#!/usr/bin/python3
#==================================================================#
# KoboldAI
# Version: 1.19.0
# By: The KoboldAI Community
#==================================================================#
# External packages
from dataclasses import dataclass
import eventlet
eventlet.monkey_patch(all=True, thread=False, os=False)
import os, inspect
os.system("")
__file__ = os.path.dirname(os.path.realpath(__file__))
os.chdir(__file__)
os.environ['EVENTLET_THREADPOOL_SIZE'] = '1'
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
from eventlet import tpool
import logging
from logger import logger, set_logger_verbosity, quiesce_logger
from ansi2html import Ansi2HTMLConverter
logging.getLogger("urllib3").setLevel(logging.ERROR)
from os import path, getcwd
import time
import re
import json
import datetime
import collections
import zipfile
import packaging
import packaging.version
import contextlib
import traceback
import threading
import markdown
import bleach
import itertools
import bisect
import functools
import traceback
import inspect
import warnings
import multiprocessing
import copy
import numpy as np
from collections.abc import Iterable
from collections import OrderedDict
from typing import Any, Callable, TypeVar, Tuple, Union, Dict, Set, List, Optional, Type
import requests
import html
import argparse
import sys
import gc
import lupa
import importlib
# KoboldAI
import fileops
import gensettings
from utils import debounce
import utils
import koboldai_settings
import torch
from transformers import StoppingCriteria, GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, PreTrainedModel, modeling_utils, AutoModelForTokenClassification
from transformers import __version__ as transformers_version
import transformers
try:
from transformers.models.opt.modeling_opt import OPTDecoder
except:
pass
import transformers.generation_utils
# Text2img
import base64
from PIL import Image, ImageFont, ImageDraw, ImageFilter, ImageOps
from io import BytesIO
global tpu_mtj_backend
if lupa.LUA_VERSION[:2] != (5, 4):
logger.error(f"Please install lupa==1.10. You have lupa {lupa.__version__}.")
patch_causallm_patched = False
# Make sure tqdm progress bars display properly in Colab
from tqdm.auto import tqdm
old_init = tqdm.__init__
def new_init(self, *args, **kwargs):
old_init(self, *args, **kwargs)
if(self.ncols == 0 and kwargs.get("ncols") != 0):
self.ncols = 99
tqdm.__init__ = new_init
# Fix some issues with the OPT tokenizer
from transformers import PreTrainedTokenizerBase
old_pretrainedtokenizerbase_from_pretrained = PreTrainedTokenizerBase.from_pretrained.__func__
@classmethod
def new_pretrainedtokenizerbase_from_pretrained(cls, *args, **kwargs):
tokenizer = old_pretrainedtokenizerbase_from_pretrained(cls, *args, **kwargs)
tokenizer._koboldai_header = tokenizer.encode("")
tokenizer.add_bos_token = False
tokenizer.add_prefix_space = False
return tokenizer
PreTrainedTokenizerBase.from_pretrained = new_pretrainedtokenizerbase_from_pretrained
#==================================================================#
# Variables & Storage
#==================================================================#
# Terminal tags for colored text
class colors:
PURPLE = '\033[95m'
BLUE = '\033[94m'
CYAN = '\033[96m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
RED = '\033[91m'
END = '\033[0m'
UNDERLINE = '\033[4m'
# AI models Menu
# This is a dict of lists where they key is the menu name, and the list is the menu items.
# Each item takes the 4 elements, 1: Text to display, 2: Model Name (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 OPT", "optlist", "", True],
["Untuned GPT-Neo/J", "gptneolist", "", True],
["Untuned Fairseq Dense", "fsdlist", "", True],
["Untuned Bloom", "bloomlist", "", True],
["Untuned XGLM", "xglmlist", "", True],
["Untuned RWKV-4", "rwkvlist", "", True],
["Untuned GPT2", "gpt2list", "", True],
["Online Services", "apilist", "", True],
["Read Only (No AI)", "ReadOnly", "", False]
],
'adventurelist': [
["Skein 20B", "KoboldAI/GPT-NeoX-20B-Skein", "64GB", False],
["Nerys OPT 13B V2 (Hybrid)", "KoboldAI/OPT-13B-Nerys-v2", "32GB", False],
["Nerys FSD 13B V2 (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys-v2", "32GB", False],
["Nerys FSD 13B (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys", "32GB", False],
["Skein 6B", "KoboldAI/GPT-J-6B-Skein", "16GB", False],
["OPT Nerys 6B V2 (Hybrid)", "KoboldAI/OPT-6B-nerys-v2", "16GB", False],
["Adventure 6B", "KoboldAI/GPT-J-6B-Adventure", "16GB", False],
["Nerys FSD 2.7B (Hybrid)", "KoboldAI/fairseq-dense-2.7B-Nerys", "8GB", False],
["Adventure 2.7B", "KoboldAI/GPT-Neo-2.7B-AID", "8GB", False],
["Adventure 1.3B", "KoboldAI/GPT-Neo-1.3B-Adventure", "6GB", False],
["Adventure 125M (Mia)", "Merry/AID-Neo-125M", "2GB", False],
["Return to Main Menu", "mainmenu", "", True],
],
'novellist': [
["Nerys OPT 13B V2 (Hybrid)", "KoboldAI/OPT-13B-Nerys-v2", "32GB", False],
["Nerys FSD 13B V2 (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys-v2", "32GB", False],
["Janeway FSD 13B", "KoboldAI/fairseq-dense-13B-Janeway", "32GB", False],
["Nerys FSD 13B (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys", "32GB", False],
["OPT Nerys 6B V2 (Hybrid)", "KoboldAI/OPT-6B-nerys-v2", "16GB", False],
["Janeway FSD 6.7B", "KoboldAI/fairseq-dense-6.7B-Janeway", "16GB", False],
["Janeway Neo 6B", "KoboldAI/GPT-J-6B-Janeway", "16GB", False],
["Janeway Neo 2.7B", "KoboldAI/GPT-Neo-2.7B-Janeway", "8GB", False],
["Janeway FSD 2.7B", "KoboldAI/fairseq-dense-2.7B-Janeway", "8GB", False],
["Nerys FSD 2.7B (Hybrid)", "KoboldAI/fairseq-dense-2.7B-Nerys", "8GB", False],
["Horni-LN 2.7B", "KoboldAI/GPT-Neo-2.7B-Horni-LN", "8GB", False],
["Picard 2.7B (Older Janeway)", "KoboldAI/GPT-Neo-2.7B-Picard", "8GB", False],
["Return to Main Menu", "mainmenu", "", True],
],
'nsfwlist': [
["Erebus 20B (NSFW)", "KoboldAI/GPT-NeoX-20B-Erebus", "64GB", False],
["Erebus 13B (NSFW)", "KoboldAI/OPT-13B-Erebus", "32GB", False],
["Shinen FSD 13B (NSFW)", "KoboldAI/fairseq-dense-13B-Shinen", "32GB", False],
["Erebus 6.7B (NSFW)", "KoboldAI/OPT-6.7B-Erebus", "16GB", False],
["Shinen FSD 6.7B (NSFW)", "KoboldAI/fairseq-dense-6.7B-Shinen", "16GB", False],
["Lit V2 6B (NSFW)", "hakurei/litv2-6B-rev3", "16GB", False],
["Lit 6B (NSFW)", "hakurei/lit-6B", "16GB", False],
["Shinen 6B (NSFW)", "KoboldAI/GPT-J-6B-Shinen", "16GB", False],
["Erebus 2.7B (NSFW)", "KoboldAI/OPT-2.7B-Erebus", "8GB", False],
["Horni 2.7B (NSFW)", "KoboldAI/GPT-Neo-2.7B-Horni", "8GB", False],
["Shinen 2.7B (NSFW)", "KoboldAI/GPT-Neo-2.7B-Shinen", "8GB", False],
["Return to Main Menu", "mainmenu", "", True],
],
'chatlist': [
["Convo 6B (Chatbot)", "hitomi-team/convo-6B", "16GB", False],
["C1 6B (Chatbot)", "hakurei/c1-6B", "16GB", False],
["C1 1.3B (Chatbot)", "iokru/c1-1.3B", "6GB", False],
["Return to Main Menu", "mainmenu", "", True],
],
'gptneolist': [
["GPT-NeoX 20B", "EleutherAI/gpt-neox-20b", "64GB", False],
["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],
],
'bloomlist': [
["Bloom 176B", "bigscience/bloom", "", False],
["Bloom 7.1B", "bigscience/bloom-7b1", "", False],
["Bloom 3B", "bigscience/bloom-3b", "", False],
["Bloom 1.7B", "bigscience/bloom-1b7", "", False],
["Bloom 560M", "bigscience/bloom-560m", "", False],
["Return to Main Menu", "mainmenu", "", True],
],
'optlist': [
["OPT 66B", "facebook/opt-66b", "128GB", False],
["OPT 30B", "facebook/opt-30b", "64GB", False],
["OPT 13B", "facebook/opt-13b", "32GB", False],
["OPT 6.7B", "facebook/opt-6.7b", "16GB", False],
["OPT 2.7B", "facebook/opt-2.7b", "8GB", False],
["OPT 1.3B", "facebook/opt-1.3b", "4GB", False],
["OPT 350M", "facebook/opt-350m", "2GB", False],
["OPT 125M", "facebook/opt-125m", "1GB", False],
["Return to Main Menu", "mainmenu", "", True],
],
'fsdlist': [
["Fairseq Dense 13B", "KoboldAI/fairseq-dense-13B", "32GB", False],
["Fairseq Dense 6.7B", "KoboldAI/fairseq-dense-6.7B", "16GB", False],
["Fairseq Dense 2.7B", "KoboldAI/fairseq-dense-2.7B", "8GB", False],
["Fairseq Dense 1.3B", "KoboldAI/fairseq-dense-1.3B", "4GB", False],
["Fairseq Dense 355M", "KoboldAI/fairseq-dense-355M", "2GB", False],
["Fairseq Dense 125M", "KoboldAI/fairseq-dense-125M", "1GB", False],
["Return to Main Menu", "mainmenu", "", True],
],
'xglmlist': [
["XGLM 4.5B (Larger Dataset)", "facebook/xglm-4.5B", "12GB", False],
["XGLM 7.5B", "facebook/xglm-7.5B", "18GB", False],
["XGLM 2.9B", "facebook/xglm-2.9B", "10GB", False],
["XGLM 1.7B", "facebook/xglm-1.7B", "6GB", False],
["XGLM 564M", "facebook/xglm-564M", "4GB", False],
["Return to Main Menu", "mainmenu", "", True],
],
'rwkvlist': [
["RWKV-4 7B (GPU)", "RWKV-7B-GPU", "??GB", False],
["RWKV-4 7B (CPU)", "RWKV-7B-CPU", "??GB", False],
["RWKV-4 3B (GPU)", "RWKV-3B-GPU", "?GB", False],
["RWKV-4 3B (CPU)", "RWKV-3B-CPU", "?GB", False],
["RWKV-4 1.5B (GPU)", "RWKV-1B5-GPU", "9GB", False],
["RWKV-4 1.5B (CPU)", "RWKV-1B5-CPU", "6GB", False],
["RWKV-4 340M (GPU)", "RWKV-340M-GPU", "?GB", False],
["RWKV-4 340M (CPU)", "RWKV-340M-CPU", "?GB", False],
["RWKV-4 169M (GPU)", "RWKV-169M-GPU", "?GB", False],
["RWKV-4 169M (CPU)", "RWKV-169M-CPU", "?GB", False],
["Return to Main Menu", "mainmenu", "", True],
],
'apilist': [
["GooseAI API (requires API key)", "GooseAI", "", False],
["OpenAI API (requires API key)", "OAI", "", False],
["InferKit API (requires API key)", "InferKit", "", False],
# ["KoboldAI Server API (Old Google Colab)", "Colab", "", False],
["KoboldAI API", "API", "", False],
["KoboldAI Horde", "CLUSTER", "", False],
["Return to Main Menu", "mainmenu", "", True],
]
}
class Send_to_socketio(object):
def write(self, bar):
print('\r' + bar, end='')
time.sleep(0.01)
try:
gui_msg = bar.replace(f"{colors.PURPLE}INIT{colors.END} | ","").replace(" ", " ")
emit('from_server', {'cmd': 'model_load_status', 'data': gui_msg}, broadcast=True, room="UI_1")
except:
pass
def flush(self):
pass
@dataclass
class ImportBuffer:
# Singleton!!!
prompt: Optional[str] = None
memory: Optional[str] = None
authors_note: Optional[str] = None
notes: Optional[str] = None
world_infos: Optional[dict] = None
@dataclass
class PromptPlaceholder:
id: str
order: Optional[int] = None
default: Optional[str] = None
title: Optional[str] = None
description: Optional[str] = None
value: Optional[str] = None
def to_json(self) -> dict:
return {key: getattr(self, key) for key in [
"id",
"order",
"default",
"title",
"description"
]}
def request_client_configuration(self, placeholders: List[PromptPlaceholder]) -> None:
emit("request_prompt_config", [x.to_json() for x in placeholders], broadcast=False, room="UI_2")
def extract_placeholders(self, text: str) -> List[PromptPlaceholder]:
placeholders = []
for match in re.finditer(r"\${(.*?)}", text):
ph_text = match.group(1)
try:
ph_order, ph_text = ph_text.split("#")
except ValueError:
ph_order = None
if "[" not in ph_text:
ph_id = ph_text
# Already have it!
if any([x.id == ph_id for x in placeholders]):
continue
# Apparently, none of these characters are supported:
# "${}[]#:@^|", however I have found some prompts using these,
# so they will be allowed.
for char in "${}[]":
if char in ph_text:
print("[eph] Weird char")
print(f"Char: {char}")
print(f"Ph_id: {ph_id}")
return
placeholders.append(self.PromptPlaceholder(
id=ph_id,
order=int(ph_order) if ph_order else None,
))
continue
ph_id, _ = ph_text.split("[")
ph_text = ph_text.replace(ph_id, "", 1)
# Already have it!
if any([x.id == ph_id for x in placeholders]):
continue
# Match won't match it for some reason (???), so we use finditer and next()
try:
default_match = next(re.finditer(r"\[(.*?)\]", ph_text))
except StopIteration:
print("[eph] Weird brackets")
return placeholders
ph_default = default_match.group(1)
ph_text = ph_text.replace(default_match.group(0), "")
try:
ph_title, ph_desc = ph_text.split(":")
except ValueError:
ph_title = ph_text or None
ph_desc=None
placeholders.append(self.PromptPlaceholder(
id=ph_id,
order=int(ph_order) if ph_order else None,
default=ph_default,
title=ph_title,
description=ph_desc
))
return placeholders
def _replace_placeholders(self, text: str, ph_ids: dict):
for ph_id, value in ph_ids.items():
pattern = "\${(?:\d#)?%s.*?}" % re.escape(ph_id)
for ph_text in re.findall(pattern, text):
text = text.replace(ph_text, value)
return text
def replace_placeholders(self, ph_ids: dict):
self.prompt = self._replace_placeholders(self.prompt, ph_ids)
self.memory = self._replace_placeholders(self.memory, ph_ids)
self.authors_note = self._replace_placeholders(self.authors_note, ph_ids)
for i in range(len(self.world_infos)):
for key in ["content", "comment"]:
self.world_infos[i][key] = self._replace_placeholders(self.world_infos[i][key])
def from_club(self, club_id):
# Maybe it is a better to parse the NAI Scenario (if available), it has more data
r = requests.get(f"https://aetherroom.club/api/{club_id}")
if not r.ok:
# TODO: Show error message on client
print(f"[import] Got {r.status_code} on request to club :^(")
return
j = r.json()
self.prompt = j["promptContent"]
self.memory = j["memory"]
self.authors_note = j["authorsNote"]
self.notes = j["description"]
self.world_infos = []
for wi in j["worldInfos"]:
self.world_infos.append({
"key_list": wi["keysList"],
"keysecondary": [],
"content": wi["entry"],
"comment": "",
"folder": wi.get("folder", None),
"num": 0,
"init": True,
"selective": wi.get("selective", False),
"constant": wi.get("constant", False),
"uid": None,
})
placeholders = self.extract_placeholders(self.prompt)
if not placeholders:
self.commit()
else:
self.request_client_configuration(placeholders)
def commit(self):
# Push buffer story to actual story
exitModes()
koboldai_vars.create_story("")
koboldai_vars.gamestarted = True
koboldai_vars.prompt = self.prompt
koboldai_vars.memory = self.memory or ""
koboldai_vars.authornote = self.authors_note or ""
koboldai_vars.notes = self.notes
for wi in self.world_infos:
koboldai_vars.worldinfo_v2.add_item(
wi["key_list"][0],
wi["key_list"],
wi.get("keysecondary", []),
wi.get("folder", "root"),
wi.get("constant", False),
wi["content"],
wi.get("comment", "")
)
# Reset current save
koboldai_vars.savedir = getcwd()+"\\stories"
# Refresh game screen
koboldai_vars.laststory = None
setgamesaved(False)
sendwi()
refresh_story()
import_buffer = ImportBuffer()
# Set logging level to reduce chatter from Flask
import logging
log = logging.getLogger('werkzeug')
log.setLevel(logging.ERROR)
def UI_2_logger(message):
conv = Ansi2HTMLConverter(inline=True, dark_bg=True)
data = json.loads(message)
data['html'] = [conv.convert(text, full=False) for text in data['text'].split("\n")]
if not has_request_context():
if koboldai_settings.queue is not None:
koboldai_settings.queue.put(["log_message", data, {"broadcast":True, "room":"UI_2"}])
else:
socketio.emit("log_message", data, broadcast=True, room="UI_2")
web_log_history = []
def UI_2_log_history(message):
conv = Ansi2HTMLConverter(inline=True, dark_bg=True)
data = json.loads(message)
data['html'] = [conv.convert(text, full=False) for text in data['text'].split("\n")]
if len(web_log_history) >= 100:
del web_log_history[0]
web_log_history.append(data)
from flask import Flask, render_template, Response, request, copy_current_request_context, send_from_directory, session, jsonify, abort, redirect, has_request_context
from flask_socketio import SocketIO, emit, join_room, leave_room
from flask_socketio import emit as _emit
from flask_session import Session
from flask_compress import Compress
from werkzeug.exceptions import HTTPException, NotFound, InternalServerError
import secrets
app = Flask(__name__, root_path=os.getcwd())
app.secret_key = secrets.token_hex()
app.config['SESSION_TYPE'] = 'filesystem'
app.config['TEMPLATES_AUTO_RELOAD'] = True
Compress(app)
socketio = SocketIO(app, async_method="eventlet", manage_session=False, cors_allowed_origins='*', max_http_buffer_size=10_000_000)
#socketio = SocketIO(app, async_method="eventlet", manage_session=False, cors_allowed_origins='*', max_http_buffer_size=10_000_000, logger=logger, engineio_logger=True)
logger.add(UI_2_log_history, serialize=True, colorize=True, enqueue=True, level="INFO")
#logger.add("log_file_1.log", rotation="500 MB") # Automatically rotate too big file
koboldai_vars = koboldai_settings.koboldai_vars(socketio)
utils.koboldai_vars = koboldai_vars
old_socketio_on = socketio.on
def new_socketio_on(*a, **k):
decorator = old_socketio_on(*a, **k)
def new_decorator(f):
@functools.wraps(f)
def g(*a, **k):
if args.no_ui:
return
return f(*a, **k)
return decorator(g)
return new_decorator
socketio.on = new_socketio_on
def emit(*args, **kwargs):
try:
return _emit(*args, **kwargs)
except AttributeError:
return socketio.emit(*args, **kwargs)
#replacement for tpool.execute to maintain request contexts
def replacement_tpool_execute(function, *args, **kwargs):
temp = {}
socketio.start_background_task(tpool.execute_2, function, temp, *args, **kwargs).join()
print(temp)
return temp[1]
def replacement_tpool_execute_2(function, temp, *args, **kwargs):
temp[1] = function(*args, **kwargs)
# marshmallow/apispec setup
from apispec import APISpec
from apispec.ext.marshmallow import MarshmallowPlugin
from apispec.ext.marshmallow.field_converter import make_min_max_attributes
from apispec_webframeworks.flask import FlaskPlugin
from marshmallow import Schema, fields, validate, EXCLUDE
from marshmallow.exceptions import ValidationError
class KoboldSchema(Schema):
pass
def new_make_min_max_attributes(validators, min_attr, max_attr) -> dict:
# Patched apispec function that creates "exclusiveMinimum"/"exclusiveMaximum" OpenAPI attributes insteaed of "minimum"/"maximum" when using validators.Range or validators.Length with min_inclusive=False or max_inclusive=False
attributes = {}
min_list = [validator.min for validator in validators if validator.min is not None]
max_list = [validator.max for validator in validators if validator.max is not None]
min_inclusive_list = [getattr(validator, "min_inclusive", True) for validator in validators if validator.min is not None]
max_inclusive_list = [getattr(validator, "max_inclusive", True) for validator in validators if validator.max is not None]
if min_list:
if min_attr == "minimum" and not min_inclusive_list[max(range(len(min_list)), key=min_list.__getitem__)]:
min_attr = "exclusiveMinimum"
attributes[min_attr] = max(min_list)
if max_list:
if min_attr == "maximum" and not max_inclusive_list[min(range(len(max_list)), key=max_list.__getitem__)]:
min_attr = "exclusiveMaximum"
attributes[max_attr] = min(max_list)
return attributes
make_min_max_attributes.__code__ = new_make_min_max_attributes.__code__
def api_format_docstring(f):
f.__doc__ = eval('f"""{}"""'.format(f.__doc__.replace("\\", "\\\\")))
return f
def api_catch_out_of_memory_errors(f):
@functools.wraps(f)
def decorated(*args, **kwargs):
try:
return f(*args, **kwargs)
except Exception as e:
if any (s in traceback.format_exc().lower() for s in ("out of memory", "not enough memory")):
for line in reversed(traceback.format_exc().split("\n")):
if any(s in line.lower() for s in ("out of memory", "not enough memory")) and line.count(":"):
line = line.split(":", 1)[1]
line = re.sub(r"\[.+?\] +data\.", "", line).strip()
raise KoboldOutOfMemoryError("KoboldAI ran out of memory: " + line, type="out_of_memory.gpu.cuda" if "cuda out of memory" in line.lower() else "out_of_memory.gpu.hip" if "hip out of memory" in line.lower() else "out_of_memory.tpu.hbm" if "memory space hbm" in line.lower() else "out_of_memory.cpu.default_memory_allocator" if "defaultmemoryallocator" in line.lower() else "out_of_memory.unknown.unknown")
raise KoboldOutOfMemoryError(type="out_of_memory.unknown.unknown")
raise e
return decorated
def api_schema_wrap(f):
try:
input_schema: Type[Schema] = next(iter(inspect.signature(f).parameters.values())).annotation
except:
HAS_SCHEMA = False
else:
HAS_SCHEMA = inspect.isclass(input_schema) and issubclass(input_schema, Schema)
f = api_format_docstring(f)
f = api_catch_out_of_memory_errors(f)
@functools.wraps(f)
def decorated(*args, **kwargs):
if HAS_SCHEMA:
body = request.get_json()
schema = input_schema.from_dict(input_schema().load(body))
response = f(schema, *args, **kwargs)
else:
response = f(*args, **kwargs)
if not isinstance(response, Response):
response = jsonify(response)
return response
return decorated
@app.errorhandler(HTTPException)
def handler(e):
if request.path != "/api" and not request.path.startswith("/api/"):
return e
resp = jsonify(detail={"msg": str(e), "type": "generic.error_" + str(e.code)})
if e.code == 405 and e.valid_methods is not None:
resp.headers["Allow"] = ", ".join(e.valid_methods)
return resp, e.code
class KoboldOutOfMemoryError(HTTPException):
code = 507
description = "KoboldAI ran out of memory."
type = "out_of_memory.unknown.unknown"
def __init__(self, *args, type=None, **kwargs):
super().__init__(*args, **kwargs)
if type is not None:
self.type = type
@app.errorhandler(KoboldOutOfMemoryError)
def handler(e):
if request.path != "/api" and not request.path.startswith("/api/"):
return InternalServerError()
return jsonify(detail={"type": e.type, "msg": e.description}), e.code
@app.errorhandler(ValidationError)
def handler(e):
if request.path != "/api" and not request.path.startswith("/api/"):
return InternalServerError()
return jsonify(detail=e.messages), 422
@app.errorhandler(NotImplementedError)
def handler(e):
if request.path != "/api" and not request.path.startswith("/api/"):
return InternalServerError()
return jsonify(detail={"type": "not_implemented", "msg": str(e).strip()}), 501
api_versions: List[str] = []
class KoboldAPISpec(APISpec):
class KoboldFlaskPlugin(FlaskPlugin):
def __init__(self, api: "KoboldAPISpec", *args, **kwargs):
self._kobold_api_spec = api
super().__init__(*args, **kwargs)
def path_helper(self, *args, **kwargs):
return super().path_helper(*args, **kwargs)[len(self._kobold_api_spec._prefixes[0]):]
def __init__(self, *args, title: str = "KoboldAI API", openapi_version: str = "3.0.3", version: str = "1.0.0", prefixes: List[str] = None, **kwargs):
plugins = [KoboldAPISpec.KoboldFlaskPlugin(self), MarshmallowPlugin()]
self._prefixes = prefixes if prefixes is not None else [""]
self._kobold_api_spec_version = version
api_versions.append(version)
api_versions.sort(key=lambda x: [int(e) for e in x.split(".")])
super().__init__(*args, title=title, openapi_version=openapi_version, version=version, plugins=plugins, servers=[{"url": self._prefixes[0]}], **kwargs)
for prefix in self._prefixes:
app.route(prefix, endpoint="~KoboldAPISpec~" + prefix)(lambda: redirect(request.path + "/docs/"))
app.route(prefix + "/", endpoint="~KoboldAPISpec~" + prefix + "/")(lambda: redirect("docs/"))
app.route(prefix + "/docs", endpoint="~KoboldAPISpec~" + prefix + "/docs")(lambda: redirect("docs/"))
app.route(prefix + "/docs/", endpoint="~KoboldAPISpec~" + prefix + "/docs/")(lambda: render_template("swagger-ui.html", url=self._prefixes[0] + "/openapi.json"))
app.route(prefix + "/openapi.json", endpoint="~KoboldAPISpec~" + prefix + "/openapi.json")(lambda: jsonify(self.to_dict()))
def route(self, rule: str, methods=["GET"], **kwargs):
__F = TypeVar("__F", bound=Callable[..., Any])
if "strict_slashes" not in kwargs:
kwargs["strict_slashes"] = False
def new_decorator(f: __F) -> __F:
@functools.wraps(f)
def g(*args, **kwargs):
global api_version
api_version = self._kobold_api_spec_version
try:
return f(*args, **kwargs)
finally:
api_version = None
for prefix in self._prefixes:
g = app.route(prefix + rule, methods=methods, **kwargs)(g)
with app.test_request_context():
self.path(view=g, **kwargs)
return g
return new_decorator
def get(self, rule: str, **kwargs):
return self.route(rule, methods=["GET"], **kwargs)
def post(self, rule: str, **kwargs):
return self.route(rule, methods=["POST"], **kwargs)
def put(self, rule: str, **kwargs):
return self.route(rule, methods=["PUT"], **kwargs)
def patch(self, rule: str, **kwargs):
return self.route(rule, methods=["PATCH"], **kwargs)
def delete(self, rule: str, **kwargs):
return self.route(rule, methods=["DELETE"], **kwargs)
tags = [
{"name": "info", "description": "Metadata about this API"},
{"name": "generate", "description": "Text generation endpoints"},
{"name": "model", "description": "Information about the current text generation model"},
{"name": "story", "description": "Endpoints for managing the story in the KoboldAI GUI"},
{"name": "world_info", "description": "Endpoints for managing the world info in the KoboldAI GUI"},
{"name": "config", "description": "Allows you to get/set various setting values"},
]
api_version = None # This gets set automatically so don't change this value
api_v1 = KoboldAPISpec(
version="1.2.0",
prefixes=["/api/v1", "/api/latest"],
tags=tags,
)
# Returns the expected config filename for the current setup.
# If the model_name is specified, it returns what the settings file would be for that model
def get_config_filename(model_name = None):
if model_name:
return(f"settings/{model_name.replace('/', '_')}.settings")
elif args.configname:
return(f"settings/{args.configname.replace('/', '_')}.settings")
elif koboldai_vars.configname != '':
return(f"settings/{koboldai_vars.configname.replace('/', '_')}.settings")
else:
logger.warning(f"Empty configfile name sent back. Defaulting to ReadOnly")
return(f"settings/ReadOnly.settings")
#==================================================================#
# Function to get model selection at startup
#==================================================================#
def sendModelSelection(menu="mainmenu", folder="./models"):
#If we send one of the manual load options, send back the list of model directories, otherwise send the menu
if menu in ('NeoCustom', 'GPT2Custom'):
(paths, breadcrumbs) = get_folder_path_info(folder)
if 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)
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)
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 koboldai_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):
return os.path.exists(os.path.join(path, 'config.json'))
#==================================================================#
# Return all keys in tokenizer dictionary containing char
#==================================================================#
#def gettokenids(char):
# keys = []
# for key in vocab_keys:
# if(key.find(char) != -1):
# keys.append(key)
# return keys
#==================================================================#
# Return Model Name
#==================================================================#
def getmodelname():
if(koboldai_vars.online_model != ''):
return(f"{koboldai_vars.model}/{koboldai_vars.online_model}")
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.cpu:
breakmodel.gpu_blocks = [0]*n_layers
return
elif(args.breakmodel_gpulayers is not None or (utils.HAS_ACCELERATE and args.breakmodel_disklayers is not None)):
try:
if(not args.breakmodel_gpulayers):
breakmodel.gpu_blocks = []
else:
breakmodel.gpu_blocks = list(map(int, args.breakmodel_gpulayers.split(',')))
assert len(breakmodel.gpu_blocks) <= torch.cuda.device_count()
s = n_layers
for i in range(len(breakmodel.gpu_blocks)):
if(breakmodel.gpu_blocks[i] <= -1):
breakmodel.gpu_blocks[i] = s
break
else:
s -= breakmodel.gpu_blocks[i]
assert sum(breakmodel.gpu_blocks) <= n_layers
n_layers -= sum(breakmodel.gpu_blocks)
if(args.breakmodel_disklayers is not None):
assert args.breakmodel_disklayers <= n_layers
breakmodel.disk_blocks = args.breakmodel_disklayers
n_layers -= args.breakmodel_disklayers
except:
logger.warning("--breakmodel_gpulayers is malformatted. Please use the --help option to see correct usage of --breakmodel_gpulayers. Defaulting to all layers on device 0.")
breakmodel.gpu_blocks = [n_layers]
n_layers = 0
elif(args.breakmodel_layers is not None):
breakmodel.gpu_blocks = [n_layers - max(0, min(n_layers, args.breakmodel_layers))]
n_layers -= sum(breakmodel.gpu_blocks)
elif(args.model is not None):
logger.info("Breakmodel not specified, assuming GPU 0")
breakmodel.gpu_blocks = [n_layers]
n_layers = 0
else:
device_count = torch.cuda.device_count()
if(device_count > 1):
print(colors.CYAN + "\nPlease select one of your GPUs to be your primary GPU.")
print("VRAM usage in your primary GPU will be higher than for your other ones.")
print("It is recommended you make your fastest GPU your primary GPU.")
device_list(n_layers)
while(True):
primaryselect = input("device ID> ")
if(primaryselect.isnumeric() and 0 <= int(primaryselect) < device_count):
breakmodel.primary_device = int(primaryselect)
break
else:
print(f"{colors.RED}Please enter an integer between 0 and {device_count-1}.{colors.END}")
else:
breakmodel.primary_device = 0
print(colors.PURPLE + "\nIf you don't have enough VRAM to run the model on a single GPU")
print("you can split the model between your CPU and your GPU(s), or between")
print("multiple GPUs if you have more than one.")
print("By putting more 'layers' on a GPU or CPU, more computations will be")
print("done on that device and more VRAM or RAM will be required on that device")
print("(roughly proportional to number of layers).")
print("It should be noted that GPUs are orders of magnitude faster than the CPU.")
print(f"This model has{colors.YELLOW} {n_layers} {colors.PURPLE}layers.{colors.END}\n")
for i in range(device_count):
device_list(n_layers, primary=breakmodel.primary_device, selected=i)
print(f"{colors.CYAN}\nHow many of the remaining{colors.YELLOW} {n_layers} {colors.CYAN}layers would you like to put into device {i}?\nYou can also enter -1 to allocate all remaining layers to this device.{colors.END}\n")
while(True):
layerselect = input("# of layers> ")
if((layerselect.isnumeric() or layerselect.strip() == '-1') and -1 <= int(layerselect) <= n_layers):
layerselect = int(layerselect)
layerselect = n_layers if layerselect == -1 else layerselect
breakmodel.gpu_blocks.append(layerselect)
n_layers -= layerselect
break
else:
print(f"{colors.RED}Please enter an integer between -1 and {n_layers}.{colors.END}")
if(n_layers == 0):
break
if(utils.HAS_ACCELERATE and n_layers > 0):
device_list(n_layers, primary=breakmodel.primary_device, selected=-1)
print(f"{colors.CYAN}\nHow many of the remaining{colors.YELLOW} {n_layers} {colors.CYAN}layers would you like to put into the disk cache?\nYou can also enter -1 to allocate all remaining layers to this device.{colors.END}\n")
while(True):
layerselect = input("# of layers> ")
if((layerselect.isnumeric() or layerselect.strip() == '-1') and -1 <= int(layerselect) <= n_layers):
layerselect = int(layerselect)
layerselect = n_layers if layerselect == -1 else layerselect
breakmodel.disk_blocks = layerselect
n_layers -= layerselect
break
else:
print(f"{colors.RED}Please enter an integer between -1 and {n_layers}.{colors.END}")
logger.init_ok("Final device configuration:", status="Info")
device_list(n_layers)
# 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):
logger.warning("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):
import accelerate.utils
for key, value in model.state_dict().items():
target_dtype = torch.float32 if breakmodel.primary_device == "cpu" else torch.float16
if(value.dtype is not target_dtype):
accelerate.utils.set_module_tensor_to_device(model, key, target_dtype)
disk_blocks = breakmodel.disk_blocks
gpu_blocks = breakmodel.gpu_blocks
ram_blocks = len(utils.layers_module_names) - sum(gpu_blocks)
cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks))
device_map = {}
for name in utils.layers_module_names:
layer = int(name.rsplit(".", 1)[1])
device = ("disk" if layer < disk_blocks else "cpu") if layer < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, layer - ram_blocks)
device_map[name] = device
for name in utils.get_missing_module_names(model, list(device_map.keys())):
device_map[name] = breakmodel.primary_device
breakmodel.dispatch_model_ex(model, device_map, main_device=breakmodel.primary_device, offload_buffers=True, offload_dir="accelerate-disk-cache")
gc.collect()
generator = model.generate
return
model.half()
gc.collect()
if(hasattr(model, "transformer")):
model.transformer.wte.to(breakmodel.primary_device)
model.transformer.ln_f.to(breakmodel.primary_device)
if(hasattr(model, 'lm_head')):
model.lm_head.to(breakmodel.primary_device)
if(hasattr(model.transformer, 'wpe')):
model.transformer.wpe.to(breakmodel.primary_device)
elif(not hasattr(model.model, "decoder")):
model.model.embed_tokens.to(breakmodel.primary_device)
model.model.layer_norm.to(breakmodel.primary_device)
model.lm_head.to(breakmodel.primary_device)
model.model.embed_positions.to(breakmodel.primary_device)
else:
model.model.decoder.embed_tokens.to(breakmodel.primary_device)
if(model.model.decoder.project_in is not None):
model.model.decoder.project_in.to(breakmodel.primary_device)
if(model.model.decoder.project_out is not None):
model.model.decoder.project_out.to(breakmodel.primary_device)
model.model.decoder.embed_positions.to(breakmodel.primary_device)
gc.collect()
GPTNeoModel.forward = breakmodel.new_forward_neo
if("GPTJModel" in globals()):
GPTJModel.forward = breakmodel.new_forward_neo # type: ignore
if("XGLMModel" in globals()):
XGLMModel.forward = breakmodel.new_forward_xglm # type: ignore
if("OPTDecoder" in globals()):
OPTDecoder.forward = breakmodel.new_forward_opt # type: ignore
generator = model.generate
if(hasattr(model, "transformer")):
breakmodel.move_hidden_layers(model.transformer)
elif(not hasattr(model.model, "decoder")):
breakmodel.move_hidden_layers(model.model, model.model.layers)
else:
breakmodel.move_hidden_layers(model.model.decoder, model.model.decoder.layers)
#==================================================================#
# Allow the models to override some settings
#==================================================================#
def loadmodelsettings():
try:
js = json.loads(str(model_config).partition(' ')[2])
except Exception as e:
try:
try:
js = json.load(open(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 newline mode if using XGLM
if koboldai_vars.model_type == "opt" or koboldai_vars.model_type == "bloom":
koboldai_vars.newlinemode = "ns" # Handle 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):
sampler_order = koboldai_vars.sampler_order
if(len(sampler_order) < 7):
sampler_order = [6] + sampler_order
koboldai_vars.sampler_order = 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):
for setting in ['frmttriminc', 'frmtrmblln', 'frmtrmspch', 'frmtadsnsp', 'singleline']:
if setting in js["formatoptns"]:
setattr(koboldai_vars, setting, js["formatoptns"][setting])
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 koboldai_vars and write them to client settings file
#==================================================================#
def savesettings():
# Build json to write
for setting in ['model_settings', 'user_settings', 'system_settings']:
if setting == "model_settings":
filename = "settings/{}.v2_settings".format(koboldai_vars.model.replace("/", "_"))
else:
filename = "settings/{}.v2_settings".format(setting)
with open(filename, "w") as settings_file:
settings_file.write(getattr(koboldai_vars, "_{}".format(setting)).to_json())
#==================================================================#
# Don't save settings unless 2 seconds have passed without modification
#==================================================================#
@debounce(2)
def settingschanged():
logger.info("Saving settings.")
savesettings()
#==================================================================#
# Read settings from client file JSON and send to koboldai_vars
#==================================================================#
def loadsettings():
if(path.exists("settings/" + getmodelname().replace('/', '_') + ".v2_settings")):
with open("settings/" + getmodelname().replace('/', '_') + ".v2_settings", "r") as file:
getattr(koboldai_vars, "_model_settings").from_json(file.read())
#==================================================================#
# Load a soft prompt from a file
#==================================================================#
#def check_for_sp_change():
# while(True):
# time.sleep(0.05)
#
# 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
z, version, shape, fortran_order, dtype = fileops.checksp("./softprompts/"+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("--apikey", help="Specify the API key to use for online services")
parser.add_argument("--sh_apikey", help="Specify the API key to use for txt2img from the Stable Horde. Get a key from https://stablehorde.net/register")
parser.add_argument("--req_model", type=str, action='append', required=False, help="Which models which we allow to generate for us during cluster mode. Can be specified multiple times.")
parser.add_argument("--revision", help="Specify the model revision for huggingface models (can be a git branch/tag name or a git commit hash)")
parser.add_argument("--cpu", action='store_true', help="By default unattended launches are on the GPU use this option to force CPU usage.")
parser.add_argument("--breakmodel", action='store_true', help=argparse.SUPPRESS)
parser.add_argument("--breakmodel_layers", type=int, help=argparse.SUPPRESS)
parser.add_argument("--breakmodel_gpulayers", type=str, help="If using a model that supports hybrid generation, this is a comma-separated list that specifies how many layers to put on each GPU device. For example to put 8 layers on device 0, 9 layers on device 1 and 11 layers on device 2, use --breakmodel_gpulayers 8,9,11")
parser.add_argument("--breakmodel_disklayers", type=int, help="If using a model that supports hybrid generation, this is the number of layers to put in disk cache.")
parser.add_argument("--override_delete", action='store_true', help="Deleting stories from inside the browser is disabled if you are using --remote and enabled otherwise. Using this option will instead allow deleting stories if using --remote and prevent deleting stories otherwise.")
parser.add_argument("--override_rename", action='store_true', help="Renaming stories from inside the browser is disabled if you are using --remote and enabled otherwise. Using this option will instead allow renaming stories if using --remote and prevent renaming stories otherwise.")
parser.add_argument("--configname", help="Force a fixed configuration name to aid with config management.")
parser.add_argument("--colab", action='store_true', help="Optimize for Google Colab.")
parser.add_argument("--nobreakmodel", action='store_true', help="Disables Breakmodel support completely.")
parser.add_argument("--unblock", action='store_true', default=False, help="Unblocks the KoboldAI port to be accessible from other machines without optimizing for remote play (It is recommended to use --host instead)")
parser.add_argument("--quiet", action='store_true', default=False, help="If present will suppress any story related text from showing on the console")
parser.add_argument("--no_aria2", action='store_true', default=False, help="Prevents KoboldAI from using aria2 to download huggingface models more efficiently, in case aria2 is causing you issues")
parser.add_argument("--lowmem", action='store_true', help="Extra Low Memory loading for the GPU, slower but memory does not peak to twice the usage")
parser.add_argument("--savemodel", action='store_true', help="Saves the model to the models folder even if --colab is used (Allows you to save models to Google Drive)")
parser.add_argument("--customsettings", help="Preloads arguements from json file. You only need to provide the location of the json file. Use customsettings.json template file. It can be renamed if you wish so that you can store multiple configurations. Leave any settings you want as default as null. Any values you wish to set need to be in double quotation marks")
parser.add_argument("--no_ui", action='store_true', default=False, help="Disables the GUI and Socket.IO server while leaving the API server running.")
parser.add_argument("--summarizer_model", action='store', default="philschmid/bart-large-cnn-samsum", help="Huggingface model to use for summarization. Defaults to sshleifer/distilbart-cnn-12-6")
parser.add_argument("--max_summary_length", action='store', default=75, help="Maximum size for summary to send to image generation")
parser.add_argument("--multi_story", action='store_true', default=False, help="Allow multi-story mode (experimental)")
parser.add_argument('-f', action='store', help="option for compatability with colab memory profiles")
parser.add_argument('-v', '--verbosity', action='count', default=0, help="The default logging level is ERROR or higher. This value increases the amount of logging seen in your screen")
parser.add_argument('-q', '--quiesce', action='count', default=0, help="The default logging level is ERROR or higher. This value decreases the amount of logging seen in your screen")
#args: argparse.Namespace = None
if "pytest" in sys.modules and override_args is None:
args = parser.parse_args([])
return
if override_args is not None:
import shlex
args = parser.parse_args(shlex.split(override_args))
elif(os.environ.get("KOBOLDAI_ARGS") is not None):
import shlex
args = parser.parse_args(shlex.split(os.environ["KOBOLDAI_ARGS"]))
else:
args = parser.parse_args()
#load system and user settings
for setting in ['user_settings', 'system_settings']:
if os.path.exists("settings/{}.v2_settings".format(setting)):
with open("settings/{}.v2_settings".format(setting), "r") as settings_file:
getattr(koboldai_vars, "_{}".format(setting)).from_json(settings_file.read())
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])
set_logger_verbosity(args.verbosity)
quiesce_logger(args.quiesce)
if args.customsettings:
f = open (args.customsettings)
importedsettings = json.load(f)
for items in importedsettings:
if importedsettings[items] is not None:
setattr(args, items, importedsettings[items])
f.close()
if args.no_ui:
def new_emit(*args, **kwargs):
return
old_emit = socketio.emit
socketio.emit = new_emit
args.max_summary_length = int(args.max_summary_length)
koboldai_vars.model = args.model;
koboldai_vars.revision = args.revision
koboldai_settings.multi_story = args.multi_story
if args.apikey:
koboldai_vars.apikey = args.apikey
if args.sh_apikey:
koboldai_vars.sh_apikey = args.sh_apikey
if args.req_model:
koboldai_vars.cluster_requested_models = args.req_model
if args.colab:
args.remote = True;
args.override_rename = True;
args.override_delete = True;
args.nobreakmodel = True;
args.quiet = True;
args.lowmem = True;
args.noaimenu = True;
if args.quiet:
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 koboldai_vars
koboldai_vars.model = "NeoCustom"
koboldai_vars.custmodpth = modpath
elif args.model:
logger.message(f"Welcome to KoboldAI!")
logger.message(f"You have selected the following Model: {koboldai_vars.model}")
if args.path:
logger.message(f"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
#setup socketio relay queue
koboldai_settings.queue = multiprocessing.Queue()
socketio.start_background_task(socket_io_relay, koboldai_settings.queue, socketio)
#==================================================================#
# Load Model
#==================================================================#
def tpumtjgetsofttokens():
soft_tokens = None
if(koboldai_vars.sp is None):
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
default_url = None
models_on_url = False
multi_online_models = False
show_online_model_select=False
gpu_count = torch.cuda.device_count()
gpu_names = []
send_horde_models = False
for i in range(gpu_count):
gpu_names.append(torch.cuda.get_device_name(i))
if model in ['Colab', 'API']:
url = True
elif model == 'CLUSTER':
models_on_url = True
show_online_model_select=True
url = True
key = True
default_url = 'https://koboldai.net'
multi_online_models = True
if path.exists(get_config_filename(model)):
with open(get_config_filename(model), "r") as file:
# Check if API key exists
js = json.load(file)
if("apikey" in js and js["apikey"] != ""):
# API key exists, grab it and close the file
key_value = js["apikey"]
elif 'oaiapikey' in js and js['oaiapikey'] != "":
key_value = js["oaiapikey"]
if 'url' in js and js['url'] != "":
url = js['url']
if key_value != "":
send_horde_models = True
elif model in [x[1] for x in model_menu['apilist']]:
show_online_model_select=True
if path.exists("settings/{}.v2_settings".format(model)):
with open("settings/{}.v2_settings".format(model), "r") as file:
# Check if API key exists
try:
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})
except json.decoder.JSONDecodeError:
print(":(")
pass
key = True
elif model.startswith("RWKV"):
pass
elif model == 'ReadOnly':
pass
elif not utils.HAS_ACCELERATE and not torch.cuda.is_available():
pass
elif args.cpu:
pass
else:
layer_count = get_layer_count(model, directory=directory)
if layer_count is None:
breakmodel = False
gpu = True
else:
breakmodel = True
if model in ["NeoCustom", "GPT2Custom"]:
filename = "settings/{}.breakmodel".format(os.path.basename(os.path.normpath(directory)))
else:
filename = "settings/{}.breakmodel".format(model.replace("/", "_"))
if path.exists(filename):
with open(filename, "r") as file:
data = [x for x in file.read().split("\n")[:2] if x != '']
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 if x != '']
break_values += [0] * (gpu_count - len(break_values))
emit('from_server', {'cmd': 'selected_model_info', 'key_value': key_value, 'key':key, 'multi_online_models': multi_online_models, 'default_url': default_url,
'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, 'models_on_url': models_on_url}, broadcast=True, room="UI_1")
emit('selected_model_info', {'key_value': key_value, 'key':key,
'gpu':gpu, 'layer_count':layer_count, 'breakmodel':breakmodel, 'multi_online_models': multi_online_models, 'default_url': default_url,
'disk_break_value': disk_blocks, 'disk_break': utils.HAS_ACCELERATE,
'break_values': break_values, 'gpu_count': gpu_count,
'url': url, 'gpu_names': gpu_names, 'models_on_url': models_on_url, 'show_online_model_select': show_online_model_select})
if send_horde_models:
get_cluster_models({'key': key_value, 'url': default_url})
elif key_value != "" and model in [x[1] for x in model_menu['apilist']] and model != 'CLUSTER':
get_oai_models(key_value)
def get_layer_count(model, directory=""):
if(model not in ["InferKit", "Colab", "API", "CLUSTER", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ"]):
if(model == "GPT2Custom"):
with open(os.path.join(directory, "config.json"), "r") as f:
model_config = json.load(f)
# Get the model_type from the config or assume a model type if it isn't present
else:
if(directory):
model = directory
from transformers import AutoConfig
if(os.path.isdir(model.replace('/', '_'))):
model_config = AutoConfig.from_pretrained(model.replace('/', '_'), revision=koboldai_vars.revision, cache_dir="cache")
elif(os.path.isdir("models/{}".format(model.replace('/', '_')))):
model_config = AutoConfig.from_pretrained("models/{}".format(model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
elif(os.path.isdir(directory)):
model_config = AutoConfig.from_pretrained(directory, revision=koboldai_vars.revision, cache_dir="cache")
elif(os.path.isdir(koboldai_vars.custmodpth.replace('/', '_'))):
model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth.replace('/', '_'), revision=koboldai_vars.revision, cache_dir="cache")
else:
model_config = AutoConfig.from_pretrained(model, revision=koboldai_vars.revision, cache_dir="cache")
try:
if ((utils.HAS_ACCELERATE and model_config.model_type != 'gpt2') or model_config.model_type in ("gpt_neo", "gptj", "xglm", "opt")) and not koboldai_vars.nobreakmodel:
return utils.num_layers(model_config)
else:
return None
except:
return None
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
logger.init("OAI Engines", status="Retrieving")
req = requests.get(
url,
headers = {
'Authorization': 'Bearer '+key
}
)
if(req.status_code == 200):
r = req.json()
engines = r["data"]
try:
engines = [[en["id"], "{} ({})".format(en['id'], "Ready" if en["ready"] == True else "Not Ready")] for en in engines]
except:
logger.error(engines)
raise
online_model = ""
changed=False
#Save the key
if not path.exists("settings"):
# If the client settings file doesn't exist, create it
# Write API key to file
os.makedirs('settings', exist_ok=True)
if path.exists("settings/{}.v2_settings".format(model)):
with open("settings/{}.v2_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
else:
js = {}
changed=True
if changed:
with open("settings/{}.v2_settings".format(model), "w") as file:
js["apikey"] = key
file.write(json.dumps(js, indent=3))
logger.init_ok("OAI Engines", status="OK")
emit('from_server', {'cmd': 'oai_engines', 'data': engines, 'online_model': online_model}, broadcast=True, 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
logger.init_err("OAI Engines", status="Failed")
logger.error(req.json())
emit('from_server', {'cmd': 'errmsg', 'data': req.json()})
@socketio.on("get_cluster_models")
def get_cluster_models(msg):
koboldai_vars.oaiapikey = msg['key']
koboldai_vars.apikey = koboldai_vars.oaiapikey
model = msg['model']
url = msg['url']
# Get list of models from public cluster
print("{0}Retrieving engine list...{1}".format(colors.PURPLE, colors.END), end="")
try:
req = requests.get("{}/api/v1/models".format(url))
except:
logger.init_err("KAI Horde Models", status="Failed")
logger.error("Provided KoboldAI Horde URL unreachable")
emit('from_server', {'cmd': 'errmsg', 'data': "Provided KoboldAI Horde URL unreachable"})
return
if(req.status_code == 200):
engines = req.json()
print(engines)
try:
engines = [[en, en] for en in engines]
except:
print(engines)
raise
print(engines)
online_model = ""
changed=False
#Save the key
if not path.exists("settings"):
# If the client settings file doesn't exist, create it
# Write API key to file
os.makedirs('settings', exist_ok=True)
if path.exists(get_config_filename(model)):
with open(get_config_filename(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'] != koboldai_vars.oaiapikey:
changed=True
else:
changed=True
if changed:
js={}
with open(get_config_filename(model), "w") as file:
js["apikey"] = koboldai_vars.oaiapikey
file.write(json.dumps(js, indent=3))
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
logger.init_err("KAI Horde Models", status="Failed")
logger.error(req.json())
emit('from_server', {'cmd': 'errmsg', 'data': req.json()}, room="UI_1")
return
engines = req.json()
logger.debug(engines)
try:
engines = [[en, en] for en in engines]
except:
logger.error(engines)
raise
online_model = ""
changed=False
#Save the key
if not path.exists("settings"):
# If the client settings file doesn't exist, create it
# Write API key to file
os.makedirs('settings', exist_ok=True)
if path.exists(get_config_filename(model)):
with open(get_config_filename(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'] != koboldai_vars.oaiapikey:
changed=True
else:
changed=True
if changed:
js={}
with open(get_config_filename(model), "w") as file:
js["apikey"] = koboldai_vars.oaiapikey
js["url"] = url
file.write(json.dumps(js, indent=3))
logger.init_ok("KAI Horde Models", status="OK")
emit('from_server', {'cmd': 'oai_engines', 'data': engines, 'online_model': online_model}, broadcast=True)
# Function to patch transformers to use our soft prompt
def patch_causallm(model):
from torch.nn import Embedding
if(getattr(Embedding, "_koboldai_patch_causallm_model", None)):
Embedding._koboldai_patch_causallm_model = model
return model
old_embedding_call = Embedding.__call__
def new_embedding_call(self, input_ids, *args, **kwargs):
if(Embedding._koboldai_patch_causallm_model.get_input_embeddings() is not self):
return old_embedding_call(self, input_ids, *args, **kwargs)
assert input_ids is not None
if(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('\r' + bar, end='')
emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True, room="UI_1")
eventlet.sleep(seconds=0)
except:
pass
def flush(self):
pass
def http_get(
url: str,
temp_file,
proxies=None,
resume_size=0,
headers=None,
file_name=None,
):
"""
Download remote file. Do not gobble up errors.
"""
headers = copy.deepcopy(headers)
if resume_size > 0:
headers["Range"] = f"bytes={resume_size}-"
r = requests.get(url, stream=True, proxies=proxies, headers=headers)
transformers.utils.hub._raise_for_status(r)
content_length = r.headers.get("Content-Length")
total = resume_size + int(content_length) if content_length is not None else None
# `tqdm` behavior is determined by `utils.logging.is_progress_bar_enabled()`
# and can be set using `utils.logging.enable/disable_progress_bar()`
if url[-11:] != 'config.json':
progress = tqdm.tqdm(
unit="B",
unit_scale=True,
unit_divisor=1024,
total=total,
initial=resume_size,
desc=f"Downloading {file_name}" if file_name is not None else "Downloading",
file=Send_to_socketio(),
)
koboldai_vars.total_download_chunks = total
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
if url[-11:] != 'config.json':
progress.update(len(chunk))
koboldai_vars.downloaded_chunks += len(chunk)
temp_file.write(chunk)
if url[-11:] != 'config.json':
progress.close()
# def http_get(
# url: str,
# temp_file: BinaryIO,
# *,
# proxies=None,
# resume_size=0,
# headers: Optional[Dict[str, str]] = None,
# timeout=10.0,
# max_retries=0,
# ):
# """
# Donwload a remote file. Do not gobble up errors, and will return errors tailored to the Hugging Face Hub.
# """
# headers = copy.deepcopy(headers)
# if resume_size > 0:
# headers["Range"] = "bytes=%d-" % (resume_size,)
# r = _request_wrapper(
# method="GET",
# url=url,
# stream=True,
# proxies=proxies,
# headers=headers,
# timeout=timeout,
# max_retries=max_retries,
# )
# hf_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
# progress = tqdm(
# unit="B",
# unit_scale=True,
# total=total,
# initial=resume_size,
# desc="Downloading",
# file=Send_to_socketio(),
# disable=bool(logger.getEffectiveLevel() == logging.NOTSET),
# )
# for chunk in r.iter_content(chunk_size=1024):
# if chunk: # filter out keep-alive new chunks
# progress.update(len(chunk))
# temp_file.write(chunk)
# progress.close()
transformers.utils.hub.http_get = http_get
def patch_transformers():
global transformers
global old_transfomers_functions
old_transfomers_functions = {}
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)
old_transfomers_functions['PreTrainedModel.from_pretrained'] = PreTrainedModel.from_pretrained
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
old_transfomers_functions['modeling_utils.get_checkpoint_shard_files'] = old_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()
old_transfomers_functions['XGLMSinusoidalPositionalEmbedding.forward'] = XGLMSinusoidalPositionalEmbedding.forward
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()
old_transfomers_functions['OPTForCausalLM.__init__'] = OPTForCausalLM.__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)
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
if(koboldai_vars.standalone):
return scores
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
from torch.nn import functional as F
class ProbabilityVisualizerLogitsProcessor(LogitsProcessor):
def __init__(self):
pass
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
assert scores.ndim == 2
assert input_ids.ndim == 2
if not koboldai_vars.show_probs:
return scores
for batch_index, batch in enumerate(scores):
probs = F.softmax(batch, dim = -1).cpu().numpy()
token_prob_info = []
for token_id, score in sorted(enumerate(probs), key=lambda x: x[1], reverse=True)[:8]:
token_prob_info.append({
"tokenId": token_id,
"decoded": utils.decodenewlines(tokenizer.decode(token_id)),
"score": float(score),
})
if len(scores) == 1:
koboldai_vars.actions.set_probabilities(token_prob_info)
else:
koboldai_vars.actions.set_option_probabilities(token_prob_info, batch_index)
return scores
def new_get_logits_processor(*args, **kwargs) -> LogitsProcessorList:
processors = new_get_logits_processor.old_get_logits_processor(*args, **kwargs)
processors.insert(0, LuaLogitsProcessor())
processors.append(ProbabilityVisualizerLogitsProcessor())
return processors
new_get_logits_processor.old_get_logits_processor = transformers.generation_utils.GenerationMixin._get_logits_processor
transformers.generation_utils.GenerationMixin._get_logits_processor = new_get_logits_processor
class KoboldLogitsWarperList(LogitsProcessorList):
def __init__(self, beams: int = 1, **kwargs):
self.__warper_list: List[LogitsWarper] = []
self.__warper_list.append(TopKLogitsWarper(top_k=1, min_tokens_to_keep=1 + (beams > 1)))
self.__warper_list.append(TopALogitsWarper(top_a=0.5, min_tokens_to_keep=1 + (beams > 1)))
self.__warper_list.append(TopPLogitsWarper(top_p=0.5, min_tokens_to_keep=1 + (beams > 1)))
self.__warper_list.append(TailFreeLogitsWarper(tfs=0.5, min_tokens_to_keep=1 + (beams > 1)))
self.__warper_list.append(TypicalLogitsWarper(typical=0.5, min_tokens_to_keep=1 + (beams > 1)))
self.__warper_list.append(TemperatureLogitsWarper(temperature=0.5))
self.__warper_list.append(AdvancedRepetitionPenaltyLogitsProcessor())
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, *args, **kwargs):
sampler_order = koboldai_vars.sampler_order[:]
if len(sampler_order) < 7: # Add repetition penalty at beginning if it's not present
sampler_order = [6] + sampler_order
for k in sampler_order:
scores = self.__warper_list[k](input_ids, scores, *args, **kwargs)
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.inference_config.do_streaming:
return False
if not koboldai_vars.output_streaming:
return False
if koboldai_vars.chatmode:
return False
koboldai_vars.actions.stream_tokens([utils.decodenewlines(tokenizer.decode(x[-1])) for x in input_ids])
return False
class CoreStopper(StoppingCriteria):
# Controls core generation stuff; aborting, counting generated tokens, etc
def __init__(self):
self.regeneration_required = False
self.halt = False
def __call__(
self,
input_ids: torch.LongTensor,
scores: torch.FloatTensor,
**kwargs,
) -> bool:
koboldai_vars.generated_tkns += 1
if (
not koboldai_vars.standalone
and 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.inference_config.stop_at_genamt
and
koboldai_vars.generated_tkns >= koboldai_vars.genamt
)
):
koboldai_vars.abort = False
self.regeneration_required = False
self.halt = False
return True
if koboldai_vars.standalone:
return False
assert input_ids.ndim == 2
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())
return self.regeneration_required or self.halt
# Sets up dynamic world info scanner
class DynamicWorldInfoScanCriteria(StoppingCriteria):
def __init__(
self,
tokenizer,
excluded_world_info: List[Set],
):
self.tokenizer = tokenizer
self.excluded_world_info = excluded_world_info
def __call__(
self,
input_ids: torch.LongTensor,
scores: torch.FloatTensor,
**kwargs,
) -> bool:
if not koboldai_vars.inference_config.do_dynamic_wi:
return False
if len(self.excluded_world_info) != input_ids.shape[0]:
print(tokenizer.decode(self.excluded_world_info))
print(tokenizer.decode(input_ids.shape[0]))
assert len(self.excluded_world_info) == input_ids.shape[0]
if not koboldai_vars.dynamicscan:
return False
tail = input_ids[..., -koboldai_vars.generated_tkns:]
for i, t in enumerate(tail):
decoded = utils.decodenewlines(tokenizer.decode(t))
_, _, _, found = koboldai_vars.calc_ai_text(submitted_text=decoded)
found = list(set(found) - set(self.excluded_world_info[i]))
if len(found) != 0:
print("Found: {}".format(found))
model.core_stopper.regeneration_required = True
return True
return False
old_get_stopping_criteria = transformers.generation_utils.GenerationMixin._get_stopping_criteria
old_transfomers_functions['transformers.generation_utils.GenerationMixin._get_stopping_criteria'] = old_get_stopping_criteria
def new_get_stopping_criteria(self, *args, **kwargs):
global tokenizer
stopping_criteria = old_get_stopping_criteria(self, *args, **kwargs)
self.core_stopper = CoreStopper()
self.kai_scanner = DynamicWorldInfoScanCriteria(
tokenizer=tokenizer,
excluded_world_info=self.kai_scanner_excluded_world_info,
)
token_streamer = TokenStreamer(tokenizer=tokenizer)
stopping_criteria.insert(0, self.core_stopper)
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 reset_model_settings():
koboldai_vars.socketio = socketio
koboldai_vars.max_length = 1024 # Maximum number of tokens to submit per action
koboldai_vars.ikmax = 3000 # Maximum number of characters to submit to InferKit
koboldai_vars.genamt = 80 # Amount of text for each action to generate
koboldai_vars.ikgen = 200 # Number of characters for InferKit to generate
koboldai_vars.rep_pen = 1.1 # Default generator repetition_penalty
koboldai_vars.rep_pen_slope = 0.7 # Default generator repetition penalty slope
koboldai_vars.rep_pen_range = 1024 # Default generator repetition penalty range
koboldai_vars.temp = 0.5 # Default generator temperature
koboldai_vars.top_p = 0.9 # Default generator top_p
koboldai_vars.top_k = 0 # Default generator top_k
koboldai_vars.top_a = 0.0 # Default generator top-a
koboldai_vars.tfs = 1.0 # Default generator tfs (tail-free sampling)
koboldai_vars.typical = 1.0 # Default generator typical sampling threshold
koboldai_vars.numseqs = 1 # Number of sequences to ask the generator to create
koboldai_vars.generated_tkns = 0 # If using a backend that supports Lua generation modifiers, how many tokens have already been generated, otherwise 0
koboldai_vars.badwordsids = []
koboldai_vars.fp32_model = False # Whether or not the most recently loaded HF model was in fp32 format
koboldai_vars.modeldim = -1 # Embedding dimension of your model (e.g. it's 4096 for GPT-J-6B and 2560 for GPT-Neo-2.7B)
koboldai_vars.sampler_order = [0, 1, 2, 3, 4, 5]
koboldai_vars.newlinemode = "n"
koboldai_vars.revision = None
koboldai_vars.lazy_load = True
def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=False, online_model="", use_breakmodel_args=False, breakmodel_args_default_to_cpu=False, url=None):
global model
global generator
global torch
global model_config
global GPT2Tokenizer
global tokenizer
koboldai_vars.aibusy = True
koboldai_vars.horde_share = False
if(initial_load):
use_breakmodel_args = True
reset_model_settings()
if not utils.HAS_ACCELERATE:
disk_layers = None
koboldai_vars.reset_model()
koboldai_vars.cluster_requested_models = [online_model] if isinstance(online_model, str) else online_model
if koboldai_vars.cluster_requested_models == [""]:
koboldai_vars.cluster_requested_models = []
koboldai_vars.noai = False
if not use_breakmodel_args:
set_aibusy(True)
if koboldai_vars.model != 'ReadOnly':
emit('from_server', {'cmd': 'model_load_status', 'data': "Loading {}".format(koboldai_vars.model)}, broadcast=True)
#Have to add a sleep so the server will send the emit for some reason
time.sleep(0.1)
if gpu_layers is not None:
args.breakmodel_gpulayers = gpu_layers
elif use_breakmodel_args:
gpu_layers = args.breakmodel_gpulayers
if breakmodel_args_default_to_cpu and gpu_layers is None:
gpu_layers = args.breakmodel_gpulayers = []
if disk_layers is not None:
args.breakmodel_disklayers = int(disk_layers)
elif use_breakmodel_args:
disk_layers = args.breakmodel_disklayers
if breakmodel_args_default_to_cpu and disk_layers is None:
disk_layers = args.breakmodel_disklayers = 0
#We need to wipe out the existing model and refresh the cuda cache
model = None
generator = None
model_config = None
koboldai_vars.online_model = ''
with torch.no_grad():
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="torch.distributed.reduce_op is deprecated")
for tensor in gc.get_objects():
try:
if torch.is_tensor(tensor):
tensor.set_(torch.tensor((), device=tensor.device, dtype=tensor.dtype))
except:
pass
gc.collect()
try:
with torch.no_grad():
torch.cuda.empty_cache()
except:
pass
#Reload our badwords
koboldai_vars.badwordsids = koboldai_settings.badwordsids_default
if online_model == "":
koboldai_vars.configname = getmodelname()
#Let's set the GooseAI or OpenAI server URLs if that's applicable
else:
koboldai_vars.online_model = online_model
# 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"
koboldai_vars.configname = f"GooseAI_{online_model.replace('/', '_')}"
elif(koboldai_vars.model == "CLUSTER") and type(online_model) is list:
if len(online_model) != 1:
koboldai_vars.configname = koboldai_vars.model
else:
koboldai_vars.configname = f"{koboldai_vars.model}_{online_model[0].replace('/', '_')}"
else:
koboldai_vars.configname = f"{koboldai_vars.model}_{online_model.replace('/', '_')}"
if path.exists(get_config_filename()):
changed=False
with open(get_config_filename(), "r") as file:
# Check if API key exists
js = json.load(file)
if 'online_model' in js:
if js['online_model'] != online_model:
changed=True
js['online_model'] = online_model
else:
changed=True
js['online_model'] = online_model
if changed:
with open("settings/{}.v2_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
elif koboldai_vars.model != "CLUSTER":
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", "API", "CLUSTER", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"] and not koboldai_vars.model.startswith("RWKV")):
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"):
logger.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", "API", "CLUSTER", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
loadmodelsettings()
loadsettings()
logger.init("GPU support", status="Searching")
koboldai_vars.hascuda = torch.cuda.is_available() and not args.cpu
koboldai_vars.bmsupported = ((utils.HAS_ACCELERATE and koboldai_vars.model_type != 'gpt2') 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):
logger.warning("--breakmodel is no longer supported. Breakmodel mode is now automatically enabled when --breakmodel_gpulayers is used (see --help for details).")
if(args.breakmodel_layers is not None):
logger.warning("--breakmodel_layers is deprecated. Use --breakmodel_gpulayers instead (see --help for details).")
if(args.model and koboldai_vars.bmsupported and not args.breakmodel_gpulayers and not args.breakmodel_layers and (not utils.HAS_ACCELERATE or not args.breakmodel_disklayers)):
logger.warning("Model launched without the --breakmodel_gpulayers argument, defaulting to GPU only mode.")
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)):
logger.warning("This model does not support hybrid generation. --breakmodel_gpulayers will be ignored.")
if(koboldai_vars.hascuda):
logger.init_ok("GPU support", status="Found")
else:
logger.init_warn("GPU support", status="Not Found")
if args.cpu:
koboldai_vars.usegpu = False
gpu_layers = None
disk_layers = None
koboldai_vars.breakmodel = False
elif koboldai_vars.hascuda:
if(koboldai_vars.bmsupported):
koboldai_vars.usegpu = False
koboldai_vars.breakmodel = True
else:
koboldai_vars.breakmodel = False
koboldai_vars.usegpu = use_gpu
# 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"
koboldai_vars.configname = "GooseAI"
# Ask for API key if OpenAI was selected
if(koboldai_vars.model == "OAI"):
if not koboldai_vars.configname:
koboldai_vars.configname = "OAI"
if(koboldai_vars.model == "ReadOnly"):
koboldai_vars.noai = True
# Start transformers and create pipeline
if koboldai_vars.model.startswith("RWKV"):
_, model_class, device = koboldai_vars.model.split("-")
model, tokenizer = rwkv_init(
model_class=model_class,
use_gpu=(device == "GPU")
)
global breakmodel
import breakmodel
elif (not koboldai_vars.use_colab_tpu and koboldai_vars.model not in ["InferKit", "Colab", "API", "CLUSTER", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
if(not koboldai_vars.noai):
logger.init("Transformers", status='Starting')
for m in ("GPTJModel", "XGLMModel"):
try:
globals()[m] = getattr(__import__("transformers"), m)
except:
pass
# Lazy loader
import torch_lazy_loader
def get_lazy_load_callback(n_layers, convert_to_float16=True):
if not 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=f"{colors.PURPLE}INIT{colors.END} | Loading model tensors", file=Send_to_socketio())
with zipfile.ZipFile(f, "r") as z:
try:
last_storage_key = None
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):
return model.get_input_embeddings().embedding_dim
def maybe_low_cpu_mem_usage() -> Dict[str, Any]:
if(packaging.version.parse(transformers_version) < packaging.version.parse("4.11.0")):
logger.warning(f"Please upgrade to transformers 4.11.0 for lower RAM usage. You have transformers {transformers_version}.")
return {}
return {"low_cpu_mem_usage": True}
@contextlib.contextmanager
def maybe_use_float16(always_use=False):
if(always_use or (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_type == "gpt2"):
koboldai_vars.lazy_load = False
if os.path.exists(koboldai_vars.custmodpth):
model_config = open(koboldai_vars.custmodpth + "/config.json", "r")
elif os.path.exists(os.path.join("models/", koboldai_vars.custmodpth)):
config_path = os.path.join("models/", koboldai_vars.custmodpth)
config_path = os.path.join(config_path, "config.json").replace("\\", "//")
model_config = open(config_path, "r")
#js = json.load(model_config)
with(maybe_use_float16()):
try:
if os.path.exists(koboldai_vars.custmodpth):
model = GPT2LMHeadModel.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
elif os.path.exists(os.path.join("models/", koboldai_vars.custmodpth)):
model = GPT2LMHeadModel.from_pretrained(os.path.join("models/", koboldai_vars.custmodpth), revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained(os.path.join("models/", koboldai_vars.custmodpth), revision=koboldai_vars.revision, cache_dir="cache")
else:
model = GPT2LMHeadModel.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.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 = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
model.save_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), max_shard_size="500MiB")
tokenizer.save_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')))
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) and not koboldai_vars.nobreakmodel:
device_config(model_config)
# Download model from Huggingface if it does not exist, otherwise load locally
#If we specify a model and it's in the root directory, we need to move it to the models directory (legacy folder structure to new)
if os.path.isdir(koboldai_vars.model.replace('/', '_')):
import shutil
shutil.move(koboldai_vars.model.replace('/', '_'), "models/{}".format(koboldai_vars.model.replace('/', '_')))
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", use_fast=False)
except Exception as e:
try:
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
try:
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2Tokenizer.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", use_fast=False)
except Exception as e:
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
try:
tokenizer = GPT2Tokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2Tokenizer.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", use_fast=False)
except Exception as e:
try:
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
try:
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2Tokenizer.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
import huggingface_hub
legacy = packaging.version.parse(transformers_version) < packaging.version.parse("4.22.0.dev0")
# Save the config.json
shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, transformers.configuration_utils.CONFIG_NAME, revision=koboldai_vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), 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(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, transformers.modeling_utils.WEIGHTS_NAME, revision=koboldai_vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), 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(os.path.realpath(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(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, filename, revision=koboldai_vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), 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) != ""]
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()
#koboldai_vars.badwords = gettokenids("[")
#for key in koboldai_vars.badwords:
# koboldai_vars.badwordsids.append([vocab[key]])
logger.info(f"Pipeline created: {koboldai_vars.model}")
else:
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.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 = koboldai_vars.calc_ai_text(submitted_text=decoded)
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:
print(colors.GREEN + "TPU backend compilation stopped" + colors.END)
koboldai_vars.compiling = False
def tpumtjgenerate_settings_callback() -> dict:
sampler_order = koboldai_vars.sampler_order[:]
if len(sampler_order) < 7: # Add repetition penalty at beginning if it's not present
sampler_order = [6] + sampler_order
return {
"sampler_order": 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 in ("Colab", "API", "CLUSTER")):
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B", revision=koboldai_vars.revision, cache_dir="cache")
loadsettings()
koboldai_vars.colaburl = url if url is not None else koboldai_vars.colaburl
elif(koboldai_vars.model == "OAI"):
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
loadsettings()
koboldai_vars.colaburl = url if url is not None else koboldai_vars.colaburl
# 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_vars.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)
#tpool.execute(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) != ""]
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
presets = []
current_max_uid = 0
for file in os.listdir("./presets"):
if file[-8:] == '.presets':
with open("./presets/{}".format(file)) as f:
data = json.load(f)
for preset in data:
preset['uid'] += current_max_uid
presets.append(preset)
current_max_uid = max([preset['uid'] for preset in presets])
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'] == get_model_size(koboldai_vars.model) 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
koboldai_vars.aibusy = False
if not os.path.exists("./softprompts"):
os.mkdir("./softprompts")
koboldai_vars.splist = [[f, get_softprompt_desc(os.path.join("./softprompts", f),None,True)] for f in os.listdir("./softprompts") if os.path.isfile(os.path.join("./softprompts", f)) and valid_softprompt(os.path.join("./softprompts", f))]
if initial_load and koboldai_vars.cloudflare_link != "":
print(format(colors.GREEN) + "KoboldAI has finished loading and is available at the following link for UI 1: " + koboldai_vars.cloudflare_link + format(colors.END))
print(format(colors.GREEN) + "KoboldAI has finished loading and is available at the following link for UI 2: " + koboldai_vars.cloudflare_link + "/new_ui" + format(colors.END))
# Set up Flask routes
@app.route('/')
@app.route('/index')
def index():
if args.no_ui:
return redirect('/api/latest')
else:
return render_template('index.html', hide_ai_menu=args.noaimenu)
@app.route('/api', strict_slashes=False)
def api():
return redirect('/api/latest')
@app.route('/favicon.ico')
def favicon():
return send_from_directory(app.root_path,
'koboldai.ico', mimetype='image/vnd.microsoft.icon')
@app.route('/download')
def download():
if args.no_ui:
raise NotFound()
save_format = request.args.get("format", "json").strip().lower()
if(save_format == "plaintext"):
txt = 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)
logger.init("LUA bridge", status="Starting")
# 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()
logger.debug('LUA ERROR: ' + str(e).replace("\033", ""))
logger.warning("Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.")
socketio.emit("error", str(e), broadcast=True, room="UI_2")
exit(1)
logger.init_ok("LUA bridge", status="OK")
def lua_log_format_name(name):
return f"[{name}]" if type(name) is str else "CORE"
def bridged_kwarg(name=None):
def _bridged_kwarg(f: F):
_bridged[name if name is not None else f.__name__[4:] if f.__name__[:4] == "lua_" else f.__name__] = f
return f
return _bridged_kwarg
#==================================================================#
# Event triggered when a userscript is loaded
#==================================================================#
@bridged_kwarg()
def load_callback(filename, modulename):
print(colors.GREEN + f"Loading Userscript [{modulename}] <{filename}>" + colors.END)
#==================================================================#
# Load all Lua scripts
#==================================================================#
def load_lua_scripts():
logger.init("LUA Scripts", status="Starting")
filenames = []
modulenames = []
descriptions = []
lst = fileops.getusfiles(long_desc=True)
filenames_dict = {ob["filename"]: i for i, ob in enumerate(lst)}
for filename in 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()
logger.debug('LUA ERROR: ' + str(e).replace("\033", ""))
logger.warning("Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.")
socketio.emit("error", str(e), broadcast=True, room="UI_2")
if(koboldai_vars.serverstarted):
set_aibusy(0)
logger.init_ok("LUA Scripts", status="OK")
#==================================================================#
# Print message that originates from the userscript with the given name
#==================================================================#
@bridged_kwarg()
def lua_print(msg):
if(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 GPT2Tokenizer
global tokenizer
tokenizer = GPT2Tokenizer.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 GPT2Tokenizer
global tokenizer
tokenizer = GPT2Tokenizer.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
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, _, _, found_entries = koboldai_vars.calc_ai_text()
#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)
koboldai_vars.sync_worldinfo_v1_to_v2()
sendwi()
#==================================================================#
# 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)
koboldai_vars.sync_worldinfo_v1_to_v2()
sendwi()
#==================================================================#
# 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",
"show_probs"
)
#==================================================================#
# Return the setting with the given name if it exists
#==================================================================#
@bridged_kwarg()
def lua_get_setting(setting):
if(setting in ("settemp", "temp")): return 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.frmttriminc
if(setting in ("frmtrmblln", "rmblln")): return koboldai_vars.frmttrmblln
if(setting in ("frmtrmspch", "rmspch")): return koboldai_vars.frmttrmspch
if(setting in ("frmtadsnsp", "adsnsp")): return koboldai_vars.frmtadsnsp
if(setting in ("frmtsingleline", "singleline")): return koboldai_vars.singleline
if(setting == "output_streaming"): return koboldai_vars.output_streaming
if(setting == "show_probs"): return koboldai_vars.show_probs
#==================================================================#
# Set the setting with the given name if it exists
#==================================================================#
@bridged_kwarg()
def lua_set_setting(setting, v):
actual_type = type(lua_get_setting(setting))
assert v is not None and (actual_type is type(v) or (actual_type is int and type(v) is float))
v = actual_type(v)
print(colors.GREEN + f"{lua_log_format_name(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.frmttriminc = v
if(setting in ("frmtrmblln", "rmblln")): koboldai_vars.frmttrmblln = v
if(setting in ("frmtrmspch", "rmspch")): koboldai_vars.frmttrmspch = v
if(setting in ("frmtadsnsp", "adsnsp")): koboldai_vars.frmtadsnsp = v
if(setting in ("frmtsingleline", "singleline")): koboldai_vars.singleline = v
if(setting == "output_streaming"): koboldai_vars.output_streaming = v
if(setting == "show_probs"): koboldai_vars.show_probs = 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)
koboldai_vars.actions.delete_action(chunk-1)
koboldai_vars.lua_deleted.add(chunk)
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:
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", "API", "CLUSTER", "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", "API", "CLUSTER", "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()
logger.debug('LUA ERROR: ' + str(e).replace("\033", ""))
logger.warning("Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.")
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()
logger.debug('LUA ERROR: ' + str(e).replace("\033", ""))
logger.warning("Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.")
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():
logger.info("Client connected!")
if request.args.get("rely") == "true":
return
join_room("UI_{}".format(request.args.get('ui')))
if 'story' not in session:
session['story'] = 'default'
join_room(session['story'])
logger.debug("Joining Room UI_{}".format(request.args.get('ui')))
logger.debug("Session['Story']: {}".format(session['story']))
if request.args.get("ui") == "2":
ui2_connect()
return
logger.debug("{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:
logger.debug(f"Data received: {msg}")
# 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'):
koboldai_vars.frmttriminc = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'frmtrmblln'):
koboldai_vars.frmtrmblln = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'frmtrmspch'):
koboldai_vars.frmtrmspch = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'frmtadsnsp'):
koboldai_vars.frmtadsnsp = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'singleline'):
koboldai_vars.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"]
sampler_order_min_length = 6
sampler_order_max_length = 7
if(not isinstance(sampler_order, list)):
raise ValueError(f"Sampler order must be a list, but got a {type(sampler_order)}")
if(not (sampler_order_min_length <= len(sampler_order) <= sampler_order_max_length)):
raise ValueError(f"Sampler order must be a list of length greater than or equal to {sampler_order_min_length} and less than or equal to {sampler_order_max_length}, but got a list of length {len(sampler_order)}")
if(not all(isinstance(e, int) for e in sampler_order)):
raise ValueError(f"Sampler order must be a list of ints, but got a list with at least one non-int element")
if(min(sampler_order) != 0 or max(sampler_order) != len(sampler_order) - 1 or len(set(sampler_order)) != len(sampler_order)):
raise ValueError(f"Sampler order list of length {len(sampler_order)} must be a permutation of the first {len(sampler_order)} nonnegative integers")
koboldai_vars.sampler_order = sampler_order
settingschanged()
elif(msg['cmd'] == 'list_model'):
sendModelSelection(menu=msg['data'])
elif(msg['cmd'] == 'load_model'):
logger.debug(f"Selected Model: {koboldai_vars.model_selected}")
if not os.path.exists("settings/"):
os.mkdir("settings")
changed = True
if not utils.HAS_ACCELERATE:
msg['disk_layers'] = "0"
if os.path.exists("settings/" + koboldai_vars.model_selected.replace('/', '_') + ".breakmodel"):
with open("settings/" + koboldai_vars.model_selected.replace('/', '_') + ".breakmodel", "r") as file:
data = file.read().split('\n')[:2]
if len(data) < 2:
data.append("0")
gpu_layers, disk_layers = data
if gpu_layers == msg['gpu_layers'] and disk_layers == msg['disk_layers']:
changed = False
if changed:
if koboldai_vars.model_selected in ["NeoCustom", "GPT2Custom"]:
filename = "settings/{}.breakmodel".format(os.path.basename(os.path.normpath(koboldai_vars.custmodpth)))
else:
filename = "settings/{}.breakmodel".format(koboldai_vars.model_selected.replace('/', '_'))
f = open(filename, "w")
f.write(str(msg['gpu_layers']) + '\n' + str(msg['disk_layers']))
f.close()
koboldai_vars.colaburl = msg['url'] + "/request"
koboldai_vars.model = koboldai_vars.model_selected
if koboldai_vars.model == "CLUSTER":
if type(msg['online_model']) is not list:
if msg['online_model'] == '':
koboldai_vars.cluster_requested_models = []
else:
koboldai_vars.cluster_requested_models = [msg['online_model']]
else:
koboldai_vars.cluster_requested_models = msg['online_model']
load_model(use_gpu=msg['use_gpu'], gpu_layers=msg['gpu_layers'], disk_layers=msg['disk_layers'], online_model=msg['online_model'])
elif(msg['cmd'] == 'show_model'):
logger.info(f"Model Name: {getmodelname()}")
emit('from_server', {'cmd': 'show_model_name', 'data': getmodelname()}, broadcast=True, 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_selected = msg['data']
koboldai_vars.custmodpth = msg['path_modelname']
get_model_info(msg['data'], directory=msg['path'])
else:
koboldai_vars.model_selected = msg['path_modelname']
try:
get_model_info(koboldai_vars.model_selected)
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_selected = 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_selected = 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_selected)
elif(msg['cmd'] == 'delete_model'):
if "{}/models".format(os.getcwd()) in os.path.abspath(msg['data']) or "{}\\models".format(os.getcwd()) in os.path.abspath(msg['data']):
if check_if_dir_is_model(msg['data']):
logger.warning(f"Someone deleted {msg['data']}")
import shutil
shutil.rmtree(msg['data'])
sendModelSelection(menu=msg['menu'])
else:
logger.error(f"Someone attempted to delete {msg['data']} but this is not a valid model")
else:
logger.critical(f"Someone maliciously attempted to delete {msg['data']}. The attempt has been blocked.")
elif(msg['cmd'] == 'OAI_Key_Update'):
get_oai_models({'model': koboldai_vars.model, 'key': msg['key']})
elif(msg['cmd'] == 'Cluster_Key_Update'):
get_cluster_models({'model': koboldai_vars.model, 'key': msg['key'], 'url': msg['url']})
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'] == 'setfulldeterminism'):
koboldai_vars.full_determinism = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setoutputstreaming'):
koboldai_vars.output_streaming = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setshowbudget'):
koboldai_vars.show_budget = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setshowprobs'):
koboldai_vars.show_probs = 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()
elif(msg['cmd'] == 'getfieldbudget'):
unencoded = msg["data"]["unencoded"]
field = msg["data"]["field"]
# Tokenizer may be undefined here when a model has not been chosen.
if "tokenizer" not in globals():
# We don't have a tokenizer, just return nulls.
emit(
'from_server',
{'cmd': 'showfieldbudget', 'data': {"length": None, "max": None, "field": field}},
)
return
header_length = len(tokenizer._koboldai_header)
max_tokens = koboldai_vars.max_length - header_length - koboldai_vars.sp_length - koboldai_vars.genamt
if not unencoded:
# Unencoded is empty, just return 0
emit(
'from_server',
{'cmd': 'showfieldbudget', 'data': {"length": 0, "max": max_tokens, "field": field}},
broadcast=True
)
else:
if field == "anoteinput":
unencoded = buildauthorsnote(unencoded, msg["data"]["anotetemplate"])
tokens_length = len(tokenizer.encode(unencoded))
emit(
'from_server',
{'cmd': 'showfieldbudget', 'data': {"length": tokens_length, "max": max_tokens, "field": field}},
broadcast=True
)
#==================================================================#
# Send userscripts list to client
#==================================================================#
def sendUSStatItems():
_, loaded = getuslist()
loaded = loaded if 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('>', '>')
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 and isinstance(koboldai_vars.welcome, str):
txt = kml(koboldai_vars.welcome) + "
"
else:
txt = "Welcome to KoboldAI! You are running "+getmodelname()+".
"
if(not koboldai_vars.noai and not koboldai_vars.welcome):
txt = txt + "Please load a game or enter a prompt below to begin!"
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:
if 'UI_V2_Only' not in set:
emit('from_server', {'cmd': 'addsetting', 'data': set}, room="UI_1")
else:
for set in gensettings.gensettingsik:
if 'UI_V2_Only' not in set:
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 hasattr(koboldai_vars, frm["id"])):
setattr(koboldai_vars, 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—this usually takes 1–2 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, no_generate=False, ignore_aibusy=False):
# Ignore new submissions if the AI is currently busy
if(koboldai_vars.aibusy):
return
while(True):
set_aibusy(1)
koboldai_vars.actions.clear_unused_options()
if(koboldai_vars.model in ["API","CLUSTER"]):
global tokenizer
if koboldai_vars.model == "API":
tokenizer_id = requests.get(
koboldai_vars.colaburl[:-8] + "/api/v1/model",
).json()["result"]
elif len(koboldai_vars.cluster_requested_models) >= 1:
# If the player has requested one or more models, we use the first one for the tokenizer
tokenizer_id = koboldai_vars.cluster_requested_models[0]
# The cluster can return any number of possible models for each gen, but this happens after this step
# So at this point, this is unknown
else:
tokenizer_id = ""
if tokenizer_id != koboldai_vars.api_tokenizer_id:
try:
if(os.path.isdir(tokenizer_id)):
try:
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache")
except:
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
elif(os.path.isdir("models/{}".format(tokenizer_id.replace('/', '_')))):
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
except:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
else:
try:
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache")
except:
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
except:
logger.warning(f"Unknown tokenizer {repr(tokenizer_id)}")
koboldai_vars.api_tokenizer_id = tokenizer_id
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
if(not no_generate):
execute_inmod()
koboldai_vars.submission = re.sub(r"[^\S\r\n]*([\r\n]*)$", r"\1", koboldai_vars.submission) # Remove trailing whitespace, excluding newlines
data = koboldai_vars.submission
if(not force_submit and len(data.strip()) == 0):
set_aibusy(0)
socketio.emit("error", "No prompt or random story theme entered", broadcast=True, room="UI_2")
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] = ""
if(not no_generate):
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.append_options([applyoutputformatting(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
if(not no_generate):
execute_inmod()
koboldai_vars.submission = re.sub(r"[^\S\r\n]*([\r\n]*)$", r"\1", koboldai_vars.submission) # Remove trailing whitespace, excluding newlines
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 no_generate and 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:
if(not no_generate):
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] if not no_generate else ""})
assert type(genout[-1]["generated_text"]) is str
koboldai_vars.actions.append_options([applyoutputformatting(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 no_generate and 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 no_generate and 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 apiactionsubmit_generate(txt, minimum, maximum):
koboldai_vars.generated_tkns = 0
if not koboldai_vars.quiet:
logger.debug(f"Prompt Min:{minimum}, Max:{maximum}")
logger.prompt(utils.decodenewlines(tokenizer.decode(txt)).encode("unicode_escape").decode("utf-8"))
# Clear CUDA cache if using GPU
if(koboldai_vars.hascuda and (koboldai_vars.usegpu or koboldai_vars.breakmodel)):
gc.collect()
torch.cuda.empty_cache()
# Submit input text to generator
_genout, already_generated = tpool.execute(core_generate, txt, minimum, maximum, set())
genout = [applyoutputformatting(utils.decodenewlines(tokenizer.decode(tokens[-already_generated:]))) for tokens in _genout]
# Clear CUDA cache again if using GPU
if(koboldai_vars.hascuda and (koboldai_vars.usegpu or koboldai_vars.breakmodel)):
del _genout
gc.collect()
torch.cuda.empty_cache()
return genout
def apiactionsubmit_tpumtjgenerate(txt, minimum, maximum):
koboldai_vars.generated_tkns = 0
if(koboldai_vars.full_determinism):
tpu_mtj_backend.set_rng_seed(koboldai_vars.seed)
if not koboldai_vars.quiet:
logger.debug(f"Prompt Min:{minimum}, Max:{maximum}")
logger.prompt(utils.decodenewlines(tokenizer.decode(txt)).encode("unicode_escape").decode("utf-8"))
koboldai_vars._prompt = koboldai_vars.prompt
# Submit input text to generator
soft_tokens = tpumtjgetsofttokens()
genout = tpool.execute(
tpu_mtj_backend.infer_static,
np.uint32(txt),
gen_len = maximum-minimum+1,
temp=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,
)
genout = [applyoutputformatting(utils.decodenewlines(tokenizer.decode(txt))) for txt in genout]
return genout
def apiactionsubmit(data, use_memory=False, use_world_info=False, use_story=False, use_authors_note=False):
if(koboldai_vars.model == "Colab"):
raise NotImplementedError("API generation is not supported in old Colab API mode.")
elif(koboldai_vars.model == "API"):
raise NotImplementedError("API generation is not supported in API mode.")
elif(koboldai_vars.model == "CLUSTER"):
raise NotImplementedError("API generation is not supported in API mode.")
elif(koboldai_vars.model == "OAI"):
raise NotImplementedError("API generation is not supported in OpenAI/GooseAI mode.")
elif(koboldai_vars.model == "ReadOnly"):
raise NotImplementedError("API generation is not supported in read-only mode; please load a model and then try again.")
data = applyinputformatting(data)
if(koboldai_vars.memory != "" and koboldai_vars.memory[-1] != "\n"):
mem = koboldai_vars.memory + "\n"
else:
mem = koboldai_vars.memory
if(use_authors_note and koboldai_vars.authornote != ""):
anotetxt = ("\n" + koboldai_vars.authornotetemplate + "\n").replace("<|>", koboldai_vars.authornote)
else:
anotetxt = ""
MIN_STORY_TOKENS = 8
story_tokens = []
mem_tokens = []
wi_tokens = []
story_budget = lambda: koboldai_vars.max_length - koboldai_vars.sp_length - koboldai_vars.genamt - len(tokenizer._koboldai_header) - len(story_tokens) - len(mem_tokens) - len(wi_tokens)
budget = lambda: story_budget() + MIN_STORY_TOKENS
if budget() < 0:
abort(Response(json.dumps({"detail": {
"msg": f"Your Max Tokens setting is too low for your current soft prompt and tokenizer to handle. It needs to be at least {koboldai_vars.max_length - budget()}.",
"type": "token_overflow",
}}), mimetype="application/json", status=500))
if use_memory:
mem_tokens = tokenizer.encode(utils.encodenewlines(mem))[-budget():]
if use_world_info:
#world_info, _ = checkworldinfo(data, force_use_txt=True, scan_story=use_story)
world_info = koboldai_vars.worldinfo_v2.get_used_wi()
wi_tokens = tokenizer.encode(utils.encodenewlines(world_info))[-budget():]
if use_story:
if koboldai_vars.useprompt:
story_tokens = tokenizer.encode(utils.encodenewlines(koboldai_vars.prompt))[-budget():]
story_tokens = tokenizer.encode(utils.encodenewlines(data))[-story_budget():] + story_tokens
if use_story:
for i, action in enumerate(reversed(koboldai_vars.actions.values())):
if story_budget() <= 0:
assert story_budget() == 0
break
story_tokens = tokenizer.encode(utils.encodenewlines(action))[-story_budget():] + story_tokens
if i == koboldai_vars.andepth - 1:
story_tokens = tokenizer.encode(utils.encodenewlines(anotetxt))[-story_budget():] + story_tokens
if not koboldai_vars.useprompt:
story_tokens = tokenizer.encode(utils.encodenewlines(koboldai_vars.prompt))[-budget():] + story_tokens
tokens = tokenizer._koboldai_header + mem_tokens + wi_tokens + story_tokens
assert story_budget() >= 0
minimum = len(tokens) + 1
maximum = len(tokens) + koboldai_vars.genamt
if(not koboldai_vars.use_colab_tpu and koboldai_vars.model not in ["Colab", "API", "CLUSTER", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
genout = apiactionsubmit_generate(tokens, minimum, maximum)
elif(koboldai_vars.use_colab_tpu or koboldai_vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")):
genout = apiactionsubmit_tpumtjgenerate(tokens, minimum, maximum)
return genout
#==================================================================#
#
#==================================================================#
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 buildauthorsnote(authorsnote, template):
# Build Author's Note if set
if authorsnote == "":
return ""
return ("\n" + template + "\n").replace("<|>", authorsnote)
def calcsubmitbudgetheader(txt, **kwargs):
# Scan for WorldInfo matches
winfo, found_entries = checkworldinfo(txt, **kwargs)
# Add a newline to the end of memory
if(koboldai_vars.memory != "" and koboldai_vars.memory[-1] != "\n"):
mem = koboldai_vars.memory + "\n"
else:
mem = koboldai_vars.memory
anotetxt = buildauthorsnote(koboldai_vars.authornote, koboldai_vars.authornotetemplate)
return winfo, mem, anotetxt, found_entries
def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions, submission=None, budget_deduction=0):
forceanote = False # In case we don't have enough actions to hit A.N. depth
anoteadded = False # In case our budget runs out before we hit A.N. depth
anotetkns = [] # Placeholder for Author's Note tokens
lnanote = 0 # Placeholder for Author's Note length
lnsp = koboldai_vars.sp_length
if("tokenizer" not in globals()):
from transformers import GPT2Tokenizer
global tokenizer
tokenizer = GPT2Tokenizer.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 - lnheader - 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 if koboldai_vars.model not in ("Colab", "API", "CLUSTER", "OAI") else []) + 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
truncated_action_tokens = acttkns[count:]
tokens = truncated_action_tokens + 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 if koboldai_vars.model not in ("Colab", "API", "CLUSTER", "OAI") else []) + memtokens + witokens + anotetkns + prompttkns + tokens
else:
tokens = (tokenizer._koboldai_header if koboldai_vars.model not in ("Colab", "API", "CLUSTER", "OAI") else []) + memtokens + witokens + prompttkns + tokens
else:
# Prepend Memory, WI, and Prompt before action tokens
tokens = (tokenizer._koboldai_header if koboldai_vars.model not in ("Colab", "API", "CLUSTER", "OAI") else []) + 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)
subtxt, min, max, found_entries = koboldai_vars.calc_ai_text(submitted_text=txt)
generate(subtxt, min, max, 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)
def core_generate(text: list, min: int, max: int, found_entries: set):
# This generation function is tangled with koboldai_vars intentionally. It
# is meant for the story and nothing else.
gen_in = torch.tensor(text, dtype=torch.long)[None]
if koboldai_vars.is_model_torch():
# Torch stuff
if koboldai_vars.full_determinism:
torch.manual_seed(koboldai_vars.seed)
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)
elif koboldai_vars.use_colab_tpu:
if koboldai_vars.full_determinism:
tpu_mtj_backend.set_rng_seed(koboldai_vars.seed)
if gen_in.shape[-1] + koboldai_vars.genamt > koboldai_vars.max_length:
logger.error("gen_in.shape[-1]: {}".format(gen_in.shape[-1]))
logger.error("koboldai_vars.genamt: {}".format(koboldai_vars.genamt))
logger.error("koboldai_vars.max_length: {}".format(koboldai_vars.max_length))
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")
found_entries = found_entries or set()
if model:
model.kai_scanner_excluded_world_info = found_entries
koboldai_vars._prompt = koboldai_vars.prompt
with torch.no_grad():
already_generated = 0
numseqs = koboldai_vars.numseqs
while True:
# The reason this is a loop is due to how Dynamic WI works. We
# cannot simply add the WI to the context mid-generation, so we
# stop early, and then insert WI, then continue generating. That
# stopping and continuing is this loop.
result = raw_generate(
gen_in[0],
max_new=koboldai_vars.genamt,
do_streaming=True,
do_dynamic_wi=True,
batch_count=numseqs,
# Real max length is handled by CoreStopper.
bypass_hf_maxlength=True,
)
genout = result.encoded
already_generated += len(genout[0])
try:
assert already_generated <= koboldai_vars.genamt
except AssertionError:
print("AlreadyGenerated", already_generated)
print("genamt", koboldai_vars.genamt)
raise
if result.is_whole_generation:
break
# Generation stopped; why?
# If we have been told to halt, we have reached our target token
# amount (controlled by halt), or Dynamic WI has not told us to
# stop temporarily to insert WI, we can assume that we are done
# generating. We shall break.
if model.core_stopper.halt or not model.core_stopper.regeneration_required:
break
# Now we are doing stuff for Dynamic WI.
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)
txt, _, _, found_entries = koboldai_vars.calc_ai_text(submitted_text=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
gen_in = genout
numseqs = 1
return genout, already_generated
class GenerationResult:
def __init__(
self,
out_batches: list,
prompt: list,
# Controls if generate() does it's looping thing. This should only be
# done for HF models that use that StoppingCondition
is_whole_generation: bool,
# Controls if we should trim output by prompt length
output_includes_prompt: bool = False,
):
# Shave prompt off of encoded response when needed (HF). Decoded does
# not return prompt.
if output_includes_prompt:
self.encoded = out_batches[:, len(prompt):]
else:
self.encoded = out_batches
self.prompt = prompt
self.is_whole_generation = is_whole_generation
self.decoded = [utils.decodenewlines(tokenizer.decode(enc)) for enc in self.encoded]
class GenerationSettings:
def __init__(self, **overrides) -> None:
for setting in [
"temp",
"top_p",
"top_k",
"tfs",
"typical",
"top_a",
"rep_pen",
"rep_pen_slope",
"rep_pen_range",
"sampler_order",
]:
setattr(
self,
setting,
overrides.get(setting, getattr(koboldai_vars, setting))
)
def raw_generate(
# prompt is either a string (text) or a list (token ids)
prompt: Union[str, list, np.ndarray],
max_new: int,
do_streaming: bool = False,
do_dynamic_wi: bool = False,
batch_count: int = 1,
bypass_hf_maxlength: bool = False,
generation_settings: Optional[dict] = None
) -> GenerationResult:
gen_settings = GenerationSettings(*(generation_settings or {}))
model_functions = {
"GooseAI": oai_raw_generate,
"OAI": oai_raw_generate,
"CLUSTER": cluster_raw_generate,
"Colab": colab_raw_generate,
"API": api_raw_generate,
}
if isinstance(prompt, torch.Tensor):
prompt_tokens = prompt.cpu().numpy()
elif isinstance(prompt, list):
prompt_tokens = np.array(prompt)
elif isinstance(prompt, str):
prompt_tokens = np.array(tokenizer.encode(prompt))
else:
raise ValueError(f"Prompt is {type(prompt)}. Not a fan!")
assert isinstance(prompt_tokens, np.ndarray)
assert len(prompt_tokens.shape) == 1
if koboldai_vars.model == "ReadOnly":
raise NotImplementedError("No loaded model")
result: GenerationResult
time_start = time.time()
if koboldai_vars.use_colab_tpu or koboldai_vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"):
batch_encoded = tpu_raw_generate(
prompt_tokens=prompt_tokens,
max_new=max_new,
batch_count=batch_count,
gen_settings=gen_settings
)
result = GenerationResult(
out_batches=batch_encoded, prompt=prompt_tokens, is_whole_generation=True
)
elif koboldai_vars.model in model_functions:
batch_encoded = model_functions[koboldai_vars.model](
prompt_tokens=prompt_tokens,
max_new=max_new,
batch_count=batch_count,
gen_settings=gen_settings
)
result = GenerationResult(
out_batches=batch_encoded, prompt=prompt_tokens, is_whole_generation=True
)
elif koboldai_vars.model.startswith("RWKV"):
batch_encoded = rwkv_raw_generate(
prompt_tokens=prompt_tokens,
max_new=max_new,
batch_count=batch_count,
gen_settings=gen_settings
)
result = GenerationResult(
out_batches=batch_encoded, prompt=prompt_tokens, is_whole_generation=True, output_includes_prompt=True
)
else:
# Torch HF
batch_encoded = torch_raw_generate(
prompt_tokens=prompt_tokens,
max_new=max_new if not bypass_hf_maxlength else int(2e9),
do_streaming=do_streaming,
do_dynamic_wi=do_dynamic_wi,
batch_count=batch_count,
gen_settings=gen_settings
)
result = GenerationResult(
out_batches=batch_encoded,
prompt=prompt_tokens,
is_whole_generation=False,
output_includes_prompt=True,
)
time_end = round(time.time() - time_start, 2)
tokens_per_second = round(len(result.encoded[0]) / time_end, 2)
if not koboldai_vars.quiet:
logger.info(f"Generated {len(result.encoded[0])} tokens in {time_end} seconds, for an average rate of {tokens_per_second} tokens per second.")
return result
def tpu_raw_generate(
prompt_tokens: List[int],
max_new: int,
batch_count: int,
gen_settings: GenerationSettings
):
# Mostly lifted from apiactionsubmit_tpumtjgenerate
soft_tokens = tpumtjgetsofttokens()
genout = tpool.execute(
tpu_mtj_backend.infer_static,
np.uint32(prompt_tokens),
gen_len = max_new,
temp=gen_settings.temp,
top_p=gen_settings.top_p,
top_k=gen_settings.top_k,
tfs=gen_settings.tfs,
typical=gen_settings.typical,
top_a=gen_settings.top_a,
numseqs=batch_count,
repetition_penalty=gen_settings.rep_pen,
rpslope=gen_settings.rep_pen_slope,
rprange=gen_settings.rep_pen_range,
soft_embeddings=koboldai_vars.sp,
soft_tokens=soft_tokens,
sampler_order=gen_settings.sampler_order,
)
genout = np.array(genout)
return genout
def torch_raw_generate(
prompt_tokens: Union[List[int], torch.Tensor],
max_new: int,
gen_settings: GenerationSettings,
do_streaming: bool = False,
do_dynamic_wi: bool = False,
batch_count: int = 1,
):
koboldai_vars.inference_config.do_streaming = do_streaming
koboldai_vars.inference_config.do_dynamic_wi = do_dynamic_wi
# Dynamic WI depends on this!!! This is a main gen call.
koboldai_vars.inference_config.stop_at_genamt = do_dynamic_wi
# Makes stopping criteria hook happy
model.kai_scanner_excluded_world_info = model.kai_scanner_excluded_world_info or set()
if not isinstance(prompt_tokens, torch.Tensor):
gen_in = torch.tensor(prompt_tokens, dtype=torch.long)[None]
else:
gen_in = prompt_tokens
device = "cpu"
if koboldai_vars.hascuda and koboldai_vars.usegpu:
device = koboldai_vars.gpu_device
elif koboldai_vars.hascuda and koboldai_vars.breakmodel:
device = breakmodel.primary_device
gen_in = gen_in.to(device)
with torch.no_grad():
genout = generator(
gen_in,
do_sample=True,
max_length=min(len(prompt_tokens) + max_new, koboldai_vars.max_length),
repetition_penalty=1.0,
bad_words_ids=koboldai_vars.badwordsids,
use_cache=True,
num_return_sequences=batch_count,
)
return genout
def oai_raw_generate(
prompt_tokens: List[int],
max_new: int,
batch_count: int,
gen_settings: GenerationSettings,
):
# Taken mainly from oairequest()
decoded_prompt = utils.decodenewlines(tokenizer.decode(prompt_tokens))
# Log request to console
if not koboldai_vars.quiet:
print("{0}Len:{1}, Txt:{2}{3}".format(colors.YELLOW, len(decoded_prompt), decoded_prompt, colors.END))
# Store context in memory to use it for comparison with generated content
koboldai_vars.lastctx = decoded_prompt
# Build request JSON data
# GooseAI is a subntype of OAI. So to check if it's this type, we check the configname as a workaround
# as the koboldai_vars.model will always be OAI
if 'GooseAI' in koboldai_vars.configname:
reqdata = {
'prompt': decoded_prompt,
'max_tokens': max_new,
'temperature': gen_settings.temp,
'top_a': gen_settings.top_a,
'top_p': gen_settings.top_p,
'top_k': gen_settings.top_k,
'tfs': gen_settings.tfs,
'typical_p': gen_settings.typical,
'repetition_penalty': gen_settings.rep_pen,
'repetition_penalty_slope': gen_settings.rep_pen_slope,
'repetition_penalty_range': gen_settings.rep_pen_range,
'n': batch_count,
# TODO: Implement streaming
'stream': False
}
else:
reqdata = {
'prompt': decoded_prompt,
'max_tokens': max_new,
'temperature': gen_settings.temp,
'top_p': gen_settings.top_p,
'n': batch_count,
'stream': False
}
req = requests.post(
koboldai_vars.oaiurl,
json = reqdata,
headers = {
'Authorization': 'Bearer '+koboldai_vars.oaiapikey,
'Content-Type': 'application/json'
}
)
j = req.json()
# Deal with the response
if req.ok:
outputs = [out["text"] for out in j["choices"]]
if not koboldai_vars.quiet:
print("{0}{1}{2}".format(colors.CYAN, outputs, colors.END))
return np.array([tokenizer.encode(x) for x in outputs])
else:
# Send error message to web client
if "error" in j:
error_type = j["error"]["type"]
error_message = j["error"]["message"]
else:
error_type = "Unknown"
error_message = "Unknown"
emit('from_server', {
'cmd': 'errmsg',
'data': f"OpenAI API Error: {error_type} - {error_message}"
}, broadcast=True, room="UI_1")
set_aibusy(0)
return []
class HordeException(Exception):
pass
def cluster_raw_generate(
prompt_tokens: List[int],
max_new: int,
batch_count: int,
gen_settings: GenerationSettings,
):
decoded_prompt = utils.decodenewlines(tokenizer.decode(prompt_tokens))
# Store context in memory to use it for comparison with generated content
koboldai_vars.lastctx = decoded_prompt
# Build request JSON data
reqdata = {
'max_length': max_new,
'max_context_length': koboldai_vars.max_length,
'rep_pen': gen_settings.rep_pen,
'rep_pen_slope': gen_settings.rep_pen_slope,
'rep_pen_range': gen_settings.rep_pen_range,
'temperature': gen_settings.temp,
'top_p': gen_settings.top_p,
'top_k': gen_settings.top_k,
'top_a': gen_settings.top_a,
'tfs': gen_settings.tfs,
'typical': gen_settings.typical,
'n': batch_count,
}
cluster_metadata = {
'prompt': decoded_prompt,
'params': reqdata,
'api_key': koboldai_vars.apikey,
'models': [x for x in koboldai_vars.cluster_requested_models if x],
}
try:
# Create request
req = requests.post(
koboldai_vars.colaburl[:-8] + "/api/v1/generate/async",
json=cluster_metadata,
)
except requests.exceptions.ConnectionError:
errmsg = f"Horde unavailable. Please try again later"
logger.error(errmsg)
raise HordeException(errmsg)
if req.status_code == 503:
errmsg = f"KoboldAI API Error: No available KoboldAI servers found in Horde to fulfil this request using the selected models or other properties."
logger.error(errmsg)
raise HordeException(errmsg)
elif not req.ok:
errmsg = f"KoboldAI API Error: Failed to get a standard reply from the Horde. Please check the console."
logger.error(errmsg)
raise HordeException(errmsg)
try:
js = req.json()
except requests.exceptions.JSONDecodeError:
errmsg = f"Unexpected message received from the Horde: '{req.text}'"
logger.error(errmsg)
raise HordeException(errmsg)
request_id = js["id"]
logger.debug("Horde Request ID: {}".format(request_id))
# We've sent the request and got the ID back, now we need to watch it to see when it finishes
finished = False
while not finished:
try:
req = requests.get(koboldai_vars.colaburl[:-8] + "/api/v1/generate/check/" + request_id)
except requests.exceptions.ConnectionError:
errmsg = f"Horde unavailable. Please try again later"
logger.error(errmsg)
raise HordeException(errmsg)
if not req.ok:
errmsg = f"KoboldAI API Error: Failed to get a standard reply from the Horde. Please check the console."
logger.error(req.text)
raise HordeException(errmsg)
try:
js = req.json()
except requests.exceptions.JSONDecodeError:
errmsg = f"Unexpected message received from the KoboldAI Horde: '{req.text}'"
logger.error(errmsg)
raise HordeException(errmsg)
if "done" not in js:
errmsg = f"Unexpected response received from the KoboldAI Horde: '{js}'"
logger.error(errmsg )
raise HordeException(errmsg)
finished = js["done"]
koboldai_vars.horde_wait_time = js["wait_time"]
koboldai_vars.horde_queue_position = js["queue_position"]
koboldai_vars.horde_queue_size = js["waiting"]
if not finished:
logger.debug(js)
time.sleep(1)
logger.debug("Last Horde Status Message: {}".format(js))
js = requests.get(koboldai_vars.colaburl[:-8] + "/api/v1/generate/prompt/" + request_id).json()['generations']
logger.debug("Horde Result: {}".format(js))
gen_servers = [(cgen['server_name'],cgen['server_id']) for cgen in js]
logger.info(f"Generations by: {gen_servers}")
# TODO: Fix this, using tpool so it's a context error
# Just in case we want to announce it to the user
# if len(js) == 1:
# warnmsg = f"Text generated by {js[0]['server_name']}"
# emit('from_server', {'cmd': 'warnmsg', 'data': warnmsg}, broadcast=True)
return np.array([tokenizer.encode(cgen["text"]) for cgen in js])
def colab_raw_generate(
prompt_tokens: List[int],
max_new: int,
batch_count: int,
gen_settings: GenerationSettings,
):
decoded_prompt = utils.decodenewlines(tokenizer.decode(prompt_tokens))
# Store context in memory to use it for comparison with generated content
koboldai_vars.lastctx = decoded_prompt
# Build request JSON data
reqdata = {
'text': decoded_prompt,
'min': 0,
'max': max_new,
'rep_pen': gen_settings.rep_pen,
'rep_pen_slope': gen_settings.rep_pen_slope,
'rep_pen_range': gen_settings.rep_pen_range,
'temperature': gen_settings.temp,
'top_p': gen_settings.top_p,
'top_k': gen_settings.top_k,
'tfs': gen_settings.tfs,
'typical': gen_settings.typical,
'topa': gen_settings.top_a,
'numseqs': batch_count,
'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"]
return np.array([tokenizer.encode(x) for x in genout])
def api_raw_generate(
prompt_tokens: List[int],
max_new: int,
batch_count: int,
gen_settings: GenerationSettings,
):
decoded_prompt = utils.decodenewlines(tokenizer.decode(prompt_tokens))
# Store context in memory to use it for comparison with generated content
koboldai_vars.lastctx = decoded_prompt
# Build request JSON data
reqdata = {
'prompt': decoded_prompt,
'max_length': max_new,
'max_context_length': koboldai_vars.max_length,
'rep_pen': gen_settings.rep_pen,
'rep_pen_slope': gen_settings.rep_pen_slope,
'rep_pen_range': gen_settings.rep_pen_range,
'temperature': gen_settings.temp,
'top_p': gen_settings.top_p,
'top_k': gen_settings.top_k,
'top_a': gen_settings.top_a,
'tfs': gen_settings.tfs,
'typical': gen_settings.typical,
'n': batch_count,
}
# Create request
while True:
req = requests.post(
koboldai_vars.colaburl[:-8] + "/api/v1/generate",
json=reqdata,
)
if(req.status_code == 503): # Server is currently generating something else so poll until it's our turn
time.sleep(1)
continue
js = req.json()
if(req.status_code != 200):
errmsg = "KoboldAI API Error: Failed to get a reply from the server. Please check the console."
print("{0}{1}{2}".format(colors.RED, json.dumps(js, indent=2), colors.END))
emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True)
emit("error", errmsg, broadcast=True, room="UI_2")
set_aibusy(0)
return
genout = [obj["text"] for obj in js["results"]]
return np.array([tokenizer.encode(x) for x in genout])
def rwkv_raw_generate(
prompt_tokens: List[int],
max_new: int,
batch_count: int,
gen_settings: GenerationSettings,
):
import types
model.clear()
context = list(prompt_tokens)
input_length = len(prompt_tokens)
# TODO: Not needed every run? I think this is creating that huge wait time
# between generations.
init_state = types.SimpleNamespace()
for i in range(input_length):
x = context[:i+1]
if i == input_length - 1:
init_state.out = model.run(x)
else:
model.run(x)
model.save(init_state)
for ni, i in enumerate(range(input_length, input_length + max_new)):
x = context[:i+1]
x = x[-model.ctx_len:]
if i == input_length:
out = copy.deepcopy(init_state.out)
else:
out = model.run(x)
# Don't generate EOS
out[0] = -9999999
char = tokenizer.sample_logits(
out=out,
x=x,
ctx_len=model.ctx_len,
temperature=gen_settings.temp,
top_p=gen_settings.top_p,
)
char = char.item()
context.append(char)
if koboldai_vars.output_streaming:
koboldai_vars.actions.stream_tokens([utils.decodenewlines(tokenizer.decode(char))])
# HACK
if ni > max_new:
break
return np.array([context])
@dataclass
class RWKVConfig:
n_layer: int
n_embed: int
ctx_len: int
def rwkv_init(model_class: str, use_gpu: bool = False):
torch.backends.cudnn.allow_tf32 = True
torch.backends.cuda.matmul.allow_tf32 = True
os.environ["RWKV_FLOAT_MODE"] = "bf16"
logger.info("[RWKV] RWKV support is in super-duper-uber-schmoober alpha and will ignore many options.")
device = "cpu"
if use_gpu:
logger.warning("[RWKV] Using GPU. This may not work out of the box and may require significant setup.")
device = "cuda"
os.environ["RWKV_RUN_DEVICE"] = device
TOKENIZER_PATH = "models/RWKV4/20B_tokenizer.json"
MODEL_DIR = "models/RWKV4/models"
model_files = os.listdir(MODEL_DIR)
matching_models = [f for f in model_files if f.startswith(f"RWKV-4-Pile-{model_class}")]
if not matching_models:
raise RuntimeError(f"No models of class '{model_class}' found in '{MODEL_DIR}'. Did you rename the model?")
model_path = os.path.join(MODEL_DIR, sorted(matching_models)[-1])
model_config = {
"169M": RWKVConfig(n_layer=12, n_embed=768, ctx_len=1024),
"430M": RWKVConfig(n_layer=24, n_embed=1024, ctx_len=1024),
"1B5": RWKVConfig(n_layer=24, n_embed=2048, ctx_len=1024),
"3B": RWKVConfig(n_layer=32, n_embed=2560, ctx_len=1024),
"7B": RWKVConfig(n_layer=32, n_embed=4096, ctx_len=1024),
}.get(model_class)
if not model_config:
raise RuntimeError(f"No config for model '{model_class}' found!")
if not os.path.exists(TOKENIZER_PATH):
raise RuntimeError(f"Can't find tokenizer at '{TOKENIZER_PATH}'. Did you download it and put it at that location?")
# Model stuff
from models.RWKV4.src.model_run import RWKV_RNN
from transformers import PreTrainedTokenizerFast
from torch.nn import functional as F
model = RWKV_RNN(
model_path.split(".")[0],
device,
"RWKV",
model_config.n_layer,
model_config.n_embed,
model_config.ctx_len,
)
tokenizer = PreTrainedTokenizerFast(tokenizer_file=TOKENIZER_PATH)
# We'll just patch tokenizer ourselves to make it easier
def _sample_logits(self, out, x, ctx_len, temperature, top_p):
last_char = int(x[-1])
probs = F.softmax(torch.tensor(out), dim=-1)
sorted_probs, s_index = torch.sort(probs, descending=True)
cumulative_probs = torch.cumsum(sorted_probs, dim=-1).numpy()
cutoff = float(sorted_probs[np.argmax(cumulative_probs > top_p)])
probs[probs < cutoff] = 0
if temperature != 1.0:
probs = probs.pow(1.0 / temperature)
return torch.multinomial(probs, num_samples=1)[0]
tokenizer.sample_logits = _sample_logits.__get__(tokenizer, AutoTokenizer)
tokenizer._koboldai_header = []
tokenizer.add_bos_token = False
tokenizer.add_prefix_space = False
logger.info("[RWKV] Loaded :^)")
return model, tokenizer
#==================================================================#
# Send text to generator and deal with output
#==================================================================#
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:
logger.debug(f"Prompt Min:{minimum}, Max:{maximum}")
logger.prompt(utils.decodenewlines(tokenizer.decode(txt)).encode("unicode_escape").decode("utf-8"))
# Store context in memory to use it for comparison with generated content
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(core_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()
logger.debug('LUA ERROR: ' + str(e).replace("\033", ""))
logger.warning("Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.")
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")
logger.error(traceback.format_exc().replace("\033", ""))
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]
if(len(genout) == 1):
genresult(genout[0]["generated_text"])
#koboldai_vars.actions.append(applyoutputformatting(genout[0]["generated_text"]))
else:
koboldai_vars.actions.append_options([applyoutputformatting(x["generated_text"]) for x in genout])
genout = [{"generated_text": x['text']} for x in koboldai_vars.actions.get_current_options()]
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:
logger.generation(genout.encode("unicode_escape").decode("utf-8"))
# 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:
logger.info(f"Generation Result {i}")
logger.generation(result["generated_text"].encode("unicode_escape").decode("utf-8"))
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 text to TPU mesh transformer backend
#==================================================================#
def tpumtjgenerate(txt, minimum, maximum, found_entries=None):
if(koboldai_vars.full_determinism):
tpu_mtj_backend.set_rng_seed(koboldai_vars.seed)
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:
logger.debug(f"Prompt Min:{minimum}, Max:{maximum}")
logger.prompt(utils.decodenewlines(tokenizer.decode(txt)).encode("unicode_escape").decode("utf-8"))
koboldai_vars._prompt = koboldai_vars.prompt
# 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)
txt, _, _, found_entries = koboldai_vars.calc_ai_text(submitted_text=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()
logger.debug('LUA ERROR: ' + str(e).replace("\033", ""))
logger.warning("Lua engine stopped; please open 'Userscripts' and press Load to reinitialize scripts.")
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.append_options([applyoutputformatting(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", "
").replace("\\r", "
").replace("\\n", "
").replace("\r\n", "
").replace('\n', '
').replace('\r', '
').replace('</s>', '
')
#==================================================================#
# Strips submitted text from the text returned by the AI
#==================================================================#
def getnewcontent(txt):
# If the submitted context was blank, then everything is new
if(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.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.frmttriminc and not koboldai_vars.chatmode):
txt = utils.trimincompletesentence(txt)
# Replace blank lines
if(koboldai_vars.frmtrmblln or koboldai_vars.chatmode):
txt = utils.replaceblanklines(txt)
# Remove special characters
if(koboldai_vars.frmtrmspch):
txt = utils.removespecialchars(txt, koboldai_vars)
# Single Line Mode
if(koboldai_vars.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 = ['', koboldai_vars.comregex_ui.sub(lambda m: '\n'.join('' + l + '' for l in m.group().split('\n')), html.escape(koboldai_vars.prompt)), '']
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('' + l + '' for l in m.group().split('\n')), item) # Add special formatting to comments
item = koboldai_vars.acregex_ui.sub('\\1', item) # Add special formatting to adventure actions
text_parts.extend(('', item, ''))
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('' + l + '' for l in m.group().split('\n')), item) # Add special formatting to comments
item = koboldai_vars.acregex_ui.sub('\\1', item) # Add special formatting to adventure actions
chunk_text = f'{formatforhtml(item)}'
emit('from_server', {'cmd': 'updatechunk', 'data': {'index': idx, 'html': chunk_text}}, broadcast=True, 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': 'updatefulldeterminism', 'data': koboldai_vars.full_determinism}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatefrmttriminc', 'data': koboldai_vars.frmttriminc}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatefrmtrmblln', 'data': koboldai_vars.frmtrmblln}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatefrmtrmspch', 'data': koboldai_vars.frmtrmspch}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatefrmtadsnsp', 'data': koboldai_vars.frmtadsnsp}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updatesingleline', 'data': koboldai_vars.singleline}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updateoutputstreaming', 'data': koboldai_vars.output_streaming}, broadcast=True, room="UI_1")
emit('from_server', {'cmd': 'updateshowprobs', 'data': koboldai_vars.show_probs}, 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(koboldai_vars.disable_set_aibusy):
return
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:
logger.warning(f"Attempted to edit non-existent chunk {chunk}")
setgamesaved(False)
update_story_chunk(chunk)
emit('from_server', {'cmd': 'texteffect', 'data': chunk}, broadcast=True, 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:
logger.warning(f"Attempted to delete non-existent chunk {chunk}")
setgamesaved(False)
remove_story_chunk(chunk)
emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True, 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"]]
koboldai_vars.sync_worldinfo_v1_to_v2()
sendwi()
#==================================================================#
#
#==================================================================#
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 = actions[-depth:]
#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.lower() in txt.lower():
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.lower() in txt.lower():
wimem = wimem + wi["content"] + "\n"
found_entries.add(id(wi))
found = True
break
if found:
break
else:
wimem = wimem + wi["content"] + "\n"
found_entries.add(id(wi))
break
return wimem, found_entries
#==================================================================#
# Commit changes to Memory storage
#==================================================================#
def memsubmit(data):
emit('from_server', {'cmd': 'setinputtext', 'data': data}, broadcast=True, 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)
#==================================================================#
# 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):
logger.debug("Load Request")
logger.debug("Called from {}".format(inspect.stack()[1].function))
start_time = time.time()
if(loadpath):
# Leave Edit/Memory mode before continuing
exitModes()
# Read file contents into JSON object
start_time = time.time()
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
logger.debug("Loading JSON data took {}s".format(time.time()-start_time))
loadJSON(js)
logger.debug("Time to load story: {}s".format(time.time()-start_time))
def loadJSON(json_text_or_dict):
logger.debug("Loading JSON Story")
logger.debug("Called from {}".format(inspect.stack()[1].function))
start_time = time.time()
if isinstance(json_text_or_dict, str):
json_data = json.loads(json_text_or_dict)
else:
json_data = json_text_or_dict
logger.debug("Loading JSON data took {}s".format(time.time()-start_time))
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)
logger.debug("Calcing AI Text from Story Load")
ignore = koboldai_vars.calc_ai_text()
def load_story_v1(js):
logger.debug("Loading V1 Story")
logger.debug("Called from {}".format(inspect.stack()[1].function))
loadpath = js['v1_loadpath'] if 'v1_loadpath' in js else koboldai_vars.savedir
filename = js['v1_filename'] if 'v1_filename' in js else 'untitled.json'
_filename = filename
if(filename.endswith('.json')):
_filename = filename[:-5]
leave_room(session['story'])
session['story'] = _filename
join_room(_filename)
#create the story
#koboldai_vars.create_story(session['story'])
koboldai_vars.create_story(session['story'])
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):
#We set the action count higher so that we don't trigger a scroll in the UI.
#Once all but the last is loaded we can bring it back down and do the last one so we scroll to it
logger.debug("Created temp story class")
temp_story_class = koboldai_settings.KoboldStoryRegister(None, None, koboldai_vars, tokenizer=None)
for i in range(len(js["actions"])):
temp_story_class.append(js["actions"][i], recalc=False)
logger.debug("Added actions to temp story class")
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"]
for i in range(len(js["actions_metadata"][key]["Alternative Text"])):
data[i]["text"] = data[i].pop("Text")
temp_story_class.set_options(data, int(key))
koboldai_vars.actions.load_json(temp_story_class.to_json())
logger.debug("Saved temp story class")
del temp_story_class
# 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"]:
if wi.get("folder", "root") == 'root':
folder = "root"
else:
if 'wifolders_d' in js:
if wi['folder'] in js['wifolders_d']:
folder = js['wifolders_d'][wi['folder']]['name']
else:
folder = "root"
else:
folder = "root"
koboldai_vars.worldinfo_v2.add_item([x.strip() for x in wi["key"].split(",")][0], wi["key"], wi.get("keysecondary", ""),
folder, wi.get("constant", False),
wi["content"], wi.get("comment", ""), recalc=False)
# 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):
logger.debug("Loading V2 Story")
logger.debug("Called from {}".format(inspect.stack()[1].function))
leave_room(session['story'])
session['story'] = js['story_name']
join_room(session['story'])
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 koboldai_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 koboldai_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()
tpool.execute(__preempt_tokenizer)
# Load soft prompt specified by the settings file, if applicable
if(path.exists("settings/" + getmodelname().replace('/', '_') + ".v2_settings")):
file = open("settings/" + getmodelname().replace('/', '_') + ".v2_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 ("/", "\\")))):
if valid_softprompt("softprompts/"+js["softprompt"]):
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()
tpool.execute(tpu_mtj_backend.infer_dynamic, np.tile(np.uint32((23403, 727, 20185)), (koboldai_vars.numseqs, 1)),
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))
)
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()
tpool.execute(
tpu_mtj_backend.infer_static,
np.uint32((23403, 727, 20185)),
soft_embeddings= koboldai_vars.sp,
soft_tokens= soft_tokens,
gen_len= 1,
numseqs= koboldai_vars.numseqs
)
# Set the initial RNG seed
if(koboldai_vars.seed is not None):
if(koboldai_vars.use_colab_tpu):
if(koboldai_vars.seed_specified):
__import__("tpu_mtj_backend").set_rng_seed(koboldai_vars.seed)
else:
__import__("tpu_mtj_backend").randomize_rng_seed()
else:
if(koboldai_vars.seed_specified):
__import__("torch").manual_seed(koboldai_vars.seed)
else:
__import__("torch").seed()
koboldai_vars.seed = __import__("tpu_mtj_backend").get_rng_seed() if koboldai_vars.use_colab_tpu else __import__("torch").initial_seed()
def send_debug():
if koboldai_vars.debug:
debug_info = ""
try:
debug_info = "{}Seed: {} ({})\n".format(debug_info, repr(__import__("tpu_mtj_backend").get_rng_seed() if koboldai_vars.use_colab_tpu else __import__("torch").initial_seed()), "specified by user in settings file" if koboldai_vars.seed_specified else "randomly generated")
except:
pass
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")
#==================================================================#
# Load file browser for soft prompts
#==================================================================#
@socketio.on('show_folder_soft_prompt')
def show_folder_soft_prompt(data):
file_popup("Load Softprompt", "./softprompts", "", renameable=True, folder_only=False, editable=False, deleteable=True, jailed=True, item_check=None)
#==================================================================#
# Load file browser for user scripts
#==================================================================#
@socketio.on('show_folder_usersripts')
def show_folder_usersripts(data):
file_popup("Load Softprompt", "./userscripts", "", renameable=True, folder_only=False, editable=True, deleteable=True, jailed=True, item_check=None)
# UI V2 CODE
#==================================================================#
@app.route('/ai_text')
def ai_text():
start_time = time.time()
text = koboldai_vars.calc_ai_text(return_text=True)
logger.debug("Generating Game Text took {} seconds".format(time.time()-start_time))
return "{}\n\n\n{}".format(text, "Generating Game Text took {} seconds".format(time.time()-start_time))
#==================================================================#
# UI V2 CODE
#==================================================================#
@app.route('/new_ui')
@logger.catch
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, on_colab=koboldai_vars.on_colab )
@logger.catch
def ui2_connect():
#Send all variables to client
logger.debug("Sending full data to client for story {}".format(session['story']))
koboldai_vars.send_to_ui()
UI_2_load_cookies()
UI_2_theme_list_refresh(None)
pass
#==================================================================#
# UI V2 CODE Themes
#==================================================================#
@app.route('/themes/')
@logger.catch
def ui2_serve_themes(path):
return send_from_directory('themes', path)
#==================================================================#
# File Popup options
#==================================================================#
@socketio.on('upload_file')
@logger.catch
def upload_file(data):
logger.debug("upload_file {}".format(data['filename']))
if data['upload_no_save']:
json_data = json.loads(data['data'].decode("utf-8"))
loadJSON(json_data)
else:
if 'current_folder' in session:
path = os.path.abspath(os.path.join(session['current_folder'], data['filename']).replace("\\", "/")).replace("\\", "/")
if koboldai_vars.debug:
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')
@logger.catch
def popup_change_folder(data):
if koboldai_vars.debug:
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')
@logger.catch
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')
@logger.catch
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')
@logger.catch
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')
@logger.catch
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))
@logger.catch
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, show_folders=True,
column_widths=["100%"], sort="Modified", advanced_sort=None, desc=False):
#starting_folder = The folder we're going to get folders and/or items from
#return_event = the socketio event that will be emitted when the load button is clicked
#jailed = if set to true will look for the session variable jailed_folder and prevent navigation outside of that folder
#folder_only = will only show folders, no files
#deletable = will show the delete icons/methods.
#editable = will show the edit icons/methods
#show_breadcrumbs = will show the breadcrumbs at the top of the screen
#item_check will call this function to check if the item is valid as a selection if not none. Will pass absolute directory as only argument to function
#show_hidden = ... really, you have to ask?
#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
session['sort'] = sort
session['desc'] = desc
session['show_folders'] = show_folders
session['advanced_sort'] = advanced_sort
emit("load_popup", {"popup_title": popup_title, "call_back": return_event, "renameable": renameable, "deleteable": deleteable, "editable": editable, 'upload': upload}, broadcast=False)
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)
@logger.catch
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']
sort = session['sort']
desc = session['desc']
show_folders = session['show_folders']
advanced_sort = session['advanced_sort']
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("\\", "/")
if advanced_sort is not None:
files_to_check = advanced_sort(base_path, desc=desc)
else:
files_to_check = get_files_sorted(base_path, sort, desc=desc)
for item in files_to_check:
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])
if show_folders:
items = folders
else:
items = []
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]]
emit("popup_items", {"items": items, "column_names": column_names, "show_filename": show_filename, "column_widths": column_widths}, broadcast=False)
socketio.emit("popup_items", items, broadcast=True, include_self=True, room="UI_1")
if show_breadcrumbs:
emit("popup_breadcrumbs", breadcrumbs, broadcast=False)
socketio.emit("popup_breadcrumbs", breadcrumbs, broadcast=True, room="UI_1")
@logger.catch
def get_files_sorted(path, sort, desc=False):
data = {}
for file in os.scandir(path=path):
if sort == "Modified":
data[file.name] = datetime.datetime.fromtimestamp(file.stat().st_mtime)
elif sort == "Accessed":
data[file.name] = datetime.datetime.fromtimestamp(file.stat().st_atime)
elif sort == "Created":
data[file.name] = datetime.datetime.fromtimestamp(file.stat().st_ctime)
elif sort == "Name":
data[file.name] = file.name
return [key[0] for key in sorted(data.items(), key=lambda kv: (kv[1], kv[0]), reverse=desc)]
@socketio.on("configure_prompt")
@logger.catch
def UI_2_configure_prompt(data):
import_buffer.replace_placeholders(data)
import_buffer.commit()
#==================================================================#
# Event triggered when browser SocketIO detects a variable change
#==================================================================#
@socketio.on('var_change')
@logger.catch
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":
if classname == "model_settings":
filename = "settings/{}.v2_settings".format(koboldai_vars.model.replace("/", "_"))
else:
filename = "settings/{}.v2_settings".format(classname)
if not os.path.exists("settings"):
os.mkdir("settings")
with open(filename, "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')
@logger.catch
def UI_2_save_story(data):
if koboldai_vars.debug:
print("Saving Story")
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")
@logger.catch
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')
@logger.catch
def UI_2_Set_Selected_Text(data):
if not koboldai_vars.quiet:
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')
@logger.catch
def UI_2_Use_Option_Text(data):
if koboldai_vars.prompt == "":
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 Option is Selected
#==================================================================#
@socketio.on('delete_option')
@logger.catch
def UI_2_delete_option(data):
koboldai_vars.actions.delete_option(int(data['option']), action_step=int(data['chunk']))
#==================================================================#
# Event triggered when user clicks the submit button
#==================================================================#
@socketio.on('submit')
@logger.catch
def UI_2_submit(data):
if not koboldai_vars.noai and data['theme'] != "":
if koboldai_vars.debug:
print("doing random prompt")
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:
if koboldai_vars.debug:
print("doing normal input")
koboldai_vars.actions.clear_unused_options()
koboldai_vars.lua_koboldbridge.feedback = None
koboldai_vars.recentrng = koboldai_vars.recentrngm = None
if koboldai_vars.actions.action_count == -1:
actionsubmit(data['data'], actionmode=koboldai_vars.actionmode)
else:
actionsubmit(data['data'], actionmode=koboldai_vars.actionmode)
#==================================================================#
# Event triggered when user clicks the submit button
#==================================================================#
@socketio.on('abort')
@logger.catch
def UI_2_abort(data):
if koboldai_vars.debug:
print("got abort")
koboldai_vars.abort = True
#==================================================================#
# Event triggered when user clicks the pin button
#==================================================================#
@socketio.on('Pinning')
@logger.catch
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')
@logger.catch
def UI_2_back(data):
if koboldai_vars.debug:
print("back")
koboldai_vars.actions.clear_unused_options()
ignore = koboldai_vars.actions.pop()
#==================================================================#
# Event triggered when user clicks the redo button
#==================================================================#
@socketio.on('redo')
@logger.catch
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')
@logger.catch
def UI_2_retry(data):
if len(koboldai_vars.actions.get_current_options_no_edits()) == 0:
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')
@logger.catch
def UI_2_load_model_button(data):
sendModelSelection()
#==================================================================#
# Event triggered when user clicks the a model
#==================================================================#
@socketio.on('select_model')
@logger.catch
def UI_2_select_model(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 folder
elif data['model'] in ("NeoCustom", "GPT2Custom") and 'path' in data:
sendModelSelection(menu=data['model'], folder=data['path'])
#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')
@logger.catch
def UI_2_load_model(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("{}\n{}".format(data['gpu_layers'], 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'], url=koboldai_vars.colaburl)
#==================================================================#
# Event triggered when load story is clicked
#==================================================================#
@socketio.on('load_story_list')
@logger.catch
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_listing_data,
column_names=['Story Name', 'Action Count', 'Last Loaded'], show_filename=False,
column_widths=['minmax(150px, auto)', '150px', '150px'], advanced_sort=story_sort,
sort="Modified", desc=True)
@logger.catch
def get_story_listing_data(item_full_path, item, valid_selection):
title = ""
action_count = -1
last_loaded = ""
if not valid_selection:
return [title, action_count, last_loaded]
if os.path.getsize(item_full_path) < 2*1024*1024: #2MB
with open(item_full_path, "r") as f:
js = json.load(f)
else:
js = {}
title = js['story_name'] if 'story_name' in js else ".".join(item.split(".")[:-1])
if "actions" in js:
action_count = len(js["actions"])
else:
action_count = "{}MB".format(round(os.path.getsize(item_full_path)/1024/1024,1))
if title in koboldai_vars._system_settings.story_loads:
# UNIX Timestamp
last_loaded = int(time.mktime(time.strptime(koboldai_vars._system_settings.story_loads[title], "%m/%d/%Y, %H:%M:%S")))
else:
last_loaded = os.path.getmtime(item_full_path)
if js.get("file_version", 1) == 1 or os.path.getsize(item_full_path) >= 2*1024*1024:
return [title, action_count, last_loaded]
action_count = js['actions']['action_count']+1
return [title, action_count, last_loaded]
@logger.catch
def valid_story(file):
if file.endswith(".json"):
if os.path.getsize(file) < 2*1024*1024: #2MB
with open(file, "r") as f:
try:
js = json.load(f)
except:
pass
return False
return 'actions' in js
else:
return True
@logger.catch
def story_sort(base_path, desc=False):
files = {}
for file in os.scandir(path=base_path):
if file.name.endswith(".json"):
filename = os.path.join(base_path, file.name).replace("\\", "/")
if os.path.getsize(filename) < 2*1024*1024: #2MB
with open(filename, "r") as f:
try:
js = json.load(f)
if 'story_name' in js and js['story_name'] in koboldai_vars.story_loads:
files[file.name] = datetime.datetime.strptime(koboldai_vars.story_loads[js['story_name']], "%m/%d/%Y, %H:%M:%S")
else:
files[file.name] = datetime.datetime.fromtimestamp(file.stat().st_mtime)
except:
pass
else:
files[file.name] = datetime.datetime.fromtimestamp(file.stat().st_mtime)
return [key[0] for key in sorted(files.items(), key=lambda kv: (kv[1], kv[0]), reverse=desc)]
#==================================================================#
# Event triggered on load story
#==================================================================#
@socketio.on('load_story')
@logger.catch
def UI_2_load_story(file):
start_time = time.time()
logger.debug("got a call or loading a story: {}".format(file))
if koboldai_vars.debug:
print("loading {}".format(file))
loadRequest(file)
logger.debug("Load Story took {}s".format(time.time()-start_time))
#==================================================================#
# Event triggered on new story
#==================================================================#
@socketio.on('new_story')
@logger.catch
def UI_2_new_story(data):
logger.info("Starting new story")
koboldai_vars.create_story("")
#==================================================================#
# Event triggered when user moves world info
#==================================================================#
@socketio.on('move_wi')
@logger.catch
def UI_2_move_wi(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')
@logger.catch
def UI_2_wi_set_folder(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')
@logger.catch
def UI_2_Rename_World_Info_Folder(data):
if koboldai_vars.debug:
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')
@logger.catch
def UI_2_edit_world_info(data):
if koboldai_vars.debug:
print("edit_world_info")
print(data)
if data['uid'] < 0:
koboldai_vars.worldinfo_v2.add_item(data['title'], data['key'],
data['keysecondary'], data['folder'],
data['constant'], data['manual_text'],
data['comment'], wpp=data['wpp'], use_wpp=data['use_wpp'])
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['manual_text'],
data['comment'], wpp=data['wpp'], use_wpp=data['use_wpp'])
#==================================================================#
# Event triggered when user creates world info folder
#==================================================================#
@socketio.on('create_world_info_folder')
@logger.catch
def UI_2_create_world_info_folder(data):
koboldai_vars.worldinfo_v2.add_folder("New Folder")
#==================================================================#
# Event triggered when user deletes world info item
#==================================================================#
@socketio.on('delete_world_info')
@logger.catch
def UI_2_delete_world_info(uid):
koboldai_vars.worldinfo_v2.delete(int(uid))
#==================================================================#
# Event triggered when user deletes world info folder
#==================================================================#
@socketio.on('delete_wi_folder')
@logger.catch
def UI_2_delete_wi_folder(folder):
koboldai_vars.worldinfo_v2.delete_folder(folder)
#==================================================================#
# Event triggered when user exports world info folder
#==================================================================#
@app.route('/export_world_info_folder')
@logger.catch
def UI_2_export_world_info_folder():
if 'folder' in request.args:
data = koboldai_vars.worldinfo_v2.to_json(folder=request.args['folder'])
folder = request.args['folder']
else:
data = koboldai_vars.worldinfo_v2.to_json()
folder = koboldai_vars.story_name
return Response(
json.dumps(data, indent="\t"),
mimetype="application/json",
headers={"Content-disposition":
"attachment; filename={}_world_info.json".format(folder)}
)
#==================================================================#
# Event triggered when user exports world info folder
#==================================================================#
@socketio.on('upload_world_info_folder')
@logger.catch
def UI_2_upload_world_info_folder(data):
json_data = json.loads(data['data'])
koboldai_vars.worldinfo_v2.load_json(json_data, folder=data['folder'])
logger.debug("Calcing AI Text from WI Upload")
koboldai_vars.calc_ai_text()
@socketio.on('import_world_info')
@logger.catch
def UI_2_import_world_info(data):
wi_data = data["data"]
uids = {}
for folder_name, children in wi_data["folders"].items():
koboldai_vars.worldinfo_v2.add_folder(folder_name)
for child in children:
# Child is index
if child not in uids:
entry_data = wi_data["entries"][str(child)]
uids[child] = koboldai_vars.worldinfo_v2.add_item(
title=entry_data["title"],
key=entry_data["key"],
keysecondary=entry_data["keysecondary"],
folder=folder_name,
constant=entry_data["constant"],
manual_text=entry_data["manual_text"],
comment=entry_data["comment"],
use_wpp=entry_data["use_wpp"],
wpp=entry_data["wpp"],
)
koboldai_vars.worldinfo_v2.add_item_to_folder(uids[child], folder_name)
@socketio.on("search_wi")
@logger.catch
def UI_2_search_wi(data):
query = data["query"].lower()
full_data = koboldai_vars.worldinfo_v2.to_json()
results = {"title": [], "key": [], "keysecondary": [], "manual_text": []}
for entry in full_data["entries"].values():
# Order matters for what's more important.
if query in entry["title"].lower():
results["title"].append(entry)
elif any([query in k.lower() for k in entry["key"]]):
results["key"].append(entry)
elif any([query in k.lower() for k in entry["keysecondary"]]):
results["keysecondary"].append(entry)
elif query in entry["content"].lower():
results["manual_text"].append(entry)
elif query in entry["manual_text"].lower():
results["comment"].append(entry)
emit("wi_results", results, broadcast=True, room="UI_2")
@socketio.on("update_wi_attribute")
@logger.catch
def UI_2_update_wi_attribute(data):
uid, key, value = data["uid"], data["key"], data["value"]
koboldai_vars.worldinfo_v2.world_info[uid][key] = value
socketio.emit("world_info_entry", koboldai_vars.worldinfo_v2.world_info[uid], broadcast=True, room="UI_2")
@socketio.on("update_wi_keys")
@logger.catch
def UI_2_update_wi_keys(data):
uid, key, is_secondary, operation = data["uid"], data["key"], data["is_secondary"], data["operation"]
keykey = "key" if not is_secondary else "keysecondary"
key_exists = key in koboldai_vars.worldinfo_v2.world_info[uid][keykey]
if operation == "add":
if not key_exists:
koboldai_vars.worldinfo_v2.world_info[uid][keykey].append(key)
elif operation == "remove":
if key_exists:
koboldai_vars.worldinfo_v2.world_info[uid][keykey].remove(key)
if keykey == "keysecondary":
koboldai_vars.worldinfo_v2.world_info[uid]["selective"] = len(koboldai_vars.worldinfo_v2.world_info[uid]["keysecondary"]) > 0
# Send to UI
socketio.emit("world_info_entry", koboldai_vars.worldinfo_v2.world_info[uid], broadcast=True, room="UI_2")
@socketio.on("scratchpad_prompt")
@logger.catch
def UI_2_scratchpad_prompt(data):
print(data)
out_text = raw_generate(
data,
max_new=80,
).decoded
print("data", data, "out", out_text)
socketio.emit("scratchpad_response", out_text, broadcast=True, room="UI_2")
#==================================================================#
# Event triggered when user edits phrase biases
#==================================================================#
@socketio.on('phrase_bias_update')
@logger.catch
def UI_2_phrase_bias_update(biases):
koboldai_vars.biases = biases
#==================================================================#
# Event triggered to rely a message
#==================================================================#
@logger.catch
def socket_io_relay(queue, socketio):
while True:
if not queue.empty():
while not queue.empty():
data = queue.get()
socketio.emit(data[0], data[1], **data[2])
#socketio.emit(data[0], data[1], broadcast=True, room="UI_2")
time.sleep(0.2)
#==================================================================#
# Event triggered when Softprompt load menu is clicked
#==================================================================#
@socketio.on('load_softprompt_list')
@logger.catch
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'])
@logger.catch
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
@logger.catch
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 loaded
#==================================================================#
@socketio.on('load_softprompt')
@logger.catch
def UI_2_load_softprompt(data):
if koboldai_vars.debug:
print("Load softprompt: {}".format(data))
spRequest(data)
#==================================================================#
# Event triggered when load userscripts is clicked
#==================================================================#
@socketio.on('load_userscripts_list')
@logger.catch
def UI_2_load_userscripts_list(data):
file_popup("Select Userscripts to Load", "./userscripts", "load_userscripts", upload=True, jailed=True, folder_only=False, renameable=True, editable=True,
deleteable=True, show_breadcrumbs=False, item_check=valid_userscripts_to_load,
valid_only=True, hide_extention=True, extra_parameter_function=get_userscripts_desc,
column_names=['Module Name', 'Description'],
show_filename=True, show_folders=False,
column_widths=['200px', '150px', 'auto'])
@logger.catch
def valid_userscripts_to_load(file):
if koboldai_vars.debug:
print("{} is valid: {}".format(file, file.endswith(".lua") and os.path.basename(file) not in koboldai_vars.userscripts))
return file.endswith(".lua") and os.path.basename(file) not in koboldai_vars.userscripts
@logger.catch
def valid_userscripts_to_unload(file):
return file.endswith(".lua") and os.path.basename(file) in koboldai_vars.userscripts
@logger.catch
def get_userscripts_desc(item_full_path, item, valid_selection):
if not valid_selection:
return [None, None]
ob = ["", ""]
description = []
multiline = False
with open(item_full_path) as f:
ob[0] = f.readline().strip().replace("\033", "")
if ob[0][:2] != "--":
ob[0] = file
else:
ob[0] = ob[0][2:]
if ob[0][:2] == "[[":
ob[0] = ob[0][2:]
multiline = True
ob[0] = ob[0].lstrip("-").strip()
for line in f:
line = line.strip().replace("\033", "")
if multiline:
index = line.find("]]")
if index > -1:
description.append(line[:index])
if index != len(line) - 2:
break
multiline = False
else:
description.append(line)
else:
if line[:2] != "--":
break
line = line[2:]
if line[:2] == "[[":
multiline = True
line = line[2:]
description.append(line.strip())
ob[1] = "\n".join(description)
if len(ob[1]) > 250:
ob[1] = ob[1][:247] + "..."
return ob
#==================================================================#
# Event triggered when userscript's are loaded
#==================================================================#
@socketio.on('load_userscripts')
@logger.catch
def UI_2_load_userscripts(data):
if koboldai_vars.debug:
print("Loading Userscripts: {}".format(os.path.basename(data)))
koboldai_vars.userscripts = [x for x in koboldai_vars.userscripts if x != os.path.basename(data)]+[os.path.basename(data)]
load_lua_scripts()
#==================================================================#
# Event triggered when userscript's are unloaded
#==================================================================#
@socketio.on('unload_userscripts')
@logger.catch
def UI_2_unload_userscripts(data):
if koboldai_vars.debug:
print("Unloading Userscript: {}".format(data))
koboldai_vars.userscripts = [x for x in koboldai_vars.userscripts if x != data]
load_lua_scripts()
#==================================================================#
# Event triggered when aidg.club loaded
#==================================================================#
@socketio.on('load_aidg_club')
@logger.catch
def UI_2_load_aidg_club(data):
if koboldai_vars.debug:
print("Load aidg.club: {}".format(data))
import_buffer.from_club(data)
# importAidgRequest(data)
#==================================================================#
# Event triggered when Theme Changed
#==================================================================#
@socketio.on('theme_change')
@logger.catch
def UI_2_theme_change(data):
with open("themes/{}.css".format(data['name']), "w") as f:
f.write(":root {\n")
for key, value in data['theme'].items():
f.write("\t{}: {};\n".format(key, value.replace(";", "").replace("--", "-")))
f.write("}")
f.write("--------Special Rules from Original Theme---------\n")
for rule in data['special_rules']:
f.write(rule)
f.write("\n")
if koboldai_vars.debug:
print("Theme Saved")
#==================================================================#
# Refresh SP List
#==================================================================#
@socketio.on('sp_list_refresh')
@logger.catch
def UI_2_sp_list_refresh(data):
koboldai_vars.splist = [[f, get_softprompt_desc(os.path.join("./softprompts", f),None,True)] for f in os.listdir("./softprompts") if os.path.isfile(os.path.join("./softprompts", f)) and valid_softprompt(os.path.join("./softprompts", f))]
#==================================================================#
# Refresh Theme List
#==================================================================#
@socketio.on('theme_list_refresh')
@logger.catch
def UI_2_theme_list_refresh(data):
koboldai_vars.theme_list = [".".join(f.split(".")[:-1]) for f in os.listdir("./themes") if os.path.isfile(os.path.join("./themes", f))]
#==================================================================#
# Save Tweaks
#==================================================================#
@socketio.on('save_cookies')
@logger.catch
def UI_2_save_cookies(data):
for key in data:
#Note this won't sync to the client automatically as we're modifying a variable rather than setting it
koboldai_vars.cookies[key] = data[key]
with open("./settings/cookies.settings", "w") as f:
json.dump(koboldai_vars.cookies, f)
@app.route("/generate_raw", methods=["GET"])
def UI_2_generate_raw():
prompt = request.args.get("prompt")
if not prompt:
return Response(json.dumps({"error": "No prompt"}), status=400)
if not model:
return Response(json.dumps({"error": "No model"}), status=500)
try:
out = raw_generate(prompt, max_new=80)
except NotImplementedError as e:
return Response(json.dumps({"error": str(e)}), status=500)
return out
#==================================================================#
# Load Tweaks
#==================================================================#
@logger.catch
def UI_2_load_cookies():
if koboldai_vars.on_colab:
if os.path.exists("./settings/cookies.settings"):
with open("./settings/cookies.settings", "r") as f:
data = json.load(f)
socketio.emit('load_cookies', data, room="UI_2")
#==================================================================#
# Save New Preset
#==================================================================#
@socketio.on('save_new_preset')
@logger.catch
def UI_2_save_new_preset(data):
preset = {}
#Data to get from current settings
for item in ["genamt", "rep_pen", "rep_pen_range", "rep_pen_slope", "sampler_order", "temp", "tfs", "top_a", "top_k", "top_p", "typical"]:
preset[item] = getattr(koboldai_vars, item)
#Data to get from UI
for item in ['preset', 'description']:
preset[item] = data[item]
preset['Model Size'] = get_model_size(koboldai_vars.model)
preset['Model Category'] = 'Custom'
preset['Model Type'] = koboldai_vars.model
preset['uid'] = 0
preset = [preset]
with open("./presets/{}.presets".format(data['preset']), "w") as f:
json.dump(preset, f, indent="\t")
@logger.catch
def get_model_size(model_name):
if "30B" in model_name:
return "30B"
elif "20B" in model_name:
return "20B"
elif "13B" in model_name:
return "13B"
elif "6B" in model_name.replace("6.7B", "6B"):
return "6B"
elif "2.7B" in model_name:
return "2.7B"
elif "1.3B" in model_name:
return "1.3B"
#==================================================================#
# Save New Preset
#==================================================================#
@socketio.on('save_revision')
@logger.catch
def UI_2_save_revision(data):
koboldai_vars.save_revision()
#==================================================================#
# Generate Image
#==================================================================#
@socketio.on("generate_image")
@logger.catch
def UI_2_generate_image(data):
koboldai_vars.generating_image = True
eventlet.sleep(0)
art_guide = 'fantasy illustration, artstation, by jason felix by steve argyle by tyler jacobson by peter mohrbacher, cinematic lighting'
#get latest action
if len(koboldai_vars.actions) > 0:
action = koboldai_vars.actions[-1]
else:
action = koboldai_vars.prompt
#Get matching world info entries
keys = []
for wi in koboldai_vars.worldinfo_v2:
for key in wi['key']:
if key in action:
#Check to make sure secondary keys are present if needed
if len(wi['keysecondary']) > 0:
for keysecondary in wi['keysecondary']:
if keysecondary in action:
keys.append(key)
break
break
else:
keys.append(key)
break
#If we have > 4 keys, use those otherwise use sumarization
if len(keys) < 4:
start_time = time.time()
if os.path.exists("models/{}".format(args.summarizer_model.replace('/', '_'))):
koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained("models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
else:
koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained(args.summarizer_model, cache_dir="cache")
#text to summarize (get 1000 tokens worth of text):
text = []
text_length = 0
for item in reversed(koboldai_vars.actions.to_sentences()):
if len(koboldai_vars.summary_tokenizer.encode(item[0])) + text_length <= 1000:
text.append(item[0])
text_length += len(koboldai_vars.summary_tokenizer.encode(item[0]))
else:
break
text = "".join(text)
logger.debug("Text to summarizer: {}".format(text))
max_length = args.max_summary_length - len(koboldai_vars.summary_tokenizer.encode(art_guide))
keys = [summarize(text, max_length=max_length)]
logger.debug("Text from summarizer: {}".format(keys[0]))
#If we don't have a GPU, use horde if we're allowed to
start_time = time.time()
if ((not koboldai_vars.hascuda or not os.path.exists("models/stable-diffusion-v1-4")) and koboldai_vars.img_gen_priority != 0) or koboldai_vars.img_gen_priority == 3:
b64_data = text2img_horde(", ".join(keys), art_guide = art_guide)
else:
import psutil
#We aren't being forced to use horde, so now let's figure out if we should use local
if torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_reserved(0) >= 6000000000:
#He have enough vram, just do it locally
b64_data = text2img_local(", ".join(keys), art_guide = art_guide)
elif torch.cuda.get_device_properties(0).total_memory > 6000000000 and koboldai_vars.img_gen_priority <= 1:
#We could do it locally by swapping the model out
print("Could do local or online")
b64_data = text2img_horde(", ".join(keys), art_guide = art_guide)
elif koboldai_vars.img_gen_priority != 0:
b64_data = text2img_horde(", ".join(keys), art_guide = art_guide)
logger.debug("Time to Generate Image {}".format(time.time()-start_time))
koboldai_vars.picture = b64_data
koboldai_vars.picture_prompt = ", ".join(keys)
koboldai_vars.generating_image = False
@logger.catch
def text2img_local(prompt, art_guide="", filename="new.png"):
start_time = time.time()
logger.debug("Generating Image")
koboldai_vars.aibusy = True
koboldai_vars.generating_image = True
from diffusers import StableDiffusionPipeline
import base64
from io import BytesIO
if koboldai_vars.image_pipeline is None:
pipe = tpool.execute(StableDiffusionPipeline.from_pretrained, "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, cache="models/stable-diffusion-v1-4").to("cuda")
else:
pipe = koboldai_vars.image_pipeline.to("cuda")
logger.debug("time to load: {}".format(time.time() - start_time))
start_time = time.time()
def get_image(pipe, prompt, num_inference_steps):
from torch import autocast
with autocast("cuda"):
return pipe(prompt, num_inference_steps=num_inference_steps)["sample"][0]
image = tpool.execute(get_image, pipe, prompt, num_inference_steps=35)
buffered = BytesIO()
image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue()).decode('ascii')
logger.debug("time to generate: {}".format(time.time() - start_time))
start_time = time.time()
if koboldai_vars.keep_img_gen_in_memory:
pipe.to("cpu")
if koboldai_vars.image_pipeline is None:
koboldai_vars.image_pipeline = pipe
else:
koboldai_vars.image_pipeline = None
del pipe
torch.cuda.empty_cache()
koboldai_vars.generating_image = False
koboldai_vars.aibusy = False
logger.debug("time to unload: {}".format(time.time() - start_time))
return img_str
@logger.catch
def text2img_horde(prompt,
art_guide = 'fantasy illustration, artstation, by jason felix by steve argyle by tyler jacobson by peter mohrbacher, cinematic lighting',
filename = "story_art.png"):
logger.debug("Generating Image using Horde")
koboldai_vars.generating_image = True
final_imgen_params = {
"n": 1,
"width": 512,
"height": 512,
"steps": 50,
}
final_submit_dict = {
"prompt": "{}, {}".format(prompt, art_guide),
"api_key": koboldai_vars.sh_apikey if koboldai_vars.sh_apikey != '' else "0000000000",
"params": final_imgen_params,
}
logger.debug(final_submit_dict)
submit_req = requests.post('https://stablehorde.net/api/v1/generate/sync', json = final_submit_dict)
if submit_req.ok:
results = submit_req.json()
for iter in range(len(results)):
b64img = results[iter]["img"]
base64_bytes = b64img.encode('utf-8')
img_bytes = base64.b64decode(base64_bytes)
img = Image.open(BytesIO(img_bytes))
if len(results) > 1:
final_filename = f"{iter}_{filename}"
else:
final_filename = filename
img.save(final_filename)
logger.debug("Saved Image")
koboldai_vars.generating_image = False
return(b64img)
else:
koboldai_vars.generating_image = False
logger.error(submit_req.text)
#@logger.catch
def get_items_locations_from_text(text):
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
nlp = transformers.pipeline("ner", model=model, tokenizer=tokenizer)
# input example sentence
ner_results = nlp(text)
orgs = []
last_org_position = -2
loc = []
last_loc_position = -2
per = []
last_per_position = -2
for i, result in enumerate(ner_results):
if result['entity'] in ('B-ORG', 'I-ORG'):
if result['start']-1 <= last_org_position:
if result['start'] != last_org_position:
orgs[-1] = "{} ".format(orgs[-1])
orgs[-1] = "{}{}".format(orgs[-1], result['word'].replace("##", ""))
else:
orgs.append(result['word'])
last_org_position = result['end']
elif result['entity'] in ('B-LOC', 'I-LOC'):
if result['start']-1 <= last_loc_position:
if result['start'] != last_loc_position:
loc[-1] = "{} ".format(loc[-1])
loc[-1] = "{}{}".format(loc[-1], result['word'].replace("##", ""))
else:
loc.append(result['word'])
last_loc_position = result['end']
elif result['entity'] in ('B-PER', 'I-PER'):
if result['start']-1 <= last_per_position:
if result['start'] != last_per_position:
per[-1] = "{} ".format(per[-1])
per[-1] = "{}{}".format(per[-1], result['word'].replace("##", ""))
else:
per.append(result['word'])
last_per_position = result['end']
print("Orgs: {}".format(orgs))
print("Locations: {}".format(loc))
print("People: {}".format(per))
#==================================================================#
# summarizer
#==================================================================#
def summarize(text, max_length=100, min_length=30):
from transformers import pipeline as summary_pipeline
start_time = time.time()
if koboldai_vars.summarizer is None:
if os.path.exists("models/{}".format(args.summarizer_model.replace('/', '_'))):
koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained("models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
koboldai_vars.summarizer = AutoModelForSeq2SeqLM.from_pretrained("models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
else:
koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained(args.summarizer_model, cache_dir="cache")
koboldai_vars.summarizer = AutoModelForSeq2SeqLM.from_pretrained(args.summarizer_model, cache_dir="cache")
koboldai_vars.summary_tokenizer.save_pretrained("models/{}".format(args.summarizer_model.replace('/', '_')), max_shard_size="500MiB")
koboldai_vars.summarizer.save_pretrained("models/{}".format(args.summarizer_model.replace('/', '_')), max_shard_size="500MiB")
#Try GPU accel
if koboldai_vars.hascuda and torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_reserved(0) >= 1645778560:
koboldai_vars.summarizer.to(0)
device=0
else:
device=-1
summarizer = tpool.execute(summary_pipeline, task="summarization", model=koboldai_vars.summarizer, tokenizer=koboldai_vars.summary_tokenizer, device=device)
logger.debug("Time to load summarizer: {}".format(time.time()-start_time))
#Actual sumarization
start_time = time.time()
global old_transfomers_functions
temp = transformers.generation_utils.GenerationMixin._get_stopping_criteria
transformers.generation_utils.GenerationMixin._get_stopping_criteria = old_transfomers_functions['transformers.generation_utils.GenerationMixin._get_stopping_criteria']
#make sure text is less than 1024 tokens, otherwise we'll crash
if len(koboldai_vars.summary_tokenizer.encode(text)) > 1000:
text = koboldai_vars.summary_tokenizer.decode(koboldai_vars.summary_tokenizer.encode(text)[:1000])
output = tpool.execute(summarizer, text, max_length=max_length, min_length=min_length, do_sample=False)[0]['summary_text']
transformers.generation_utils.GenerationMixin._get_stopping_criteria = temp
logger.debug("Time to summarize: {}".format(time.time()-start_time))
#move model back to CPU to save precious vram
torch.cuda.empty_cache()
logger.debug("VRAM used by summarization: {}".format(torch.cuda.memory_reserved(0)))
koboldai_vars.summarizer.to("cpu")
torch.cuda.empty_cache()
logger.debug("Original Text: {}".format(text))
logger.debug("Summarized Text: {}".format(output))
return output
#==================================================================#
# Auto-memory function
#==================================================================#
@socketio.on("refresh_auto_memory")
@logger.catch
def UI_2_refresh_auto_memory(data):
koboldai_vars.auto_memory = "Generating..."
if koboldai_vars.summary_tokenizer is None:
koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained("models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
#first, let's get all of our game text and split it into sentences
sentences = [x[0] for x in koboldai_vars.actions.to_sentences()]
sentences_lengths = [len(koboldai_vars.summary_tokenizer.encode(x)) for x in sentences]
while len(koboldai_vars.summary_tokenizer.encode("".join(sentences))) > 1000:
#Now let's split them into 1000 token chunks
summary_chunks = [""]
summary_chunk_lengths = [0]
for i in range(len(sentences)):
if summary_chunk_lengths[-1] + sentences_lengths[i] <= 1000:
summary_chunks[-1] += sentences[i]
summary_chunk_lengths[-1] += sentences_lengths[i]
else:
summary_chunks.append(sentences[i])
summary_chunk_lengths.append(sentences_lengths[i])
new_sentences = []
i=0
for summary_chunk in summary_chunks:
logger.debug("summarizing chunk {}".format(i))
new_sentences.extend(re.split("(?<=[.!?])\s+", summarize(summary_chunk)))
i+=1
logger.debug("Summarized to {} sentencees from {}".format(len(new_sentences), len(sentences)))
sentences = new_sentences
koboldai_vars.auto_memory = "\n".join(sentences)
logger.debug("OK, doing final summarization")
output = summarize(" ".join(sentences))
koboldai_vars.auto_memory += "\n\n Final Result:\n" + output
#==================================================================#
# Get next 100 actions for infinate scroll
#==================================================================#
@socketio.on("get_next_100_actions")
@logger.catch
def UI_2_get_next_100_actions(data):
logger.debug("Sending an additional 100 actions, starting at action {}".format(data-1))
sent = 0
data_to_send = []
for i in reversed(list(koboldai_vars.actions.actions)):
if i < data:
if sent >= 100:
break
data_to_send.append({"id": i, "action": koboldai_vars.actions.actions[i]})
sent += 1
logger.debug("data_to_send length: {}".format(len(data_to_send)))
emit("var_changed", {"classname": "story", "name": "actions", "old_value": None, "value":data_to_send})
#==================================================================#
# Get next 100 actions for infinate scroll
#==================================================================#
@socketio.on("update_tokens")
@logger.catch
def UI_2_update_tokens(data):
ignore = koboldai_vars.calc_ai_text(submitted_text=data)
#==================================================================#
# Test
#==================================================================#
@socketio.on("get_log")
def UI_2_get_log(data):
emit("log_message", web_log_history)
#==================================================================#
# Test
#==================================================================#
@app.route("/vars")
@logger.catch
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
@socketio.on("trigger_error")
@logger.catch
def trigger_error(data):
temp = this_var_doesnt_exist
#==================================================================#
class EmptySchema(KoboldSchema):
pass
class BasicTextResultInnerSchema(KoboldSchema):
text: str = fields.String(required=True)
class BasicTextResultSchema(KoboldSchema):
result: BasicTextResultInnerSchema = fields.Nested(BasicTextResultInnerSchema)
class BasicResultInnerSchema(KoboldSchema):
result: str = fields.String(required=True)
class BasicResultSchema(KoboldSchema):
result: BasicResultInnerSchema = fields.Nested(BasicResultInnerSchema, required=True)
class BasicResultsSchema(KoboldSchema):
results: BasicResultInnerSchema = fields.List(fields.Nested(BasicResultInnerSchema), required=True)
class BasicStringSchema(KoboldSchema):
value: str = fields.String(required=True)
class BasicBooleanSchema(KoboldSchema):
value: bool = fields.Boolean(required=True)
class BasicUIDSchema(KoboldSchema):
uid: str = fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info entry/folder."})
class BasicErrorSchema(KoboldSchema):
msg: str = fields.String(required=True)
type: str = fields.String(required=True)
class StoryEmptyErrorSchema(KoboldSchema):
detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True)
class StoryTooShortErrorSchema(KoboldSchema):
detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True)
class OutOfMemoryErrorSchema(KoboldSchema):
detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True)
class NotFoundErrorSchema(KoboldSchema):
detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True)
api_out_of_memory_response = """507:
description: Out of memory
content:
application/json:
schema: OutOfMemoryErrorSchema
examples:
gpu.cuda:
value:
detail:
msg: "KoboldAI ran out of memory: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 4.00 GiB total capacity; 2.97 GiB already allocated; 0 bytes free; 2.99 GiB reserved in total by PyTorch)"
type: out_of_memory.gpu.cuda
gpu.hip:
value:
detail:
msg: "KoboldAI ran out of memory: HIP out of memory. Tried to allocate 20.00 MiB (GPU 0; 4.00 GiB total capacity; 2.97 GiB already allocated; 0 bytes free; 2.99 GiB reserved in total by PyTorch)"
type: out_of_memory.gpu.hip
tpu.hbm:
value:
detail:
msg: "KoboldAI ran out of memory: Compilation failed: Compilation failure: Ran out of memory in memory space hbm. Used 8.83G of 8.00G hbm. Exceeded hbm capacity by 848.88M."
type: out_of_memory.tpu.hbm
cpu.default_cpu_allocator:
value:
detail:
msg: "KoboldAI ran out of memory: DefaultCPUAllocator: not enough memory: you tried to allocate 209715200 bytes."
type: out_of_memory.cpu.default_cpu_allocator
unknown.unknown:
value:
detail:
msg: "KoboldAI ran out of memory."
type: out_of_memory.unknown.unknown"""
class ValidationErrorSchema(KoboldSchema):
detail: Dict[str, List[str]] = fields.Dict(keys=fields.String(), values=fields.List(fields.String(), validate=validate.Length(min=1)), required=True)
api_validation_error_response = """422:
description: Validation error
content:
application/json:
schema: ValidationErrorSchema"""
class ServerBusyErrorSchema(KoboldSchema):
detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True)
api_server_busy_response = """503:
description: Server is busy
content:
application/json:
schema: ServerBusyErrorSchema
example:
detail:
msg: Server is busy; please try again later.
type: service_unavailable"""
class NotImplementedErrorSchema(KoboldSchema):
detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True)
api_not_implemented_response = """501:
description: Not implemented
content:
application/json:
schema: NotImplementedErrorSchema
example:
detail:
msg: API generation is not supported in read-only mode; please load a model and then try again.
type: not_implemented"""
class SamplerSettingsSchema(KoboldSchema):
rep_pen: Optional[float] = fields.Float(validate=validate.Range(min=1), metadata={"description": "Base repetition penalty value."})
rep_pen_range: Optional[int] = fields.Integer(validate=validate.Range(min=0), metadata={"description": "Repetition penalty range."})
rep_pen_slope: Optional[float] = fields.Float(validate=validate.Range(min=0), metadata={"description": "Repetition penalty slope."})
top_k: Optional[int] = fields.Integer(validate=validate.Range(min=0), metadata={"description": "Top-k sampling value."})
top_a: Optional[float] = fields.Float(validate=validate.Range(min=0), metadata={"description": "Top-a sampling value."})
top_p: Optional[float] = fields.Float(validate=validate.Range(min=0, max=1), metadata={"description": "Top-p sampling value."})
tfs: Optional[float] = fields.Float(validate=validate.Range(min=0, max=1), metadata={"description": "Tail free sampling value."})
typical: Optional[float] = fields.Float(validate=validate.Range(min=0, max=1), metadata={"description": "Typical sampling value."})
temperature: Optional[float] = fields.Float(validate=validate.Range(min=0, min_inclusive=False), metadata={"description": "Temperature value."})
def soft_prompt_validator(soft_prompt: str):
if len(soft_prompt.strip()) == 0:
return
if not koboldai_vars.allowsp:
raise ValidationError("Cannot use soft prompts with current backend.")
if any(q in soft_prompt for q in ("/", "\\")):
return
z, _, _, _, _ = fileops.checksp(soft_prompt.strip(), koboldai_vars.modeldim)
if isinstance(z, int):
raise ValidationError("Must be a valid soft prompt name.")
z.close()
return True
def story_load_validator(name: str):
if any(q in name for q in ("/", "\\")):
return
if len(name.strip()) == 0 or not os.path.isfile(fileops.storypath(name)):
raise ValidationError("Must be a valid story name.")
return True
def permutation_validator(lst: list):
if any(not isinstance(e, int) for e in lst):
return
if min(lst) != 0 or max(lst) != len(lst) - 1 or len(set(lst)) != len(lst):
raise ValidationError("Must be a permutation of the first N non-negative integers, where N is the length of this array")
return True
class GenerationInputSchema(SamplerSettingsSchema):
prompt: str = fields.String(required=True, metadata={"description": "This is the submission."})
use_memory: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the memory from the KoboldAI GUI when generating text."})
use_story: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the story from the KoboldAI GUI when generating text."})
use_authors_note: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the author's note from the KoboldAI GUI when generating text. This has no effect unless `use_story` is also enabled."})
use_world_info: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the world info from the KoboldAI GUI when generating text."})
use_userscripts: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the userscripts from the KoboldAI GUI when generating text."})
soft_prompt: Optional[str] = fields.String(metadata={"description": "Soft prompt to use when generating. If set to the empty string or any other string containing no non-whitespace characters, uses no soft prompt."}, validate=[soft_prompt_validator, validate.Regexp(r"^[^/\\]*$")])
max_length: int = fields.Integer(validate=validate.Range(min=1, max=512), metadata={"description": "Number of tokens to generate."})
max_context_length: int = fields.Integer(validate=validate.Range(min=512, max=2048), metadata={"description": "Maximum number of tokens to send to the model."})
n: int = fields.Integer(validate=validate.Range(min=1, max=5), metadata={"description": "Number of outputs to generate."})
disable_output_formatting: bool = fields.Boolean(load_default=True, metadata={"description": "When enabled, all output formatting options default to `false` instead of the value in the KoboldAI GUI."})
frmttriminc: Optional[bool] = fields.Boolean(metadata={"description": "Output formatting option. When enabled, removes some characters from the end of the output such that the output doesn't end in the middle of a sentence. If the output is less than one sentence long, does nothing.\n\nIf `disable_output_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."})
frmtrmblln: Optional[bool] = fields.Boolean(metadata={"description": "Output formatting option. When enabled, replaces all occurrences of two or more consecutive newlines in the output with one newline.\n\nIf `disable_output_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."})
frmtrmspch: Optional[bool] = fields.Boolean(metadata={"description": "Output formatting option. When enabled, removes `#/@%{}+=~|\^<>` from the output.\n\nIf `disable_output_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."})
singleline: Optional[bool] = fields.Boolean(metadata={"description": "Output formatting option. When enabled, removes everything after the first line of the output, including the newline.\n\nIf `disable_output_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."})
disable_input_formatting: bool = fields.Boolean(load_default=True, metadata={"description": "When enabled, all input formatting options default to `false` instead of the value in the KoboldAI GUI"})
frmtadsnsp: Optional[bool] = fields.Boolean(metadata={"description": "Input formatting option. When enabled, adds a leading space to your input if there is no trailing whitespace at the end of the previous action.\n\nIf `disable_input_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."})
quiet: Optional[bool] = fields.Boolean(metadata={"description": "When enabled, Generated output will not be displayed in the console."})
sampler_order: Optional[List[int]] = fields.List(fields.Integer(), validate=[validate.Length(min=6), permutation_validator], metadata={"description": "Sampler order to be used. If N is the length of this array, then N must be greater than or equal to 6 and the array must be a permutation of the first N non-negative integers."})
sampler_seed: Optional[int] = fields.Integer(validate=validate.Range(min=0, max=2**64 - 1), metadata={"description": "RNG seed to use for sampling. If not specified, the global RNG will be used."})
sampler_full_determinism: Optional[bool] = fields.Boolean(metadata={"description": "If enabled, the generated text will always be the same as long as you use the same RNG seed, input and settings. If disabled, only the *sequence* of generated texts that you get when repeatedly generating text will be the same given the same RNG seed, input and settings."})
class GenerationResultSchema(KoboldSchema):
text: str = fields.String(required=True, metadata={"description": "Generated output as plain text."})
class GenerationOutputSchema(KoboldSchema):
results: List[GenerationResultSchema] = fields.List(fields.Nested(GenerationResultSchema), required=True, metadata={"description": "Array of generated outputs."})
class StoryNumsChunkSchema(KoboldSchema):
num: int = fields.Integer(required=True, metadata={"description": "Guaranteed to not equal the `num` of any other active story chunk. Equals 0 iff this is the first action of the story (the prompt)."})
class StoryChunkSchema(StoryNumsChunkSchema, KoboldSchema):
text: str = fields.String(required=True, metadata={"description": "The text inside this story chunk."})
class StorySchema(KoboldSchema):
results: List[StoryChunkSchema] = fields.List(fields.Nested(StoryChunkSchema), required=True, metadata={"description": "Array of story actions. The array is sorted such that actions closer to the end of this array are closer to the end of the story."})
class BasicBooleanSchema(KoboldSchema):
result: bool = fields.Boolean(required=True)
class StoryNumsSchema(KoboldSchema):
results: List[int] = fields.List(fields.Integer(), required=True, metadata={"description": "Array of story action nums. The array is sorted such that actions closer to the end of this array are closer to the end of the story."})
class StoryChunkResultSchema(KoboldSchema):
result: StoryChunkSchema = fields.Nested(StoryChunkSchema, required=True)
class StoryChunkNumSchema(KoboldSchema):
value: int = fields.Integer(required=True)
class StoryChunkTextSchema(KoboldSchema):
value: str = fields.String(required=True)
class StoryChunkSetTextSchema(KoboldSchema):
value: str = fields.String(required=True, validate=validate.Regexp(r"^(.|\n)*\S$"))
class StoryLoadSchema(KoboldSchema):
name: str = fields.String(required=True, validate=[story_load_validator, validate.Regexp(r"^[^/\\]*$")])
class StorySaveSchema(KoboldSchema):
name: str = fields.String(required=True, validate=validate.Regexp(r"^(?=.*\S)(?!.*[/\\]).*$"))
class WorldInfoEntrySchema(KoboldSchema):
uid: int = fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info entry."})
content: str = fields.String(required=True, metadata={"description": "The \"What To Remember\" for this entry."})
key: str = fields.String(required=True, metadata={"description": "Comma-separated list of keys, or of primary keys if selective mode is enabled."})
keysecondary: str = fields.String(metadata={"description": "Comma-separated list of secondary keys if selective mode is enabled."})
selective: bool = fields.Boolean(required=True, metadata={"description": "Whether or not selective mode is enabled for this world info entry."})
constant: bool = fields.Boolean(required=True, metadata={"description": "Whether or not constant mode is enabled for this world info entry."})
comment: bool = fields.String(required=True, metadata={"description": "The comment/description/title for this world info entry."})
class WorldInfoEntryResultSchema(KoboldSchema):
result: WorldInfoEntrySchema = fields.Nested(WorldInfoEntrySchema, required=True)
class WorldInfoFolderBasicSchema(KoboldSchema):
uid: int = fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info folder."})
name: str = fields.String(required=True, metadata={"description": "Name of this world info folder."})
class WorldInfoFolderSchema(WorldInfoFolderBasicSchema):
entries: List[WorldInfoEntrySchema] = fields.List(fields.Nested(WorldInfoEntrySchema), required=True)
class WorldInfoFolderUIDsSchema(KoboldSchema):
uid: int = fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info folder."})
entries: List[int] = fields.List(fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info entry."}), required=True)
class WorldInfoEntriesSchema(KoboldSchema):
entries: List[WorldInfoEntrySchema] = fields.List(fields.Nested(WorldInfoEntrySchema), required=True)
class WorldInfoFoldersSchema(KoboldSchema):
folders: List[WorldInfoFolderBasicSchema] = fields.List(fields.Nested(WorldInfoFolderBasicSchema), required=True)
class WorldInfoSchema(WorldInfoEntriesSchema):
folders: List[WorldInfoFolderSchema] = fields.List(fields.Nested(WorldInfoFolderSchema), required=True)
class WorldInfoEntriesUIDsSchema(KoboldSchema):
entries: List[int] = fields.List(fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info entry."}), required=True)
class WorldInfoFoldersUIDsSchema(KoboldSchema):
folders: List[int] = fields.List(fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info folder."}), required=True)
class WorldInfoUIDsSchema(WorldInfoEntriesUIDsSchema):
folders: List[WorldInfoFolderSchema] = fields.List(fields.Nested(WorldInfoFolderUIDsSchema), required=True)
class ModelSelectionSchema(KoboldSchema):
model: str = fields.String(required=True, validate=validate.Regexp(r"^(?!\s*NeoCustom)(?!\s*GPT2Custom)(?!\s*TPUMeshTransformerGPTJ)(?!\s*TPUMeshTransformerGPTNeoX)(?!\s*GooseAI)(?!\s*OAI)(?!\s*InferKit)(?!\s*Colab)(?!\s*API).*$"), metadata={"description": 'Hugging Face model ID, the path to a model folder (relative to the "models" folder in the KoboldAI root folder) or "ReadOnly" for no model'})
def _generate_text(body: GenerationInputSchema):
if koboldai_vars.aibusy or koboldai_vars.genseqs:
abort(Response(json.dumps({"detail": {
"msg": "Server is busy; please try again later.",
"type": "service_unavailable",
}}), mimetype="application/json", status=503))
if vars.use_colab_tpu:
import tpu_mtj_backend
if hasattr(body, "sampler_seed"):
# If a seed was specified, we need to save the global RNG state so we
# can restore it later
old_seed = vars.seed
old_rng_state = tpu_mtj_backend.get_rng_state() if vars.use_colab_tpu else torch.get_rng_state()
vars.seed = body.sampler_seed
# We should try to use a previously saved RNG state with the same seed
if body.sampler_seed in vars.rng_states:
if vars.use_colab_tpu:
tpu_mtj_backend.set_rng_state(vars.rng_states[body.sampler_seed])
else:
torch.set_rng_state(vars.rng_states[body.sampler_seed])
else:
if vars.use_colab_tpu:
tpu_mtj_backend.set_rng_state(tpu_mtj_backend.new_rng_state(body.sampler_seed))
else:
torch.manual_seed(body.sampler_seed)
vars.rng_states[body.sampler_seed] = tpu_mtj_backend.get_rng_state() if vars.use_colab_tpu else torch.get_rng_state()
if hasattr(body, "sampler_order"):
if len(body.sampler_order) < 7:
body.sampler_order = [6] + body.sampler_order
# This maps each property of the setting to use when sending the generate idempotently
# To the object which typically contains it's value
# This allows to set the property only for the API generation, and then revert the setting
# To what it was before.
mapping = {
"disable_input_formatting": ("koboldai_vars", "disable_input_formatting", None),
"disable_output_formatting": ("koboldai_vars", "disable_output_formatting", None),
"rep_pen": ("koboldai_vars", "rep_pen", None),
"rep_pen_range": ("koboldai_vars", "rep_pen_range", None),
"rep_pen_slope": ("koboldai_vars", "rep_pen_slope", None),
"top_k": ("koboldai_vars", "top_k", None),
"top_a": ("koboldai_vars", "top_a", None),
"top_p": ("koboldai_vars", "top_p", None),
"tfs": ("koboldai_vars", "tfs", None),
"typical": ("koboldai_vars", "typical", None),
"temperature": ("koboldai_vars", "temp", None),
"frmtadsnsp": ("koboldai_vars", "frmtadsnsp", "input"),
"frmttriminc": ("koboldai_vars", "frmttriminc", "output"),
"frmtrmblln": ("koboldai_vars", "frmtrmblln", "output"),
"frmtrmspch": ("koboldai_vars", "frmtrmspch", "output"),
"singleline": ("koboldai_vars", "singleline", "output"),
"max_length": ("koboldai_vars", "genamt", None),
"max_context_length": ("koboldai_vars", "max_length", None),
"n": ("koboldai_vars", "numseqs", None),
"quiet": ("koboldai_vars", "quiet", None),
"sampler_order": ("vars", "sampler_order", None),
"sampler_full_determinism": ("vars", "full_determinism", None),
}
saved_settings = {}
set_aibusy(1)
disable_set_aibusy = koboldai_vars.disable_set_aibusy
koboldai_vars.disable_set_aibusy = True
_standalone = koboldai_vars.standalone
koboldai_vars.standalone = True
show_probs = koboldai_vars.show_probs
koboldai_vars.show_probs = False
output_streaming = koboldai_vars.output_streaming
koboldai_vars.output_streaming = False
for key, entry in mapping.items():
obj = {"koboldai_vars": koboldai_vars}[entry[0]]
if entry[2] == "input" and koboldai_vars.disable_input_formatting and not hasattr(body, key):
setattr(body, key, False)
if entry[2] == "output" and koboldai_vars.disable_output_formatting and not hasattr(body, key):
setattr(body, key, False)
if getattr(body, key, None) is not None:
if entry[1].startswith("@"):
saved_settings[key] = obj[entry[1][1:]]
obj[entry[1][1:]] = getattr(body, key)
else:
saved_settings[key] = getattr(obj, entry[1])
setattr(obj, entry[1], getattr(body, key))
try:
if koboldai_vars.allowsp and getattr(body, "soft_prompt", None) is not None:
if any(q in body.soft_prompt for q in ("/", "\\")):
raise RuntimeError
old_spfilename = koboldai_vars.spfilename
spRequest(body.soft_prompt.strip())
genout = apiactionsubmit(body.prompt, use_memory=body.use_memory, use_story=body.use_story, use_world_info=body.use_world_info, use_authors_note=body.use_authors_note)
output = {"results": [{"text": txt} for txt in genout]}
finally:
for key in saved_settings:
entry = mapping[key]
obj = {"koboldai_vars": koboldai_vars}[entry[0]]
if getattr(body, key, None) is not None:
if entry[1].startswith("@"):
if obj[entry[1][1:]] == getattr(body, key):
obj[entry[1][1:]] = saved_settings[key]
else:
if getattr(obj, entry[1]) == getattr(body, key):
setattr(obj, entry[1], saved_settings[key])
koboldai_vars.disable_set_aibusy = disable_set_aibusy
koboldai_vars.standalone = _standalone
koboldai_vars.show_probs = show_probs
koboldai_vars.output_streaming = output_streaming
if koboldai_vars.allowsp and getattr(body, "soft_prompt", None) is not None:
spRequest(old_spfilename)
if hasattr(body, "sampler_seed"):
vars.seed = old_seed
if vars.use_colab_tpu:
tpu_mtj_backend.set_rng_state(old_rng_state)
else:
torch.set_rng_state(old_rng_state)
set_aibusy(0)
return output
@api_v1.get("/info/version")
@api_schema_wrap
def get_version():
"""---
get:
summary: Current API version
tags:
- info
description: |-2
Returns the version of the API that you are currently using.
responses:
200:
description: Successful request
content:
application/json:
schema: BasicResultSchema
example:
result: 1.0.0
"""
return {"result": api_version}
@api_v1.get("/info/version/latest")
@api_schema_wrap
def get_version_latest():
"""---
get:
summary: Latest API version
tags:
- info
description: |-2
Returns the latest API version available.
responses:
200:
description: Successful request
content:
application/json:
schema: BasicResultSchema
example:
result: 1.0.0
"""
return {"result": api_versions[-1]}
@api_v1.get("/info/version/list")
@api_schema_wrap
def get_version_list():
"""---
get:
summary: List API versions
tags:
- info
description: |-2
Returns a list of available API versions sorted in ascending order.
responses:
200:
description: Successful request
content:
application/json:
schema: BasicResultsSchema
example:
results:
- 1.0.0
"""
return {"results": api_versions}
@api_v1.post("/generate")
@api_schema_wrap
def post_generate(body: GenerationInputSchema):
"""---
post:
summary: Generate text
tags:
- generate
description: |-2
Generates text given a submission, sampler settings, soft prompt and number of return sequences.
By default, the story, userscripts, memory, author's note and world info are disabled.
Unless otherwise specified, optional values default to the values in the KoboldAI GUI.
requestBody:
required: true
content:
application/json:
schema: GenerationInputSchema
example:
prompt: |-2
Niko the kobold stalked carefully down the alley, his small scaly figure obscured by a dusky cloak that fluttered lightly in the cold winter breeze.
top_p: 0.9
temperature: 0.5
responses:
200:
description: Successful request
content:
application/json:
schema: GenerationOutputSchema
example:
results:
- text: |-2
Holding up his tail to keep it from dragging in the dirty snow that covered the cobblestone, he waited patiently for the butcher to turn his attention from his stall so that he could pilfer his next meal: a tender-looking chicken.
{api_validation_error_response}
{api_not_implemented_response}
{api_server_busy_response}
{api_out_of_memory_response}
"""
return _generate_text(body)
@api_v1.get("/model")
@api_schema_wrap
def get_model():
"""---
get:
summary: Retrieve the current model string
description: |-2
Gets the current model string, which is shown in the title of the KoboldAI GUI in parentheses, e.g. "KoboldAI Client (KoboldAI/fairseq-dense-13B-Nerys-v2)".
tags:
- model
responses:
200:
description: Successful request
content:
application/json:
schema: BasicResultSchema
example:
result: KoboldAI/fairseq-dense-13B-Nerys-v2
"""
return {"result": koboldai_vars.model}
@api_v1.put("/model")
@api_schema_wrap
def put_model(body: ModelSelectionSchema):
"""---
put:
summary: Load a model
description: |-2
Loads a model given its Hugging Face model ID, the path to a model folder (relative to the "models" folder in the KoboldAI root folder) or "ReadOnly" for no model.
tags:
- model
requestBody:
required: true
content:
application/json:
schema: ModelSelectionSchema
example:
model: ReadOnly
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
{api_validation_error_response}
{api_server_busy_response}
"""
if koboldai_vars.aibusy or koboldai_vars.genseqs:
abort(Response(json.dumps({"detail": {
"msg": "Server is busy; please try again later.",
"type": "service_unavailable",
}}), mimetype="application/json", status=503))
set_aibusy(1)
old_model = koboldai_vars.model
koboldai_vars.model = body.model.strip()
try:
load_model(use_breakmodel_args=True, breakmodel_args_default_to_cpu=True)
except Exception as e:
koboldai_vars.model = old_model
raise e
set_aibusy(0)
return {}
def prompt_validator(prompt: str):
if len(prompt.strip()) == 0:
raise ValidationError("String does not match expected pattern.")
class SubmissionInputSchema(KoboldSchema):
prompt: str = fields.String(required=True, validate=prompt_validator, metadata={"pattern": r"^.*\S.*$", "description": "This is the submission."})
disable_input_formatting: bool = fields.Boolean(load_default=True, metadata={"description": "When enabled, disables all input formatting options, overriding their individual enabled/disabled states."})
frmtadsnsp: Optional[bool] = fields.Boolean(metadata={"description": "Input formatting option. When enabled, adds a leading space to your input if there is no trailing whitespace at the end of the previous action."})
@api_v1.post("/story/end")
@api_schema_wrap
def post_story_end(body: SubmissionInputSchema):
"""---
post:
summary: Add an action to the end of the story
tags:
- story
description: |-2
Inserts a single action at the end of the story in the KoboldAI GUI without generating text.
requestBody:
required: true
content:
application/json:
schema: SubmissionInputSchema
example:
prompt: |-2
This is some text to put at the end of the story.
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
{api_validation_error_response}
{api_server_busy_response}
"""
if koboldai_vars.aibusy or koboldai_vars.genseqs:
abort(Response(json.dumps({"detail": {
"msg": "Server is busy; please try again later.",
"type": "service_unavailable",
}}), mimetype="application/json", status=503))
set_aibusy(1)
disable_set_aibusy = koboldai_vars.disable_set_aibusy
koboldai_vars.disable_set_aibusy = True
_standalone = koboldai_vars.standalone
koboldai_vars.standalone = True
numseqs = koboldai_vars.numseqs
koboldai_vars.numseqs = 1
try:
actionsubmit(body.prompt, force_submit=True, no_generate=True, ignore_aibusy=True)
finally:
koboldai_vars.disable_set_aibusy = disable_set_aibusy
koboldai_vars.standalone = _standalone
koboldai_vars.numseqs = numseqs
set_aibusy(0)
return {}
@api_v1.get("/story/end")
@api_schema_wrap
def get_story_end():
"""---
get:
summary: Retrieve the last action of the story
tags:
- story
description: |-2
Returns the last action of the story in the KoboldAI GUI.
responses:
200:
description: Successful request
content:
application/json:
schema: StoryChunkResultSchema
510:
description: Story is empty
content:
application/json:
schema: StoryEmptyErrorSchema
example:
detail:
msg: Could not retrieve the last action of the story because the story is empty.
type: story_empty
"""
if not koboldai_vars.gamestarted:
abort(Response(json.dumps({"detail": {
"msg": "Could not retrieve the last action of the story because the story is empty.",
"type": "story_empty",
}}), mimetype="application/json", status=510))
if len(koboldai_vars.actions) == 0:
return {"result": {"text": koboldai_vars.prompt, "num": 0}}
return {"result": {"text": koboldai_vars.actions[koboldai_vars.actions.get_last_key()], "num": koboldai_vars.actions.get_last_key() + 1}}
@api_v1.get("/story/end/num")
@api_schema_wrap
def get_story_end_num():
"""---
get:
summary: Retrieve the num of the last action of the story
tags:
- story
description: |-2
Returns the `num` of the last action of the story in the KoboldAI GUI.
responses:
200:
description: Successful request
content:
application/json:
schema: StoryChunkNumSchema
510:
description: Story is empty
content:
application/json:
schema: StoryEmptyErrorSchema
example:
detail:
msg: Could not retrieve the last action of the story because the story is empty.
type: story_empty
"""
if not koboldai_vars.gamestarted:
abort(Response(json.dumps({"detail": {
"msg": "Could not retrieve the last action of the story because the story is empty.",
"type": "story_empty",
}}), mimetype="application/json", status=510))
if len(koboldai_vars.actions) == 0:
return {"result": {"text": 0}}
return {"result": {"text": koboldai_vars.actions.get_last_key() + 1}}
@api_v1.get("/story/end/text")
@api_schema_wrap
def get_story_end_text():
"""---
get:
summary: Retrieve the text of the last action of the story
tags:
- story
description: |-2
Returns the text of the last action of the story in the KoboldAI GUI.
responses:
200:
description: Successful request
content:
application/json:
schema: StoryChunkTextSchema
510:
description: Story is empty
content:
application/json:
schema: StoryEmptyErrorSchema
example:
detail:
msg: Could not retrieve the last action of the story because the story is empty.
type: story_empty
"""
if not koboldai_vars.gamestarted:
abort(Response(json.dumps({"detail": {
"msg": "Could not retrieve the last action of the story because the story is empty.",
"type": "story_empty",
}}), mimetype="application/json", status=510))
if len(koboldai_vars.actions) == 0:
return {"result": {"text": koboldai_vars.prompt}}
return {"result": {"text": koboldai_vars.actions[koboldai_vars.actions.get_last_key()]}}
@api_v1.put("/story/end/text")
@api_schema_wrap
def put_story_end_text(body: StoryChunkSetTextSchema):
"""---
put:
summary: Set the text of the last action of the story
tags:
- story
description: |-2
Sets the text of the last action of the story in the KoboldAI GUI to the desired value.
requestBody:
required: true
content:
application/json:
schema: StoryChunkSetTextSchema
example:
value: string
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
510:
description: Story is empty
content:
application/json:
schema: StoryEmptyErrorSchema
example:
detail:
msg: Could not retrieve the last action of the story because the story is empty.
type: story_empty
{api_validation_error_response}
"""
if not koboldai_vars.gamestarted:
abort(Response(json.dumps({"detail": {
"msg": "Could not retrieve the last action of the story because the story is empty.",
"type": "story_empty",
}}), mimetype="application/json", status=510))
value = body.value.rstrip()
if len(koboldai_vars.actions) == 0:
inlineedit(0, value)
else:
inlineedit(koboldai_vars.actions.get_last_key() + 1, value)
return {}
@api_v1.post("/story/end/delete")
@api_schema_wrap
def post_story_end_delete(body: EmptySchema):
"""---
post:
summary: Remove the last action of the story
tags:
- story
description: |-2
Removes the last action of the story in the KoboldAI GUI.
requestBody:
required: true
content:
application/json:
schema: EmptySchema
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
510:
description: Story too short
content:
application/json:
schema: StoryTooShortErrorSchema
example:
detail:
msg: Could not delete the last action of the story because the number of actions in the story is less than or equal to 1.
type: story_too_short
{api_validation_error_response}
{api_server_busy_response}
"""
if koboldai_vars.aibusy or koboldai_vars.genseqs:
abort(Response(json.dumps({"detail": {
"msg": "Server is busy; please try again later.",
"type": "service_unavailable",
}}), mimetype="application/json", status=503))
if not koboldai_vars.gamestarted or not len(koboldai_vars.actions):
abort(Response(json.dumps({"detail": {
"msg": "Could not delete the last action of the story because the number of actions in the story is less than or equal to 1.",
"type": "story_too_short",
}}), mimetype="application/json", status=510))
actionback()
return {}
@api_v1.get("/story")
@api_schema_wrap
def get_story():
"""---
get:
summary: Retrieve the entire story
tags:
- story
description: |-2
Returns the entire story currently shown in the KoboldAI GUI.
responses:
200:
description: Successful request
content:
application/json:
schema: StorySchema
"""
chunks = []
if koboldai_vars.gamestarted:
chunks.append({"num": 0, "text": koboldai_vars.prompt})
for num, action in koboldai_vars.actions.items():
chunks.append({"num": num + 1, "text": action})
return {"results": chunks}
@api_v1.get("/story/nums")
@api_schema_wrap
def get_story_nums():
"""---
get:
summary: Retrieve a list of the nums of the chunks in the current story
tags:
- story
description: |-2
Returns the `num`s of the story chunks currently shown in the KoboldAI GUI.
responses:
200:
description: Successful request
content:
application/json:
schema: StorySchema
"""
chunks = []
if koboldai_vars.gamestarted:
chunks.append(0)
for num in koboldai_vars.actions.keys():
chunks.append(num + 1)
return {"results": chunks}
@api_v1.get("/story/nums/")
@api_schema_wrap
def get_story_nums_num(num: int):
"""---
get:
summary: Determine whether or not there is a story chunk with the given num
tags:
- story
parameters:
- name: num
in: path
description: |-2
`num` of the desired story chunk.
schema:
type: integer
responses:
200:
description: Successful request
content:
application/json:
schema: BasicBooleanSchema
"""
if num == 0:
return {"result": koboldai_vars.gamestarted}
return {"result": num - 1 in koboldai_vars.actions}
@api_v1.get("/story/")
@api_schema_wrap
def get_story_num(num: int):
"""---
get:
summary: Retrieve a story chunk
tags:
- story
description: |-2
Returns information about a story chunk given its `num`.
parameters:
- name: num
in: path
description: |-2
`num` of the desired story chunk.
schema:
type: integer
responses:
200:
description: Successful request
content:
application/json:
schema: StoryChunkResultSchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No chunk with the given num exists.
type: key_error
"""
if num == 0:
if not koboldai_vars.gamestarted:
abort(Response(json.dumps({"detail": {
"msg": "No chunk with the given num exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
return {"result": {"text": koboldai_vars.prompt, "num": num}}
if num - 1 not in koboldai_vars.actions:
abort(Response(json.dumps({"detail": {
"msg": "No chunk with the given num exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
return {"result": {"text": koboldai_vars.actions[num - 1], "num": num}}
@api_v1.get("/story//text")
@api_schema_wrap
def get_story_num_text(num: int):
"""---
get:
summary: Retrieve the text of a story chunk
tags:
- story
description: |-2
Returns the text inside a story chunk given its `num`.
parameters:
- name: num
in: path
description: |-2
`num` of the desired story chunk.
schema:
type: integer
responses:
200:
description: Successful request
content:
application/json:
schema: StoryChunkTextSchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No chunk with the given num exists.
type: key_error
"""
if num == 0:
if not koboldai_vars.gamestarted:
abort(Response(json.dumps({"detail": {
"msg": "No chunk with the given num exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
return {"value": koboldai_vars.prompt}
if num - 1 not in koboldai_vars.actions:
abort(Response(json.dumps({"detail": {
"msg": "No chunk with the given num exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
return {"value": koboldai_vars.actions[num - 1]}
@api_v1.put("/story//text")
@api_schema_wrap
def put_story_num_text(body: StoryChunkSetTextSchema, num: int):
"""---
put:
summary: Set the text of a story chunk
tags:
- story
description: |-2
Sets the text inside a story chunk given its `num`.
parameters:
- name: num
in: path
description: |-2
`num` of the desired story chunk.
schema:
type: integer
requestBody:
required: true
content:
application/json:
schema: StoryChunkSetTextSchema
example:
value: string
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No chunk with the given num exists.
type: key_error
{api_validation_error_response}
"""
if num == 0:
if not koboldai_vars.gamestarted:
abort(Response(json.dumps({"detail": {
"msg": "No chunk with the given num exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
inlineedit(0, body.value.rstrip())
return {}
if num - 1 not in koboldai_vars.actions:
abort(Response(json.dumps({"detail": {
"msg": "No chunk with the given num exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
inlineedit(num, body.value.rstrip())
return {}
@api_v1.delete("/story/")
@api_schema_wrap
def post_story_num_delete(num: int):
"""---
delete:
summary: Remove a story chunk
tags:
- story
description: |-2
Removes a story chunk from the story in the KoboldAI GUI given its `num`. Cannot be used to delete the first action (the prompt).
parameters:
- name: num
in: path
description: |-2
`num` of the desired story chunk. Must be larger than or equal to 1.
schema:
type: integer
minimum: 1
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No chunk with the given num exists.
type: key_error
{api_server_busy_response}
"""
if num < 1:
abort(Response(json.dumps({"detail": {
"num": ["Must be greater than or equal to 1."],
}}), mimetype="application/json", status=422))
if num - 1 not in koboldai_vars.actions:
abort(Response(json.dumps({"detail": {
"msg": "No chunk with the given num exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
if koboldai_vars.aibusy or koboldai_vars.genseqs:
abort(Response(json.dumps({"detail": {
"msg": "Server is busy; please try again later.",
"type": "service_unavailable",
}}), mimetype="application/json", status=503))
inlinedelete(num)
return {}
@api_v1.delete("/story")
@api_schema_wrap
def delete_story():
"""---
delete:
summary: Clear the story
tags:
- story
description: |-2
Starts a new blank story.
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
{api_server_busy_response}
"""
if koboldai_vars.aibusy or koboldai_vars.genseqs:
abort(Response(json.dumps({"detail": {
"msg": "Server is busy; please try again later.",
"type": "service_unavailable",
}}), mimetype="application/json", status=503))
newGameRequest()
return {}
@api_v1.put("/story/load")
@api_schema_wrap
def put_story_load(body: StoryLoadSchema):
"""---
put:
summary: Load a story
tags:
- story
description: |-2
Loads a story given its filename (without the .json).
requestBody:
required: true
content:
application/json:
schema: StoryLoadSchema
example:
name: string
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
{api_validation_error_response}
{api_server_busy_response}
"""
if koboldai_vars.aibusy or koboldai_vars.genseqs:
abort(Response(json.dumps({"detail": {
"msg": "Server is busy; please try again later.",
"type": "service_unavailable",
}}), mimetype="application/json", status=503))
loadRequest(fileops.storypath(body.name.strip()))
return {}
@api_v1.put("/story/save")
@api_schema_wrap
def put_story_save(body: StorySaveSchema):
"""---
put:
summary: Save the current story
tags:
- story
description: |-2
Saves the current story given its destination filename (without the .json).
requestBody:
required: true
content:
application/json:
schema: StorySaveSchema
example:
name: string
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
{api_validation_error_response}
"""
saveRequest(fileops.storypath(body.name.strip()))
return {}
@api_v1.get("/world_info")
@api_schema_wrap
def get_world_info():
"""---
get:
summary: Retrieve all world info entries
tags:
- world_info
description: |-2
Returns all world info entries currently shown in the KoboldAI GUI.
The `folders` are sorted in the same order as they are in the GUI and the `entries` within the folders and within the parent `result` object are all sorted in the same order as they are in their respective parts of the GUI.
responses:
200:
description: Successful request
content:
application/json:
schema: WorldInfoSchema
"""
folders = []
entries = []
ln = len(koboldai_vars.worldinfo)
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_vars.worldinfo if wi["init"]]
folder: Optional[list] = None
if ln:
last_folder = ...
for wi in koboldai_vars.worldinfo_i:
if wi["folder"] != last_folder:
folder = []
if wi["folder"] is not None:
folders.append({"uid": wi["folder"], "name": koboldai_vars.wifolders_d[wi["folder"]]["name"], "entries": folder})
last_folder = wi["folder"]
(folder if wi["folder"] is not None else entries).append({k: v for k, v in wi.items() if k not in ("init", "folder", "num") and (wi["selective"] or k != "keysecondary")})
return {"folders": folders, "entries": entries}
@api_v1.get("/world_info/uids")
@api_schema_wrap
def get_world_info_uids():
"""---
get:
summary: Retrieve the UIDs of all world info entries
tags:
- world_info
description: |-2
Returns in a similar format as GET /world_info except only the `uid`s are returned.
responses:
200:
description: Successful request
content:
application/json:
schema: WorldInfoUIDsSchema
"""
folders = []
entries = []
ln = len(koboldai_vars.worldinfo)
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_vars.worldinfo if wi["init"]]
folder: Optional[list] = None
if ln:
last_folder = ...
for wi in koboldai_vars.worldinfo_i:
if wi["folder"] != last_folder:
folder = []
if wi["folder"] is not None:
folders.append({"uid": wi["folder"], "entries": folder})
last_folder = wi["folder"]
(folder if wi["folder"] is not None else entries).append(wi["uid"])
return {"folders": folders, "entries": entries}
@api_v1.get("/world_info/uids/")
@api_schema_wrap
def get_world_info_uids_uid(uid: int):
"""---
get:
summary: Determine whether or not there is a world info entry with the given UID
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: BasicBooleanSchema
"""
return {"result": uid in koboldai_vars.worldinfo_u and koboldai_vars.worldinfo_u[uid]["init"]}
@api_v1.get("/world_info/folders")
@api_schema_wrap
def get_world_info_folders():
"""---
get:
summary: Retrieve all world info folders
tags:
- world_info
description: |-2
Returns details about all world info folders currently shown in the KoboldAI GUI.
The `folders` are sorted in the same order as they are in the GUI.
responses:
200:
description: Successful request
content:
application/json:
schema: WorldInfoFoldersSchema
"""
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_vars.worldinfo if wi["init"]]
return {"folders": [{"uid": folder, **{k: v for k, v in koboldai_vars.wifolders_d[folder].items() if k != "collapsed"}} for folder in koboldai_vars.wifolders_l]}
@api_v1.get("/world_info/folders/uids")
@api_schema_wrap
def get_world_info_folders_uids():
"""---
get:
summary: Retrieve the UIDs all world info folders
tags:
- world_info
description: |-2
Returns the `uid`s of all world info folders currently shown in the KoboldAI GUI.
The `folders` are sorted in the same order as they are in the GUI.
responses:
200:
description: Successful request
content:
application/json:
schema: WorldInfoFoldersUIDsSchema
"""
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_vars.worldinfo if wi["init"]]
return {"folders": koboldai_vars.wifolders_l}
@api_v1.get("/world_info/folders/none")
@api_schema_wrap
def get_world_info_folders_none():
"""---
get:
summary: Retrieve all world info entries not in a folder
tags:
- world_info
description: |-2
Returns all world info entries that are not in a world info folder.
The `entries` are sorted in the same order as they are in the KoboldAI GUI.
responses:
200:
description: Successful request
content:
application/json:
schema: WorldInfoEntriesSchema
"""
entries = []
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_vars.worldinfo if wi["init"]]
for wi in reversed(koboldai_vars.worldinfo_i):
if wi["folder"] is not None:
break
entries.append({k: v for k, v in wi.items() if k not in ("init", "folder", "num") and (wi["selective"] or k != "keysecondary")})
return {"entries": list(reversed(entries))}
@api_v1.get("/world_info/folders/none/uids")
@api_schema_wrap
def get_world_info_folders_none_uids():
"""---
get:
summary: Retrieve the UIDs of all world info entries not in a folder
tags:
- world_info
description: |-2
Returns the `uid`s of all world info entries that are not in a world info folder.
The `entries` are sorted in the same order as they are in the KoboldAI GUI.
responses:
200:
description: Successful request
content:
application/json:
schema: WorldInfoEntriesUIDsSchema
"""
entries = []
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_vars.worldinfo if wi["init"]]
for wi in reversed(koboldai_vars.worldinfo_i):
if wi["folder"] is not None:
break
entries.append(wi["uid"])
return {"entries": list(reversed(entries))}
@api_v1.get("/world_info/folders/none/uids/")
@api_schema_wrap
def get_world_info_folders_none_uids_uid(uid: int):
"""---
get:
summary: Determine whether or not there is a world info entry with the given UID that is not in a world info folder
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: BasicBooleanSchema
"""
return {"result": uid in koboldai_vars.worldinfo_u and koboldai_vars.worldinfo_u[uid]["folder"] is None and koboldai_vars.worldinfo_u[uid]["init"]}
@api_v1.get("/world_info/folders/")
@api_schema_wrap
def get_world_info_folders_uid(uid: int):
"""---
get:
summary: Retrieve all world info entries in the given folder
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info folder.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
description: |-2
Returns all world info entries that are in the world info folder with the given `uid`.
The `entries` are sorted in the same order as they are in the KoboldAI GUI.
responses:
200:
description: Successful request
content:
application/json:
schema: WorldInfoEntriesSchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info folder with the given uid exists.
type: key_error
"""
if uid not in koboldai_vars.wifolders_d:
abort(Response(json.dumps({"detail": {
"msg": "No world info folder with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
entries = []
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_vars.worldinfo if wi["init"]]
for wi in koboldai_vars.wifolders_u[uid]:
if wi["init"]:
entries.append({k: v for k, v in wi.items() if k not in ("init", "folder", "num") and (wi["selective"] or k != "keysecondary")})
return {"entries": entries}
@api_v1.get("/world_info/folders//uids")
@api_schema_wrap
def get_world_info_folders_uid_uids(uid: int):
"""---
get:
summary: Retrieve the UIDs of all world info entries in the given folder
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info folder.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
description: |-2
Returns the `uid`s of all world info entries that are in the world info folder with the given `uid`.
The `entries` are sorted in the same order as they are in the KoboldAI GUI.
responses:
200:
description: Successful request
content:
application/json:
schema: WorldInfoEntriesUIDsSchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info folder with the given uid exists.
type: key_error
"""
if uid not in koboldai_vars.wifolders_d:
abort(Response(json.dumps({"detail": {
"msg": "No world info folder with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
entries = []
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_vars.worldinfo if wi["init"]]
for wi in koboldai_vars.wifolders_u[uid]:
if wi["init"]:
entries.append(wi["uid"])
return {"entries": entries}
@api_v1.get("/world_info/folders//uids/")
@api_schema_wrap
def get_world_info_folders_folder_uid_uids_entry_uid(folder_uid: int, entry_uid: int):
"""---
get:
summary: Determine whether or not there is a world info entry with the given UID in the world info folder with the given UID
tags:
- world_info
parameters:
- name: folder_uid
in: path
description: |-2
`uid` of the desired world info folder.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
- name: entry_uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: BasicBooleanSchema
"""
return {"result": entry_uid in koboldai_vars.worldinfo_u and koboldai_vars.worldinfo_u[entry_uid]["folder"] == folder_uid and koboldai_vars.worldinfo_u[entry_uid]["init"]}
@api_v1.get("/world_info/folders//name")
@api_schema_wrap
def get_world_info_folders_uid_name(uid: int):
"""---
get:
summary: Retrieve the name of the world info folder with the given UID
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info folder.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: BasicStringSchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info folder with the given uid exists.
type: key_error
"""
if uid not in koboldai_vars.wifolders_d:
abort(Response(json.dumps({"detail": {
"msg": "No world info folder with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
return {"value": koboldai_vars.wifolders_d[uid]["name"]}
@api_v1.put("/world_info/folders//name")
@api_schema_wrap
def put_world_info_folders_uid_name(body: BasicStringSchema, uid: int):
"""---
put:
summary: Set the name of the world info folder with the given UID to the specified value
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info folder.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
requestBody:
required: true
content:
application/json:
schema: BasicStringSchema
example:
value: string
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info folder with the given uid exists.
type: key_error
{api_validation_error_response}
"""
if uid not in koboldai_vars.wifolders_d:
abort(Response(json.dumps({"detail": {
"msg": "No world info folder with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
koboldai_vars.wifolders_d[uid]["name"] = body.value
setgamesaved(False)
return {}
@api_v1.get("/world_info/")
@api_schema_wrap
def get_world_info_uid(uid: int):
"""---
get:
summary: Retrieve information about the world info entry with the given UID
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: WorldInfoEntrySchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
wi = koboldai_vars.worldinfo_u[uid]
return {k: v for k, v in wi.items() if k not in ("init", "folder", "num") and (wi["selective"] or k != "keysecondary")}
@api_v1.get("/world_info//comment")
@api_schema_wrap
def get_world_info_uid_comment(uid: int):
"""---
get:
summary: Retrieve the comment of the world info entry with the given UID
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: BasicStringSchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
return {"value": koboldai_vars.worldinfo_u[uid]["comment"]}
@api_v1.put("/world_info//comment")
@api_schema_wrap
def put_world_info_uid_comment(body: BasicStringSchema, uid: int):
"""---
put:
summary: Set the comment of the world info entry with the given UID to the specified value
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
requestBody:
required: true
content:
application/json:
schema: BasicStringSchema
example:
value: string
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
{api_validation_error_response}
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
koboldai_vars.worldinfo_u[uid]["comment"] = body.value
setgamesaved(False)
return {}
@api_v1.get("/world_info//content")
@api_schema_wrap
def get_world_info_uid_content(uid: int):
"""---
get:
summary: Retrieve the content of the world info entry with the given UID
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: BasicStringSchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
return {"value": koboldai_vars.worldinfo_u[uid]["content"]}
@api_v1.put("/world_info//content")
@api_schema_wrap
def put_world_info_uid_content(body: BasicStringSchema, uid: int):
"""---
put:
summary: Set the content of the world info entry with the given UID to the specified value
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
requestBody:
required: true
content:
application/json:
schema: BasicStringSchema
example:
value: string
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
{api_validation_error_response}
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
koboldai_vars.worldinfo_u[uid]["content"] = body.value
setgamesaved(False)
return {}
@api_v1.get("/world_info//key")
@api_schema_wrap
def get_world_info_uid_key(uid: int):
"""---
get:
summary: Retrieve the keys or primary keys of the world info entry with the given UID
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: BasicStringSchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
return {"value": koboldai_vars.worldinfo_u[uid]["key"]}
@api_v1.put("/world_info//key")
@api_schema_wrap
def put_world_info_uid_key(body: BasicStringSchema, uid: int):
"""---
put:
summary: Set the keys or primary keys of the world info entry with the given UID to the specified value
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
requestBody:
required: true
content:
application/json:
schema: BasicStringSchema
example:
value: string
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
{api_validation_error_response}
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
koboldai_vars.worldinfo_u[uid]["key"] = body.value
setgamesaved(False)
return {}
@api_v1.get("/world_info//keysecondary")
@api_schema_wrap
def get_world_info_uid_keysecondary(uid: int):
"""---
get:
summary: Retrieve the secondary keys of the world info entry with the given UID
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: BasicStringSchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
return {"value": koboldai_vars.worldinfo_u[uid]["keysecondary"]}
@api_v1.put("/world_info//keysecondary")
@api_schema_wrap
def put_world_info_uid_keysecondary(body: BasicStringSchema, uid: int):
"""---
put:
summary: Set the secondary keys of the world info entry with the given UID to the specified value
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
requestBody:
required: true
content:
application/json:
schema: BasicStringSchema
example:
value: string
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
{api_validation_error_response}
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
koboldai_vars.worldinfo_u[uid]["keysecondary"] = body.value
setgamesaved(False)
return {}
@api_v1.get("/world_info//selective")
@api_schema_wrap
def get_world_info_uid_selective(uid: int):
"""---
get:
summary: Retrieve the selective mode state of the world info entry with the given UID
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: BasicBooleanSchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
return {"value": koboldai_vars.worldinfo_u[uid]["selective"]}
@api_v1.put("/world_info//selective")
@api_schema_wrap
def put_world_info_uid_selective(body: BasicBooleanSchema, uid: int):
"""---
put:
summary: Set the selective mode state of the world info entry with the given UID to the specified value
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
requestBody:
required: true
content:
application/json:
schema: BasicBooleanSchema
example:
value: true
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
{api_validation_error_response}
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
koboldai_vars.worldinfo_u[uid]["selective"] = body.value
setgamesaved(False)
return {}
@api_v1.get("/world_info//constant")
@api_schema_wrap
def get_world_info_uid_constant(uid: int):
"""---
get:
summary: Retrieve the constant mode state of the world info entry with the given UID
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: BasicBooleanSchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
return {"value": koboldai_vars.worldinfo_u[uid]["constant"]}
@api_v1.put("/world_info//constant")
@api_schema_wrap
def put_world_info_uid_constant(body: BasicBooleanSchema, uid: int):
"""---
put:
summary: Set the constant mode state of the world info entry with the given UID to the specified value
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
requestBody:
required: true
content:
application/json:
schema: BasicBooleanSchema
example:
value: true
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
{api_validation_error_response}
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
koboldai_vars.worldinfo_u[uid]["constant"] = body.value
setgamesaved(False)
return {}
@api_v1.post("/world_info/folders/none")
@api_schema_wrap
def post_world_info_folders_none(body: EmptySchema):
"""---
post:
summary: Create a new world info entry outside of a world info folder, at the end of the world info
tags:
- world_info
requestBody:
required: true
content:
application/json:
schema: EmptySchema
responses:
200:
description: Successful request
content:
application/json:
schema: BasicUIDSchema
{api_validation_error_response}
"""
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_vars.worldinfo if wi["init"]]
setgamesaved(False)
emit('from_server', {'cmd': 'wiexpand', 'data': koboldai_vars.worldinfo[-1]["num"]}, broadcast=True)
koboldai_vars.worldinfo[-1]["init"] = True
addwiitem(folder_uid=None)
return {"uid": koboldai_vars.worldinfo[-2]["uid"]}
@api_v1.post("/world_info/folders/")
@api_schema_wrap
def post_world_info_folders_uid(body: EmptySchema, uid: int):
"""---
post:
summary: Create a new world info entry at the end of the world info folder with the given UID
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info folder.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
requestBody:
required: true
content:
application/json:
schema: EmptySchema
responses:
200:
description: Successful request
content:
application/json:
schema: BasicUIDSchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info folder with the given uid exists.
type: key_error
{api_validation_error_response}
"""
if uid not in koboldai_vars.wifolders_d:
abort(Response(json.dumps({"detail": {
"msg": "No world info folder with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
stablesortwi()
koboldai_vars.worldinfo_i = [wi for wi in koboldai_vars.worldinfo if wi["init"]]
setgamesaved(False)
emit('from_server', {'cmd': 'wiexpand', 'data': koboldai_vars.wifolders_u[uid][-1]["num"]}, broadcast=True)
koboldai_vars.wifolders_u[uid][-1]["init"] = True
addwiitem(folder_uid=uid)
return {"uid": koboldai_vars.wifolders_u[uid][-2]["uid"]}
@api_v1.delete("/world_info/")
@api_schema_wrap
def delete_world_info_uid(uid: int):
"""---
delete:
summary: Delete the world info entry with the given UID
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info entry.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info entry with the given uid exists.
type: key_error
"""
if uid not in koboldai_vars.worldinfo_u:
abort(Response(json.dumps({"detail": {
"msg": "No world info entry with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
deletewi(uid)
return {}
@api_v1.post("/world_info/folders")
@api_schema_wrap
def post_world_info_folders(body: EmptySchema):
"""---
post:
summary: Create a new world info folder at the end of the world info
tags:
- world_info
requestBody:
required: true
content:
application/json:
schema: EmptySchema
responses:
200:
description: Successful request
content:
application/json:
schema: BasicUIDSchema
{api_validation_error_response}
"""
addwifolder()
return {"uid": koboldai_vars.wifolders_l[-1]}
@api_v1.delete("/world_info/folders/")
@api_schema_wrap
def delete_world_info_folders_uid(uid: int):
"""---
delete:
summary: Delete the world info folder with the given UID
tags:
- world_info
parameters:
- name: uid
in: path
description: |-2
`uid` of the desired world info folder.
schema:
type: integer
minimum: -2147483648
maximum: 2147483647
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
404:
description: Not found
content:
application/json:
schema: NotFoundErrorSchema
example:
detail:
msg: No world info folders with the given uid exists.
type: key_error
"""
if uid not in koboldai_vars.wifolders_d:
abort(Response(json.dumps({"detail": {
"msg": "No world info folder with the given uid exists.",
"type": "key_error",
}}), mimetype="application/json", status=404))
deletewifolder(uid)
return {}
def _make_f_get(obj, _var_name, _name, _schema, _example_yaml_value):
def f_get():
"""---
get:
summary: Retrieve the current {} setting value
tags:
- config
responses:
200:
description: Successful request
content:
application/json:
schema: {}
example:
value: {}
"""
_obj = {"koboldai_vars": koboldai_vars}[obj]
if _var_name.startswith("@"):
return {"value": _obj[_var_name[1:]]}
else:
return {"value": getattr(_obj, _var_name)}
f_get.__doc__ = f_get.__doc__.format(_name, _schema, _example_yaml_value)
return f_get
def _make_f_put(schema_class: Type[KoboldSchema], obj, _var_name, _name, _schema, _example_yaml_value):
def f_put(body: schema_class):
"""---
put:
summary: Set {} setting to specified value
tags:
- config
requestBody:
required: true
content:
application/json:
schema: {}
example:
value: {}
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
{api_validation_error_response}
"""
_obj = {"koboldai_vars": koboldai_vars}[obj]
if _var_name.startswith("@"):
_obj[_var_name[1:]] = body.value
else:
setattr(_obj, _var_name, body.value)
settingschanged()
refresh_settings()
return {}
f_put.__doc__ = f_put.__doc__.format(_name, _schema, _example_yaml_value, api_validation_error_response=api_validation_error_response)
return f_put
def create_config_endpoint(method="GET", schema="MemorySchema"):
_name = globals()[schema].KoboldMeta.name
_var_name = globals()[schema].KoboldMeta.var_name
_route_name = globals()[schema].KoboldMeta.route_name
_obj = globals()[schema].KoboldMeta.obj
_example_yaml_value = globals()[schema].KoboldMeta.example_yaml_value
_schema = schema
f = _make_f_get(_obj, _var_name, _name, _schema, _example_yaml_value) if method == "GET" else _make_f_put(globals()[schema], _obj, _var_name, _name, _schema, _example_yaml_value)
f.__name__ = f"{method.lower()}_config_{_name}"
f = api_schema_wrap(f)
for api in (api_v1,):
f = api.route(f"/config/{_route_name}", methods=[method])(f)
class SoftPromptSettingSchema(KoboldSchema):
value: str = fields.String(required=True, validate=[soft_prompt_validator, validate.Regexp(r"^[^/\\]*$")], metadata={"description": "Soft prompt name, or a string containing only whitespace for no soft prompt. If using the GET method and no soft prompt is loaded, this will always be the empty string."})
@api_v1.get("/config/soft_prompt")
@api_schema_wrap
def get_config_soft_prompt():
"""---
get:
summary: Retrieve the current soft prompt name
tags:
- config
responses:
200:
description: Successful request
content:
application/json:
schema: SoftPromptSettingSchema
example:
value: ""
"""
return {"value": koboldai_vars.spfilename.strip()}
class SoftPromptsListSchema(KoboldSchema):
values: List[SoftPromptSettingSchema] = fields.List(fields.Nested(SoftPromptSettingSchema), required=True, metadata={"description": "Array of available softprompts."})
@api_v1.get("/config/soft_prompts_list")
@api_schema_wrap
def get_config_soft_prompts_list():
"""---
get:
summary: Retrieve all available softprompt filenames
tags:
- config
responses:
200:
description: Successful request
content:
application/json:
schema: SoftPromptsListSchema
example:
values: []
"""
splist = []
for sp in fileops.getspfiles(koboldai_vars.modeldim):
splist.append({"value":sp["filename"]})
return {"values": splist}
@api_v1.put("/config/soft_prompt")
@api_schema_wrap
def put_config_soft_prompt(body: SoftPromptSettingSchema):
"""---
put:
summary: Set soft prompt by name
tags:
- config
requestBody:
required: true
content:
application/json:
schema: SoftPromptSettingSchema
example:
value: ""
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
{api_validation_error_response}
"""
if koboldai_vars.allowsp:
spRequest(body.value)
settingschanged()
return {}
class SamplerSeedSettingSchema(KoboldSchema):
value: int = fields.Integer(validate=validate.Range(min=0, max=2**64 - 1), required=True)
@api_v1.get("/config/sampler_seed")
@api_schema_wrap
def get_config_sampler_seed():
"""---
get:
summary: Retrieve the current global sampler seed value
tags:
- config
responses:
200:
description: Successful request
content:
application/json:
schema: SamplerSeedSettingSchema
example:
value: 3475097509890965500
"""
return {"value": __import__("tpu_mtj_backend").get_rng_seed() if vars.use_colab_tpu else __import__("torch").initial_seed()}
@api_v1.put("/config/sampler_seed")
@api_schema_wrap
def put_config_sampler_seed(body: SamplerSeedSettingSchema):
"""---
put:
summary: Set the global sampler seed value
tags:
- config
requestBody:
required: true
content:
application/json:
schema: SamplerSeedSettingSchema
example:
value: 3475097509890965500
responses:
200:
description: Successful request
content:
application/json:
schema: EmptySchema
{api_validation_error_response}
"""
if vars.use_colab_tpu:
import tpu_mtj_backend
tpu_mtj_backend.set_rng_seed(body.value)
else:
import torch
torch.manual_seed(body.value)
vars.seed = body.value
return {}
config_endpoint_schemas: List[Type[KoboldSchema]] = []
def config_endpoint_schema(c: Type[KoboldSchema]):
config_endpoint_schemas.append(c)
return c
@config_endpoint_schema
class MemorySettingSchema(KoboldSchema):
value = fields.String(required=True)
class KoboldMeta:
route_name = "memory"
obj = "koboldai_vars"
var_name = "memory"
name = "memory"
example_yaml_value = "Memory"
@config_endpoint_schema
class AuthorsNoteSettingSchema(KoboldSchema):
value = fields.String(required=True)
class KoboldMeta:
route_name = "authors_note"
obj = "koboldai_vars"
var_name = "authornote"
name = "author's note"
example_yaml_value = "''"
@config_endpoint_schema
class AuthorsNoteTemplateSettingSchema(KoboldSchema):
value = fields.String(required=True)
class KoboldMeta:
route_name = "authors_note_template"
obj = "koboldai_vars"
var_name = "authornotetemplate"
name = "author's note template"
example_yaml_value = "\"[Author's note: <|>]\""
@config_endpoint_schema
class TopKSamplingSettingSchema(KoboldSchema):
value = fields.Integer(validate=validate.Range(min=0), required=True)
class KoboldMeta:
route_name = "top_k"
obj = "koboldai_vars"
var_name = "top_k"
name = "top-k sampling"
example_yaml_value = "0"
@config_endpoint_schema
class TopASamplingSettingSchema(KoboldSchema):
value = fields.Float(validate=validate.Range(min=0), required=True)
class KoboldMeta:
route_name = "top_a"
obj = "koboldai_vars"
var_name = "top_a"
name = "top-a sampling"
example_yaml_value = "0.0"
@config_endpoint_schema
class TopPSamplingSettingSchema(KoboldSchema):
value = fields.Float(validate=validate.Range(min=0, max=1), required=True)
class KoboldMeta:
route_name = "top_p"
obj = "koboldai_vars"
var_name = "top_p"
name = "top-p sampling"
example_yaml_value = "0.9"
@config_endpoint_schema
class TailFreeSamplingSettingSchema(KoboldSchema):
value = fields.Float(validate=validate.Range(min=0, max=1), required=True)
class KoboldMeta:
route_name = "tfs"
obj = "koboldai_vars"
var_name = "tfs"
name = "tail free sampling"
example_yaml_value = "1.0"
@config_endpoint_schema
class TypicalSamplingSettingSchema(KoboldSchema):
value = fields.Float(validate=validate.Range(min=0, max=1), required=True)
class KoboldMeta:
route_name = "typical"
obj = "koboldai_vars"
var_name = "typical"
name = "typical sampling"
example_yaml_value = "1.0"
@config_endpoint_schema
class TemperatureSamplingSettingSchema(KoboldSchema):
value = fields.Float(validate=validate.Range(min=0, min_inclusive=False), required=True)
class KoboldMeta:
route_name = "temperature"
obj = "koboldai_vars"
var_name = "temp"
name = "temperature"
example_yaml_value = "0.5"
@config_endpoint_schema
class GensPerActionSettingSchema(KoboldSchema):
value = fields.Integer(validate=validate.Range(min=0, max=5), required=True)
class KoboldMeta:
route_name = "n"
obj = "koboldai_vars"
var_name = "numseqs"
name = "Gens Per Action"
example_yaml_value = "1"
@config_endpoint_schema
class MaxLengthSettingSchema(KoboldSchema):
value = fields.Integer(validate=validate.Range(min=1, max=512), required=True)
class KoboldMeta:
route_name = "max_length"
obj = "koboldai_vars"
var_name = "genamt"
name = "max length"
example_yaml_value = "80"
@config_endpoint_schema
class WorldInfoDepthSettingSchema(KoboldSchema):
value = fields.Integer(validate=validate.Range(min=1, max=5), required=True)
class KoboldMeta:
route_name = "world_info_depth"
obj = "koboldai_vars"
var_name = "widepth"
name = "world info depth"
example_yaml_value = "3"
@config_endpoint_schema
class AuthorsNoteDepthSettingSchema(KoboldSchema):
value = fields.Integer(validate=validate.Range(min=1, max=5), required=True)
class KoboldMeta:
route_name = "authors_note_depth"
obj = "koboldai_vars"
var_name = "andepth"
name = "author's note depth"
example_yaml_value = "3"
@config_endpoint_schema
class MaxContextLengthSettingSchema(KoboldSchema):
value = fields.Integer(validate=validate.Range(min=512, max=2048), required=True)
class KoboldMeta:
route_name = "max_context_length"
obj = "koboldai_vars"
var_name = "max_length"
name = "max context length"
example_yaml_value = "2048"
@config_endpoint_schema
class TrimIncompleteSentencesSettingsSchema(KoboldSchema):
value = fields.Boolean(required=True)
class KoboldMeta:
route_name = "frmttriminc"
obj = "koboldai_vars"
var_name = "frmttriminc"
name = "trim incomplete sentences (output formatting)"
example_yaml_value = "false"
@config_endpoint_schema
class RemoveBlankLinesSettingsSchema(KoboldSchema):
value = fields.Boolean(required=True)
class KoboldMeta:
route_name = "frmtrmblln"
obj = "koboldai_vars"
var_name = "frmtrmblln"
name = "remove blank lines (output formatting)"
example_yaml_value = "false"
@config_endpoint_schema
class RemoveSpecialCharactersSettingsSchema(KoboldSchema):
value = fields.Boolean(required=True)
class KoboldMeta:
route_name = "frmtrmspch"
obj = "koboldai_vars"
var_name = "frmtrmspch"
name = "remove special characters (output formatting)"
example_yaml_value = "false"
@config_endpoint_schema
class SingleLineSettingsSchema(KoboldSchema):
value = fields.Boolean(required=True)
class KoboldMeta:
route_name = "singleline"
obj = "koboldai_vars"
var_name = "singleline"
name = "single line (output formatting)"
example_yaml_value = "false"
@config_endpoint_schema
class AddSentenceSpacingSettingsSchema(KoboldSchema):
value = fields.Boolean(required=True)
class KoboldMeta:
route_name = "frmtadsnsp"
obj = "koboldai_vars"
var_name = "frmtadsnsp"
name = "add sentence spacing (input formatting)"
example_yaml_value = "false"
@config_endpoint_schema
class SamplerOrderSettingSchema(KoboldSchema):
value = fields.List(fields.Integer(), validate=[validate.Length(min=6), permutation_validator], required=True)
class KoboldMeta:
route_name = "sampler_order"
obj = "vars"
var_name = "sampler_order"
name = "sampler order"
example_yaml_value = "[6, 0, 1, 2, 3, 4, 5]"
@config_endpoint_schema
class SamplerFullDeterminismSettingSchema(KoboldSchema):
value = fields.Boolean(required=True)
class KoboldMeta:
route_name = "sampler_full_determinism"
obj = "vars"
var_name = "full_determinism"
name = "sampler full determinism"
example_yaml_value = "false"
for schema in config_endpoint_schemas:
create_config_endpoint(schema=schema.__name__, method="GET")
create_config_endpoint(schema=schema.__name__, method="PUT")
#==================================================================#
# Final startup commands to launch Flask app
#==================================================================#
def startup():
if koboldai_vars.model == "" or koboldai_vars.model is None:
koboldai_vars.model = "ReadOnly"
socketio.start_background_task(load_model, **{'initial_load':True})
print("", end="", flush=True)
@logger.catch
def run():
general_startup()
# Start flask & SocketIO
logger.init("Flask", status="Starting")
Session(app)
logger.init_ok("Flask", status="OK")
logger.init("Webserver", status="Starting")
patch_transformers()
startup()
# 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_vars.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("(?Phttps?:\/\/[^\s]+loca.lt)", cloudflare).group("url"))
koboldai_vars.cloudflare_link = cloudflare
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)
koboldai_vars.cloudflare_link = cloudflare
elif(args.ngrok):
from flask_ngrok import _run_ngrok
cloudflare = _run_ngrok()
koboldai_vars.cloudflare_link = cloudflare
elif(args.remote):
from flask_cloudflared import _run_cloudflared
cloudflare = _run_cloudflared(port)
koboldai_vars.cloudflare_link = cloudflare
if(args.localtunnel or args.ngrok or args.remote):
with open('cloudflare.log', 'w') as cloudflarelog:
cloudflarelog.write("KoboldAI has finished loading and is available at the following link : " + cloudflare)
logger.init_ok("Webserver", status="OK")
logger.message(f"KoboldAI has finished loading and is available at the following link for UI 1: {cloudflare}")
logger.message(f"KoboldAI has finished loading and is available at the following link for UI 2: {cloudflare}/new_ui")
else:
logger.init_ok("Webserver", status="OK")
logger.message(f"Webserver has started, you can now connect to this machine at port: {port}")
koboldai_vars.serverstarted = True
socketio.run(app, host='0.0.0.0', port=port)
else:
if args.unblock:
if not args.no_ui:
try:
import webbrowser
webbrowser.open_new('http://localhost:{0}'.format(port))
except:
pass
logger.init_ok("Webserver", status="OK")
logger.message(f"Webserver started! You may now connect with a browser at http://127.0.0.1:{port}")
koboldai_vars.serverstarted = True
socketio.run(app, port=port, host='0.0.0.0')
else:
if not args.no_ui:
try:
import webbrowser
webbrowser.open_new('http://localhost:{0}'.format(port))
except:
pass
logger.init_ok("Webserver", status="OK")
logger.message(f"Webserver started! You may now connect with a browser at http://127.0.0.1:{port}")
koboldai_vars.serverstarted = True
socketio.run(app, port=port)
logger.init("Webserver", status="Closed")
if __name__ == "__main__":
run()
else:
general_startup()
# Start flask & SocketIO
logger.init("Flask", status="Starting")
Session(app)
logger.init_ok("Flask", status="OK")
patch_transformers()
startup()
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)