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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
class load_model_inputs(ctypes.Structure):
_fields_ = [("threads", ctypes.c_int),
("blasthreads", ctypes.c_int),
("max_context_length", ctypes.c_int),
("batch_size", ctypes.c_int),
("f16_kv", ctypes.c_bool),
("low_vram", ctypes.c_bool),
("use_mmq", 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),
("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),
("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),
("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),
("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)
("user_top", ctypes.c_float),
("slope", ctypes.c_float),
("top_correction", ctypes.c_float),]
class generation_outputs(ctypes.Structure):
_fields_ = [("status", ctypes.c_int),
("text", ctypes.c_char * 32768)]
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")
def init_library():
global handle, args
global lib_default,lib_failsafe,lib_openblas,lib_noavx2,lib_clblast,lib_clblast_noavx2,lib_cublas,lib_hipblas
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
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
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
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
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
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, ctypes.c_wchar_p] #apparently needed for osx to work. i duno why they need to interpret it that way but whatever
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_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 = ctypes.c_int
handle.get_pending_output.restype = ctypes.c_char_p
def load_model(model_filename):
global args
inputs = load_model_inputs()
inputs.model_filename = model_filename.encode("UTF-8")
inputs.batch_size = 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.blasthreads = args.blasthreads
inputs.f16_kv = True
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.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
clblastids = 0
if args.useclblast:
clblastids = 100 + int(args.useclblast[0])*10 + int(args.useclblast[1])
inputs.clblast_info = clblastids
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
# 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
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="", 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=1.1, rep_pen_range=128, 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, user_top=0.75, slope=0.1, top_correction=0.5):
global maxctx, args, currentusergenkey, totalgens
inputs = generation_inputs()
outputs = ctypes.create_unicode_buffer(ctypes.sizeof(generation_outputs))
inputs.prompt = prompt.encode("UTF-8")
inputs.memory = memory.encode("UTF-8")
if max_length >= max_context_length:
max_length = max_context_length-1
inputs.max_context_length = max_context_length # this will resize the context buffer if changed
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_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.user_top = user_top
inputs.slope = slope
inputs.top_correction = top_correction
inputs.rep_pen = rep_pen
inputs.rep_pen_range = rep_pen_range
inputs.stream_sse = stream_sse
inputs.quiet = quiet
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")
else:
inputs.stop_sequence[n] = stop_sequence[n].encode("UTF-8")
currentusergenkey = genkey
totalgens += 1
ret = handle.generate(inputs,outputs)
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 utfprint(str):
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 = "concedo/koboldcpp" # local kobold api apparently needs a hardcoded known HF model name
maxctx = 2048
maxhordectx = 2048
maxhordelen = 256
modelbusy = threading.Lock()
requestsinqueue = 0
defaultport = 5001
KcppVersion = "1.51.1"
showdebug = True
showsamplerwarning = True
showmaxctxwarning = True
session_kudos_earned = 0
session_jobs = 0
session_starttime = None
exitcounter = 0
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
args = None #global args
gui_layers_untouched = True
preloaded_story = None
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):
global friendlymodelname
is_quiet = args.quiet
def run_blocking(): #api format 1=basic,2=kai,3=oai,4=oai-chat
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:
frqp = genparams.get('frequency_penalty', 0.1)
scaled_rep_pen = genparams.get('presence_penalty', frqp) + 1
genparams["max_length"] = genparams.get('max_tokens', 100)
genparams["rep_pen"] = scaled_rep_pen
# 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', [])
adapter_obj = genparams.get('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", "")
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
messages_string += message['content']
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
return generate(
prompt=genparams.get('prompt', ""),
memory=genparams.get('memory', ""),
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),
user_top=genparams.get('user_top', 0.75),
slope=genparams.get('slope', 0.1),
top_correction=genparams.get('top_correction', 0.5),
rep_pen=genparams.get('rep_pen', 1.1),
rep_pen_range=genparams.get('rep_pen_range', 256),
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)
recvtxt = ""
if stream_flag:
loop = asyncio.get_event_loop()
executor = ThreadPoolExecutor()
recvtxt = await loop.run_in_executor(executor, run_blocking)
else:
recvtxt = run_blocking()
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,
"choices": [{"text": recvtxt, "index": 0, "finish_reason": "length"}]}
elif api_format==4:
res = {"id": "chatcmpl-1", "object": "chat.completion", "created": 1, "model": friendlymodelname,
"choices": [{"index": 0, "message":{"role": "assistant", "content": recvtxt,}, "finish_reason": "length"}]}
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}\r\n\r\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(0.05) #anti race condition, prevent check from overtaking generate
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: # if oai chat, send last [DONE] message consistent with openai format
await self.send_oai_sse_event('[DONE]')
break
# 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 = []
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)
try:
await asyncio.gather(*tasks)
generate_result = generate_task.result()
return generate_result
except ConnectionAbortedError as cae: # attempt to abort if connection lost
print(cae)
handle.abort_generate()
time.sleep(0.1) #short delay
except Exception as e:
print(e)
def do_GET(self):
global maxctx, maxhordelen, friendlymodelname, KcppVersion, totalgens, preloaded_story
self.path = self.path.rstrip('/')
response_body = None
content_type = 'application/json'
if self.path in ["", "/?"] or self.path.startswith(('/?','?')): #it's possible for the root url to have ?params without /
content_type = 'text/html'
if self.embedded_kailite is None:
response_body = (f"Embedded Kobold Lite is not found.<br>You will have to connect via the main KoboldAI client, or <a href='https://lite.koboldai.net?local=1&port={self.port}'>use this URL</a> to connect.").encode()
else:
response_body = self.embedded_kailite
elif self.path.endswith(('/api/v1/model', '/api/latest/model')):
response_body = (json.dumps({'result': friendlymodelname }).encode())
elif self.path.endswith(('/api/v1/config/max_length', '/api/latest/config/max_length')):
response_body = (json.dumps({"value": maxhordelen}).encode())
elif self.path.endswith(('/api/v1/config/max_context_length', '/api/latest/config/max_context_length')):
response_body = (json.dumps({"value": min(maxctx,maxhordectx)}).encode())
elif self.path.endswith(('/api/v1/config/soft_prompt', '/api/latest/config/soft_prompt')):
response_body = (json.dumps({"value":""}).encode())
elif self.path.endswith(('/api/v1/config/soft_prompts_list', '/api/latest/config/soft_prompts_list')):
response_body = (json.dumps({"values": []}).encode())
elif self.path.endswith(('/api/v1/info/version', '/api/latest/info/version')):
response_body = (json.dumps({"result":"1.2.5"}).encode())
elif self.path.endswith(('/api/extra/true_max_context_length')): #do not advertise this to horde
response_body = (json.dumps({"value": maxctx}).encode())
elif self.path.endswith(('/api/extra/version')):
response_body = (json.dumps({"result":"KoboldCpp","version":KcppVersion}).encode())
elif self.path.endswith(('/api/extra/perf')):
lastp = handle.get_last_process_time()
laste = handle.get_last_eval_time()
lastc = handle.get_last_token_count()
totalgens = handle.get_total_gens()
stopreason = handle.get_last_stop_reason()
response_body = (json.dumps({"last_process":lastp,"last_eval":laste,"last_token_count":lastc, "total_gens":totalgens, "stop_reason":stopreason, "queue":requestsinqueue, "idle":(0 if modelbusy.locked() else 1)}).encode())
elif self.path.endswith('/api/extra/generate/check'):
pendtxtStr = ""
if requestsinqueue==0 and totalgens>0:
pendtxt = handle.get_pending_output()
pendtxtStr = ctypes.string_at(pendtxt).decode("UTF-8","ignore")
response_body = (json.dumps({"results": [{"text": pendtxtStr}]}).encode())
elif self.path.endswith('/v1/models'):
response_body = (json.dumps({"object":"list","data":[{"id":friendlymodelname,"object":"model","created":1,"owned_by":"koboldcpp","permission":[],"root":"koboldcpp"}]}).encode())
elif self.path=="/api":
content_type = 'text/html'
if self.embedded_kcpp_docs is None:
response_body = (f"KoboldCpp API is running!\n\nAPI usage reference can be found at the wiki: https://github.com/LostRuins/koboldcpp/wiki").encode()
else:
response_body = self.embedded_kcpp_docs
elif self.path=="/v1":
content_type = 'text/html'
response_body = (f"KoboldCpp OpenAI compatible endpoint is running!\n\nFor usage reference, see https://platform.openai.com/docs/api-reference").encode()
elif self.path=="/api/extra/preloadstory":
if preloaded_story is None:
response_body = (json.dumps({}).encode())
else:
response_body = preloaded_story
elif self.path.endswith(('/api')) or self.path.endswith(('/api/v1')):
self.path = "/api"
self.send_response(302)
self.send_header("location", self.path)
self.end_headers(content_type='text/html')
return None
if response_body is None:
self.send_response(404)
self.end_headers(content_type='text/html')
rp = 'Error: HTTP Server is running, but this endpoint does not exist. Please check the URL.'
self.wfile.write(rp.encode())
else:
self.send_response(200)
self.send_header('content-length', str(len(response_body)))
self.end_headers(content_type=content_type)
self.wfile.write(response_body)
return
def do_POST(self):
global modelbusy, requestsinqueue, currentusergenkey, totalgens
content_length = int(self.headers['content-length'])
body = self.rfile.read(content_length)
self.path = self.path.rstrip('/')
response_body = None
response_code = 200
if self.path.endswith(('/api/extra/tokencount')):
try:
genparams = json.loads(body)
countprompt = genparams.get('prompt', "")
count = handle.token_count(countprompt.encode("UTF-8"))
response_body = (json.dumps({"value": count}).encode())
except Exception as e:
utfprint("Count Tokens - Body Error: " + str(e))
response_code = 400
response_body = (json.dumps({"value": -1}).encode())
elif self.path.endswith('/api/extra/abort'):
multiuserkey = ""
try:
tempbody = json.loads(body)
if isinstance(tempbody, dict):
multiuserkey = tempbody.get('genkey', "")
except Exception as e:
multiuserkey = ""
pass
if (multiuserkey=="" and requestsinqueue==0) or (multiuserkey!="" and multiuserkey==currentusergenkey):
ag = handle.abort_generate()
time.sleep(0.1) #short delay before replying
response_body = (json.dumps({"success": ("true" if ag else "false")}).encode())
print("\nGeneration Aborted")
else:
response_body = (json.dumps({"success": "false"}).encode())
elif self.path.endswith('/api/extra/generate/check'):
pendtxtStr = ""
multiuserkey = ""
try:
tempbody = json.loads(body)
if isinstance(tempbody, dict):
multiuserkey = tempbody.get('genkey', "")
except Exception as e:
multiuserkey = ""
if totalgens>0:
if (multiuserkey=="" and requestsinqueue==0) or (multiuserkey!="" and multiuserkey==currentusergenkey):
pendtxt = handle.get_pending_output()
pendtxtStr = ctypes.string_at(pendtxt).decode("UTF-8","ignore")
response_body = (json.dumps({"results": [{"text": pendtxtStr}]}).encode())
if response_body is not None:
self.send_response(response_code)
self.send_header('content-length', str(len(response_body)))
self.end_headers(content_type='application/json')
self.wfile.write(response_body)
return
reqblocking = False
muint = int(args.multiuser)
multiuserlimit = ((muint-1) if muint > 1 else 4)
#backwards compatibility for up to 5 concurrent requests, use default limit of 5 if multiuser set to 1
if muint > 0 and requestsinqueue < multiuserlimit:
reqblocking = True
requestsinqueue += 1
if not modelbusy.acquire(blocking=reqblocking):
self.send_response(503)
self.end_headers(content_type='application/json')
self.wfile.write(json.dumps({"detail": {
"msg": "Server is busy; please try again later.",
"type": "service_unavailable",
}}).encode())
return
if reqblocking:
requestsinqueue = (requestsinqueue - 1) if requestsinqueue > 0 else 0
try:
sse_stream_flag = False
api_format = 0 #1=basic,2=kai,3=oai,4=oai-chat
if self.path.endswith('/request'):
api_format = 1
if self.path.endswith(('/api/v1/generate', '/api/latest/generate')):
api_format = 2
if self.path.endswith('/api/extra/generate/stream'):
api_format = 2
sse_stream_flag = True
if self.path.endswith('/v1/completions'):
api_format = 3
if self.path.endswith('/v1/chat/completions'):
api_format = 4
if api_format > 0:
genparams = None
try:
genparams = json.loads(body)
except Exception as e:
utfprint("Body Err: " + str(body))
return self.send_response(503)
is_quiet = args.quiet
if (args.debugmode != -1 and not is_quiet) or args.debugmode >= 1:
utfprint("\nInput: " + json.dumps(genparams))
if args.foreground:
bring_terminal_to_foreground()
# Check if streaming chat completions, if so, set stream mode to true
if (api_format == 4 or api_format == 3) and "stream" in genparams and genparams["stream"]:
sse_stream_flag = True
gen = asyncio.run(self.handle_request(genparams, api_format, sse_stream_flag))
try:
# Headers are already sent when streaming
if not sse_stream_flag:
self.send_response(200)
genresp = (json.dumps(gen).encode())
self.send_header('content-length', str(len(genresp)))
self.end_headers(content_type='application/json')
self.wfile.write(genresp)
except:
print("Generate: The response could not be sent, maybe connection was terminated?")
return
finally:
modelbusy.release()
self.send_response(404)
self.end_headers(content_type='text/html')
def do_OPTIONS(self):
self.send_response(200)
self.end_headers(content_type='text/html')
def do_HEAD(self):
self.send_response(200)
self.end_headers(content_type='text/html')
def end_headers(self, content_type=None):
self.send_header('access-control-allow-origin', '*')
self.send_header('access-control-allow-methods', '*')
self.send_header('access-control-allow-headers', '*, Accept, Content-Type, Content-Length, Cache-Control, Accept-Encoding, X-CSRF-Token, Client-Agent, X-Fields, Content-Type, Authorization, X-Requested-With, X-HTTP-Method-Override, apikey, genkey')
self.send_header("cache-control", "no-store")
if content_type is not None:
self.send_header('content-type', content_type)
return super(ServerRequestHandler, self).end_headers()
def RunServerMultiThreaded(addr, port, embedded_kailite = None, embedded_kcpp_docs = None):
global exitcounter
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
sock.bind((addr, port))
sock.listen(5)
class Thread(threading.Thread):
def __init__(self, i):
threading.Thread.__init__(self)
self.i = i
self.daemon = True
self.start()
def run(self):
global exitcounter
handler = ServerRequestHandler(addr, port, embedded_kailite, embedded_kcpp_docs)
with http.server.HTTPServer((addr, port), handler, False) as self.httpd:
try:
self.httpd.socket = sock
self.httpd.server_bind = self.server_close = lambda self: None
self.httpd.serve_forever()
except (KeyboardInterrupt,SystemExit):
exitcounter = 999
self.httpd.server_close()
sys.exit(0)
finally:
exitcounter = 999
self.httpd.server_close()
sys.exit(0)
def stop(self):
global exitcounter
exitcounter = 999
self.httpd.server_close()
numThreads = 12
threadArr = []
for i in range(numThreads):
threadArr.append(Thread(i))
while 1:
try:
time.sleep(10)
except KeyboardInterrupt:
global exitcounter
exitcounter = 999
for i in range(numThreads):
threadArr[i].stop()
sys.exit(0)
# note: customtkinter-5.2.0
def show_new_gui():
from tkinter.filedialog import askopenfilename
from tkinter.filedialog import asksaveasfile
# if args received, launch
if len(sys.argv) != 1:
import tkinter as tk
root = tk.Tk() #we dont want the useless window to be visible, but we want it in taskbar
root.attributes("-alpha", 0)
args.model_param = askopenfilename(title="Select ggml model .bin or .gguf file or .kcpps config")
root.destroy()
if args.model_param and args.model_param!="" and args.model_param.lower().endswith('.kcpps'):
loadconfigfile(args.model_param)
if not args.model_param:
global exitcounter
exitcounter = 999
print("\nNo ggml model or kcpps file was selected. Exiting.")
time.sleep(3)
sys.exit(2)
return
import customtkinter as ctk
nextstate = 0 #0=exit, 1=launch
windowwidth = 540
windowheight = 500
ctk.set_appearance_mode("dark")
root = ctk.CTk()
root.geometry(str(windowwidth) + "x" + str(windowheight))
root.title("KoboldCpp v"+KcppVersion)
root.resizable(False,False)
tabs = ctk.CTkFrame(root, corner_radius = 0, width=windowwidth, height=windowheight-50)
tabs.grid(row=0, stick="nsew")
tabnames= ["Quick Launch", "Hardware", "Tokens", "Model", "Network"]
navbuttons = {}
navbuttonframe = ctk.CTkFrame(tabs, width=100, height=int(tabs.cget("height")))
navbuttonframe.grid(row=0, column=0, padx=2,pady=2)
navbuttonframe.grid_propagate(False)
tabcontentframe = ctk.CTkFrame(tabs, width=windowwidth - int(navbuttonframe.cget("width")), height=int(tabs.cget("height")))
tabcontentframe.grid(row=0, column=1, sticky="nsew", padx=2, pady=2)
tabcontentframe.grid_propagate(False)
CLDevices = ["1","2","3","4"]
CUDevices = ["1","2","3","4","All"]
CLDevicesNames = ["","","",""]
CUDevicesNames = ["","","","",""]
MaxMemory = [0]
tabcontent = {}
lib_option_pairs = [
(lib_openblas, "Use OpenBLAS"),
(lib_clblast, "Use CLBlast"),
(lib_cublas, "Use CuBLAS"),
(lib_hipblas, "Use hipBLAS (ROCm)"),
(lib_default, "Use No BLAS"),
(lib_clblast_noavx2, "CLBlast NoAVX2 (Old CPU)"),
(lib_noavx2, "NoAVX2 Mode (Old CPU)"),
(lib_failsafe, "Failsafe Mode (Old CPU)")]
openblas_option, clblast_option, cublas_option, hipblas_option, default_option, clblast_noavx2_option, 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)
# slider data
blasbatchsize_values = ["-1", "32", "64", "128", "256", "512", "1024", "2048"]
blasbatchsize_text = ["Don't Batch BLAS","32","64","128","256","512","1024","2048"]
contextsize_text = ["256", "512", "1024", "2048", "3072", "4096", "6144", "8192", "12288", "16384", "24576", "32768", "65536"]
runopts = [opt for lib, opt in lib_option_pairs if file_exists(lib)]