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koboldcpp.py
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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
sampler_order_max = 7
stop_token_max = 16
ban_token_max = 16
tensor_split_max = 16
logit_bias_max = 16
images_max = 4
bias_min_value = -100.0
bias_max_value = 100.0
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),
("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)]
handle = None
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 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_openblas = pick_existant_file("koboldcpp_openblas.dll","koboldcpp_openblas.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 = ""
def init_library():
global handle, args, libname
global lib_default,lib_failsafe,lib_openblas,lib_noavx2,lib_clblast,lib_clblast_noavx2,lib_cublas,lib_hipblas,lib_vulkan,lib_vulkan_noavx2
libname = ""
use_openblas = False # if true, uses OpenBLAS for acceleration. libopenblas.dll must exist in the same dir.
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. Non-BLAS 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. Non-BLAS 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.noblas and args.nommap):
use_failsafe = True
print("!!! Attempting to use FAILSAFE MODE !!!")
else:
print("Attempting to use non-avx2 compatibility library.")
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. Non-BLAS library will be used.")
else:
print("Attempting to use CLBlast library for faster prompt ingestion. A compatible clblast will be required.")
use_clblast = True
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. Non-BLAS 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. Non-BLAS library will be used.")
else:
print("Attempting to use Vulkan library for faster prompt ingestion. A compatible Vulkan will be required.")
use_vulkan = True
else:
if not file_exists(lib_openblas) or (os.name=='nt' and not file_exists("libopenblas.dll")):
print("Warning: OpenBLAS library file not found. Non-BLAS library will be used.")
elif args.noblas:
print("Attempting to library without OpenBLAS.")
else:
use_openblas = True
print("Attempting to use OpenBLAS library for faster prompt ingestion. A compatible libopenblas will be required.")
if sys.platform=="darwin":
print("Mac OSX note: Some people have found Accelerate actually faster than OpenBLAS. To compare, run Koboldcpp with --noblas instead.")
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_openblas:
libname = lib_openblas
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_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 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 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(prompt, memory="", images=[], max_length=32, max_context_length=512, temperature=0.7, top_k=100, top_a=0.0, top_p=0.92, min_p=0.0, typical_p=1.0, tfs=1.0, rep_pen=1.0, rep_pen_range=128, rep_pen_slope=1.0, presence_penalty=0.0, mirostat=0, mirostat_tau=5.0, mirostat_eta=0.1, sampler_order=[6,0,1,3,4,2,5], seed=-1, stop_sequence=[], use_default_badwordsids=False, stream_sse=False, grammar='', grammar_retain_state=False, genkey='', trimstop=False, quiet=False, dynatemp_range=0.0, dynatemp_exponent=1.0, smoothing_factor=0.0, logit_biases={}, render_special=False, banned_tokens=[], bypass_eos_token=False):
global maxctx, args, currentusergenkey, totalgens, pendingabortkey
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")
if max_length >= (max_context_length-1):
max_length = max_context_length-1
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.")
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
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 use_default_badwordsids
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
if sampler_order and 0 < len(sampler_order) <= sampler_order_max:
try:
for i, sampler in enumerate(sampler_order):
inputs.sampler_order[i] = sampler
inputs.sampler_len = len(sampler_order)
global showsamplerwarning
if showsamplerwarning and inputs.mirostat==0 and inputs.sampler_len>0 and (inputs.sampler_order[0]!=6 or inputs.sampler_order[inputs.sampler_len-1]!=5):
print("\n(Note: Non-default sampler_order detected. Recommended sampler values are [6,0,1,3,4,2,5]. This message will only show once per session.)")
showsamplerwarning = False
except TypeError as e:
print("ERROR: sampler_order must be a list of integers: " + str(e))
inputs.seed = seed
for n in range(stop_token_max):
if not stop_sequence or n >= len(stop_sequence):
inputs.stop_sequence[n] = "".encode("UTF-8")
elif stop_sequence[n]==None:
inputs.stop_sequence[n] = "".encode("UTF-8")
else:
inputs.stop_sequence[n] = stop_sequence[n].encode("UTF-8")
bias_list = []
try:
if logit_biases and len(logit_biases) > 0:
bias_list = [{"key": key, "value": value} for key, value in logit_biases.items()]
except Exception as ex:
print(f"Logit bias dictionary is invalid: {ex}")
for n in range(logit_bias_max):
if n >= len(bias_list):
inputs.logit_biases[n] = logit_bias(-1, 0.0)
else:
try:
t_id = int(bias_list[n]['key'])
bias = float(bias_list[n]['value'])
t_id = -1 if t_id < 0 else t_id
bias = (bias_max_value if bias > bias_max_value else (bias_min_value if bias < bias_min_value else bias))
inputs.logit_biases[n] = logit_bias(t_id, bias)
except Exception as ex:
inputs.logit_biases[n] = logit_bias(-1, 0.0)
print(f"Skipped unparsable logit bias:{ex}")
for n in range(ban_token_max):
if not banned_tokens or n >= len(banned_tokens):
inputs.banned_tokens[n] = "".encode("UTF-8")
else:
inputs.banned_tokens[n] = banned_tokens[n].encode("UTF-8")
currentusergenkey = genkey
totalgens += 1
#early exit if aborted
if pendingabortkey!="" and pendingabortkey==genkey:
print(f"\nDeferred Abort for GenKey: {pendingabortkey}")
pendingabortkey = ""
return {"text":"","status":-1,"stopreason":-1}
else:
ret = handle.generate(inputs)
outstr = ""
if ret.status==1:
outstr = ret.text.decode("UTF-8","ignore")
if trimstop:
for trim_str in stop_sequence:
sindex = outstr.find(trim_str)
if sindex != -1 and trim_str!="":
outstr = outstr[:sindex]
return {"text":outstr,"status":ret.status,"stopreason":ret.stopreason}
def sd_load_model(model_filename,vae_filename,lora_filename):
global args
inputs = sd_load_model_inputs()
inputs.debugmode = args.debugmode
inputs.executable_path = (getdirpath()+"/").encode("UTF-8")
inputs.model_filename = model_filename.encode("UTF-8")
thds = args.threads
quant = 0
if args.sdthreads and args.sdthreads > 0:
sdt = int(args.sdthreads)
if sdt > 0:
thds = sdt
if args.sdquant:
quant = 1
inputs.threads = thds
inputs.quant = quant
inputs.taesd = True if args.sdvaeauto else False
inputs.vae_filename = vae_filename.encode("UTF-8")
inputs.lora_filename = lora_filename.encode("UTF-8")
inputs.lora_multiplier = args.sdloramult
inputs = set_backend_props(inputs)
ret = handle.sd_load_model(inputs)
return ret
def sd_generate(genparams):
global maxctx, args, currentusergenkey, totalgens, pendingabortkey, chatcompl_adapter
default_adapter = {} if chatcompl_adapter is None else chatcompl_adapter
adapter_obj = genparams.get('adapter', default_adapter)
forced_negprompt = adapter_obj.get("add_sd_negative_prompt", "")
forced_posprompt = adapter_obj.get("add_sd_prompt", "")
prompt = genparams.get("prompt", "high quality")
negative_prompt = genparams.get("negative_prompt", "")
if forced_negprompt!="":
if negative_prompt!="":
negative_prompt += ", " + forced_negprompt
else:
negative_prompt = forced_negprompt
if forced_posprompt!="":
if prompt!="":
prompt += ", " + forced_posprompt
else:
prompt = forced_posprompt
init_images_arr = genparams.get("init_images", [])
init_images = ("" if (not init_images_arr or len(init_images_arr)==0 or not init_images_arr[0]) else init_images_arr[0])
denoising_strength = genparams.get("denoising_strength", 0.6)
cfg_scale = genparams.get("cfg_scale", 5)
sample_steps = tryparseint(genparams.get("steps", 20))
width = tryparseint(genparams.get("width", 512))
height = tryparseint(genparams.get("height", 512))
seed = tryparseint(genparams.get("seed", -1))
sample_method = genparams.get("sampler_name", "k_euler_a")
is_quiet = True if (args.quiet or args.debugmode == -1) else False
clip_skip = tryparseint(genparams.get("clip_skip", -1))
#clean vars
width = width - (width%64)
height = height - (height%64)
cfg_scale = (1 if cfg_scale < 1 else (25 if cfg_scale > 25 else cfg_scale))
sample_steps = (1 if sample_steps < 1 else (80 if sample_steps > 80 else sample_steps))
reslimit = 1024
width = (64 if width < 64 else width)
height = (64 if height < 64 else height)
if args.sdclamped:
sample_steps = (40 if sample_steps > 40 else sample_steps)
reslimit = int(args.sdclamped)
reslimit = (512 if reslimit<512 else reslimit)
print(f"\nImgGen: Clamped Mode (For Shared Use). Step counts and resolution are clamped to {reslimit}x{reslimit}.")
biggest = max(width,height)
if biggest > reslimit:
scaler = biggest / reslimit
width = int(width / scaler)
height = int(height / scaler)
width = width - (width%64)
height = height - (height%64)
inputs = sd_generation_inputs()
inputs.prompt = prompt.encode("UTF-8")
inputs.negative_prompt = negative_prompt.encode("UTF-8")
inputs.init_images = init_images.encode("UTF-8")
inputs.cfg_scale = cfg_scale
inputs.denoising_strength = denoising_strength
inputs.sample_steps = sample_steps
inputs.width = width
inputs.height = height
inputs.seed = seed
inputs.sample_method = sample_method.lower().encode("UTF-8")
inputs.quiet = is_quiet
inputs.clip_skip = clip_skip
ret = handle.sd_generate(inputs)
outstr = ""
if ret.status==1:
outstr = ret.data.decode("UTF-8","ignore")
return outstr
def whisper_load_model(model_filename):
global args
inputs = whisper_load_model_inputs()
inputs.debugmode = args.debugmode
inputs.executable_path = (getdirpath()+"/").encode("UTF-8")
inputs.model_filename = model_filename.encode("UTF-8")
inputs = set_backend_props(inputs)
ret = handle.whisper_load_model(inputs)
return ret
def whisper_generate(genparams):
global args
is_quiet = True if (args.quiet or args.debugmode == -1) else False
prompt = genparams.get("prompt", "")
audio_data = genparams.get("audio_data", "")
if audio_data.startswith("data:audio"):
audio_data = audio_data.split(",", 1)[1]
inputs = whisper_generation_inputs()
inputs.prompt = prompt.encode("UTF-8")
inputs.audio_data = audio_data.encode("UTF-8")
inputs.quiet = is_quiet
ret = handle.whisper_generate(inputs)
outstr = ""
if ret.status==1:
outstr = ret.data.decode("UTF-8","ignore")
return outstr
def utfprint(str):
maxlen = 25000
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
#################################################################
### A hacky simple HTTP server simulating a kobold api by Concedo
### we are intentionally NOT using flask, because we want MINIMAL dependencies
#################################################################
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 = 2048
maxhordectx = 2048
maxhordelen = 256
modelbusy = threading.Lock()
requestsinqueue = 0
defaultport = 5001
KcppVersion = "1.69.1"
showdebug = True
showsamplerwarning = True
showmaxctxwarning = 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
gui_layers_untouched = True
runmode_untouched = True
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
def transform_genparams(genparams, api_format):
#api format 1=basic,2=kai,3=oai,4=oai-chat,5=interrogate
#alias all nonstandard alternative names for rep pen.
rp1 = genparams.get('repeat_penalty', 1.0)
rp2 = genparams.get('repetition_penalty', 1.0)
rp3 = genparams.get('rep_pen', 1.0)
rp_max = max(rp1,rp2,rp3)
genparams["rep_pen"] = rp_max
if api_format==1:
genparams["prompt"] = genparams.get('text', "")
genparams["top_k"] = int(genparams.get('top_k', 120))
genparams["max_length"] = genparams.get('max', 150)
elif api_format==2:
if "ignore_eos" in genparams and not ("use_default_badwordsids" in genparams):
genparams["use_default_badwordsids"] = genparams.get('ignore_eos', False)
elif api_format==3 or api_format==4:
genparams["max_length"] = genparams.get('max_tokens', (256 if api_format==4 else 150))
presence_penalty = genparams.get('presence_penalty', genparams.get('frequency_penalty', 0.0))
genparams["presence_penalty"] = presence_penalty
# openai allows either a string or a list as a stop sequence
if isinstance(genparams.get('stop',[]), list):
genparams["stop_sequence"] = genparams.get('stop', [])
else:
genparams["stop_sequence"] = [genparams.get('stop')]
genparams["sampler_seed"] = tryparseint(genparams.get('seed', -1))
genparams["use_default_badwordsids"] = genparams.get('ignore_eos', False)
genparams["mirostat"] = genparams.get('mirostat_mode', 0)
if api_format==4:
# translate openai chat completion messages format into one big string.
messages_array = genparams.get('messages', [])
default_adapter = {} if chatcompl_adapter is None else chatcompl_adapter
adapter_obj = genparams.get('adapter', default_adapter)
messages_string = ""
system_message_start = adapter_obj.get("system_start", "\n### Instruction:\n")
system_message_end = adapter_obj.get("system_end", "")
user_message_start = adapter_obj.get("user_start", "\n### Instruction:\n")
user_message_end = adapter_obj.get("user_end", "")
assistant_message_start = adapter_obj.get("assistant_start", "\n### Response:\n")
assistant_message_end = adapter_obj.get("assistant_end", "")
images_added = []
for message in messages_array:
if message['role'] == "system":
messages_string += system_message_start
elif message['role'] == "user":
messages_string += user_message_start
elif message['role'] == "assistant":
messages_string += assistant_message_start
# content can be a string or an array of objects
curr_content = message['content']
if isinstance(curr_content, str):
messages_string += curr_content
elif isinstance(curr_content, list): #is an array
for item in curr_content:
if item['type']=="text":
messages_string += item['text']
elif item['type']=="image_url":
if item['image_url'] and item['image_url']['url'] and item['image_url']['url'].startswith("data:image"):
images_added.append(item['image_url']['url'].split(",", 1)[1])
if message['role'] == "system":
messages_string += system_message_end
elif message['role'] == "user":
messages_string += user_message_end
elif message['role'] == "assistant":
messages_string += assistant_message_end
messages_string += assistant_message_start
genparams["prompt"] = messages_string
if len(images_added)>0:
genparams["images"] = images_added
if len(genparams.get('stop_sequence', []))==0: #only set stop seq if it wont overwrite existing
genparams["stop_sequence"] = [user_message_start.strip(),assistant_message_start.strip()]
else:
genparams["stop_sequence"].append(user_message_start.strip())
genparams["stop_sequence"].append(assistant_message_start.strip())
genparams["trim_stop"] = True
elif api_format==5:
firstimg = genparams.get('image', "")
genparams["images"] = [firstimg]
genparams["max_length"] = 42
genparams["prompt"] = "### Instruction: In one sentence, write a descriptive caption for this image.\n### Response:"
return genparams
class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
sys_version = ""
server_version = "ConcedoLlamaForKoboldServer"
def __init__(self, addr, port):
self.addr = addr
self.port = port
def __call__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def log_message(self, format, *args):
global showdebug
if showdebug:
super().log_message(format, *args)
pass
def extract_b64string_from_file_upload(self, body):
try:
if 'content-type' in self.headers and self.headers['content-type']:
boundary = self.headers['content-type'].split("=")[1].encode()
if boundary:
fparts = body.split(boundary)
for fpart in fparts:
detected_upload_filename = re.findall(r'Content-Disposition.*name="file"; filename="(.*)"', fpart.decode('utf-8',errors='ignore'))
if detected_upload_filename and len(detected_upload_filename)>0:
utfprint(f"Detected uploaded file: {detected_upload_filename[0]}")
file_data = fpart.split(b'\r\n\r\n')[1].rsplit(b'\r\n', 1)[0]
file_data_base64 = base64.b64encode(file_data).decode('utf-8',"ignore")
base64_string = f"data:audio/wav;base64,{file_data_base64}"
return base64_string
print("Uploaded file not found.")
return None
except Exception as e:
print(f"File Upload Process Error: {e}")
return None
async def generate_text(self, genparams, api_format, stream_flag):
from datetime import datetime
global friendlymodelname, chatcompl_adapter, currfinishreason
is_quiet = args.quiet
currfinishreason = "null"
def run_blocking(): #api format 1=basic,2=kai,3=oai,4=oai-chat
#flag instance as non-idle for a while
washordereq = genparams.get('genkey', '').startswith('HORDEREQ_')
if not washordereq:
global last_non_horde_req_time
last_non_horde_req_time = time.time()
return generate(
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', 150),
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', 256),
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),
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', []),
use_default_badwordsids=genparams.get('use_default_badwordsids', 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_tokens=genparams.get('banned_tokens', []),
bypass_eos_token=genparams.get('bypass_eos', False),
)
genout = {"text":"","status":-1,"stopreason":-1}
if stream_flag:
loop = asyncio.get_event_loop()
executor = ThreadPoolExecutor()
genout = await loop.run_in_executor(executor, run_blocking)
else:
genout = run_blocking()
recvtxt = genout['text']
currfinishreason = ("length" if (genout['stopreason']!=1) else "stop")
#flag instance as non-idle for a while
washordereq = genparams.get('genkey', '').startswith('HORDEREQ_')
if not washordereq:
global last_non_horde_req_time
last_non_horde_req_time = time.time()
if (args.debugmode != -1 and not is_quiet) or args.debugmode >= 1:
utfprint("\nOutput: " + recvtxt)
if api_format==1:
res = {"data": {"seqs":[recvtxt]}}
elif api_format==3:
res = {"id": "cmpl-1", "object": "text_completion", "created": 1, "model": friendlymodelname,
"usage": {"prompt_tokens": 100,"completion_tokens": 100,"total_tokens": 200},
"choices": [{"text": recvtxt, "index": 0, "finish_reason": currfinishreason}]}
elif api_format==4:
res = {"id": "chatcmpl-1", "object": "chat.completion", "created": 1, "model": friendlymodelname,
"usage": {"prompt_tokens": 100,"completion_tokens": 100,"total_tokens": 200},
"choices": [{"index": 0, "message":{"role": "assistant", "content": recvtxt,}, "finish_reason": currfinishreason}]}
elif api_format==5:
res = {"caption": end_trim_to_sentence(recvtxt)}
else:
res = {"results": [{"text": recvtxt, "finish_reason":currfinishreason}]}
try:
return res
except Exception as e:
print(f"Generate: Error while generating: {e}")
async def send_oai_sse_event(self, data):
if data=="[DONE]":
self.wfile.write(f'data: {data}'.encode())
else:
self.wfile.write(f'data: {data}\n\n'.encode())
self.wfile.flush()
async def send_kai_sse_event(self, data):
self.wfile.write(f'event: message\n'.encode())
self.wfile.write(f'data: {data}\n\n'.encode())
self.wfile.flush()
async def handle_sse_stream(self, genparams, api_format):