mirror of
https://github.com/KoboldAI/KoboldAI-Client.git
synced 2025-06-05 21:59:24 +02:00
Need to set vars.allowsp to True before calling loadsettings()
This commit is contained in:
Gnome Ann
222
aiserver.py
222
aiserver.py
@ -461,6 +461,62 @@ def loadmodelsettings():
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if(not vars.gamestarted):
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if(not vars.gamestarted):
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vars.authornotetemplate = vars.setauthornotetemplate
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vars.authornotetemplate = vars.setauthornotetemplate
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#==================================================================#
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# Take settings from vars and write them to client settings file
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#==================================================================#
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def savesettings():
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# Build json to write
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js = {}
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js["apikey"] = vars.apikey
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js["andepth"] = vars.andepth
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js["temp"] = vars.temp
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js["top_p"] = vars.top_p
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js["top_k"] = vars.top_k
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js["tfs"] = vars.tfs
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js["rep_pen"] = vars.rep_pen
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js["rep_pen_slope"] = vars.rep_pen_slope
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js["rep_pen_range"] = vars.rep_pen_range
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js["genamt"] = vars.genamt
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js["max_length"] = vars.max_length
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js["ikgen"] = vars.ikgen
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js["formatoptns"] = vars.formatoptns
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js["numseqs"] = vars.numseqs
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js["widepth"] = vars.widepth
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js["useprompt"] = vars.useprompt
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js["adventure"] = vars.adventure
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js["chatmode"] = vars.chatmode
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js["chatname"] = vars.chatname
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js["dynamicscan"] = vars.dynamicscan
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js["nopromptgen"] = vars.nopromptgen
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js["rngpersist"] = vars.rngpersist
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js["nogenmod"] = vars.nogenmod
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js["autosave"] = vars.autosave
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js["welcome"] = vars.welcome
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js["newlinemode"] = vars.newlinemode
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js["antemplate"] = vars.setauthornotetemplate
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js["userscripts"] = vars.userscripts
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js["corescript"] = vars.corescript
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js["softprompt"] = vars.spfilename
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# Write it
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if not os.path.exists('settings'):
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os.mkdir('settings')
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file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "w")
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try:
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file.write(json.dumps(js, indent=3))
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finally:
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file.close()
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#==================================================================#
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# Don't save settings unless 2 seconds have passed without modification
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#==================================================================#
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@debounce(2)
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def settingschanged():
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print("{0}Saving settings!{1}".format(colors.GREEN, colors.END))
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savesettings()
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#==================================================================#
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#==================================================================#
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# Read settings from client file JSON and send to vars
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# Read settings from client file JSON and send to vars
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#==================================================================#
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#==================================================================#
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@ -550,6 +606,60 @@ def loadsettings():
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file.close()
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file.close()
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#==================================================================#
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# Load a soft prompt from a file
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#==================================================================#
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def spRequest(filename):
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vars.spfilename = ""
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settingschanged()
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if(len(filename) == 0):
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vars.sp = None
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vars.sp_length = 0
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return
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global np
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if 'np' not in globals():
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import numpy as np
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z, version, shape, fortran_order, dtype = fileops.checksp(filename, vars.modeldim)
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assert isinstance(z, zipfile.ZipFile)
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with z.open('meta.json') as f:
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vars.spmeta = json.load(f)
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z.close()
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with np.load(fileops.sppath(filename), allow_pickle=False) as f:
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tensor = f['tensor.npy']
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# If the tensor is in bfloat16 format, convert it to float32
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if(tensor.dtype == 'V2'):
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tensor.dtype = np.uint16
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tensor = np.uint32(tensor) << 16
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tensor.dtype = np.float32
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if(tensor.dtype != np.float16):
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tensor = np.float32(tensor)
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assert not np.isinf(tensor).any() and not np.isnan(tensor).any()
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vars.sp_length = tensor.shape[-2]
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vars.spmeta["n_tokens"] = vars.sp_length
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if(vars.model in ("TPUMeshTransformerGPTJ",)):
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rows = tensor.shape[0]
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padding_amount = tpu_mtj_backend.params["seq"] - (tpu_mtj_backend.params["seq"] % -tpu_mtj_backend.params["cores_per_replica"]) - rows
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tensor = np.pad(tensor, ((0, padding_amount), (0, 0)))
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tensor = tensor.reshape(
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tpu_mtj_backend.params["cores_per_replica"],
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-1,
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tpu_mtj_backend.params["d_model"],
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)
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vars.sp = tpu_mtj_backend.shard_xmap(np.float32(tensor))
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else:
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vars.sp = torch.from_numpy(tensor)
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vars.spfilename = filename
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settingschanged()
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#==================================================================#
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#==================================================================#
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# Startup
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# Startup
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#==================================================================#
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#==================================================================#
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@ -1298,10 +1408,10 @@ else:
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tpu_mtj_backend.compiling_callback = tpumtjgenerate_compiling_callback
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tpu_mtj_backend.compiling_callback = tpumtjgenerate_compiling_callback
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tpu_mtj_backend.stopped_compiling_callback = tpumtjgenerate_stopped_compiling_callback
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tpu_mtj_backend.stopped_compiling_callback = tpumtjgenerate_stopped_compiling_callback
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tpu_mtj_backend.settings_callback = tpumtjgenerate_settings_callback
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tpu_mtj_backend.settings_callback = tpumtjgenerate_settings_callback
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vars.allowsp = True
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loadmodelsettings()
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loadmodelsettings()
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loadsettings()
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loadsettings()
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tpu_mtj_backend.load_model(vars.custmodpth, **vars.modelconfig)
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tpu_mtj_backend.load_model(vars.custmodpth, **vars.modelconfig)
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vars.allowsp = True
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vars.modeldim = int(tpu_mtj_backend.params["d_model"])
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vars.modeldim = int(tpu_mtj_backend.params["d_model"])
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tokenizer = tpu_mtj_backend.tokenizer
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tokenizer = tpu_mtj_backend.tokenizer
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else:
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else:
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@ -2397,62 +2507,6 @@ def sendsettings():
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if(not frm["id"] in vars.formatoptns):
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if(not frm["id"] in vars.formatoptns):
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vars.formatoptns[frm["id"]] = False;
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vars.formatoptns[frm["id"]] = False;
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#==================================================================#
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# Take settings from vars and write them to client settings file
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#==================================================================#
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def savesettings():
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# Build json to write
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js = {}
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js["apikey"] = vars.apikey
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js["andepth"] = vars.andepth
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js["temp"] = vars.temp
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js["top_p"] = vars.top_p
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js["top_k"] = vars.top_k
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js["tfs"] = vars.tfs
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js["rep_pen"] = vars.rep_pen
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js["rep_pen_slope"] = vars.rep_pen_slope
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js["rep_pen_range"] = vars.rep_pen_range
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js["genamt"] = vars.genamt
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js["max_length"] = vars.max_length
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js["ikgen"] = vars.ikgen
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js["formatoptns"] = vars.formatoptns
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js["numseqs"] = vars.numseqs
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js["widepth"] = vars.widepth
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js["useprompt"] = vars.useprompt
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js["adventure"] = vars.adventure
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js["chatmode"] = vars.chatmode
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js["chatname"] = vars.chatname
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js["dynamicscan"] = vars.dynamicscan
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js["nopromptgen"] = vars.nopromptgen
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js["rngpersist"] = vars.rngpersist
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js["nogenmod"] = vars.nogenmod
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js["autosave"] = vars.autosave
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js["welcome"] = vars.welcome
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js["newlinemode"] = vars.newlinemode
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js["antemplate"] = vars.setauthornotetemplate
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js["userscripts"] = vars.userscripts
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js["corescript"] = vars.corescript
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js["softprompt"] = vars.spfilename
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# Write it
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if not os.path.exists('settings'):
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os.mkdir('settings')
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file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "w")
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try:
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file.write(json.dumps(js, indent=3))
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finally:
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file.close()
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#==================================================================#
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# Don't save settings unless 2 seconds have passed without modification
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#==================================================================#
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@debounce(2)
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def settingschanged():
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print("{0}Saving settings!{1}".format(colors.GREEN, colors.END))
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savesettings()
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#==================================================================#
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#==================================================================#
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# Set value of gamesaved
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# Set value of gamesaved
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#==================================================================#
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#==================================================================#
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@ -4493,60 +4547,6 @@ def loadRequest(loadpath, filename=None):
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emit('from_server', {'cmd': 'hidegenseqs', 'data': ''}, broadcast=True)
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emit('from_server', {'cmd': 'hidegenseqs', 'data': ''}, broadcast=True)
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print("{0}Story loaded from {1}!{2}".format(colors.GREEN, filename, colors.END))
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print("{0}Story loaded from {1}!{2}".format(colors.GREEN, filename, colors.END))
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#==================================================================#
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# Load a soft prompt from a file
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#==================================================================#
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def spRequest(filename):
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vars.spfilename = ""
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settingschanged()
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if(len(filename) == 0):
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vars.sp = None
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vars.sp_length = 0
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return
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global np
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if 'np' not in globals():
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import numpy as np
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z, version, shape, fortran_order, dtype = fileops.checksp(filename, vars.modeldim)
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assert isinstance(z, zipfile.ZipFile)
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with z.open('meta.json') as f:
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vars.spmeta = json.load(f)
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z.close()
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with np.load(fileops.sppath(filename), allow_pickle=False) as f:
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tensor = f['tensor.npy']
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# If the tensor is in bfloat16 format, convert it to float32
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if(tensor.dtype == 'V2'):
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tensor.dtype = np.uint16
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tensor = np.uint32(tensor) << 16
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tensor.dtype = np.float32
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if(tensor.dtype != np.float16):
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tensor = np.float32(tensor)
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assert not np.isinf(tensor).any() and not np.isnan(tensor).any()
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vars.sp_length = tensor.shape[-2]
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vars.spmeta["n_tokens"] = vars.sp_length
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if(vars.model in ("TPUMeshTransformerGPTJ",)):
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rows = tensor.shape[0]
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padding_amount = tpu_mtj_backend.params["seq"] - (tpu_mtj_backend.params["seq"] % -tpu_mtj_backend.params["cores_per_replica"]) - rows
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tensor = np.pad(tensor, ((0, padding_amount), (0, 0)))
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tensor = tensor.reshape(
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tpu_mtj_backend.params["cores_per_replica"],
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-1,
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tpu_mtj_backend.params["d_model"],
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)
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vars.sp = tpu_mtj_backend.shard_xmap(np.float32(tensor))
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else:
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vars.sp = torch.from_numpy(tensor)
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vars.spfilename = filename
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settingschanged()
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#==================================================================#
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#==================================================================#
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# Import an AIDungon game exported with Mimi's tool
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# Import an AIDungon game exported with Mimi's tool
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#==================================================================#
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#==================================================================#
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