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koboldcpp.py
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#!/usr/bin/env python3
#-*- coding: utf-8 -*-
# KoboldCpp is an easy-to-use AI text-generation software for GGML models.
# It's a single self contained distributable from Concedo, that builds off llama.cpp,
# and adds a versatile Kobold API endpoint, additional format support,
# backward compatibility, as well as a fancy UI with persistent stories,
# editing tools, save formats, memory, world info, author's note, characters,
# scenarios and everything Kobold and KoboldAI Lite have to offer.
import ctypes
import os
import math
import re
import argparse
import platform
import base64
import struct
import json
import sys
import http.server
import time
import asyncio
import socket
import threading
import html
import urllib.parse as urlparse
from concurrent.futures import ThreadPoolExecutor
from datetime import datetime, timezone
# constants
sampler_order_max = 7
tensor_split_max = 16
images_max = 8
bias_min_value = -100.0
bias_max_value = 100.0
logprobs_max = 5
default_draft_amount = 8
default_ttsmaxlen = 4096
default_visionmaxres = 1024
# abuse prevention
stop_token_max = 256
ban_token_max = 512
logit_bias_max = 512
dry_seq_break_max = 128
# global vars
handle = None
friendlymodelname = "inactive"
friendlysdmodelname = "inactive"
lastgeneratedcomfyimg = b''
fullsdmodelpath = "" #if empty, it's not initialized
mmprojpath = "" #if empty, it's not initialized
password = "" #if empty, no auth key required
fullwhispermodelpath = "" #if empty, it's not initialized
ttsmodelpath = "" #if empty, not initialized
maxctx = 4096
maxhordectx = 4096
maxhordelen = 400
modelbusy = threading.Lock()
requestsinqueue = 0
defaultport = 5001
KcppVersion = "1.83"
showdebug = True
guimode = False
showsamplerwarning = True
showmaxctxwarning = True
showusedmemwarning = True
session_kudos_earned = 0
session_jobs = 0
session_starttime = None
exitcounter = -1
punishcounter = 0 #causes a timeout if too many errors
rewardcounter = 0 #reduces error counts for successful jobs
totalgens = 0
currentusergenkey = "" #store a special key so polled streaming works even in multiuser
pendingabortkey = "" #if an abort is received for the non-active request, remember it (at least 1) to cancel later
args = None #global args
runmode_untouched = True
modelfile_extracted_meta = None
importvars_in_progress = False
has_multiplayer = False
multiplayer_story_data_compressed = None #stores the full compressed story of the current multiplayer session
multiplayer_turn_major = 1 # to keep track of when a client needs to sync their stories
multiplayer_turn_minor = 1
multiplayer_dataformat = "" # used to tell what is the data payload in saved story. set by client
multiplayer_lastactive = {} # timestamp of last activity for each unique player
websearch_lastquery = ""
websearch_lastresponse = []
preloaded_story = None
chatcompl_adapter = None
embedded_kailite = None
embedded_kcpp_docs = None
embedded_kcpp_sdui = None
sslvalid = False
nocertify = False
start_time = time.time()
last_req_time = time.time()
last_non_horde_req_time = time.time()
currfinishreason = "null"
using_gui_launcher = False
using_outdated_flags = False
saved_stdout = None
saved_stderr = None
saved_stdout_py = None
saved_stderr_py = None
stdout_nullfile = None
stdout_nullfile_py = None
CLDevices = ["1","2","3","4"]
CUDevices = ["1","2","3","4","All"]
CLDevicesNames = ["","","",""]
CUDevicesNames = ["","","","",""]
VKDevicesNames = ["","","",""]
VKIsDGPU = [0,0,0,0]
MaxMemory = [0]
MaxFreeMemory = [0]
class logit_bias(ctypes.Structure):
_fields_ = [("token_id", ctypes.c_int32),
("bias", ctypes.c_float)]
class token_count_outputs(ctypes.Structure):
_fields_ = [("count", ctypes.c_int),
("ids", ctypes.POINTER(ctypes.c_int))]
# returns top 5 logprobs per token
class logprob_item(ctypes.Structure):
_fields_ = [("option_count", ctypes.c_int),
("selected_token", ctypes.c_char_p),
("selected_logprob", ctypes.c_float),
("tokens", ctypes.c_char_p * logprobs_max),
("logprobs", ctypes.POINTER(ctypes.c_float))]
class last_logprobs_outputs(ctypes.Structure):
_fields_ = [("count", ctypes.c_int),
("logprob_items", ctypes.POINTER(logprob_item))]
class load_model_inputs(ctypes.Structure):
_fields_ = [("threads", ctypes.c_int),
("blasthreads", ctypes.c_int),
("max_context_length", ctypes.c_int),
("low_vram", ctypes.c_bool),
("use_mmq", ctypes.c_bool),
("use_rowsplit", ctypes.c_bool),
("executable_path", ctypes.c_char_p),
("model_filename", ctypes.c_char_p),
("lora_filename", ctypes.c_char_p),
("lora_base", ctypes.c_char_p),
("draftmodel_filename", ctypes.c_char_p),
("draft_amount", ctypes.c_int),
("draft_gpulayers", ctypes.c_int),
("draft_gpusplit", ctypes.c_float * tensor_split_max),
("mmproj_filename", ctypes.c_char_p),
("visionmaxres", ctypes.c_int),
("use_mmap", ctypes.c_bool),
("use_mlock", ctypes.c_bool),
("use_smartcontext", ctypes.c_bool),
("use_contextshift", ctypes.c_bool),
("use_fastforward", ctypes.c_bool),
("clblast_info", ctypes.c_int),
("cublas_info", ctypes.c_int),
("vulkan_info", ctypes.c_char_p),
("blasbatchsize", ctypes.c_int),
("forceversion", ctypes.c_int),
("gpulayers", ctypes.c_int),
("rope_freq_scale", ctypes.c_float),
("rope_freq_base", ctypes.c_float),
("moe_experts", ctypes.c_int),
("flash_attention", ctypes.c_bool),
("tensor_split", ctypes.c_float * tensor_split_max),
("quant_k", ctypes.c_int),
("quant_v", ctypes.c_int),
("quiet", ctypes.c_bool),
("debugmode", ctypes.c_int)]
class generation_inputs(ctypes.Structure):
_fields_ = [("seed", ctypes.c_int),
("prompt", ctypes.c_char_p),
("memory", ctypes.c_char_p),
("images", ctypes.c_char_p * images_max),
("max_context_length", ctypes.c_int),
("max_length", ctypes.c_int),
("temperature", ctypes.c_float),
("top_k", ctypes.c_int),
("top_a", ctypes.c_float),
("top_p", ctypes.c_float),
("min_p", ctypes.c_float),
("typical_p", ctypes.c_float),
("tfs", ctypes.c_float),
("rep_pen", ctypes.c_float),
("rep_pen_range", ctypes.c_int),
("rep_pen_slope", ctypes.c_float),
("presence_penalty", ctypes.c_float),
("mirostat", ctypes.c_int),
("mirostat_tau", ctypes.c_float),
("mirostat_eta", ctypes.c_float),
("xtc_threshold", ctypes.c_float),
("xtc_probability", ctypes.c_float),
("sampler_order", ctypes.c_int * sampler_order_max),
("sampler_len", ctypes.c_int),
("allow_eos_token", ctypes.c_bool),
("bypass_eos_token", ctypes.c_bool),
("render_special", ctypes.c_bool),
("stream_sse", ctypes.c_bool),
("grammar", ctypes.c_char_p),
("grammar_retain_state", ctypes.c_bool),
("dynatemp_range", ctypes.c_float),
("dynatemp_exponent", ctypes.c_float),
("smoothing_factor", ctypes.c_float),
("dry_multiplier", ctypes.c_float),
("dry_base", ctypes.c_float),
("dry_allowed_length", ctypes.c_int),
("dry_penalty_last_n", ctypes.c_int),
("dry_sequence_breakers_len", ctypes.c_int),
("dry_sequence_breakers", ctypes.POINTER(ctypes.c_char_p)),
("stop_sequence_len", ctypes.c_int),
("stop_sequence", ctypes.POINTER(ctypes.c_char_p)),
("logit_biases_len", ctypes.c_int),
("logit_biases", ctypes.POINTER(logit_bias)),
("banned_tokens_len", ctypes.c_int),
("banned_tokens", ctypes.POINTER(ctypes.c_char_p))]
class generation_outputs(ctypes.Structure):
_fields_ = [("status", ctypes.c_int),
("stopreason", ctypes.c_int),
("prompt_tokens", ctypes.c_int),
("completion_tokens", ctypes.c_int),
("text", ctypes.c_char_p)]
class sd_load_model_inputs(ctypes.Structure):
_fields_ = [("model_filename", ctypes.c_char_p),
("executable_path", ctypes.c_char_p),
("clblast_info", ctypes.c_int),
("cublas_info", ctypes.c_int),
("vulkan_info", ctypes.c_char_p),
("threads", ctypes.c_int),
("quant", ctypes.c_int),
("taesd", ctypes.c_bool),
("notile", ctypes.c_bool),
("t5xxl_filename", ctypes.c_char_p),
("clipl_filename", ctypes.c_char_p),
("clipg_filename", ctypes.c_char_p),
("vae_filename", ctypes.c_char_p),
("lora_filename", ctypes.c_char_p),
("lora_multiplier", ctypes.c_float),
("quiet", ctypes.c_bool),
("debugmode", ctypes.c_int)]
class sd_generation_inputs(ctypes.Structure):
_fields_ = [("prompt", ctypes.c_char_p),
("negative_prompt", ctypes.c_char_p),
("init_images", ctypes.c_char_p),
("denoising_strength", ctypes.c_float),
("cfg_scale", ctypes.c_float),
("sample_steps", ctypes.c_int),
("width", ctypes.c_int),
("height", ctypes.c_int),
("seed", ctypes.c_int),
("sample_method", ctypes.c_char_p),
("clip_skip", ctypes.c_int)]
class sd_generation_outputs(ctypes.Structure):
_fields_ = [("status", ctypes.c_int),
("data", ctypes.c_char_p)]
class whisper_load_model_inputs(ctypes.Structure):
_fields_ = [("model_filename", ctypes.c_char_p),
("executable_path", ctypes.c_char_p),
("clblast_info", ctypes.c_int),
("cublas_info", ctypes.c_int),
("vulkan_info", ctypes.c_char_p),
("quiet", ctypes.c_bool),
("debugmode", ctypes.c_int)]
class whisper_generation_inputs(ctypes.Structure):
_fields_ = [("prompt", ctypes.c_char_p),
("audio_data", ctypes.c_char_p),
("suppress_non_speech", ctypes.c_bool),
("langcode", ctypes.c_char_p)]
class whisper_generation_outputs(ctypes.Structure):
_fields_ = [("status", ctypes.c_int),
("data", ctypes.c_char_p)]
class tts_load_model_inputs(ctypes.Structure):
_fields_ = [("threads", ctypes.c_int),
("ttc_model_filename", ctypes.c_char_p),
("cts_model_filename", ctypes.c_char_p),
("executable_path", ctypes.c_char_p),
("clblast_info", ctypes.c_int),
("cublas_info", ctypes.c_int),
("vulkan_info", ctypes.c_char_p),
("gpulayers", ctypes.c_int),
("flash_attention", ctypes.c_bool),
("ttsmaxlen", ctypes.c_int),
("quiet", ctypes.c_bool),
("debugmode", ctypes.c_int)]
class tts_generation_inputs(ctypes.Structure):
_fields_ = [("prompt", ctypes.c_char_p),
("speaker_seed", ctypes.c_int),
("audio_seed", ctypes.c_int),
("nocache", ctypes.c_bool)]
class tts_generation_outputs(ctypes.Structure):
_fields_ = [("status", ctypes.c_int),
("data", ctypes.c_char_p)]
def getdirpath():
return os.path.dirname(os.path.realpath(__file__))
def getabspath():
return os.path.dirname(os.path.abspath(__file__))
def file_exists(filename):
return os.path.exists(os.path.join(getdirpath(), filename))
def suppress_stdout():
global saved_stdout, saved_stderr, saved_stdout_py, saved_stderr_py, stdout_nullfile, stdout_nullfile_py
if not saved_stdout and not saved_stderr and not saved_stdout_py and not saved_stderr_py and not stdout_nullfile and not stdout_nullfile_py:
sys.stdout.flush()
sys.stderr.flush()
saved_stdout = os.dup(sys.stdout.fileno())
saved_stderr = os.dup(sys.stderr.fileno())
saved_stderr_py = sys.stderr
saved_stdout_py = sys.stdout
stdout_nullfile = os.open(os.devnull, os.O_WRONLY)
stdout_nullfile_py = open(os.devnull, 'w')
os.dup2(stdout_nullfile, sys.stdout.fileno())
os.dup2(stdout_nullfile, sys.stderr.fileno())
sys.stderr = sys.stdout = stdout_nullfile_py
def restore_stdout():
global saved_stdout, saved_stderr, saved_stdout_py, saved_stderr_py, stdout_nullfile, stdout_nullfile_py
if saved_stdout and saved_stderr and saved_stdout_py and saved_stderr_py and stdout_nullfile and stdout_nullfile_py:
sys.stdout = saved_stdout_py
sys.stderr = saved_stderr_py
os.dup2(saved_stdout, sys.stdout.fileno())
os.dup2(saved_stderr, sys.stderr.fileno())
os.close(stdout_nullfile)
stdout_nullfile_py.close()
os.close(saved_stdout)
os.close(saved_stderr)
saved_stdout = saved_stderr = saved_stdout_py = saved_stderr_py = stdout_nullfile = stdout_nullfile_py = None
def get_default_threads():
physical_core_limit = 1
if os.cpu_count() is not None and os.cpu_count()>1:
physical_core_limit = os.cpu_count() // 2
default_threads = (physical_core_limit if physical_core_limit<=3 else max(3,physical_core_limit-1))
processor = platform.processor()
if 'Intel' in processor:
default_threads = (8 if default_threads > 8 else default_threads) #this helps avoid e-cores.
return default_threads
def pick_existant_file(ntoption,nonntoption):
precompiled_prefix = "precompiled_"
ntexist = file_exists(ntoption)
nonntexist = file_exists(nonntoption)
precompiled_ntexist = file_exists(precompiled_prefix+ntoption)
precompiled_nonntexist = file_exists(precompiled_prefix+nonntoption)
if os.name == 'nt':
if not ntexist and precompiled_ntexist:
return (precompiled_prefix+ntoption)
if nonntexist and not ntexist:
return nonntoption
return ntoption
else:
if not nonntexist and precompiled_nonntexist:
return (precompiled_prefix+nonntoption)
if ntexist and not nonntexist:
return ntoption
return nonntoption
lib_default = pick_existant_file("koboldcpp_default.dll","koboldcpp_default.so")
lib_failsafe = pick_existant_file("koboldcpp_failsafe.dll","koboldcpp_failsafe.so")
lib_noavx2 = pick_existant_file("koboldcpp_noavx2.dll","koboldcpp_noavx2.so")
lib_clblast = pick_existant_file("koboldcpp_clblast.dll","koboldcpp_clblast.so")
lib_clblast_noavx2 = pick_existant_file("koboldcpp_clblast_noavx2.dll","koboldcpp_clblast_noavx2.so")
lib_clblast_failsafe = pick_existant_file("koboldcpp_clblast_failsafe.dll","koboldcpp_clblast_failsafe.so")
lib_cublas = pick_existant_file("koboldcpp_cublas.dll","koboldcpp_cublas.so")
lib_hipblas = pick_existant_file("koboldcpp_hipblas.dll","koboldcpp_hipblas.so")
lib_vulkan = pick_existant_file("koboldcpp_vulkan.dll","koboldcpp_vulkan.so")
lib_vulkan_noavx2 = pick_existant_file("koboldcpp_vulkan_noavx2.dll","koboldcpp_vulkan_noavx2.so")
libname = ""
lib_option_pairs = [
(lib_default, "Use CPU"),
(lib_cublas, "Use CuBLAS"),
(lib_hipblas, "Use hipBLAS (ROCm)"),
(lib_vulkan, "Use Vulkan"),
(lib_clblast, "Use CLBlast"),
(lib_noavx2, "Use CPU (Old CPU)"),
(lib_vulkan_noavx2, "Use Vulkan (Old CPU)"),
(lib_clblast_noavx2, "Use CLBlast (Old CPU)"),
(lib_clblast_failsafe, "Use CLBlast (Older CPU)"),
(lib_failsafe, "Failsafe Mode (Older CPU)")]
default_option, cublas_option, hipblas_option, vulkan_option, clblast_option, noavx2_option, vulkan_noavx2_option, clblast_noavx2_option, clblast_failsafe_option, failsafe_option = (opt if file_exists(lib) or (os.name == 'nt' and file_exists(opt + ".dll")) else None for lib, opt in lib_option_pairs)
runopts = [opt for lib, opt in lib_option_pairs if file_exists(lib)]
def init_library():
global handle, args, libname
global lib_default,lib_failsafe,lib_noavx2,lib_clblast,lib_clblast_noavx2,lib_clblast_failsafe,lib_cublas,lib_hipblas,lib_vulkan,lib_vulkan_noavx2
libname = lib_default
if args.noavx2:
if args.useclblast and (os.name!='nt' or file_exists("clblast.dll")):
if (args.failsafe) and file_exists(lib_clblast_failsafe):
libname = lib_clblast_failsafe
elif file_exists(lib_clblast_noavx2):
libname = lib_clblast_noavx2
elif (args.usevulkan is not None) and file_exists(lib_vulkan_noavx2):
libname = lib_vulkan_noavx2
elif (args.failsafe) and file_exists(lib_failsafe):
print("!!! Attempting to use FAILSAFE MODE !!!")
libname = lib_failsafe
elif file_exists(lib_noavx2):
libname = lib_noavx2
elif (args.usecublas is not None):
if file_exists(lib_cublas):
libname = lib_cublas
elif file_exists(lib_hipblas):
libname = lib_hipblas
elif (args.usevulkan is not None) and file_exists(lib_vulkan):
libname = lib_vulkan
elif args.useclblast and file_exists(lib_clblast) and (os.name!='nt' or file_exists("clblast.dll")):
libname = lib_clblast
print("Initializing dynamic library: " + libname)
dir_path = getdirpath()
abs_path = getabspath()
#add all potential paths
if os.name=='nt':
os.add_dll_directory(dir_path)
os.add_dll_directory(abs_path)
os.add_dll_directory(os.getcwd())
if libname == lib_cublas and "CUDA_PATH" in os.environ:
newpath = os.path.join(os.environ["CUDA_PATH"], "bin")
if os.path.exists(newpath):
os.add_dll_directory(newpath)
if libname == lib_hipblas and "HIP_PATH" in os.environ:
newpath = os.path.join(os.environ["HIP_PATH"], "bin")
if os.path.exists(newpath):
os.add_dll_directory(newpath)
handle = ctypes.CDLL(os.path.join(dir_path, libname))
handle.load_model.argtypes = [load_model_inputs]
handle.load_model.restype = ctypes.c_bool
handle.generate.argtypes = [generation_inputs]
handle.generate.restype = generation_outputs
handle.new_token.restype = ctypes.c_char_p
handle.new_token.argtypes = [ctypes.c_int]
handle.get_stream_count.restype = ctypes.c_int
handle.has_finished.restype = ctypes.c_bool
handle.get_last_eval_time.restype = ctypes.c_float
handle.get_last_process_time.restype = ctypes.c_float
handle.get_last_token_count.restype = ctypes.c_int
handle.get_last_seed.restype = ctypes.c_int
handle.get_total_gens.restype = ctypes.c_int
handle.get_last_stop_reason.restype = ctypes.c_int
handle.abort_generate.restype = ctypes.c_bool
handle.token_count.restype = token_count_outputs
handle.get_pending_output.restype = ctypes.c_char_p
handle.get_chat_template.restype = ctypes.c_char_p
handle.sd_load_model.argtypes = [sd_load_model_inputs]
handle.sd_load_model.restype = ctypes.c_bool
handle.sd_generate.argtypes = [sd_generation_inputs]
handle.sd_generate.restype = sd_generation_outputs
handle.whisper_load_model.argtypes = [whisper_load_model_inputs]
handle.whisper_load_model.restype = ctypes.c_bool
handle.whisper_generate.argtypes = [whisper_generation_inputs]
handle.whisper_generate.restype = whisper_generation_outputs
handle.tts_load_model.argtypes = [tts_load_model_inputs]
handle.tts_load_model.restype = ctypes.c_bool
handle.tts_generate.argtypes = [tts_generation_inputs]
handle.tts_generate.restype = tts_generation_outputs
handle.last_logprobs.restype = last_logprobs_outputs
handle.detokenize.argtypes = [token_count_outputs]
handle.detokenize.restype = ctypes.c_char_p
def set_backend_props(inputs):
clblastids = 0
if args.useclblast:
clblastids = 100 + int(args.useclblast[0])*10 + int(args.useclblast[1])
inputs.clblast_info = clblastids
# we must force an explicit tensor split
# otherwise the default will divide equally and multigpu crap will slow it down badly
inputs.cublas_info = 0
if args.usecublas:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
if not args.tensor_split:
if (args.usecublas and "0" in args.usecublas):
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ["HIP_VISIBLE_DEVICES"] = "0"
elif (args.usecublas and "1" in args.usecublas):
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
os.environ["HIP_VISIBLE_DEVICES"] = "1"
elif (args.usecublas and "2" in args.usecublas):
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
os.environ["HIP_VISIBLE_DEVICES"] = "2"
elif (args.usecublas and "3" in args.usecublas):
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
os.environ["HIP_VISIBLE_DEVICES"] = "3"
else:
if (args.usecublas and "0" in args.usecublas):
inputs.cublas_info = 0
elif (args.usecublas and "1" in args.usecublas):
inputs.cublas_info = 1
elif (args.usecublas and "2" in args.usecublas):
inputs.cublas_info = 2
elif (args.usecublas and "3" in args.usecublas):
inputs.cublas_info = 3
if args.usevulkan: #is an empty array if using vulkan without defined gpu
s = ""
for it in range(0,len(args.usevulkan)):
s += str(args.usevulkan[it])
inputs.vulkan_info = s.encode("UTF-8")
else:
inputs.vulkan_info = "".encode("UTF-8")
# set universal flags
inputs.quiet = args.quiet
inputs.debugmode = args.debugmode
inputs.executable_path = (getdirpath()+"/").encode("UTF-8")
return inputs
def end_trim_to_sentence(input_text):
enders = ['.', '!', '?', '*', '"', ')', '}', '`', ']', ';', '…']
last = -1
for ender in enders:
last = max(last, input_text.rfind(ender))
nl = input_text.rfind("\n")
last = max(last, nl)
if last > 0:
return input_text[:last + 1].strip()
return input_text.strip()
def tryparseint(value):
try:
return int(value)
except ValueError:
return value
def is_incomplete_utf8_sequence(byte_seq): #note, this will only flag INCOMPLETE sequences, corrupted ones will be ignored.
try:
byte_seq.decode('utf-8')
return False # Valid UTF-8
except UnicodeDecodeError as e:
if e.reason == 'unexpected end of data':
return True #incomplete sequence
return False #invalid sequence, but not incomplete
def unpack_to_dir(destpath = ""):
import shutil
srcpath = os.path.abspath(os.path.dirname(__file__))
cliunpack = False if destpath == "" else True
print("Attempt to unpack KoboldCpp into directory...")
if not cliunpack:
from tkinter.filedialog import askdirectory
from tkinter import messagebox
destpath = askdirectory(title='Select an empty folder to unpack KoboldCpp')
if not destpath:
return
if os.path.isdir(srcpath) and os.path.isdir(destpath) and not os.listdir(destpath):
try:
if cliunpack:
print(f"KoboldCpp will be extracted to {destpath}\nThis process may take several seconds to complete.")
else:
messagebox.showinfo("Unpack Starting", f"KoboldCpp will be extracted to {destpath}\nThis process may take several seconds to complete.")
for item in os.listdir(srcpath):
s = os.path.join(srcpath, item)
d = os.path.join(destpath, item)
if item.endswith('.pyd'): # Skip .pyd files
continue
if os.path.isdir(s):
shutil.copytree(s, d, False, None)
else:
shutil.copy2(s, d)
if cliunpack:
print(f"KoboldCpp successfully extracted to {destpath}")
else:
messagebox.showinfo("KoboldCpp Unpack Success", f"KoboldCpp successfully extracted to {destpath}")
except Exception as e:
if cliunpack:
print(f"An error occurred while unpacking: {e}")
else:
messagebox.showerror("Error", f"An error occurred while unpacking: {e}")
else:
if cliunpack:
print("The target folder is not empty or invalid. Please select an empty folder.")
else:
messagebox.showwarning("Invalid Selection", "The target folder is not empty or invalid. Please select an empty folder.")
def exit_with_error(code, message, title="Error"):
global guimode
print("")
time.sleep(1)
if guimode:
show_gui_msgbox(title, message)
else:
print(message, flush=True)
time.sleep(2)
sys.exit(code)
def utfprint(str, importance = 2): #0 = only debugmode, 1 = except quiet, 2 = always print
if args.quiet and importance<2: #quiet overrides debugmode
return
if args.debugmode < 1:
if importance==1 and (args.debugmode == -1 or args.quiet):
return
if importance==0:
return
maxlen = 32000
if args.debugmode >= 1:
maxlen = 64000
try:
strlength = len(str)
if strlength > maxlen: #limit max output len
str = str[:maxlen] + f"... (+{strlength-maxlen} chars)"
except Exception:
pass
try:
print(str)
except UnicodeEncodeError:
# Replace or omit the problematic character
utf_string = str.encode('ascii', 'ignore').decode('ascii',"ignore")
utf_string = utf_string.replace('\a', '') #remove bell characters
print(utf_string)
def bring_terminal_to_foreground():
if os.name=='nt':
ctypes.windll.user32.ShowWindow(ctypes.windll.kernel32.GetConsoleWindow(), 9)
ctypes.windll.user32.SetForegroundWindow(ctypes.windll.kernel32.GetConsoleWindow())
def simple_lcg_hash(input_string): #turns any string into a number between 10000 and 99999
a = 1664525
c = 1013904223
m = 89999 # Modulo
hash_value = 25343
for char in input_string:
hash_value = (a * hash_value + ord(char) + c) % m
hash_value += 10000
return hash_value
def string_has_overlap(str_a, str_b, maxcheck):
max_overlap = min(maxcheck, len(str_a), len(str_b))
for i in range(1, max_overlap + 1):
if str_a[-i:] == str_b[:i]:
return True
return False
def string_contains_or_overlaps_sequence_substring(inputstr, sequences):
if inputstr=="":
return False
for s in sequences:
if s.strip()=="":
continue
if s.strip() in inputstr.strip() or inputstr.strip() in s.strip():
return True
if string_has_overlap(inputstr, s, 10):
return True
return False
def get_capabilities():
global has_multiplayer, KcppVersion, friendlymodelname, friendlysdmodelname, fullsdmodelpath, mmprojpath, password, fullwhispermodelpath, ttsmodelpath
has_llm = not (friendlymodelname=="inactive")
has_txt2img = not (friendlysdmodelname=="inactive" or fullsdmodelpath=="")
has_vision = (mmprojpath!="")
has_password = (password!="")
has_whisper = (fullwhispermodelpath!="")
has_search = True if args.websearch else False
has_tts = (ttsmodelpath!="")
return {"result":"KoboldCpp", "version":KcppVersion, "protected":has_password, "llm":has_llm, "txt2img":has_txt2img,"vision":has_vision,"transcribe":has_whisper,"multiplayer":has_multiplayer,"websearch":has_search,"tts":has_tts}
def dump_gguf_metadata(file_path): #if you're gonna copy this into your own project at least credit concedo
chunk_size = 1024*1024*12 # read first 12mb of file
try:
data = None
fptr = 0
dt_table = ["u8","i8","u16","i16","u32","i32","f32","bool","str","arr","u64","i64","f64"] #13 types, else error
tt_table = ["f32","f16","q4_0","q4_1","q4_2","q4_3","q5_0","q5_1","q8_0","q8_1","q2_k","q3_k","q4_k","q5_k","q6_k","q8_k","iq2_xxs","iq2_xs","iq3_xxs","iq1_s","iq4_nl","iq3_s","iq2_s","iq4_xs","i8","i16","i32","i64","f64","iq1_m","bf16","q4_0_4_4","q4_0_4_8","q4_0_8_8","tq1_0","tq2_0","iq4_nl_4_4","unknown","unknown","unknown","unknown","unknown"]
def read_data(datatype):
nonlocal fptr, data, dt_table
if datatype=="u32":
val_bytes = data[fptr:fptr + 4]
val = struct.unpack('<I', val_bytes)[0]
fptr += 4
return val
if datatype=="u64":
val_bytes = data[fptr:fptr + 8]
val = struct.unpack('<Q', val_bytes)[0]
fptr += 8
return val
if datatype=="i32":
val_bytes = data[fptr:fptr + 4]
val = struct.unpack('<i', val_bytes)[0]
fptr += 4
return val
if datatype=="bool":
val_bytes = data[fptr:fptr + 1]
val = struct.unpack('<B', val_bytes)[0]
fptr += 1
return val
if datatype=="f32":
val_bytes = data[fptr:fptr + 4]
val = struct.unpack('<f', val_bytes)[0]
fptr += 4
return val
if datatype=="str":
val_bytes = data[fptr:fptr + 8]
str_len = struct.unpack('<Q', val_bytes)[0]
fptr += 8
val_bytes = data[fptr:fptr + str_len]
str_val = val_bytes.split(b'\0', 1)[0].decode('utf-8')
fptr += str_len
return str_val
if datatype=="arr":
val_bytes = data[fptr:fptr + 4]
arr_type = struct.unpack('<I', val_bytes)[0]
fptr += 4
val_bytes = data[fptr:fptr + 8]
arr_elems = struct.unpack('<Q', val_bytes)[0]
fptr += 8
arr_vals = []
for i in range(arr_elems):
dt_translated = dt_table[arr_type]
arr_val = read_data(dt_translated)
arr_vals.append(arr_val)
return arr_vals
print(f"Unknown Datatype: {datatype}")
return
fsize = os.path.getsize(file_path)
if fsize < 512: #ignore files under file size limit
print("This GGUF file is too small to analyze. Please ensure it is valid.")
return
with open(file_path, 'rb') as f:
file_header = f.read(4)
if file_header != b'GGUF': #file is not GGUF
print(f"File does not seem to be a GGUF: {file_header}")
return
data = f.read(chunk_size)
read_ver = read_data("u32")
if read_ver < 2:
print(f"This GGUF file is too old. Version detected: {read_ver}")
return
read_tensorcount = read_data("u64")
read_kvcount = read_data("u64")
print(f"*** GGUF FILE METADATA ***\nGGUF.version = {read_ver}\nGGUF.tensor_count = {read_tensorcount}\nGGUF.kv_count = {read_kvcount}")
for kn in range(read_kvcount):
curr_key = read_data("str")
curr_datatype = read_data("u32")
dt_translated = dt_table[curr_datatype]
curr_val = read_data(dt_translated)
if dt_translated=="arr":
print(f"{dt_translated}: {curr_key} = [{len(curr_val)}]")
elif dt_translated=="str":
print(f"{dt_translated}: {curr_key} = {curr_val[:100]}")
else:
print(f"{dt_translated}: {curr_key} = {curr_val}")
print("\n*** GGUF TENSOR INFO ***")
for kn in range(read_tensorcount):
tensor_name = read_data("str")
dims = read_data("u32")
dim_val_str = "["
for d in range(dims):
dim_val = read_data("u64")
dim_val_str += f"{'' if d==0 else ', '}{dim_val}"
dim_val_str += "]"
tensor_type = read_data("u32")
read_data("u64") # tensor_offset not used
tensor_type_str = tt_table[tensor_type]
print(f"{kn:<3}: {tensor_type_str:<8} | {tensor_name:<30} | {dim_val_str}")
print(f"Metadata and TensorInfo Bytes: {fptr}")
except Exception as e:
print(f"Error Analyzing File: {e}")
return
def read_gguf_metadata(file_path):
chunk_size = 8192 # read only first 8kb of file
try:
def read_gguf_key(keyname,data,maxval):
keylen = len(keyname)
index = data.find(keyname) # Search for the magic number, Read 2 chunks of 4 byte numbers
if index != -1 and index + keylen + 8 <= chunk_size:
start_index = index + keylen
first_value_bytes = data[start_index:start_index + 4]
second_value_bytes = data[start_index + 4:start_index + 8]
# Unpack each 4 bytes as an unsigned int32 in little-endian format
value1 = struct.unpack('<I', first_value_bytes)[0] #4 means its a uint32
value2 = struct.unpack('<I', second_value_bytes)[0]
if value1 == 4 and value2 > 0 and value2 <= maxval:
return value2 #contains the desired value
return 0
else:
return 0 #not found
fsize = os.path.getsize(file_path)
if fsize < 10000: #ignore files under 10kb
return None
with open(file_path, 'rb') as f:
file_header = f.read(4)
if file_header != b'GGUF': #file is not GGUF
return None
data = f.read(chunk_size)
layercount = read_gguf_key(b'.block_count',data,512)
head_count_kv = read_gguf_key(b'.attention.head_count_kv',data,8192)
key_length = read_gguf_key(b'.attention.key_length',data,8192)
val_length = read_gguf_key(b'.attention.value_length',data,8192)
return [layercount,head_count_kv, max(key_length,val_length)]
except Exception:
return None
def extract_modelfile_params(filepath,sdfilepath,whisperfilepath,mmprojfilepath,draftmodelpath,ttsmodelpath):
global modelfile_extracted_meta
modelfile_extracted_meta = None
sdfsize = 0
whisperfsize = 0
mmprojsize = 0
draftmodelsize = 0
ttsmodelsize = 0
if sdfilepath and os.path.exists(sdfilepath):
sdfsize = os.path.getsize(sdfilepath)
if whisperfilepath and os.path.exists(whisperfilepath):
whisperfsize = os.path.getsize(whisperfilepath)
if mmprojfilepath and os.path.exists(mmprojfilepath):
mmprojsize = os.path.getsize(mmprojfilepath)
if draftmodelpath and os.path.exists(draftmodelpath):
draftmodelsize = os.path.getsize(draftmodelpath)
if ttsmodelpath and os.path.exists(ttsmodelpath):
ttsmodelsize = os.path.getsize(ttsmodelpath)
if filepath and os.path.exists(filepath):
try:
fsize = os.path.getsize(filepath)
if fsize>10000000: #dont bother with models < 10mb as they are probably bad
ggufmeta = read_gguf_metadata(filepath)
modelfile_extracted_meta = [ggufmeta,fsize,sdfsize,whisperfsize,mmprojsize,draftmodelsize,ttsmodelsize] #extract done. note that meta may be null
except Exception:
modelfile_extracted_meta = None
def autoset_gpu_layers(ctxsize,sdquanted,bbs): #shitty algo to determine how many layers to use
global showusedmemwarning, modelfile_extracted_meta # reference cached values instead
gpumem = MaxMemory[0]
usedmem = 0
if MaxFreeMemory[0]>0:
usedmem = MaxMemory[0]-MaxFreeMemory[0]
if showusedmemwarning and usedmem > (2.5*1024*1024*1024):
showusedmemwarning = False
print(f"Note: KoboldCpp has detected that a significant amount of GPU VRAM ({usedmem/1024/1024} MB) is currently used by another application.\nFor best results, you may wish to close that application and then restart KoboldCpp.\n***")
reservedmem = max(1.3*1024*1024*1024,(0.5*1024*1024*1024 + usedmem)) # determine vram overhead
try:
if not modelfile_extracted_meta:
return 0
layerlimit = 0
fsize = modelfile_extracted_meta[1]
if fsize>10000000: #dont bother with models < 10mb
cs = ctxsize
mem = gpumem
if modelfile_extracted_meta[2] > 1024*1024*1024*5: #sdxl tax
mem -= 1024*1024*1024*(6 if sdquanted else 9)
elif modelfile_extracted_meta[2] > 1024*1024*512: #normal sd tax
mem -= 1024*1024*1024*(3.25 if sdquanted else 4.25)
if modelfile_extracted_meta[3] > 1024*1024*10: #whisper tax
mem -= 350*1024*1024
if modelfile_extracted_meta[4] > 1024*1024*10: #mmproj tax
mem -= 350*1024*1024
if modelfile_extracted_meta[5] > 1024*1024*10: #draft model tax
mem -= (modelfile_extracted_meta[5] * 1.5)
if modelfile_extracted_meta[6] > 1024*1024*10: #tts model tax
mem -= max(600*1024*1024, modelfile_extracted_meta[6] * 3)
mem = 0 if mem < 0 else mem
csmul = 1.0
if cs:
csmul = (cs/4096) if cs >= 8192 else 1.8 if cs > 4096 else 1.2 if cs > 2048 else 1.0
ggufmeta = modelfile_extracted_meta[0]
if not ggufmeta or ggufmeta[0]==0: #fail to read or no layers
sizeperlayer = fsize*csmul*0.052
layerlimit = int(min(200,(mem-usedmem)/sizeperlayer))
else:
layers = ggufmeta[0]
headcount = ggufmeta[1]
headkvlen = (ggufmeta[2] if ggufmeta[2] > 0 else 128)
ratio = (mem-usedmem)/(fsize*csmul*1.6*(1.0 if bbs <= 512 else 1.2))
computemem = layers*(4 if bbs <= 512 else (bbs/128))*headkvlen*cs*4*1.55 # apply blasbatchsize calculations if over 512
contextmem = layers*headcount*headkvlen*cs*4*1.15
if headcount > 0:
ratio = max(ratio, (mem - reservedmem - computemem) / (fsize + contextmem))
layerlimit = min(int(ratio*layers), (layers + 3))
layerlimit = (0 if layerlimit<=2 else layerlimit)
return layerlimit
except Exception:
return 0
def fetch_gpu_properties(testCL,testCU,testVK):
import subprocess
if testCU:
FetchedCUdevices = []
FetchedCUdeviceMem = []
FetchedCUfreeMem = []
faileddetectvram = False
AMDgpu = None
try: # Get NVIDIA GPU names
output = subprocess.run(['nvidia-smi','--query-gpu=name,memory.total,memory.free','--format=csv,noheader'], capture_output=True, text=True, check=True, encoding='utf-8').stdout
FetchedCUdevices = [line.split(",")[0].strip() for line in output.splitlines()]
FetchedCUdeviceMem = [line.split(",")[1].strip().split(" ")[0].strip() for line in output.splitlines()]
FetchedCUfreeMem = [line.split(",")[2].strip().split(" ")[0].strip() for line in output.splitlines()]
except Exception:
FetchedCUdeviceMem = []
FetchedCUfreeMem = []
faileddetectvram = True
pass
if len(FetchedCUdevices)==0:
try: # Get AMD ROCm GPU names
output = subprocess.run(['rocminfo'], capture_output=True, text=True, check=True, encoding='utf-8').stdout
device_name = None
for line in output.splitlines(): # read through the output line by line
line = line.strip()
if line.startswith("Marketing Name:"):
device_name = line.split(":", 1)[1].strip() # if we find a named device, temporarily save the name
elif line.startswith("Device Type:") and "GPU" in line and device_name is not None: # if the following Device Type is a GPU (not a CPU) then add it to devices list
FetchedCUdevices.append(device_name)
AMDgpu = True
elif line.startswith("Device Type:") and "GPU" not in line:
device_name = None
if FetchedCUdevices:
getamdvram = subprocess.run(['rocm-smi', '--showmeminfo', 'vram', '--csv'], capture_output=True, text=True, check=True, encoding='utf-8').stdout # fetch VRAM of devices
if getamdvram:
FetchedCUdeviceMem = [line.split(",")[1].strip() for line in getamdvram.splitlines()[1:] if line.strip()]
except Exception:
FetchedCUdeviceMem = []
FetchedCUfreeMem = []
faileddetectvram = True
pass
lowestcumem = 0
lowestfreecumem = 0
try:
for idx in range(0,4):
if(len(FetchedCUdevices)>idx):
CUDevicesNames[idx] = FetchedCUdevices[idx]
for idx in range(0,4):
if(len(FetchedCUdevices)>idx):
if len(FetchedCUdeviceMem)>idx:
dmem = int(FetchedCUdeviceMem[idx]) if AMDgpu else (int(FetchedCUdeviceMem[idx])*1024*1024)
lowestcumem = dmem if lowestcumem==0 else (dmem if dmem<lowestcumem else lowestcumem)
if len(FetchedCUfreeMem)>idx:
dmem = (int(FetchedCUfreeMem[idx])*1024*1024)
lowestfreecumem = dmem if lowestfreecumem==0 else (dmem if dmem<lowestfreecumem else lowestfreecumem)
except Exception:
lowestcumem = 0
lowestfreecumem = 0
faileddetectvram = True
if faileddetectvram:
print("Unable to detect VRAM, please set layers manually.")
MaxMemory[0] = max(lowestcumem,MaxMemory[0])
MaxFreeMemory[0] = max(lowestfreecumem,MaxFreeMemory[0])
if testVK:
try: # Get Vulkan names
output = subprocess.run(['vulkaninfo','--summary'], capture_output=True, text=True, check=True, encoding='utf-8').stdout
devicelist = [line.split("=")[1].strip() for line in output.splitlines() if "deviceName" in line]
devicetypes = [line.split("=")[1].strip() for line in output.splitlines() if "deviceType" in line]
idx = 0
for dname in devicelist:
if idx<len(VKDevicesNames):
VKDevicesNames[idx] = dname
idx += 1
if len(devicetypes) == len(devicelist):
idx = 0
for dvtype in devicetypes:
if idx<len(VKIsDGPU):
VKIsDGPU[idx] = (1 if dvtype=="PHYSICAL_DEVICE_TYPE_DISCRETE_GPU" else 0)
idx += 1
except Exception:
pass
if testCL:
try: # Get OpenCL GPU names on windows using a special binary. overwrite at known index if found.
basepath = os.path.abspath(os.path.dirname(__file__))
output = ""
data = None
try:
output = subprocess.run(["clinfo","--json"], capture_output=True, text=True, check=True, encoding='utf-8').stdout
data = json.loads(output)
except Exception:
output = subprocess.run([((os.path.join(basepath, "winclinfo.exe")) if os.name == 'nt' else "clinfo"),"--json"], capture_output=True, text=True, check=True, creationflags=subprocess.CREATE_NO_WINDOW | subprocess.DETACHED_PROCESS, encoding='utf-8').stdout
data = json.loads(output)