Fix for model loading by moving monkey patching functions into a run-once function
Added folder navigation to custom model loading (Needs prittying)
This commit is contained in:
ebolam
parent
190869f0d3
commit
622a3fc8db
608
aiserver.py
608
aiserver.py
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@ -45,6 +45,7 @@ import sys
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import gc
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import lupa
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import importlib
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# KoboldAI
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import fileops
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@ -53,13 +54,21 @@ from utils import debounce
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import utils
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import structures
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import torch
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from transformers import StoppingCriteria, GPT2TokenizerFast, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM, AutoTokenizer
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from transformers import StoppingCriteria, GPT2TokenizerFast, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM, AutoTokenizer, PreTrainedModel, modeling_utils
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from transformers import __version__ as transformers_version
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import transformers
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try:
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from transformers.models.opt.modeling_opt import OPTDecoder
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except:
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pass
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import transformers.generation_utils
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global tpu_mtj_backend
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if lupa.LUA_VERSION[:2] != (5, 4):
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print(f"Please install lupa==1.10. You have lupa {lupa.__version__}.", file=sys.stderr)
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patch_causallm_patched = False
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# Make sure tqdm progress bars display properly in Colab
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from tqdm.auto import tqdm
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@ -255,7 +264,8 @@ class vars:
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last_userscripts = [] # List of previous userscript filenames from the previous time userscripts were send via usstatitems
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corescript = "default.lua" # Filename of corescript to load
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# badwords = [] # Array of str/chr values that should be removed from output
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badwordsids = [[13460], [6880], [50256], [42496], [4613], [17414], [22039], [16410], [27], [29], [38430], [37922], [15913], [24618], [28725], [58], [47175], [36937], [26700], [12878], [16471], [37981], [5218], [29795], [13412], [45160], [3693], [49778], [4211], [20598], [36475], [33409], [44167], [32406], [29847], [29342], [42669], [685], [25787], [7359], [3784], [5320], [33994], [33490], [34516], [43734], [17635], [24293], [9959], [23785], [21737], [28401], [18161], [26358], [32509], [1279], [38155], [18189], [26894], [6927], [14610], [23834], [11037], [14631], [26933], [46904], [22330], [25915], [47934], [38214], [1875], [14692], [41832], [13163], [25970], [29565], [44926], [19841], [37250], [49029], [9609], [44438], [16791], [17816], [30109], [41888], [47527], [42924], [23984], [49074], [33717], [31161], [49082], [30138], [31175], [12240], [14804], [7131], [26076], [33250], [3556], [38381], [36338], [32756], [46581], [17912], [49146]] # Tokenized array of badwords used to prevent AI artifacting
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badwordsids = []
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badwordsids_default = [[13460], [6880], [50256], [42496], [4613], [17414], [22039], [16410], [27], [29], [38430], [37922], [15913], [24618], [28725], [58], [47175], [36937], [26700], [12878], [16471], [37981], [5218], [29795], [13412], [45160], [3693], [49778], [4211], [20598], [36475], [33409], [44167], [32406], [29847], [29342], [42669], [685], [25787], [7359], [3784], [5320], [33994], [33490], [34516], [43734], [17635], [24293], [9959], [23785], [21737], [28401], [18161], [26358], [32509], [1279], [38155], [18189], [26894], [6927], [14610], [23834], [11037], [14631], [26933], [46904], [22330], [25915], [47934], [38214], [1875], [14692], [41832], [13163], [25970], [29565], [44926], [19841], [37250], [49029], [9609], [44438], [16791], [17816], [30109], [41888], [47527], [42924], [23984], [49074], [33717], [31161], [49082], [30138], [31175], [12240], [14804], [7131], [26076], [33250], [3556], [38381], [36338], [32756], [46581], [17912], [49146]] # Tokenized array of badwords used to prevent AI artifacting
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badwordsids_neox = [[0], [1], [44162], [9502], [12520], [31841], [36320], [49824], [34417], [6038], [34494], [24815], [26635], [24345], [3455], [28905], [44270], [17278], [32666], [46880], [7086], [43189], [37322], [17778], [20879], [49821], [3138], [14490], [4681], [21391], [26786], [43134], [9336], [683], [48074], [41256], [19181], [29650], [28532], [36487], [45114], [46275], [16445], [15104], [11337], [1168], [5647], [29], [27482], [44965], [43782], [31011], [42944], [47389], [6334], [17548], [38329], [32044], [35487], [2239], [34761], [7444], [1084], [12399], [18990], [17636], [39083], [1184], [35830], [28365], [16731], [43467], [47744], [1138], [16079], [40116], [45564], [18297], [42368], [5456], [18022], [42696], [34476], [23505], [23741], [39334], [37944], [45382], [38709], [33440], [26077], [43600], [34418], [36033], [6660], [48167], [48471], [15775], [19884], [41533], [1008], [31053], [36692], [46576], [20095], [20629], [31759], [46410], [41000], [13488], [30952], [39258], [16160], [27655], [22367], [42767], [43736], [49694], [13811], [12004], [46768], [6257], [37471], [5264], [44153], [33805], [20977], [21083], [25416], [14277], [31096], [42041], [18331], [33376], [22372], [46294], [28379], [38475], [1656], [5204], [27075], [50001], [16616], [11396], [7748], [48744], [35402], [28120], [41512], [4207], [43144], [14767], [15640], [16595], [41305], [44479], [38958], [18474], [22734], [30522], [46267], [60], [13976], [31830], [48701], [39822], [9014], [21966], [31422], [28052], [34607], [2479], [3851], [32214], [44082], [45507], [3001], [34368], [34758], [13380], [38363], [4299], [46802], [30996], [12630], [49236], [7082], [8795], [5218], [44740], [9686], [9983], [45301], [27114], [40125], [1570], [26997], [544], [5290], [49193], [23781], [14193], [40000], [2947], [43781], [9102], [48064], [42274], [18772], [49384], [9884], [45635], [43521], [31258], [32056], [47686], [21760], [13143], [10148], [26119], [44308], [31379], [36399], [23983], [46694], [36134], [8562], [12977], [35117], [28591], [49021], [47093], [28653], [29013], [46468], [8605], [7254], [25896], [5032], [8168], [36893], [38270], [20499], [27501], [34419], [29547], [28571], [36586], [20871], [30537], [26842], [21375], [31148], [27618], [33094], [3291], [31789], [28391], [870], [9793], [41361], [47916], [27468], [43856], [8850], [35237], [15707], [47552], [2730], [41449], [45488], [3073], [49806], [21938], [24430], [22747], [20924], [46145], [20481], [20197], [8239], [28231], [17987], [42804], [47269], [29972], [49884], [21382], [46295], [36676], [34616], [3921], [26991], [27720], [46265], [654], [9855], [40354], [5291], [34904], [44342], [2470], [14598], [880], [19282], [2498], [24237], [21431], [16369], [8994], [44524], [45662], [13663], [37077], [1447], [37786], [30863], [42854], [1019], [20322], [4398], [12159], [44072], [48664], [31547], [18736], [9259], [31], [16354], [21810], [4357], [37982], [5064], [2033], [32871], [47446], [62], [22158], [37387], [8743], [47007], [17981], [11049], [4622], [37916], [36786], [35138], [29925], [14157], [18095], [27829], [1181], [22226], [5709], [4725], [30189], [37014], [1254], [11380], [42989], [696], [24576], [39487], [30119], [1092], [8088], [2194], [9899], [14412], [21828], [3725], [13544], [5180], [44679], [34398], [3891], [28739], [14219], [37594], [49550], [11326], [6904], [17266], [5749], [10174], [23405], [9955], [38271], [41018], [13011], [48392], [36784], [24254], [21687], [23734], [5413], [41447], [45472], [10122], [17555], [15830], [47384], [12084], [31350], [47940], [11661], [27988], [45443], [905], [49651], [16614], [34993], [6781], [30803], [35869], [8001], [41604], [28118], [46462], [46762], [16262], [17281], [5774], [10943], [5013], [18257], [6750], [4713], [3951], [11899], [38791], [16943], [37596], [9318], [18413], [40473], [13208], [16375]]
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badwordsids_opt = [[44717], [46613], [48513], [49923], [50185], [48755], [8488], [43303], [49659], [48601], [49817], [45405], [48742], [49925], [47720], [11227], [48937], [48784], [50017], [42248], [49310], [48082], [49895], [50025], [49092], [49007], [8061], [44226], [0], [742], [28578], [15698], [49784], [46679], [39365], [49281], [49609], [48081], [48906], [46161], [48554], [49670], [48677], [49721], [49632], [48610], [48462], [47457], [10975], [46077], [28696], [48709], [43839], [49798], [49154], [48203], [49625], [48395], [50155], [47161], [49095], [48833], [49420], [49666], [48443], [22176], [49242], [48651], [49138], [49750], [40389], [48021], [21838], [49070], [45333], [40862], [1], [49915], [33525], [49858], [50254], [44403], [48992], [48872], [46117], [49853], [47567], [50206], [41552], [50068], [48999], [49703], [49940], [49329], [47620], [49868], [49962], [2], [44082], [50236], [31274], [50260], [47052], [42645], [49177], [17523], [48691], [49900], [49069], [49358], [48794], [47529], [46479], [48457], [646], [49910], [48077], [48935], [46386], [48902], [49151], [48759], [49803], [45587], [48392], [47789], [48654], [49836], [49230], [48188], [50264], [46844], [44690], [48505], [50161], [27779], [49995], [41833], [50154], [49097], [48520], [50018], [8174], [50084], [49366], [49526], [50193], [7479], [49982], [3]]
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fp32_model = False # Whether or not the most recently loaded HF model was in fp32 format
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@ -349,14 +359,40 @@ print("{0}OK!{1}".format(colors.GREEN, colors.END))
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#==================================================================#
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# Function to get model selection at startup
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#==================================================================#
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def sendModelSelection(menu="mainmenu"):
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def sendModelSelection(menu="mainmenu", folder="./models"):
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#If we send one of the manual load options, send back the list of model directories, otherwise send the menu
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if menu in ('NeoCustom', 'GPT2Custom'):
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menu_list = [[folder, menu, "", False] for folder in next(os.walk('./models'))[1]]
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(paths, breadcrumbs) = get_folder_path_info(folder)
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menu_list = [[folder, menu, "", False] for folder in paths]
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menu_list.append(["Return to Main Menu", "mainmenu", "", True])
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emit('from_server', {'cmd': 'show_model_menu', 'data': menu_list, 'menu': 'custom'}, broadcast=True)
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emit('from_server', {'cmd': 'show_model_menu', 'data': menu_list, 'menu': menu, 'breadcrumbs': breadcrumbs}, broadcast=True)
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else:
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emit('from_server', {'cmd': 'show_model_menu', 'data': model_menu[menu], 'menu': menu}, broadcast=True)
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emit('from_server', {'cmd': 'show_model_menu', 'data': model_menu[menu], 'menu': menu, 'breadcrumbs': []}, broadcast=True)
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def get_folder_path_info(base):
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if base == 'This PC':
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breadcrumbs = [['This PC', 'This PC']]
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paths = [["{}:\\".format(chr(i)), "{}:\\".format(chr(i))] for i in range(65, 91) if os.path.exists("{}:".format(chr(i)))]
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else:
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path = os.path.abspath(base)
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if path[-1] == "\\":
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path = path[:-1]
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breadcrumbs = []
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for i in range(len(path.split("\\"))):
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breadcrumbs.append(["\\".join(path.split("\\")[:i+1]),
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path.split("\\")[i]])
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if len(breadcrumbs) == 1:
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breadcrumbs = [["{}:\\".format(chr(i)), "{}:\\".format(chr(i))] for i in range(65, 91) if os.path.exists("{}:".format(chr(i)))]
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else:
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if len([["{}:\\".format(chr(i)), "{}:\\".format(chr(i))] for i in range(65, 91) if os.path.exists("{}:".format(chr(i)))]) > 0:
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breadcrumbs.insert(0, ['This PC', 'This PC'])
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paths = []
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base_path = os.path.abspath(base)
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for item in os.listdir(base_path):
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if os.path.isdir(os.path.join(base_path, item)):
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paths.append([os.path.join(base_path, item), item])
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# Paths/breadcrumbs is a list of lists, where the first element in the sublist is the full path and the second is the folder name
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return (paths, breadcrumbs)
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def getModelSelection(modellist):
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print(" # Model\t\t\t\t\t\tVRAM\n ========================================================")
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@ -395,6 +431,15 @@ def getModelSelection(modellist):
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print("{0}Select an AI model to continue:{1}\n".format(colors.CYAN, colors.END))
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getModelSelection(mainmenu)
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def check_if_dir_is_model(path):
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try:
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from transformers import AutoConfig
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model_config = AutoConfig.from_pretrained(path, revision=vars.revision, cache_dir="cache")
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except:
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return False
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return True
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#==================================================================#
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# Return all keys in tokenizer dictionary containing char
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#==================================================================#
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@ -1024,12 +1069,12 @@ def get_layer_count(model, directory=""):
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# Get the model_type from the config or assume a model type if it isn't present
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else:
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from transformers import AutoConfig
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if vars.custmodpth == "":
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if directory == "":
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model_config = AutoConfig.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
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elif(os.path.isdir(vars.custmodpth.replace('/', '_'))):
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model_config = AutoConfig.from_pretrained(vars.custmodpth.replace('/', '_'), revision=vars.revision, cache_dir="cache")
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elif(os.path.isdir("models/{}".format(vars.custmodpth.replace('/', '_')))):
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model_config = AutoConfig.from_pretrained("models/{}".format(vars.custmodpth.replace('/', '_')), revision=vars.revision, cache_dir="cache")
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elif(os.path.isdir(directory)):
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model_config = AutoConfig.from_pretrained(directory, revision=vars.revision, cache_dir="cache")
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else:
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model_config = AutoConfig.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
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@ -1094,12 +1139,267 @@ def get_oai_models(key):
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emit('from_server', {'cmd': 'errmsg', 'data': req.json()})
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def patch_transformers():
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global transformers
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old_from_pretrained = PreTrainedModel.from_pretrained.__func__
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@classmethod
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def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
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vars.fp32_model = False
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utils.num_shards = None
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utils.current_shard = 0
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utils.from_pretrained_model_name = pretrained_model_name_or_path
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utils.from_pretrained_index_filename = None
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utils.from_pretrained_kwargs = kwargs
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utils.bar = None
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if not args.no_aria2:
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utils.aria2_hook(pretrained_model_name_or_path, **kwargs)
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return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
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PreTrainedModel.from_pretrained = new_from_pretrained
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if(hasattr(modeling_utils, "get_checkpoint_shard_files")):
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old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files
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def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs):
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utils.num_shards = utils.get_num_shards(index_filename)
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utils.from_pretrained_index_filename = index_filename
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return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs)
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modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files
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# Some versions of transformers 4.17.0.dev0 are affected by
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# https://github.com/huggingface/transformers/issues/15736
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# This is a workaround for those versions of transformers.
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if(transformers_version == "4.17.0.dev0"):
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try:
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from transformers.models.xglm.modeling_xglm import XGLMSinusoidalPositionalEmbedding
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except ImportError:
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pass
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else:
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@torch.no_grad()
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def new_forward(self, input_ids: torch.Tensor = None, inputs_embeds: torch.Tensor = None, past_key_values_length: int = 0):
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bsz, seq_len = inputs_embeds.size()[:-1]
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input_shape = inputs_embeds.size()[:-1]
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sequence_length = input_shape[1]
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position_ids = torch.arange(
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past_key_values_length + self.padding_idx + 1, past_key_values_length + sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
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).unsqueeze(0).expand(input_shape).contiguous()
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max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
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if max_pos > self.weights.size(0):
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self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
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return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach()
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XGLMSinusoidalPositionalEmbedding.forward = new_forward
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# Patch transformers to use our soft prompt
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def patch_causallm(cls):
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old_forward = cls.forward
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def new_causallm_forward(self, *args, **kwargs):
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input_ids = kwargs.get('input_ids').to(self.device)
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assert input_ids is not None
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kwargs['input_ids'] = None
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if(vars.sp is not None):
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shifted_input_ids = input_ids - self.config.vocab_size
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input_ids.clamp_(max=self.config.vocab_size-1)
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if(hasattr(self, "transformer")):
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inputs_embeds = self.transformer.wte(input_ids)
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elif(not hasattr(self.model, "decoder")):
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inputs_embeds = self.model.embed_tokens(input_ids)
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else:
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inputs_embeds = self.model.decoder.embed_tokens(input_ids)
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if(vars.sp is not None):
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vars.sp = vars.sp.to(inputs_embeds.dtype).to(inputs_embeds.device)
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inputs_embeds = torch.where(
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(shifted_input_ids >= 0)[..., None],
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vars.sp[shifted_input_ids.clamp(min=0)],
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inputs_embeds,
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)
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if(hasattr(self, "model") and hasattr(self.model, "embed_scale")):
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inputs_embeds *= self.model.embed_scale
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kwargs['inputs_embeds'] = inputs_embeds
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return old_forward(self, *args, **kwargs)
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cls.forward = new_causallm_forward
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for cls in (GPT2LMHeadModel, GPTNeoForCausalLM):
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patch_causallm(cls)
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for c in ("GPTJForCausalLM", "XGLMForCausalLM", "OPTForCausalLM"):
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try:
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patch_causallm(getattr(__import__("transformers"), c))
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except:
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pass
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# Fix a bug in OPTForCausalLM where self.lm_head is the wrong size
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if(packaging.version.parse("4.19.0.dev0") <= packaging.version.parse(transformers_version) <= packaging.version.parse("4.19.2")):
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try:
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from transformers import OPTForCausalLM, OPTModel
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except ImportError:
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pass
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||||
else:
|
||||
# This is the same as the original __init__ but with
|
||||
# config.hidden_size
|
||||
# replaced with
|
||||
# config.word_embed_proj_dim
|
||||
def new_init(self, config):
|
||||
super(OPTForCausalLM, self).__init__(config)
|
||||
self.model = OPTModel(config)
|
||||
self.lm_head = torch.nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False)
|
||||
self.post_init()
|
||||
OPTForCausalLM.__init__ = new_init
|
||||
|
||||
|
||||
# Patch transformers to use our custom logit warpers
|
||||
from transformers import LogitsProcessorList, LogitsWarper, LogitsProcessor, TopKLogitsWarper, TopPLogitsWarper, TemperatureLogitsWarper, RepetitionPenaltyLogitsProcessor
|
||||
from warpers import AdvancedRepetitionPenaltyLogitsProcessor, TailFreeLogitsWarper, TypicalLogitsWarper
|
||||
|
||||
def dynamic_processor_wrap(cls, field_name, var_name, cond=None):
|
||||
old_call = cls.__call__
|
||||
def new_call(self, *args, **kwargs):
|
||||
if(not isinstance(field_name, str) and isinstance(field_name, Iterable)):
|
||||
conds = []
|
||||
for f, v in zip(field_name, var_name):
|
||||
conds.append(getattr(vars, v))
|
||||
setattr(self, f, conds[-1])
|
||||
else:
|
||||
conds = getattr(vars, var_name)
|
||||
setattr(self, field_name, conds)
|
||||
assert len(args) == 2
|
||||
if(cond is None or cond(conds)):
|
||||
return old_call(self, *args, **kwargs)
|
||||
return args[1]
|
||||
cls.__call__ = new_call
|
||||
dynamic_processor_wrap(AdvancedRepetitionPenaltyLogitsProcessor, ("penalty", "penalty_slope", "penalty_range"), ("rep_pen", "rep_pen_slope", "rep_pen_range"), cond=lambda x: x[0] != 1.0)
|
||||
dynamic_processor_wrap(TopKLogitsWarper, "top_k", "top_k", cond=lambda x: x > 0)
|
||||
dynamic_processor_wrap(TopPLogitsWarper, "top_p", "top_p", cond=lambda x: x < 1.0)
|
||||
dynamic_processor_wrap(TailFreeLogitsWarper, "tfs", "tfs", cond=lambda x: x < 1.0)
|
||||
dynamic_processor_wrap(TypicalLogitsWarper, "typical", "typical", cond=lambda x: x < 1.0)
|
||||
dynamic_processor_wrap(TemperatureLogitsWarper, "temperature", "temp", cond=lambda x: x != 1.0)
|
||||
RepetitionPenaltyLogitsProcessor.__init__ = AdvancedRepetitionPenaltyLogitsProcessor.__init__
|
||||
RepetitionPenaltyLogitsProcessor.__call__ = AdvancedRepetitionPenaltyLogitsProcessor.__call__
|
||||
|
||||
class LuaLogitsProcessor(LogitsProcessor):
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
|
||||
assert scores.ndim == 2
|
||||
assert input_ids.ndim == 2
|
||||
self.regeneration_required = False
|
||||
self.halt = False
|
||||
|
||||
scores_shape = scores.shape
|
||||
scores_list = scores.tolist()
|
||||
vars.lua_koboldbridge.logits = vars.lua_state.table()
|
||||
for r, row in enumerate(scores_list):
|
||||
vars.lua_koboldbridge.logits[r+1] = vars.lua_state.table(*row)
|
||||
vars.lua_koboldbridge.vocab_size = scores_shape[-1]
|
||||
|
||||
execute_genmod()
|
||||
|
||||
scores = torch.tensor(
|
||||
tuple(tuple(row.values()) for row in vars.lua_koboldbridge.logits.values()),
|
||||
device=scores.device,
|
||||
dtype=scores.dtype,
|
||||
)
|
||||
assert scores.shape == scores_shape
|
||||
|
||||
return scores
|
||||
|
||||
def new_get_logits_processor(*args, **kwargs) -> LogitsProcessorList:
|
||||
processors = new_get_logits_processor.old_get_logits_processor(*args, **kwargs)
|
||||
processors.insert(0, LuaLogitsProcessor())
|
||||
return processors
|
||||
new_get_logits_processor.old_get_logits_processor = transformers.generation_utils.GenerationMixin._get_logits_processor
|
||||
transformers.generation_utils.GenerationMixin._get_logits_processor = new_get_logits_processor
|
||||
|
||||
def new_get_logits_warper(beams: int = 1,) -> LogitsProcessorList:
|
||||
warper_list = LogitsProcessorList()
|
||||
warper_list.append(TopKLogitsWarper(top_k=1, min_tokens_to_keep=1 + (beams > 1)))
|
||||
warper_list.append(TopPLogitsWarper(top_p=0.5, min_tokens_to_keep=1 + (beams > 1)))
|
||||
warper_list.append(TailFreeLogitsWarper(tfs=0.5, min_tokens_to_keep=1 + (beams > 1)))
|
||||
warper_list.append(TypicalLogitsWarper(typical=0.5, min_tokens_to_keep=1 + (beams > 1)))
|
||||
warper_list.append(TemperatureLogitsWarper(temperature=0.5))
|
||||
return warper_list
|
||||
|
||||
def new_sample(self, *args, **kwargs):
|
||||
assert kwargs.pop("logits_warper", None) is not None
|
||||
kwargs["logits_warper"] = new_get_logits_warper(
|
||||
beams=1,
|
||||
)
|
||||
if(vars.newlinemode == "s") or (vars.newlinemode == "ns"):
|
||||
kwargs["eos_token_id"] = -1
|
||||
kwargs.setdefault("pad_token_id", 2)
|
||||
return new_sample.old_sample(self, *args, **kwargs)
|
||||
new_sample.old_sample = transformers.generation_utils.GenerationMixin.sample
|
||||
transformers.generation_utils.GenerationMixin.sample = new_sample
|
||||
|
||||
|
||||
# Allow bad words filter to ban <|endoftext|> token
|
||||
import transformers.generation_logits_process
|
||||
def new_init(self, bad_words_ids: List[List[int]], eos_token_id: int):
|
||||
return new_init.old_init(self, bad_words_ids, -1)
|
||||
new_init.old_init = transformers.generation_logits_process.NoBadWordsLogitsProcessor.__init__
|
||||
transformers.generation_logits_process.NoBadWordsLogitsProcessor.__init__ = new_init
|
||||
|
||||
|
||||
# Sets up dynamic world info scanner
|
||||
class DynamicWorldInfoScanCriteria(StoppingCriteria):
|
||||
def __init__(
|
||||
self,
|
||||
tokenizer,
|
||||
excluded_world_info: List[Set],
|
||||
):
|
||||
self.regeneration_required = False
|
||||
self.halt = False
|
||||
self.tokenizer = tokenizer
|
||||
self.excluded_world_info = excluded_world_info
|
||||
def __call__(
|
||||
self,
|
||||
input_ids: torch.LongTensor,
|
||||
scores: torch.FloatTensor,
|
||||
**kwargs,
|
||||
) -> bool:
|
||||
vars.generated_tkns += 1
|
||||
if(vars.lua_koboldbridge.generated_cols and vars.generated_tkns != vars.lua_koboldbridge.generated_cols):
|
||||
raise RuntimeError(f"Inconsistency detected between KoboldAI Python and Lua backends ({vars.generated_tkns} != {vars.lua_koboldbridge.generated_cols})")
|
||||
if(vars.abort or vars.generated_tkns >= vars.genamt):
|
||||
self.regeneration_required = False
|
||||
self.halt = False
|
||||
return True
|
||||
|
||||
assert input_ids.ndim == 2
|
||||
assert len(self.excluded_world_info) == input_ids.shape[0]
|
||||
self.regeneration_required = vars.lua_koboldbridge.regeneration_required
|
||||
self.halt = not vars.lua_koboldbridge.generating
|
||||
vars.lua_koboldbridge.regeneration_required = False
|
||||
|
||||
for i in range(vars.numseqs):
|
||||
vars.lua_koboldbridge.generated[i+1][vars.generated_tkns] = int(input_ids[i, -1].item())
|
||||
|
||||
if(not vars.dynamicscan):
|
||||
return self.regeneration_required or self.halt
|
||||
tail = input_ids[..., -vars.generated_tkns:]
|
||||
for i, t in enumerate(tail):
|
||||
decoded = utils.decodenewlines(tokenizer.decode(t))
|
||||
_, found = checkworldinfo(decoded, force_use_txt=True, actions=vars._actions)
|
||||
found -= self.excluded_world_info[i]
|
||||
if(len(found) != 0):
|
||||
self.regeneration_required = True
|
||||
break
|
||||
return self.regeneration_required or self.halt
|
||||
old_get_stopping_criteria = transformers.generation_utils.GenerationMixin._get_stopping_criteria
|
||||
def new_get_stopping_criteria(self, *args, **kwargs):
|
||||
stopping_criteria = old_get_stopping_criteria(self, *args, **kwargs)
|
||||
global tokenizer
|
||||
self.kai_scanner = DynamicWorldInfoScanCriteria(
|
||||
tokenizer=tokenizer,
|
||||
excluded_world_info=self.kai_scanner_excluded_world_info,
|
||||
)
|
||||
stopping_criteria.insert(0, self.kai_scanner)
|
||||
return stopping_criteria
|
||||
transformers.generation_utils.GenerationMixin._get_stopping_criteria = new_get_stopping_criteria
|
||||
|
||||
def load_model(use_gpu=True, gpu_layers=None, initial_load=False, online_model=""):
|
||||
global model
|
||||
global generator
|
||||
global torch
|
||||
global model_config
|
||||
print("Loading vars.model: {} vars.custmodpth: {}".format(vars.model, vars.custmodpth))
|
||||
vars.noai = False
|
||||
if not initial_load:
|
||||
set_aibusy(True)
|
||||
|
|
@ -1113,10 +1413,14 @@ def load_model(use_gpu=True, gpu_layers=None, initial_load=False, online_model="
|
|||
#We need to wipe out the existing model and refresh the cuda cache
|
||||
model = None
|
||||
generator = None
|
||||
model_config = None
|
||||
try:
|
||||
torch.cuda.empty_cache()
|
||||
except:
|
||||
pass
|
||||
|
||||
#Reload our badwords
|
||||
vars.badwordsids = vars.badwordsids_default
|
||||
|
||||
#Let's set the GooseAI or OpenAI server URLs if that's applicable
|
||||
if online_model != "":
|
||||
|
|
@ -1266,42 +1570,11 @@ def load_model(use_gpu=True, gpu_layers=None, initial_load=False, online_model="
|
|||
if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
|
||||
if(not vars.noai):
|
||||
print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END))
|
||||
from transformers import StoppingCriteria, GPT2TokenizerFast, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM, AutoTokenizer
|
||||
for m in ("GPTJModel", "XGLMModel"):
|
||||
try:
|
||||
globals()[m] = getattr(__import__("transformers"), m)
|
||||
except:
|
||||
pass
|
||||
try:
|
||||
from transformers.models.opt.modeling_opt import OPTDecoder
|
||||
except:
|
||||
pass
|
||||
import transformers.generation_utils
|
||||
from transformers import __version__ as transformers_version
|
||||
|
||||
from transformers import PreTrainedModel
|
||||
from transformers import modeling_utils
|
||||
old_from_pretrained = PreTrainedModel.from_pretrained.__func__
|
||||
@classmethod
|
||||
def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
|
||||
vars.fp32_model = False
|
||||
utils.num_shards = None
|
||||
utils.current_shard = 0
|
||||
utils.from_pretrained_model_name = pretrained_model_name_or_path
|
||||
utils.from_pretrained_index_filename = None
|
||||
utils.from_pretrained_kwargs = kwargs
|
||||
utils.bar = None
|
||||
if not args.no_aria2:
|
||||
utils.aria2_hook(pretrained_model_name_or_path, **kwargs)
|
||||
return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
PreTrainedModel.from_pretrained = new_from_pretrained
|
||||
if(hasattr(modeling_utils, "get_checkpoint_shard_files")):
|
||||
old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files
|
||||
def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs):
|
||||
utils.num_shards = utils.get_num_shards(index_filename)
|
||||
utils.from_pretrained_index_filename = index_filename
|
||||
return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs)
|
||||
modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files
|
||||
|
||||
# Lazy loader
|
||||
import torch_lazy_loader
|
||||
|
|
@ -1398,236 +1671,7 @@ def load_model(use_gpu=True, gpu_layers=None, initial_load=False, online_model="
|
|||
else:
|
||||
vars.lazy_load = False
|
||||
|
||||
# Some versions of transformers 4.17.0.dev0 are affected by
|
||||
# https://github.com/huggingface/transformers/issues/15736
|
||||
# This is a workaround for those versions of transformers.
|
||||
if(transformers_version == "4.17.0.dev0"):
|
||||
try:
|
||||
from transformers.models.xglm.modeling_xglm import XGLMSinusoidalPositionalEmbedding
|
||||
except ImportError:
|
||||
pass
|
||||
else:
|
||||
@torch.no_grad()
|
||||
def new_forward(self, input_ids: torch.Tensor = None, inputs_embeds: torch.Tensor = None, past_key_values_length: int = 0):
|
||||
bsz, seq_len = inputs_embeds.size()[:-1]
|
||||
input_shape = inputs_embeds.size()[:-1]
|
||||
sequence_length = input_shape[1]
|
||||
position_ids = torch.arange(
|
||||
past_key_values_length + self.padding_idx + 1, past_key_values_length + sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
|
||||
).unsqueeze(0).expand(input_shape).contiguous()
|
||||
max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
|
||||
if max_pos > self.weights.size(0):
|
||||
self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
|
||||
return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach()
|
||||
XGLMSinusoidalPositionalEmbedding.forward = new_forward
|
||||
|
||||
# Patch transformers to use our soft prompt
|
||||
def patch_causallm(cls):
|
||||
old_forward = cls.forward
|
||||
def new_causallm_forward(self, *args, **kwargs):
|
||||
input_ids = kwargs.get('input_ids').to(self.device)
|
||||
assert input_ids is not None
|
||||
kwargs['input_ids'] = None
|
||||
if(vars.sp is not None):
|
||||
shifted_input_ids = input_ids - self.config.vocab_size
|
||||
input_ids.clamp_(max=self.config.vocab_size-1)
|
||||
if(hasattr(self, "transformer")):
|
||||
inputs_embeds = self.transformer.wte(input_ids)
|
||||
elif(not hasattr(self.model, "decoder")):
|
||||
inputs_embeds = self.model.embed_tokens(input_ids)
|
||||
else:
|
||||
inputs_embeds = self.model.decoder.embed_tokens(input_ids)
|
||||
if(vars.sp is not None):
|
||||
vars.sp = vars.sp.to(inputs_embeds.dtype).to(inputs_embeds.device)
|
||||
inputs_embeds = torch.where(
|
||||
(shifted_input_ids >= 0)[..., None],
|
||||
vars.sp[shifted_input_ids.clamp(min=0)],
|
||||
inputs_embeds,
|
||||
)
|
||||
if(hasattr(self, "model") and hasattr(self.model, "embed_scale")):
|
||||
inputs_embeds *= self.model.embed_scale
|
||||
kwargs['inputs_embeds'] = inputs_embeds
|
||||
return old_forward(self, *args, **kwargs)
|
||||
cls.forward = new_causallm_forward
|
||||
for cls in (GPT2LMHeadModel, GPTNeoForCausalLM):
|
||||
patch_causallm(cls)
|
||||
for c in ("GPTJForCausalLM", "XGLMForCausalLM", "OPTForCausalLM"):
|
||||
try:
|
||||
patch_causallm(getattr(__import__("transformers"), c))
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
# Fix a bug in OPTForCausalLM where self.lm_head is the wrong size
|
||||
if(packaging.version.parse("4.19.0.dev0") <= packaging.version.parse(transformers_version) <= packaging.version.parse("4.19.2")):
|
||||
try:
|
||||
from transformers import OPTForCausalLM, OPTModel
|
||||
except ImportError:
|
||||
pass
|
||||
else:
|
||||
# This is the same as the original __init__ but with
|
||||
# config.hidden_size
|
||||
# replaced with
|
||||
# config.word_embed_proj_dim
|
||||
def new_init(self, config):
|
||||
super(OPTForCausalLM, self).__init__(config)
|
||||
self.model = OPTModel(config)
|
||||
self.lm_head = torch.nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False)
|
||||
self.post_init()
|
||||
OPTForCausalLM.__init__ = new_init
|
||||
|
||||
|
||||
# Patch transformers to use our custom logit warpers
|
||||
from transformers import LogitsProcessorList, LogitsWarper, LogitsProcessor, TopKLogitsWarper, TopPLogitsWarper, TemperatureLogitsWarper, RepetitionPenaltyLogitsProcessor
|
||||
from warpers import AdvancedRepetitionPenaltyLogitsProcessor, TailFreeLogitsWarper, TypicalLogitsWarper
|
||||
|
||||
def dynamic_processor_wrap(cls, field_name, var_name, cond=None):
|
||||
old_call = cls.__call__
|
||||
def new_call(self, *args, **kwargs):
|
||||
if(not isinstance(field_name, str) and isinstance(field_name, Iterable)):
|
||||
conds = []
|
||||
for f, v in zip(field_name, var_name):
|
||||
conds.append(getattr(vars, v))
|
||||
setattr(self, f, conds[-1])
|
||||
else:
|
||||
conds = getattr(vars, var_name)
|
||||
setattr(self, field_name, conds)
|
||||
assert len(args) == 2
|
||||
if(cond is None or cond(conds)):
|
||||
return old_call(self, *args, **kwargs)
|
||||
return args[1]
|
||||
cls.__call__ = new_call
|
||||
dynamic_processor_wrap(AdvancedRepetitionPenaltyLogitsProcessor, ("penalty", "penalty_slope", "penalty_range"), ("rep_pen", "rep_pen_slope", "rep_pen_range"), cond=lambda x: x[0] != 1.0)
|
||||
dynamic_processor_wrap(TopKLogitsWarper, "top_k", "top_k", cond=lambda x: x > 0)
|
||||
dynamic_processor_wrap(TopPLogitsWarper, "top_p", "top_p", cond=lambda x: x < 1.0)
|
||||
dynamic_processor_wrap(TailFreeLogitsWarper, "tfs", "tfs", cond=lambda x: x < 1.0)
|
||||
dynamic_processor_wrap(TypicalLogitsWarper, "typical", "typical", cond=lambda x: x < 1.0)
|
||||
dynamic_processor_wrap(TemperatureLogitsWarper, "temperature", "temp", cond=lambda x: x != 1.0)
|
||||
RepetitionPenaltyLogitsProcessor.__init__ = AdvancedRepetitionPenaltyLogitsProcessor.__init__
|
||||
RepetitionPenaltyLogitsProcessor.__call__ = AdvancedRepetitionPenaltyLogitsProcessor.__call__
|
||||
|
||||
class LuaLogitsProcessor(LogitsProcessor):
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
|
||||
assert scores.ndim == 2
|
||||
assert input_ids.ndim == 2
|
||||
self.regeneration_required = False
|
||||
self.halt = False
|
||||
|
||||
scores_shape = scores.shape
|
||||
scores_list = scores.tolist()
|
||||
vars.lua_koboldbridge.logits = vars.lua_state.table()
|
||||
for r, row in enumerate(scores_list):
|
||||
vars.lua_koboldbridge.logits[r+1] = vars.lua_state.table(*row)
|
||||
vars.lua_koboldbridge.vocab_size = scores_shape[-1]
|
||||
|
||||
execute_genmod()
|
||||
|
||||
scores = torch.tensor(
|
||||
tuple(tuple(row.values()) for row in vars.lua_koboldbridge.logits.values()),
|
||||
device=scores.device,
|
||||
dtype=scores.dtype,
|
||||
)
|
||||
assert scores.shape == scores_shape
|
||||
|
||||
return scores
|
||||
|
||||
def new_get_logits_processor(*args, **kwargs) -> LogitsProcessorList:
|
||||
processors = new_get_logits_processor.old_get_logits_processor(*args, **kwargs)
|
||||
processors.insert(0, LuaLogitsProcessor())
|
||||
return processors
|
||||
new_get_logits_processor.old_get_logits_processor = transformers.generation_utils.GenerationMixin._get_logits_processor
|
||||
transformers.generation_utils.GenerationMixin._get_logits_processor = new_get_logits_processor
|
||||
|
||||
def new_get_logits_warper(beams: int = 1,) -> LogitsProcessorList:
|
||||
warper_list = LogitsProcessorList()
|
||||
warper_list.append(TopKLogitsWarper(top_k=1, min_tokens_to_keep=1 + (beams > 1)))
|
||||
warper_list.append(TopPLogitsWarper(top_p=0.5, min_tokens_to_keep=1 + (beams > 1)))
|
||||
warper_list.append(TailFreeLogitsWarper(tfs=0.5, min_tokens_to_keep=1 + (beams > 1)))
|
||||
warper_list.append(TypicalLogitsWarper(typical=0.5, min_tokens_to_keep=1 + (beams > 1)))
|
||||
warper_list.append(TemperatureLogitsWarper(temperature=0.5))
|
||||
return warper_list
|
||||
|
||||
def new_sample(self, *args, **kwargs):
|
||||
assert kwargs.pop("logits_warper", None) is not None
|
||||
kwargs["logits_warper"] = new_get_logits_warper(
|
||||
beams=1,
|
||||
)
|
||||
if(vars.newlinemode == "s") or (vars.newlinemode == "ns"):
|
||||
kwargs["eos_token_id"] = -1
|
||||
kwargs.setdefault("pad_token_id", 2)
|
||||
return new_sample.old_sample(self, *args, **kwargs)
|
||||
new_sample.old_sample = transformers.generation_utils.GenerationMixin.sample
|
||||
transformers.generation_utils.GenerationMixin.sample = new_sample
|
||||
|
||||
|
||||
# Allow bad words filter to ban <|endoftext|> token
|
||||
import transformers.generation_logits_process
|
||||
def new_init(self, bad_words_ids: List[List[int]], eos_token_id: int):
|
||||
return new_init.old_init(self, bad_words_ids, -1)
|
||||
new_init.old_init = transformers.generation_logits_process.NoBadWordsLogitsProcessor.__init__
|
||||
transformers.generation_logits_process.NoBadWordsLogitsProcessor.__init__ = new_init
|
||||
|
||||
|
||||
# Sets up dynamic world info scanner
|
||||
class DynamicWorldInfoScanCriteria(StoppingCriteria):
|
||||
def __init__(
|
||||
self,
|
||||
tokenizer,
|
||||
excluded_world_info: List[Set],
|
||||
):
|
||||
self.regeneration_required = False
|
||||
self.halt = False
|
||||
self.tokenizer = tokenizer
|
||||
self.excluded_world_info = excluded_world_info
|
||||
def __call__(
|
||||
self,
|
||||
input_ids: torch.LongTensor,
|
||||
scores: torch.FloatTensor,
|
||||
**kwargs,
|
||||
) -> bool:
|
||||
vars.generated_tkns += 1
|
||||
if(vars.lua_koboldbridge.generated_cols and vars.generated_tkns != vars.lua_koboldbridge.generated_cols):
|
||||
raise RuntimeError(f"Inconsistency detected between KoboldAI Python and Lua backends ({vars.generated_tkns} != {vars.lua_koboldbridge.generated_cols})")
|
||||
if(vars.abort or vars.generated_tkns >= vars.genamt):
|
||||
self.regeneration_required = False
|
||||
self.halt = False
|
||||
return True
|
||||
|
||||
assert input_ids.ndim == 2
|
||||
assert len(self.excluded_world_info) == input_ids.shape[0]
|
||||
self.regeneration_required = vars.lua_koboldbridge.regeneration_required
|
||||
self.halt = not vars.lua_koboldbridge.generating
|
||||
vars.lua_koboldbridge.regeneration_required = False
|
||||
|
||||
for i in range(vars.numseqs):
|
||||
vars.lua_koboldbridge.generated[i+1][vars.generated_tkns] = int(input_ids[i, -1].item())
|
||||
|
||||
if(not vars.dynamicscan):
|
||||
return self.regeneration_required or self.halt
|
||||
tail = input_ids[..., -vars.generated_tkns:]
|
||||
for i, t in enumerate(tail):
|
||||
decoded = utils.decodenewlines(tokenizer.decode(t))
|
||||
_, found = checkworldinfo(decoded, force_use_txt=True, actions=vars._actions)
|
||||
found -= self.excluded_world_info[i]
|
||||
if(len(found) != 0):
|
||||
self.regeneration_required = True
|
||||
break
|
||||
return self.regeneration_required or self.halt
|
||||
old_get_stopping_criteria = transformers.generation_utils.GenerationMixin._get_stopping_criteria
|
||||
def new_get_stopping_criteria(self, *args, **kwargs):
|
||||
stopping_criteria = old_get_stopping_criteria(self, *args, **kwargs)
|
||||
global tokenizer
|
||||
self.kai_scanner = DynamicWorldInfoScanCriteria(
|
||||
tokenizer=tokenizer,
|
||||
excluded_world_info=self.kai_scanner_excluded_world_info,
|
||||
)
|
||||
stopping_criteria.insert(0, self.kai_scanner)
|
||||
return stopping_criteria
|
||||
transformers.generation_utils.GenerationMixin._get_stopping_criteria = new_get_stopping_criteria
|
||||
|
||||
def get_hidden_size_from_model(model):
|
||||
try:
|
||||
|
|
@ -2983,19 +3027,23 @@ def get_message(msg):
|
|||
# If we're on a custom line that we have selected a model for, the path variable will be in msg
|
||||
# so if that's missing we need to run the menu to show the model folders in the models folder
|
||||
if msg['data'] in ('NeoCustom', 'GPT2Custom') and 'path' not in msg:
|
||||
sendModelSelection(menu=msg['data'])
|
||||
#elif msg['data'] in ('OAI', 'GooseAI'):
|
||||
# vars.model = msg['data']
|
||||
# get_oai_models()
|
||||
# emit('from_server', {'cmd': 'hide_layer_bar'}, broadcast=True)
|
||||
# emit('from_server', {'cmd': 'check_enable_model_load', 'model': vars.model}, broadcast=True)
|
||||
if 'folder' not in msg:
|
||||
folder = "./models"
|
||||
else:
|
||||
folder = msg['folder']
|
||||
sendModelSelection(menu=msg['data'], folder=folder)
|
||||
elif msg['data'] in ('NeoCustom', 'GPT2Custom'):
|
||||
if check_if_dir_is_model(msg['path']):
|
||||
vars.model = msg['data']
|
||||
vars.custmodpth = msg['path']
|
||||
get_model_info(msg['data'], directory=msg['path'])
|
||||
else:
|
||||
sendModelSelection(menu=msg['data'], folder=msg['path'])
|
||||
else:
|
||||
vars.model = msg['data']
|
||||
if 'path' in msg:
|
||||
if msg['data'] == 'NeoCustom':
|
||||
get_model_info(vars.custmodpth, directory=msg['path'])
|
||||
else:
|
||||
get_model_info(vars.model, directory=msg['path'])
|
||||
vars.custmodpth = msg['path']
|
||||
get_model_info(msg['data'], directory=msg['path'])
|
||||
else:
|
||||
get_model_info(vars.model)
|
||||
|
||||
|
|
@ -5677,6 +5725,7 @@ if __name__ == "__main__":
|
|||
print("{0}\nStarting webserver...{1}".format(colors.GREEN, colors.END), flush=True)
|
||||
|
||||
general_startup()
|
||||
patch_transformers()
|
||||
#show_select_model_list()
|
||||
if vars.model == "" or vars.model is None:
|
||||
vars.model = "ReadOnly"
|
||||
|
|
@ -5732,6 +5781,7 @@ if __name__ == "__main__":
|
|||
|
||||
else:
|
||||
general_startup()
|
||||
patch_transformers()
|
||||
#show_select_model_list()
|
||||
if vars.model == "" or vars.model is None:
|
||||
vars.model = "ReadOnly"
|
||||
|
|
|
|||
|
|
@ -991,22 +991,41 @@ function hideUSPopup() {
|
|||
}
|
||||
|
||||
|
||||
function buildLoadModelList(ar, menu) {
|
||||
function buildLoadModelList(ar, menu, breadcrumbs) {
|
||||
disableButtons([load_model_accept]);
|
||||
loadmodelcontent.html("");
|
||||
$("#loadmodellistbreadcrumbs").html("");
|
||||
var i;
|
||||
for(i=0; i<breadcrumbs.length; i++) {
|
||||
$("#loadmodellistbreadcrumbs").append("<button id='model_breadcrumbs"+i+"' name='"+ar[0][1]+"' value='"+breadcrumbs[i][0]+"'>"+breadcrumbs[i][1]+"</button>");
|
||||
$("#model_breadcrumbs"+i).off("click").on("click", (function () {
|
||||
return function () {
|
||||
socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name"), 'folder': $(this).attr("value")});
|
||||
disableButtons([load_model_accept]);
|
||||
}
|
||||
})(i));
|
||||
}
|
||||
for(i=0; i<ar.length; i++) {
|
||||
var html
|
||||
html = "<div class=\"flex\">\
|
||||
<div class=\"loadlistpadding\"></div>"
|
||||
//if the menu item is a link to another menu
|
||||
if(ar[i][3]) {
|
||||
html = html + "<span class=\"loadlisticon loadmodellisticon-folder oi oi-folder allowed\" aria-hidden=\"true\"></span>"
|
||||
} else {
|
||||
//this is a model
|
||||
html = html + "<div class=\"loadlistpadding\"></div>"
|
||||
}
|
||||
if (Array.isArray(ar[i][0])) {
|
||||
full_path = ar[i][0][0];
|
||||
folder = ar[i][0][1];
|
||||
} else {
|
||||
full_path = "";
|
||||
folder = ar[i][0];
|
||||
}
|
||||
html = html + "<div class=\"loadlistpadding\"></div>\
|
||||
<div class=\"loadlistitem\" id=\"loadmodel"+i+"\" name=\""+ar[i][1]+"\" pretty_name=\""+ar[i][0]+"\">\
|
||||
<div>"+ar[i][0]+"</div>\
|
||||
<div class=\"loadlistitem\" id=\"loadmodel"+i+"\" name=\""+ar[i][1]+"\" pretty_name=\""+full_path+"\">\
|
||||
<div>"+folder+"</div>\
|
||||
<div class=\"flex-push-right\">"+ar[i][2]+"</div>\
|
||||
</div>\
|
||||
</div>"
|
||||
|
|
@ -1020,7 +1039,7 @@ function buildLoadModelList(ar, menu) {
|
|||
}
|
||||
})(i));
|
||||
//If we're in the custom load menu (we need to send the path data back in that case)
|
||||
} else if(menu == 'custom') {
|
||||
} else if(['NeoCustom', 'GPT2Custom'].includes(menu)) {
|
||||
$("#loadmodel"+i).off("click").on("click", (function () {
|
||||
return function () {
|
||||
socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name"), 'path': $(this).attr("pretty_name")});
|
||||
|
|
@ -2472,11 +2491,12 @@ $(document).ready(function(){
|
|||
debug_area.addClass("hidden");
|
||||
}
|
||||
} else if(msg.cmd == 'show_model_menu') {
|
||||
console.log(msg)
|
||||
$("#use_gpu_div").addClass("hidden");
|
||||
$("#modelkey").addClass("hidden");
|
||||
$("#modellayers").addClass("hidden");
|
||||
$("#oaimodel").addClass("hidden")
|
||||
buildLoadModelList(msg.data, msg.menu);
|
||||
buildLoadModelList(msg.data, msg.menu, msg.breadcrumbs);
|
||||
} else if(msg.cmd == 'selected_model_info') {
|
||||
enableButtons([load_model_accept]);
|
||||
$("#oaimodel").addClass("hidden")
|
||||
|
|
|
|||
|
|
@ -279,8 +279,8 @@
|
|||
<div class="popuptitlebar">
|
||||
<div class="popuptitletext">Select A Model To Load</div>
|
||||
</div>
|
||||
<div class="loadmodellistheader">
|
||||
<div>Model</div>
|
||||
<div id="loadmodellistbreadcrumbs">
|
||||
|
||||
</div>
|
||||
<div id="loadmodellistcontent" style="overflow: scroll; height: 300px;">
|
||||
</div>
|
||||
|
|
|
|||
2
utils.py
2
utils.py
|
|
@ -149,7 +149,7 @@ def decodenewlines(txt):
|
|||
# Returns number of layers given an HF model config
|
||||
#==================================================================#
|
||||
def num_layers(config):
|
||||
return config.num_layers if hasattr(config, "num_layers") else config.n_layer if hasattr(config, "n_layer") else config.num_hidden_layers
|
||||
return config.num_layers if hasattr(config, "num_layers") else config.n_layer if hasattr(config, "n_layer") else config.num_hidden_layers if hasattr(config, 'num_hidden_layers') else None
|
||||
|
||||
#==================================================================#
|
||||
# Downloads huggingface checkpoints using aria2c if possible
|
||||
|
|
|
|||
Loading…
Reference in New Issue