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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 Kobold Lite have to offer.
import ctypes
import os
import argparse
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),
("banned_tokens", ctypes.c_char_p * ban_token_max),
("tensor_split", ctypes.c_float * tensor_split_max)]
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),
("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),
("unban_tokens_rt", 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)]
class generation_outputs(ctypes.Structure):
_fields_ = [("status", ctypes.c_int),
("text", ctypes.c_char_p)]
class sd_load_model_inputs(ctypes.Structure):
_fields_ = [("model_filename", 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),
("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),
("quiet", ctypes.c_bool)]
class sd_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_hipblas and "HIP_PATH" in os.environ:
os.add_dll_directory(os.path.join(os.environ["HIP_PATH"], "bin"))
if args.debugmode == 1:
print(f"HIP/ROCm SDK at {os.environ['HIP_PATH']} included in .DLL load path")
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
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:
s = ""
for l in range(0,len(args.usevulkan)):
s += str(args.usevulkan[l])
if s=="":
s = "0"
inputs.vulkan_info = s.encode("UTF-8")
else:
inputs.vulkan_info = "0".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 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.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
banned_tokens = args.bantokens
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")
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, 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={}):
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.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.unban_tokens_rt = not use_default_badwordsids
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: Sub-optimal sampler_order detected. You may have reduced quality. 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}")
currentusergenkey = genkey
totalgens += 1
#early exit if aborted
if pendingabortkey!="" and pendingabortkey==genkey:
print(f"\nDeferred Abort for GenKey: {pendingabortkey}")
pendingabortkey = ""
return ""
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 outstr
def sd_load_model(model_filename):
global args
inputs = sd_load_model_inputs()
inputs.debugmode = args.debugmode
inputs.model_filename = model_filename.encode("UTF-8")
thds = args.threads
quant = 0
if len(args.sdconfig) > 2:
sdt = int(args.sdconfig[2])
if sdt > 0:
thds = sdt
if len(args.sdconfig) > 3:
quant = (1 if args.sdconfig[3]=="quant" else 0)
inputs.threads = thds
inputs.quant = quant
inputs = set_backend_props(inputs)
ret = handle.sd_load_model(inputs)
return ret
def sd_generate(genparams):
global maxctx, args, currentusergenkey, totalgens, pendingabortkey
prompt = genparams.get("prompt", "high quality")
negative_prompt = genparams.get("negative_prompt", "")
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 = genparams.get("steps", 20)
width = genparams.get("width", 512)
height = genparams.get("height", 512)
seed = genparams.get("seed", -1)
sample_method = genparams.get("sampler_name", "k_euler_a")
is_quiet = True if args.quiet else False
#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)
#quick mode
if args.sdconfig and len(args.sdconfig)>1:
if args.sdconfig[1]=="quick":
cfg_scale = 1
sample_steps = 7
sample_method = "dpm++ 2m karras"
reslimit = 512
print("\nSDConfig: Quick Mode (Low Quality). Step counts, resolution, sampler, and cfg scale are fixed.")
elif args.sdconfig[1]=="clamped":
sample_steps = (40 if sample_steps > 40 else sample_steps)
reslimit = 512
print("\nSDConfig: Clamped Mode (For Shared Use). Step counts and resolution are clamped.")
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
ret = handle.sd_generate(inputs)
outstr = ""
if ret.status==1:
outstr = ret.data.decode("UTF-8","ignore")
return outstr
def utfprint(str):
maxlen = 99999
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')
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())
#################################################################
### 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
maxctx = 2048
maxhordectx = 2048
maxhordelen = 256
modelbusy = threading.Lock()
requestsinqueue = 0
defaultport = 5001
KcppVersion = "1.63"
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
sslvalid = False
nocertify = False
start_time = time.time()
last_req_time = time.time()
last_non_horde_req_time = time.time()
class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
sys_version = ""
server_version = "ConcedoLlamaForKoboldServer"
def __init__(self, addr, port, embedded_kailite, embedded_kcpp_docs):
self.addr = addr
self.port = port
self.embedded_kailite = embedded_kailite
self.embedded_kcpp_docs = embedded_kcpp_docs
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
async def generate_text(self, genparams, api_format, stream_flag):
from datetime import datetime
global friendlymodelname, chatcompl_adapter
is_quiet = args.quiet
def run_blocking(): #api format 1=basic,2=kai,3=oai,4=oai-chat
#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', 100)
elif api_format==3 or api_format==4:
genparams["max_length"] = genparams.get('max_tokens', 100)
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"] = 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"] = 32
genparams["prompt"] = "### Instruction: In one sentence, write a descriptive caption for this image.\n### Response:"
#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', 100),
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),
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=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', {})
)
recvtxt = ""
if stream_flag:
loop = asyncio.get_event_loop()
executor = ThreadPoolExecutor()
recvtxt = await loop.run_in_executor(executor, run_blocking)
else:
recvtxt = run_blocking()
#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": "length"}]}
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": "length"}]}
elif api_format==5:
res = {"caption": end_trim_to_sentence(recvtxt)}
else:
res = {"results": [{"text": recvtxt}]}
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, api_format):
global friendlymodelname
self.send_response(200)
self.send_header("cache-control", "no-cache")
self.send_header("connection", "keep-alive")
self.end_headers(content_type='text/event-stream')
current_token = 0
incomplete_token_buffer = bytearray()
await asyncio.sleep(0.25) #anti race condition, prevent check from overtaking generate
try:
while True:
streamDone = handle.has_finished() #exit next loop on done
tokenStr = ""
streamcount = handle.get_stream_count()
while current_token < streamcount:
token = handle.new_token(current_token)
if token is None: # Token isnt ready yet, received nullpointer
break
current_token += 1
newbyte = ctypes.string_at(token)
incomplete_token_buffer += bytearray(newbyte)
tokenSeg = incomplete_token_buffer.decode("UTF-8","ignore")
if tokenSeg!="":
incomplete_token_buffer.clear()
tokenStr += tokenSeg
if tokenStr!="":
if api_format == 4: # if oai chat, set format to expected openai streaming response
event_str = json.dumps({"id":"koboldcpp","object":"chat.completion.chunk","created":1,"model":friendlymodelname,"choices":[{"index":0,"finish_reason":"length","delta":{'role':'assistant','content':tokenStr}}]})
await self.send_oai_sse_event(event_str)
elif api_format == 3: # non chat completions
event_str = json.dumps({"id":"koboldcpp","object":"text_completion","created":1,"model":friendlymodelname,"choices":[{"index":0,"finish_reason":"length","text":tokenStr}]})
await self.send_oai_sse_event(event_str)
else:
event_str = json.dumps({"token": tokenStr})
await self.send_kai_sse_event(event_str)
tokenStr = ""
else:
await asyncio.sleep(0.02) #this should keep things responsive
if streamDone:
if api_format == 4 or api_format == 3: # if oai chat, send last [DONE] message consistent with openai format
await self.send_oai_sse_event('[DONE]')
break
except Exception as ex:
print("Token streaming was interrupted or aborted!")
print(ex)
handle.abort_generate()
time.sleep(0.2) #short delay
# flush buffers, sleep a bit to make sure all data sent, and then force close the connection
self.wfile.flush()
await asyncio.sleep(0.1)
self.close_connection = True
await asyncio.sleep(0.05)
async def handle_request(self, genparams, api_format, stream_flag):
tasks = []
try:
if stream_flag:
tasks.append(self.handle_sse_stream(api_format))
generate_task = asyncio.create_task(self.generate_text(genparams, api_format, stream_flag))
tasks.append(generate_task)
await asyncio.gather(*tasks)
generate_result = generate_task.result()
return generate_result
except (BrokenPipeError, ConnectionAbortedError) as cae: # attempt to abort if connection lost
print("An ongoing connection was aborted or interrupted!")
print(cae)
handle.abort_generate()
time.sleep(0.2) #short delay
except Exception as e:
print(e)
def secure_endpoint(self): #returns false if auth fails. caller should exit
#handle password stuff
if password and password !="":
auth_header = None
auth_ok = False
if 'Authorization' in self.headers:
auth_header = self.headers['Authorization']
elif 'authorization' in self.headers:
auth_header = self.headers['authorization']
if auth_header != None and auth_header.startswith('Bearer '):
token = auth_header[len('Bearer '):].strip()
if token==password:
auth_ok = True
if auth_ok==False:
self.send_response(401)
self.end_headers(content_type='application/json')
self.wfile.write(json.dumps({"detail": {
"error": "Unauthorized",
"msg": "Authentication key is missing or invalid.",
"type": "unauthorized",
}}).encode())
return False
return True
def noscript_webui(self):
global modelbusy
import html
import urllib.parse as urlparse
parsed_url = urlparse.urlparse(self.path)
parsed_dict = urlparse.parse_qs(parsed_url.query)
reply = ""
status = str(parsed_dict['status'][0]) if 'status' in parsed_dict else "Ready To Generate"
prompt = str(parsed_dict['prompt'][0]) if 'prompt' in parsed_dict else ""
max_length = int(parsed_dict['max_length'][0]) if 'max_length' in parsed_dict else 100
temperature = float(parsed_dict['temperature'][0]) if 'temperature' in parsed_dict else 0.7
top_k = int(parsed_dict['top_k'][0]) if 'top_k' in parsed_dict else 100
top_p = float(parsed_dict['top_p'][0]) if 'top_p' in parsed_dict else 0.9
rep_pen = float(parsed_dict['rep_pen'][0]) if 'rep_pen' in parsed_dict else 1.0
use_default_badwordsids = int(parsed_dict['use_default_badwordsids'][0]) if 'use_default_badwordsids' in parsed_dict else 0
gencommand = (parsed_dict['generate'][0] if 'generate' in parsed_dict else "")=="Generate"
if modelbusy.locked():
status = "Model is currently busy, try again later."
elif gencommand:
if prompt=="" or max_length<=0:
status = "Need a valid prompt and length to generate."
else:
if max_length>512:
max_length = 512
epurl = f"http://localhost:{args.port}"
if args.host!="":
epurl = f"http://{args.host}:{args.port}"
gen_payload = {"prompt": prompt,"max_length": max_length,"temperature": temperature,"prompt": prompt,"top_k": top_k,"top_p": top_p,"rep_pen": rep_pen,"use_default_badwordsids":use_default_badwordsids}
respjson = make_url_request(f'{epurl}/api/v1/generate', gen_payload)
reply = html.escape(respjson["results"][0]["text"])
status = "Generation Completed"
if "generate" in parsed_dict:
del parsed_dict["generate"]
parsed_dict["prompt"] = prompt + reply
parsed_dict["status"] = status
updated_query_string = urlparse.urlencode(parsed_dict, doseq=True)
updated_path = parsed_url._replace(query=updated_query_string).geturl()
self.path = updated_path
self.send_response(302)
self.send_header("location", self.path)
self.end_headers(content_type='text/html')
return
finalhtml = f'''<!doctype html>