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https://github.com/KoboldAI/KoboldAI-Client.git
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Merge pull request #141 from ebolam/Web-UI
Functional --model/--path, fix for switching models
This commit is contained in:
henk717
GitHub
commit
ae2ee0dd57
658
aiserver.py
658
aiserver.py
@ -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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@ -986,6 +1031,7 @@ def get_model_info(model, directory=""):
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key_value = ""
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break_values = []
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url = False
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gpu_count = torch.cuda.device_count()
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if model in [x[1] for x in model_menu['apilist']]:
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if path.exists("settings/{}.settings".format(model)):
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with open("settings/{}.settings".format(model), "r") as file:
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@ -1014,10 +1060,10 @@ def get_model_info(model, directory=""):
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break_values = file.read().split(",")
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else:
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break_values = [layer_count]
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break_values += [0] * (gpu+1 - len(break_values))
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break_values += [0] * (gpu_count - len(break_values))
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emit('from_server', {'cmd': 'selected_model_info', 'key_value': key_value, 'key':key,
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'gpu':gpu, 'layer_count':layer_count, 'breakmodel':breakmodel,
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'break_values': break_values, 'gpu_count': torch.cuda.device_count(),
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'break_values': break_values, 'gpu_count': gpu_count,
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'url': url}, broadcast=True)
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if key_value != "":
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get_oai_models(key_value)
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@ -1030,12 +1076,12 @@ 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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@ -1100,193 +1146,8 @@ def get_oai_models(key):
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emit('from_server', {'cmd': 'errmsg', 'data': req.json()})
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def load_model(use_gpu=True, gpu_layers=None, initial_load=False, online_model=""):
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global model
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global generator
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global torch
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global model_config
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vars.noai = False
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if not initial_load:
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set_aibusy(True)
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if vars.model != 'ReadOnly':
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emit('from_server', {'cmd': 'model_load_status', 'data': "Loading {}".format(vars.model)}, broadcast=True)
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#Have to add a sleep so the server will send the emit for some reason
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time.sleep(0.1)
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if gpu_layers is not None:
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args.breakmodel_gpulayers = gpu_layers
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#We need to wipe out the existing model and refresh the cuda cache
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model = None
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generator = None
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try:
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torch.cuda.empty_cache()
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except:
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pass
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#Let's set the GooseAI or OpenAI server URLs if that's applicable
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if online_model != "":
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if path.exists("settings/{}.settings".format(vars.model)):
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changed=False
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with open("settings/{}.settings".format(vars.model), "r") as file:
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# Check if API key exists
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js = json.load(file)
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if 'online_model' in js:
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if js['online_model'] != online_model:
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changed=True
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js['online_model'] = online_model
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else:
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changed=True
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js['online_model'] = online_model
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if changed:
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with open("settings/{}.settings".format(vars.model), "w") as file:
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file.write(json.dumps(js, indent=3))
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# Swap OAI Server if GooseAI was selected
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if(vars.model == "GooseAI"):
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vars.oaiengines = "https://api.goose.ai/v1/engines"
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vars.model = "OAI"
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args.configname = "GooseAI" + "/" + online_model
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else:
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args.configname = vars.model + "/" + online_model
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vars.oaiurl = vars.oaiengines + "/{0}/completions".format(online_model)
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# If transformers model was selected & GPU available, ask to use CPU or GPU
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if(vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
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vars.allowsp = True
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# Test for GPU support
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# Make model path the same as the model name to make this consistent with the other loading method if it isn't a known model type
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# This code is not just a workaround for below, it is also used to make the behavior consistent with other loading methods - Henk717
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if(not vars.model in ["NeoCustom", "GPT2Custom"]):
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vars.custmodpth = vars.model
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elif(vars.model == "NeoCustom"):
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vars.model = os.path.basename(os.path.normpath(vars.custmodpth))
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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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from transformers import AutoConfig
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if(os.path.isdir(vars.custmodpth.replace('/', '_'))):
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try:
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model_config = AutoConfig.from_pretrained(vars.custmodpth.replace('/', '_'), revision=vars.revision, cache_dir="cache")
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vars.model_type = model_config.model_type
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except ValueError as e:
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vars.model_type = "not_found"
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elif(os.path.isdir("models/{}".format(vars.custmodpth.replace('/', '_')))):
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try:
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model_config = AutoConfig.from_pretrained("models/{}".format(vars.custmodpth.replace('/', '_')), revision=vars.revision, cache_dir="cache")
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vars.model_type = model_config.model_type
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except ValueError as e:
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vars.model_type = "not_found"
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else:
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try:
|
||||
model_config = AutoConfig.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
|
||||
vars.model_type = model_config.model_type
|
||||
except ValueError as e:
|
||||
vars.model_type = "not_found"
|
||||
if(vars.model_type == "not_found" and vars.model == "NeoCustom"):
|
||||
vars.model_type = "gpt_neo"
|
||||
elif(vars.model_type == "not_found" and vars.model == "GPT2Custom"):
|
||||
vars.model_type = "gpt2"
|
||||
elif(vars.model_type == "not_found"):
|
||||
print("WARNING: No model type detected, assuming Neo (If this is a GPT2 model use the other menu option or --model GPT2Custom)")
|
||||
vars.model_type = "gpt_neo"
|
||||
|
||||
if(vars.model_type == "opt"):
|
||||
vars.badwordsids = vars.badwordsids_opt
|
||||
|
||||
if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
|
||||
loadmodelsettings()
|
||||
loadsettings()
|
||||
print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="")
|
||||
vars.hascuda = torch.cuda.is_available()
|
||||
vars.bmsupported = vars.model_type in ("gpt_neo", "gptj", "xglm", "opt") and not vars.nobreakmodel
|
||||
if(args.breakmodel is not None and args.breakmodel):
|
||||
print("WARNING: --breakmodel is no longer supported. Breakmodel mode is now automatically enabled when --breakmodel_gpulayers is used (see --help for details).", file=sys.stderr)
|
||||
if(args.breakmodel_layers is not None):
|
||||
print("WARNING: --breakmodel_layers is deprecated. Use --breakmodel_gpulayers instead (see --help for details).", file=sys.stderr)
|
||||
if(args.model and vars.bmsupported and not args.breakmodel_gpulayers and not args.breakmodel_layers):
|
||||
print("WARNING: Model launched without the --breakmodel_gpulayers argument, defaulting to GPU only mode.", file=sys.stderr)
|
||||
vars.bmsupported = False
|
||||
if(not vars.bmsupported and (args.breakmodel_gpulayers is not None or args.breakmodel_layers is not None)):
|
||||
print("WARNING: This model does not support hybrid generation. --breakmodel_gpulayers will be ignored.", file=sys.stderr)
|
||||
if(vars.hascuda):
|
||||
print("{0}FOUND!{1}".format(colors.GREEN, colors.END))
|
||||
else:
|
||||
print("{0}NOT FOUND!{1}".format(colors.YELLOW, colors.END))
|
||||
|
||||
if args.model:
|
||||
if(vars.hascuda):
|
||||
genselected = True
|
||||
vars.usegpu = True
|
||||
vars.breakmodel = False
|
||||
if(vars.bmsupported):
|
||||
vars.usegpu = False
|
||||
vars.breakmodel = True
|
||||
if(args.cpu):
|
||||
vars.usegpu = False
|
||||
vars.breakmodel = False
|
||||
elif(vars.hascuda):
|
||||
if(vars.bmsupported):
|
||||
genselected = True
|
||||
vars.usegpu = False
|
||||
vars.breakmodel = True
|
||||
else:
|
||||
genselected = False
|
||||
else:
|
||||
genselected = False
|
||||
|
||||
if(vars.hascuda):
|
||||
if(use_gpu):
|
||||
if(vars.bmsupported):
|
||||
vars.breakmodel = True
|
||||
vars.usegpu = False
|
||||
genselected = True
|
||||
else:
|
||||
vars.breakmodel = False
|
||||
vars.usegpu = True
|
||||
genselected = True
|
||||
else:
|
||||
vars.breakmodel = False
|
||||
vars.usegpu = False
|
||||
genselected = True
|
||||
|
||||
# Ask for API key if InferKit was selected
|
||||
if(vars.model == "InferKit"):
|
||||
vars.apikey = vars.oaiapikey
|
||||
|
||||
# Swap OAI Server if GooseAI was selected
|
||||
if(vars.model == "GooseAI"):
|
||||
vars.oaiengines = "https://api.goose.ai/v1/engines"
|
||||
vars.model = "OAI"
|
||||
args.configname = "GooseAI"
|
||||
|
||||
# Ask for API key if OpenAI was selected
|
||||
if(vars.model == "OAI"):
|
||||
if not args.configname:
|
||||
args.configname = "OAI"
|
||||
|
||||
if(vars.model == "ReadOnly"):
|
||||
vars.noai = True
|
||||
|
||||
# Start transformers and create pipeline
|
||||
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
|
||||
def patch_transformers():
|
||||
global transformers
|
||||
old_from_pretrained = PreTrainedModel.from_pretrained.__func__
|
||||
@classmethod
|
||||
def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
|
||||
@ -1309,101 +1170,6 @@ def load_model(use_gpu=True, gpu_layers=None, initial_load=False, online_model="
|
||||
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
|
||||
def get_lazy_load_callback(n_layers, convert_to_float16=True):
|
||||
if not vars.lazy_load:
|
||||
return
|
||||
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
if "breakmodel" in globals():
|
||||
gpu_blocks = breakmodel.gpu_blocks
|
||||
ram_blocks = ram_blocks = n_layers - sum(gpu_blocks)
|
||||
cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks))
|
||||
else:
|
||||
ram_blocks = gpu_blocks = cumulative_gpu_blocks = None
|
||||
|
||||
def lazy_load_callback(model_dict, f, **_):
|
||||
if lazy_load_callback.nested:
|
||||
return
|
||||
lazy_load_callback.nested = True
|
||||
|
||||
device_map = {}
|
||||
|
||||
for _key, spec in lazy_load_spec.get("layer_weights", {}).items():
|
||||
for layer in range(n_layers):
|
||||
key = _key.format(layer=layer)
|
||||
if key not in model_dict:
|
||||
continue
|
||||
device = vars.gpu_device if vars.hascuda and vars.usegpu else "cpu" if not vars.hascuda or not vars.breakmodel or layer < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, layer - ram_blocks)
|
||||
device_map[key] = device
|
||||
|
||||
for key, value in model_dict.items():
|
||||
if isinstance(value, torch_lazy_loader.LazyTensor) and key not in device_map:
|
||||
device_map[key] = vars.gpu_device if vars.hascuda and vars.usegpu else "cpu"
|
||||
|
||||
if utils.num_shards is None or utils.current_shard == 0:
|
||||
if utils.num_shards is not None:
|
||||
num_tensors = len(utils.get_sharded_checkpoint_num_tensors(utils.from_pretrained_model_name, utils.from_pretrained_index_filename, **utils.from_pretrained_kwargs))
|
||||
else:
|
||||
num_tensors = len(device_map)
|
||||
print(flush=True)
|
||||
utils.bar = tqdm(total=num_tensors, desc="Loading model tensors", file=Send_to_socketio())
|
||||
|
||||
with zipfile.ZipFile(f, "r") as z:
|
||||
try:
|
||||
last_storage_key = None
|
||||
f = None
|
||||
current_offset = 0
|
||||
if utils.num_shards is not None:
|
||||
utils.current_shard += 1
|
||||
for key in sorted(device_map.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)):
|
||||
storage_key = model_dict[key].key
|
||||
if storage_key != last_storage_key or model_dict[key].seek_offset < current_offset:
|
||||
last_storage_key = storage_key
|
||||
if isinstance(f, zipfile.ZipExtFile):
|
||||
f.close()
|
||||
f = z.open(f"archive/data/{storage_key}")
|
||||
current_offset = 0
|
||||
if current_offset != model_dict[key].seek_offset:
|
||||
f.read(model_dict[key].seek_offset - current_offset)
|
||||
current_offset = model_dict[key].seek_offset
|
||||
device = device_map[key]
|
||||
size = functools.reduce(lambda x, y: x * y, model_dict[key].shape, 1)
|
||||
dtype = model_dict[key].dtype
|
||||
nbytes = size if dtype is torch.bool else size * ((torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits >> 3)
|
||||
#print(f"Transferring <{key}> to {'(CPU)' if device == 'cpu' else '[device ' + str(device) + ']'} ... ", end="", flush=True)
|
||||
model_dict[key] = model_dict[key].materialize(f, map_location="cpu")
|
||||
if model_dict[key].dtype is torch.float32:
|
||||
vars.fp32_model = True
|
||||
if convert_to_float16 and vars.hascuda and (vars.breakmodel or vars.usegpu) and model_dict[key].dtype is torch.float32:
|
||||
model_dict[key] = model_dict[key].to(torch.float16)
|
||||
if not vars.usegpu and not vars.breakmodel and model_dict[key].dtype is torch.float16:
|
||||
model_dict[key] = model_dict[key].to(torch.float32)
|
||||
model_dict[key] = model_dict[key].to(device)
|
||||
#print("OK", flush=True)
|
||||
current_offset += nbytes
|
||||
utils.bar.update(1)
|
||||
finally:
|
||||
if utils.num_shards is None or utils.current_shard >= utils.num_shards:
|
||||
utils.bar.close()
|
||||
utils.bar = None
|
||||
lazy_load_callback.nested = False
|
||||
if isinstance(f, zipfile.ZipExtFile):
|
||||
f.close()
|
||||
|
||||
lazy_load_callback.nested = False
|
||||
return lazy_load_callback
|
||||
|
||||
lazy_load_config_path = os.path.join("maps", vars.model_type + ".json")
|
||||
if(vars.lazy_load and "model_config" in globals() and os.path.isfile(lazy_load_config_path)):
|
||||
with open(lazy_load_config_path) as f:
|
||||
lazy_load_spec = json.load(f)
|
||||
|
||||
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.
|
||||
@ -1635,6 +1401,286 @@ def load_model(use_gpu=True, gpu_layers=None, initial_load=False, online_model="
|
||||
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
|
||||
global GPT2TokenizerFast
|
||||
print("Loading vars.model: {} vars.custmodpth: {}".format(vars.model, vars.custmodpth))
|
||||
vars.noai = False
|
||||
if not initial_load:
|
||||
set_aibusy(True)
|
||||
if vars.model != 'ReadOnly':
|
||||
emit('from_server', {'cmd': 'model_load_status', 'data': "Loading {}".format(vars.model)}, broadcast=True)
|
||||
#Have to add a sleep so the server will send the emit for some reason
|
||||
time.sleep(0.1)
|
||||
if gpu_layers is not None:
|
||||
args.breakmodel_gpulayers = gpu_layers
|
||||
|
||||
#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 != "":
|
||||
if path.exists("settings/{}.settings".format(vars.model)):
|
||||
changed=False
|
||||
with open("settings/{}.settings".format(vars.model), "r") as file:
|
||||
# Check if API key exists
|
||||
js = json.load(file)
|
||||
if 'online_model' in js:
|
||||
if js['online_model'] != online_model:
|
||||
changed=True
|
||||
js['online_model'] = online_model
|
||||
else:
|
||||
changed=True
|
||||
js['online_model'] = online_model
|
||||
if changed:
|
||||
with open("settings/{}.settings".format(vars.model), "w") as file:
|
||||
file.write(json.dumps(js, indent=3))
|
||||
# Swap OAI Server if GooseAI was selected
|
||||
if(vars.model == "GooseAI"):
|
||||
vars.oaiengines = "https://api.goose.ai/v1/engines"
|
||||
vars.model = "OAI"
|
||||
args.configname = "GooseAI" + "/" + online_model
|
||||
else:
|
||||
args.configname = vars.model + "/" + online_model
|
||||
vars.oaiurl = vars.oaiengines + "/{0}/completions".format(online_model)
|
||||
|
||||
|
||||
# If transformers model was selected & GPU available, ask to use CPU or GPU
|
||||
if(vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
|
||||
vars.allowsp = True
|
||||
# Test for GPU support
|
||||
|
||||
# Make model path the same as the model name to make this consistent with the other loading method if it isn't a known model type
|
||||
# This code is not just a workaround for below, it is also used to make the behavior consistent with other loading methods - Henk717
|
||||
if(not vars.model in ["NeoCustom", "GPT2Custom"]):
|
||||
vars.custmodpth = vars.model
|
||||
elif(vars.model == "NeoCustom"):
|
||||
vars.model = os.path.basename(os.path.normpath(vars.custmodpth))
|
||||
|
||||
# Get the model_type from the config or assume a model type if it isn't present
|
||||
from transformers import AutoConfig
|
||||
if(os.path.isdir(vars.custmodpth.replace('/', '_'))):
|
||||
try:
|
||||
model_config = AutoConfig.from_pretrained(vars.custmodpth.replace('/', '_'), revision=vars.revision, cache_dir="cache")
|
||||
vars.model_type = model_config.model_type
|
||||
except ValueError as e:
|
||||
vars.model_type = "not_found"
|
||||
elif(os.path.isdir("models/{}".format(vars.custmodpth.replace('/', '_')))):
|
||||
try:
|
||||
model_config = AutoConfig.from_pretrained("models/{}".format(vars.custmodpth.replace('/', '_')), revision=vars.revision, cache_dir="cache")
|
||||
vars.model_type = model_config.model_type
|
||||
except ValueError as e:
|
||||
vars.model_type = "not_found"
|
||||
else:
|
||||
try:
|
||||
model_config = AutoConfig.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
|
||||
vars.model_type = model_config.model_type
|
||||
except ValueError as e:
|
||||
vars.model_type = "not_found"
|
||||
if(vars.model_type == "not_found" and vars.model == "NeoCustom"):
|
||||
vars.model_type = "gpt_neo"
|
||||
elif(vars.model_type == "not_found" and vars.model == "GPT2Custom"):
|
||||
vars.model_type = "gpt2"
|
||||
elif(vars.model_type == "not_found"):
|
||||
print("WARNING: No model type detected, assuming Neo (If this is a GPT2 model use the other menu option or --model GPT2Custom)")
|
||||
vars.model_type = "gpt_neo"
|
||||
|
||||
if(vars.model_type == "opt"):
|
||||
vars.badwordsids = vars.badwordsids_opt
|
||||
|
||||
if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
|
||||
loadmodelsettings()
|
||||
loadsettings()
|
||||
print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="")
|
||||
vars.hascuda = torch.cuda.is_available()
|
||||
vars.bmsupported = vars.model_type in ("gpt_neo", "gptj", "xglm", "opt") and not vars.nobreakmodel
|
||||
if(args.breakmodel is not None and args.breakmodel):
|
||||
print("WARNING: --breakmodel is no longer supported. Breakmodel mode is now automatically enabled when --breakmodel_gpulayers is used (see --help for details).", file=sys.stderr)
|
||||
if(args.breakmodel_layers is not None):
|
||||
print("WARNING: --breakmodel_layers is deprecated. Use --breakmodel_gpulayers instead (see --help for details).", file=sys.stderr)
|
||||
if(args.model and vars.bmsupported and not args.breakmodel_gpulayers and not args.breakmodel_layers):
|
||||
print("WARNING: Model launched without the --breakmodel_gpulayers argument, defaulting to GPU only mode.", file=sys.stderr)
|
||||
vars.bmsupported = False
|
||||
if(not vars.bmsupported and (args.breakmodel_gpulayers is not None or args.breakmodel_layers is not None)):
|
||||
print("WARNING: This model does not support hybrid generation. --breakmodel_gpulayers will be ignored.", file=sys.stderr)
|
||||
if(vars.hascuda):
|
||||
print("{0}FOUND!{1}".format(colors.GREEN, colors.END))
|
||||
else:
|
||||
print("{0}NOT FOUND!{1}".format(colors.YELLOW, colors.END))
|
||||
|
||||
if args.model:
|
||||
if(vars.hascuda):
|
||||
genselected = True
|
||||
vars.usegpu = True
|
||||
vars.breakmodel = False
|
||||
if(vars.bmsupported):
|
||||
vars.usegpu = False
|
||||
vars.breakmodel = True
|
||||
if(args.cpu):
|
||||
vars.usegpu = False
|
||||
vars.breakmodel = False
|
||||
elif(vars.hascuda):
|
||||
if(vars.bmsupported):
|
||||
genselected = True
|
||||
vars.usegpu = False
|
||||
vars.breakmodel = True
|
||||
else:
|
||||
genselected = False
|
||||
else:
|
||||
genselected = False
|
||||
|
||||
if(vars.hascuda):
|
||||
if(use_gpu):
|
||||
if(vars.bmsupported):
|
||||
vars.breakmodel = True
|
||||
vars.usegpu = False
|
||||
genselected = True
|
||||
else:
|
||||
vars.breakmodel = False
|
||||
vars.usegpu = True
|
||||
genselected = True
|
||||
else:
|
||||
vars.breakmodel = False
|
||||
vars.usegpu = False
|
||||
genselected = True
|
||||
|
||||
# Ask for API key if InferKit was selected
|
||||
if(vars.model == "InferKit"):
|
||||
vars.apikey = vars.oaiapikey
|
||||
|
||||
# Swap OAI Server if GooseAI was selected
|
||||
if(vars.model == "GooseAI"):
|
||||
vars.oaiengines = "https://api.goose.ai/v1/engines"
|
||||
vars.model = "OAI"
|
||||
args.configname = "GooseAI"
|
||||
|
||||
# Ask for API key if OpenAI was selected
|
||||
if(vars.model == "OAI"):
|
||||
if not args.configname:
|
||||
args.configname = "OAI"
|
||||
|
||||
if(vars.model == "ReadOnly"):
|
||||
vars.noai = True
|
||||
|
||||
# Start transformers and create pipeline
|
||||
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))
|
||||
for m in ("GPTJModel", "XGLMModel"):
|
||||
try:
|
||||
globals()[m] = getattr(__import__("transformers"), m)
|
||||
except:
|
||||
pass
|
||||
|
||||
# Lazy loader
|
||||
import torch_lazy_loader
|
||||
def get_lazy_load_callback(n_layers, convert_to_float16=True):
|
||||
if not vars.lazy_load:
|
||||
return
|
||||
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
if "breakmodel" in globals():
|
||||
gpu_blocks = breakmodel.gpu_blocks
|
||||
ram_blocks = ram_blocks = n_layers - sum(gpu_blocks)
|
||||
cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks))
|
||||
else:
|
||||
ram_blocks = gpu_blocks = cumulative_gpu_blocks = None
|
||||
|
||||
def lazy_load_callback(model_dict, f, **_):
|
||||
if lazy_load_callback.nested:
|
||||
return
|
||||
lazy_load_callback.nested = True
|
||||
|
||||
device_map = {}
|
||||
|
||||
for _key, spec in lazy_load_spec.get("layer_weights", {}).items():
|
||||
for layer in range(n_layers):
|
||||
key = _key.format(layer=layer)
|
||||
if key not in model_dict:
|
||||
continue
|
||||
device = vars.gpu_device if vars.hascuda and vars.usegpu else "cpu" if not vars.hascuda or not vars.breakmodel or layer < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, layer - ram_blocks)
|
||||
device_map[key] = device
|
||||
|
||||
for key, value in model_dict.items():
|
||||
if isinstance(value, torch_lazy_loader.LazyTensor) and key not in device_map:
|
||||
device_map[key] = vars.gpu_device if vars.hascuda and vars.usegpu else "cpu"
|
||||
|
||||
if utils.num_shards is None or utils.current_shard == 0:
|
||||
if utils.num_shards is not None:
|
||||
num_tensors = len(utils.get_sharded_checkpoint_num_tensors(utils.from_pretrained_model_name, utils.from_pretrained_index_filename, **utils.from_pretrained_kwargs))
|
||||
else:
|
||||
num_tensors = len(device_map)
|
||||
print(flush=True)
|
||||
utils.bar = tqdm(total=num_tensors, desc="Loading model tensors", file=Send_to_socketio())
|
||||
|
||||
with zipfile.ZipFile(f, "r") as z:
|
||||
try:
|
||||
last_storage_key = None
|
||||
f = None
|
||||
current_offset = 0
|
||||
if utils.num_shards is not None:
|
||||
utils.current_shard += 1
|
||||
for key in sorted(device_map.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)):
|
||||
storage_key = model_dict[key].key
|
||||
if storage_key != last_storage_key or model_dict[key].seek_offset < current_offset:
|
||||
last_storage_key = storage_key
|
||||
if isinstance(f, zipfile.ZipExtFile):
|
||||
f.close()
|
||||
f = z.open(f"archive/data/{storage_key}")
|
||||
current_offset = 0
|
||||
if current_offset != model_dict[key].seek_offset:
|
||||
f.read(model_dict[key].seek_offset - current_offset)
|
||||
current_offset = model_dict[key].seek_offset
|
||||
device = device_map[key]
|
||||
size = functools.reduce(lambda x, y: x * y, model_dict[key].shape, 1)
|
||||
dtype = model_dict[key].dtype
|
||||
nbytes = size if dtype is torch.bool else size * ((torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits >> 3)
|
||||
#print(f"Transferring <{key}> to {'(CPU)' if device == 'cpu' else '[device ' + str(device) + ']'} ... ", end="", flush=True)
|
||||
model_dict[key] = model_dict[key].materialize(f, map_location="cpu")
|
||||
if model_dict[key].dtype is torch.float32:
|
||||
vars.fp32_model = True
|
||||
if convert_to_float16 and vars.hascuda and (vars.breakmodel or vars.usegpu) and model_dict[key].dtype is torch.float32:
|
||||
model_dict[key] = model_dict[key].to(torch.float16)
|
||||
if not vars.usegpu and not vars.breakmodel and model_dict[key].dtype is torch.float16:
|
||||
model_dict[key] = model_dict[key].to(torch.float32)
|
||||
model_dict[key] = model_dict[key].to(device)
|
||||
#print("OK", flush=True)
|
||||
current_offset += nbytes
|
||||
utils.bar.update(1)
|
||||
finally:
|
||||
if utils.num_shards is None or utils.current_shard >= utils.num_shards:
|
||||
utils.bar.close()
|
||||
utils.bar = None
|
||||
lazy_load_callback.nested = False
|
||||
if isinstance(f, zipfile.ZipExtFile):
|
||||
f.close()
|
||||
|
||||
lazy_load_callback.nested = False
|
||||
return lazy_load_callback
|
||||
|
||||
lazy_load_config_path = os.path.join("maps", vars.model_type + ".json")
|
||||
if(vars.lazy_load and "model_config" in globals() and os.path.isfile(lazy_load_config_path)):
|
||||
with open(lazy_load_config_path) as f:
|
||||
lazy_load_spec = json.load(f)
|
||||
|
||||
else:
|
||||
vars.lazy_load = False
|
||||
|
||||
|
||||
|
||||
def get_hidden_size_from_model(model):
|
||||
try:
|
||||
return int(model.model.decoder.project_in.in_features)
|
||||
@ -2991,19 +3037,23 @@ 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)
|
||||
|
||||
@ -5685,6 +5735,7 @@ 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"
|
||||
@ -5740,6 +5791,7 @@ 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 +991,44 @@ 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 class=\"breadcrumbitem\" id='model_breadcrumbs"+i+"' name='"+ar[0][1]+"' value='"+breadcrumbs[i][0]+"'>"+breadcrumbs[i][1]+"</button><font color=white>\\</font>");
|
||||
$("#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));
|
||||
}
|
||||
if (breadcrumbs.length > 0) {
|
||||
$("#loadmodellistbreadcrumbs").append("<hr size='1'>")
|
||||
}
|
||||
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 +1042,7 @@ 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 +2494,12 @@ $(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")
|
||||
|
||||
@ -1035,7 +1035,7 @@ body.connected .statusiconlabel, .statusiconlabel.always-available {
|
||||
}
|
||||
|
||||
.loadlistitem {
|
||||
padding: 5px 10px 5px 10px;
|
||||
padding: 0px 0px 0px 0px;
|
||||
display: flex;
|
||||
flex-grow: 1;
|
||||
color: #ffffff;
|
||||
@ -1051,6 +1051,28 @@ body.connected .statusiconlabel, .statusiconlabel.always-available {
|
||||
background-color: #688f1f;
|
||||
}
|
||||
|
||||
.breadcrumbitem {
|
||||
padding: 5px 10px 5px 10px;
|
||||
color: #ffffff;
|
||||
background-color: transparent;
|
||||
border: none;
|
||||
|
||||
-moz-transition: background-color 0.25s ease-in;
|
||||
-o-transition: background-color 0.25s ease-in;
|
||||
-webkit-transition: background-color 0.25s ease-in;
|
||||
transition: background-color 0.25s ease-in;
|
||||
}
|
||||
|
||||
.breadcrumbitem:hover {
|
||||
cursor: pointer;
|
||||
background-color: #688f1f;
|
||||
}
|
||||
|
||||
hr {
|
||||
padding: 0px;
|
||||
margin: 0px;
|
||||
}
|
||||
|
||||
.loadlistpadding {
|
||||
padding-right: 10px;
|
||||
}
|
||||
|
||||
@ -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…
x
Reference in New Issue
Block a user