shape, fortran_order, dtype = fileops.checksp(file, koboldai_vars.modeldim)
if z in [1, 2, 3, 4]:
return False
elif not isinstance(z, zipfile.ZipFile):
print("not zip")
return False
else:
return True
def get_softprompt_desc(item_full_path, item, valid_selection):
if not valid_selection:
return [None, None]
z = zipfile.ZipFile(item_full_path)
with z.open('meta.json') as f:
ob = json.load(f)
return [ob['name'], ob['description']]
#==================================================================#
# Event triggered when Softprompt is clicked
#==================================================================#
@socketio.on('load_softprompt')
def UI_2_load_softprompt(data):
print("Load softprompt: {}".format(data))
spRequest(data)
#==================================================================#
# Event triggered when aidg.club loaded
#==================================================================#
@socketio.on('load_aidg_club')
def UI_2_load_aidg_club(data):
print("Load aidg.club: {}".format(data))
importAidgRequest(data)
#==================================================================#
# Test
#==================================================================#
@app.route("/actions")
def show_actions():
return koboldai_vars.actions.actions
@app.route("/vars")
def show_vars():
json_data = {}
json_data['story_settings'] = json.loads(koboldai_vars.to_json("story_settings"))
json_data['model_settings'] = json.loads(koboldai_vars.to_json("model_settings"))
json_data['user_settings'] = json.loads(koboldai_vars.to_json("user_settings"))
json_data['system_settings'] = json.loads(koboldai_vars.to_json("system_settings"))
return json_data
#==================================================================#
# 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 koboldai_vars.model == "" or koboldai_vars.model is None:
koboldai_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
koboldai_settings.port = port
if(koboldai_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))
koboldai_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))
koboldai_vars.serverstarted = True
socketio.run(app, port=port, host='0.0.0.0')
else:
try:
from flaskwebgui import FlaskUI
koboldai_vars.serverstarted = True
koboldai_vars.flaskwebgui = True
FlaskUI(app, socketio=socketio, start_server="flask-socketio", maximized=True, close_server_on_exit=True).run()
except:
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))
koboldai_vars.serverstarted = True
socketio.run(app, port=port)
else:
general_startup()
patch_transformers()
#show_select_model_list()
if koboldai_vars.model == "" or koboldai_vars.model is None:
koboldai_vars.model = "ReadOnly"
load_model(initial_load=True)
koboldai_settings.port = args.port if "port" in args and args.port is not None else 5000
print("{0}\nServer started in WSGI mode!{1}".format(colors.GREEN, colors.END), flush=True)
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