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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, math, re
import argparse
import platform
import base64
import json, sys, http.server, time, asyncio, socket, threading
from concurrent.futures import ThreadPoolExecutor
from datetime import datetime, timezone
# constants
sampler_order_max = 7
stop_token_max = 32
ban_token_max = 48
tensor_split_max = 16
logit_bias_max = 32
dry_seq_break_max = 24
images_max = 4
bias_min_value = -100.0
bias_max_value = 100.0
# global vars
handle = None
friendlymodelname = "inactive"
friendlysdmodelname = "inactive"
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
maxctx = 4096
maxhordectx = 4096
maxhordelen = 400
modelbusy = threading.Lock()
requestsinqueue = 0
defaultport = 5001
KcppVersion = "1.76.yr1-ROCm"
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
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))]
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),
("mmproj_filename", ctypes.c_char_p),
("use_mmap", ctypes.c_bool),
("use_mlock", ctypes.c_bool),
("use_smartcontext", ctypes.c_bool),
("use_contextshift", ctypes.c_bool),
("clblast_info", ctypes.c_int),
("cublas_info", ctypes.c_int),
("vulkan_info", ctypes.c_char_p),
("blasbatchsize", ctypes.c_int),
("debugmode", ctypes.c_int),
("forceversion", ctypes.c_int),
("gpulayers", ctypes.c_int),
("rope_freq_scale", ctypes.c_float),
("rope_freq_base", ctypes.c_float),
("flash_attention", ctypes.c_bool),
("tensor_split", ctypes.c_float * tensor_split_max),
("quant_k", ctypes.c_int),
("quant_v", 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),
("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", ctypes.c_char_p * dry_seq_break_max),
("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),
("stop_sequence", ctypes.c_char_p * stop_token_max),
("stream_sse", ctypes.c_bool),
("grammar", ctypes.c_char_p),
("grammar_retain_state", ctypes.c_bool),
("quiet", ctypes.c_bool),
("dynatemp_range", ctypes.c_float),
("dynatemp_exponent", ctypes.c_float),
("smoothing_factor", ctypes.c_float),
("logit_biases", logit_bias * logit_bias_max),
("banned_tokens", ctypes.c_char_p * ban_token_max)]
class generation_outputs(ctypes.Structure):
_fields_ = [("status", ctypes.c_int),
("stopreason", 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),
("vae_filename", ctypes.c_char_p),
("lora_filename", ctypes.c_char_p),
("lora_multiplier", ctypes.c_float),
("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),
("quiet", ctypes.c_bool)]
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),
("debugmode", ctypes.c_int)]
class whisper_generation_inputs(ctypes.Structure):
_fields_ = [("prompt", ctypes.c_char_p),
("audio_data", ctypes.c_char_p),
("quiet", ctypes.c_bool)]
class whisper_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()!=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_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_clblast, "Use CLBlast"),
(lib_cublas, "Use CuBLAS"),
(lib_hipblas, "Use hipBLAS (ROCm)"),
(lib_vulkan, "Use Vulkan"),
(lib_noavx2, "Use CPU (Old CPU)"),
(lib_clblast_noavx2, "Use CLBlast (Old CPU)"),
(lib_vulkan_noavx2, "Use Vulkan (Old CPU)"),
(lib_failsafe, "Failsafe Mode (Old CPU)")]
default_option, clblast_option, cublas_option, hipblas_option, vulkan_option, noavx2_option, clblast_noavx2_option, vulkan_noavx2_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 get_amd_gfx_vers_linux():
from subprocess import run
FetchedAMDgfxVersion = []
try: # Get AMD ROCm GPU gfx version
try:
output = run(['/opt/rocm/bin/rocminfo'], capture_output=True, text=True, check=True, encoding='utf-8').stdout
except Exception as e:
try:
output = run(['rocminfo'], capture_output=True, text=True, check=True, encoding='utf-8').stdout
except Exception as e:
print(f"An unexpected error occurred: {e}")
gfx_version = None
for line in output.splitlines(): # read through the output line by line
line = line.strip()
if line.startswith("Name:"):
gfx_version = line.split(":", 1)[1].strip()
elif line.startswith("Device Type:") and "GPU" in line: # if the following Device Type is a GPU (not a CPU) then add it to devices list
FetchedAMDgfxVersion.append(gfx_version)
elif line.startswith("Device Type:") and "GPU" not in line:
gfx_version = None
except Exception as e:
print(f"An unexpected error occurred: {e}")
return []
return FetchedAMDgfxVersion
def init_library():
global handle, args, libname
global lib_default,lib_failsafe,lib_noavx2,lib_clblast,lib_clblast_noavx2,lib_cublas,lib_hipblas,lib_vulkan,lib_vulkan_noavx2
libname = ""
use_clblast = False #uses CLBlast instead
use_cublas = False #uses cublas instead
use_hipblas = False #uses hipblas instead
use_noavx2 = False #uses no avx2 instructions
use_failsafe = False #uses no intrinsics, failsafe mode
use_vulkan = False #uses vulkan (needs avx2)
if args.noavx2:
use_noavx2 = True
if args.useclblast:
if not file_exists(lib_clblast_noavx2) or (os.name=='nt' and not file_exists("clblast.dll")):
print("Warning: NoAVX2 CLBlast library file not found. CPU library will be used.")
else:
print("Attempting to use NoAVX2 CLBlast library for faster prompt ingestion. A compatible clblast will be required.")
use_clblast = True
elif (args.usevulkan is not None):
if not file_exists(lib_vulkan_noavx2):
print("Warning: NoAVX2 Vulkan library file not found. CPU library will be used.")
else:
print("Attempting to use NoAVX2 Vulkan library for faster prompt ingestion. A compatible Vulkan will be required.")
use_vulkan = True
else:
if not file_exists(lib_noavx2):
print("Warning: NoAVX2 library file not found. Failsafe library will be used.")
elif (args.usecpu and args.nommap):
use_failsafe = True
print("!!! Attempting to use FAILSAFE MODE !!!")
else:
print("Attempting to use non-avx2 compatibility library.")
elif (args.usecublas is not None):
if not file_exists(lib_cublas) and not file_exists(lib_hipblas):
print("Warning: CuBLAS library file not found. CPU library will be used.")
else:
if file_exists(lib_cublas):
print("Attempting to use CuBLAS library for faster prompt ingestion. A compatible CuBLAS will be required.")
use_cublas = True
elif file_exists(lib_hipblas):
print("Attempting to use hipBLAS library for faster prompt ingestion. A compatible AMD GPU will be required.")
use_hipblas = True
elif (args.usevulkan is not None):
if not file_exists(lib_vulkan):
print("Warning: Vulkan library file not found. CPU library will be used.")
else:
print("Attempting to use Vulkan library for faster prompt ingestion. A compatible Vulkan will be required.")
use_vulkan = True
elif args.useclblast:
if not file_exists(lib_clblast) or (os.name=='nt' and not file_exists("clblast.dll")):
print("Warning: CLBlast library file not found. CPU library will be used.")
else:
print("Attempting to use CLBlast library for faster prompt ingestion. A compatible clblast will be required.")
use_clblast = True
else:
print("Attempting to use CPU library.")
if use_noavx2:
if use_failsafe:
libname = lib_failsafe
elif use_clblast:
libname = lib_clblast_noavx2
elif use_vulkan:
libname = lib_vulkan_noavx2
else:
libname = lib_noavx2
else:
if use_clblast:
libname = lib_clblast
elif use_cublas:
libname = lib_cublas
elif use_hipblas:
libname = lib_hipblas
elif use_vulkan:
libname = lib_vulkan
else:
libname = lib_default
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.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
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 not args.tensor_split:
if (args.usecublas and "0" in args.usecublas):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ["HIP_VISIBLE_DEVICES"] = "0"
elif (args.usecublas and "1" in args.usecublas):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
os.environ["HIP_VISIBLE_DEVICES"] = "1"
elif (args.usecublas and "2" in args.usecublas):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
os.environ["HIP_VISIBLE_DEVICES"] = "2"
elif (args.usecublas and "3" in args.usecublas):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
os.environ["HIP_VISIBLE_DEVICES"] = "3"
else:
if (args.usecublas and "0" in args.usecublas):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
inputs.cublas_info = 0
elif (args.usecublas and "1" in args.usecublas):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
inputs.cublas_info = 1
elif (args.usecublas and "2" in args.usecublas):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
inputs.cublas_info = 2
elif (args.usecublas and "3" in args.usecublas):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
inputs.cublas_info = 3
if args.usevulkan: #is an empty array if using vulkan without defined gpu
s = ""
for l in range(0,len(args.usevulkan)):
s += str(args.usevulkan[l])
inputs.vulkan_info = s.encode("UTF-8")
else:
inputs.vulkan_info = "".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 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(f"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):
maxlen = 32000
if args.debugmode >= 1:
maxlen = 64000
strlength = len(str)
if strlength > maxlen: #limit max output len
str = str[:maxlen] + f"... (+{strlength-maxlen} chars)"
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 string_contains_sequence_substring(inputstr,sequences):
if inputstr.strip()=="":
return False
for s in sequences:
if s.strip()=="":
continue
if s.strip() in inputstr.strip() or inputstr.strip() in s.strip():
return True
return False
import struct
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 as ex:
return None
def extract_modelfile_params(filepath,sdfilepath,whisperfilepath,mmprojfilepath):
global modelfile_extracted_meta
modelfile_extracted_meta = None
sdfsize = 0
whisperfsize = 0
mmprojsize = 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 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] #extract done. note that meta may be null
except Exception as ex:
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(1.5*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
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*1.55)
computemem = layers*(4 if bbs <= 512 else (bbs/128))*headkvlen*cs*4*1.5 # apply blasbatchsize calculations if over 512
contextmem = layers*headcount*headkvlen*cs*4*1.1
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 as ex:
return 0
def fetch_gpu_properties(testCL,testCU,testVK):
import subprocess
if testCU:
FetchedCUdevices = []
FetchedCUdeviceMem = []
FetchedCUfreeMem = []
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 as e:
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 as e:
pass
lowestcumem = 0
lowestfreecumem = 0
for idx in range(0,4):
if(len(FetchedCUdevices)>idx):
CUDevicesNames[idx] = 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)
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 as e:
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 as e1:
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)
plat = 0
dev = 0
lowestclmem = 0
for platform in data["devices"]:
dev = 0
for device in platform["online"]:
dname = device["CL_DEVICE_NAME"]
dmem = int(device["CL_DEVICE_GLOBAL_MEM_SIZE"])
idx = plat+dev*2
if idx<len(CLDevices):
CLDevicesNames[idx] = dname
lowestclmem = dmem if lowestclmem==0 else (dmem if dmem<lowestclmem else lowestclmem)
dev += 1
plat += 1
MaxMemory[0] = max(lowestclmem,MaxMemory[0])
except Exception as e:
pass
return
def auto_set_backend_cli():
fetch_gpu_properties(False,True,True)
found_new_backend = False
if exitcounter < 100 and MaxMemory[0]>3500000000 and (("Use CuBLAS" in runopts and CUDevicesNames[0]!="") or "Use hipBLAS (ROCm)" in runopts) and any(CUDevicesNames):
if "Use CuBLAS" in runopts or "Use hipBLAS (ROCm)" in runopts:
args.usecublas = ["normal"]
print("Auto Selected CUDA Backend...\n")
found_new_backend = True
elif exitcounter < 100 and (1 in VKIsDGPU) and "Use Vulkan" in runopts:
for i in range(0,len(VKIsDGPU)):
if VKIsDGPU[i]==1:
args.usevulkan = []
print("Auto Selected Vulkan Backend...\n")
found_new_backend = True
break
if not found_new_backend:
print("No GPU Backend found...\n")
def load_model(model_filename):
global args
inputs = load_model_inputs()
inputs.model_filename = model_filename.encode("UTF-8")
inputs.max_context_length = maxctx #initial value to use for ctx, can be overwritten
inputs.threads = args.threads
inputs.low_vram = (True if (args.usecublas and "lowvram" in args.usecublas) else False)
inputs.use_mmq = (True if (args.usecublas and "mmq" in args.usecublas) else False)
inputs.use_rowsplit = (True if (args.usecublas and "rowsplit" in args.usecublas) else False)
inputs.vulkan_info = "0".encode("UTF-8")
inputs.blasthreads = args.blasthreads
inputs.use_mmap = (not args.nommap)
inputs.use_mlock = args.usemlock
inputs.lora_filename = "".encode("UTF-8")
inputs.lora_base = "".encode("UTF-8")
if args.lora:
inputs.lora_filename = args.lora[0].encode("UTF-8")
inputs.use_mmap = False
if len(args.lora) > 1:
inputs.lora_base = args.lora[1].encode("UTF-8")
inputs.mmproj_filename = args.mmproj.encode("UTF-8") if args.mmproj else "".encode("UTF-8")
inputs.use_smartcontext = args.smartcontext
inputs.use_contextshift = (0 if args.noshift else 1)
inputs.flash_attention = args.flashattention
if args.quantkv>0:
inputs.quant_k = inputs.quant_v = args.quantkv
inputs.flash_attention = True
inputs.use_contextshift = 0
else:
inputs.quant_k = inputs.quant_v = 0
inputs.blasbatchsize = args.blasbatchsize
inputs.forceversion = args.forceversion
inputs.gpulayers = args.gpulayers
inputs.rope_freq_scale = args.ropeconfig[0]
if len(args.ropeconfig)>1:
inputs.rope_freq_base = args.ropeconfig[1]
else:
inputs.rope_freq_base = 10000
for n in range(tensor_split_max):
if args.tensor_split and n < len(args.tensor_split):
inputs.tensor_split[n] = float(args.tensor_split[n])
else:
inputs.tensor_split[n] = 0
inputs = set_backend_props(inputs)
inputs.executable_path = (getdirpath()+"/").encode("UTF-8")
inputs.debugmode = args.debugmode
ret = handle.load_model(inputs)
return ret
def generate(genparams, is_quiet=False, stream_flag=False):
global maxctx, args, currentusergenkey, totalgens, pendingabortkey
prompt = genparams.get('prompt', "")
memory = genparams.get('memory', "")
images = genparams.get('images', [])
max_context_length = genparams.get('max_context_length', maxctx)
max_length = genparams.get('max_length', 180)
temperature = genparams.get('temperature', 0.7)
top_k = genparams.get('top_k', 100)
top_a = genparams.get('top_a', 0.0)
top_p = genparams.get('top_p', 0.92)
min_p = genparams.get('min_p', 0.0)
typical_p = genparams.get('typical', 1.0)
tfs = genparams.get('tfs', 1.0)
rep_pen = genparams.get('rep_pen', 1.0)
rep_pen_range = genparams.get('rep_pen_range', 320)
rep_pen_slope = genparams.get('rep_pen_slope', 1.0)
presence_penalty = genparams.get('presence_penalty', 0.0)
mirostat = genparams.get('mirostat', 0)
mirostat_tau = genparams.get('mirostat_tau', 5.0)
mirostat_eta = genparams.get('mirostat_eta', 0.1)
dry_multiplier = genparams.get('dry_multiplier', 0.0)
dry_base = genparams.get('dry_base', 1.75)
dry_allowed_length = genparams.get('dry_allowed_length', 2)
dry_penalty_last_n = genparams.get('dry_penalty_last_n', 320)
dry_sequence_breakers = genparams.get('dry_sequence_breakers', [])
xtc_threshold = genparams.get('xtc_threshold', 0.2)
xtc_probability = genparams.get('xtc_probability', 0)
sampler_order = genparams.get('sampler_order', [6, 0, 1, 3, 4, 2, 5])
seed = tryparseint(genparams.get('sampler_seed', -1))
stop_sequence = genparams.get('stop_sequence', [])
ban_eos_token = genparams.get('ban_eos_token', False)
stream_sse = stream_flag
grammar = genparams.get('grammar', '')
grammar_retain_state = genparams.get('grammar_retain_state', False)
genkey = genparams.get('genkey', '')
trimstop = genparams.get('trim_stop', False)
quiet = is_quiet
dynatemp_range = genparams.get('dynatemp_range', 0.0)
dynatemp_exponent = genparams.get('dynatemp_exponent', 1.0)
smoothing_factor = genparams.get('smoothing_factor', 0.0)
logit_biases = genparams.get('logit_bias', {})
render_special = genparams.get('render_special', False)
banned_strings = genparams.get('banned_strings', []) # SillyTavern uses that name
banned_tokens = genparams.get('banned_tokens', banned_strings)
bypass_eos_token = genparams.get('bypass_eos', False)
custom_token_bans = genparams.get('custom_token_bans', '')
for tok in custom_token_bans.split(','):
tok = tok.strip() # Remove leading/trailing whitespace
if tok.isdigit():
logit_biases[tok] = bias_min_value
inputs = generation_inputs()
inputs.prompt = prompt.encode("UTF-8")
inputs.memory = memory.encode("UTF-8")
for n in range(images_max):
if not images or n >= len(images):
inputs.images[n] = "".encode("UTF-8")
else:
inputs.images[n] = images[n].encode("UTF-8")
global showmaxctxwarning
if max_context_length > maxctx:
if showmaxctxwarning:
print(f"\n(Warning! Request max_context_length={max_context_length} exceeds allocated context size of {maxctx}. It will be reduced to fit. Consider launching with increased --contextsize to avoid errors. This message will only show once per session.)")
showmaxctxwarning = False
max_context_length = maxctx
min_remain = min(max_context_length-4, 16)
if max_length >= (max_context_length-min_remain):
max_length = max_context_length-min_remain
print("\nWarning: You are trying to generate with max_length near or exceeding max_context_length. Most of the context will be removed, and your outputs will not be very coherent.")
inputs.max_context_length = max_context_length # this will resize the context buffer if changed
inputs.max_length = max_length
inputs.temperature = temperature
inputs.top_k = top_k
inputs.top_a = top_a
inputs.top_p = top_p
inputs.min_p = min_p
inputs.typical_p = typical_p
inputs.tfs = tfs
inputs.rep_pen = rep_pen
inputs.rep_pen_range = rep_pen_range
inputs.rep_pen_slope = rep_pen_slope
inputs.presence_penalty = presence_penalty
inputs.stream_sse = stream_sse
inputs.quiet = quiet
inputs.dynatemp_range = dynatemp_range
inputs.dynatemp_exponent = dynatemp_exponent
inputs.smoothing_factor = smoothing_factor
inputs.grammar = grammar.encode("UTF-8")
inputs.grammar_retain_state = grammar_retain_state
inputs.allow_eos_token = not ban_eos_token
inputs.bypass_eos_token = bypass_eos_token
inputs.render_special = render_special
if mirostat in (1, 2):
inputs.mirostat = mirostat
inputs.mirostat_tau = mirostat_tau
inputs.mirostat_eta = mirostat_eta
else:
inputs.mirostat = inputs.mirostat_tau = inputs.mirostat_eta = 0
inputs.dry_multiplier = dry_multiplier
inputs.dry_base = dry_base
inputs.xtc_threshold = xtc_threshold
inputs.xtc_probability = xtc_probability
inputs.dry_allowed_length = dry_allowed_length
inputs.dry_penalty_last_n = dry_penalty_last_n