Soft prompt support (6B Colabs not supported yet)
This commit is contained in:
Gnome Ann
parent
0f38dbc0ed
commit
1f449a9dda
93
aiserver.py
93
aiserver.py
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@ -12,7 +12,8 @@ import tkinter as tk
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from tkinter import messagebox
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import json
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import collections
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from typing import Union
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import zipfile
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from typing import Union, Tuple
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import requests
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import html
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@ -103,7 +104,9 @@ class vars:
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formatoptns = {'frmttriminc': True, 'frmtrmblln': False, 'frmtrmspch': False, 'frmtadsnsp': False} # Container for state of formatting options
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importnum = -1 # Selection on import popup list
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importjs = {} # Temporary storage for import data
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loadselect = "" # Temporary storage for filename to load
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loadselect = "" # Temporary storage for story filename to load
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spselect = "" # Temporary storage for soft prompt filename to load
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sp = None # Current soft prompt tensor (as a NumPy array)
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svowname = "" # Filename that was flagged for overwrite confirm
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saveow = False # Whether or not overwrite confirm has been displayed
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genseqs = [] # Temporary storage for generated sequences
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@ -113,6 +116,8 @@ class vars:
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bmsupported = False # Whether the breakmodel option is supported (GPT-Neo/GPT-J only, currently)
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smandelete = False # Whether stories can be deleted from inside the browser
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smanrename = False # Whether stories can be renamed from inside the browser
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allowsp = False # Whether we are allowed to use soft prompts (by default enabled if we're using GPT-2, GPT-Neo or GPT-J)
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modeldim = -1 # Embedding dimension of your model (e.g. it's 4096 for GPT-J-6B and 2560 for GPT-Neo-2.7B)
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laststory = None # Filename (without extension) of most recent story JSON file we loaded
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acregex_ai = re.compile(r'\n* *>(.|\n)*') # Pattern for matching adventure actions from the AI so we can remove them
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acregex_ui = re.compile(r'^ *(>.*)$', re.MULTILINE) # Pattern for matching actions in the HTML-escaped story so we can apply colouring, etc (make sure to encase part to format in parentheses)
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@ -311,6 +316,7 @@ else:
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# If transformers model was selected & GPU available, ask to use CPU or GPU
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if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
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vars.allowsp = True
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# Test for GPU support
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import torch
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print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="")
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@ -503,11 +509,31 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
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if(not vars.noai):
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print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END))
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from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM
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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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inputs_embeds = self.transformer.wte(input_ids)
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if(vars.sp is not None):
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inputs_embeds = torch.cat((
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vars.sp.tile((inputs_embeds.shape[0], 1, 1)),
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inputs_embeds
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), dim=1).to(self.device)
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kwargs['inputs_embeds'] = inputs_embeds
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return old_forward(*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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# If custom GPT Neo model was chosen
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if(vars.model == "NeoCustom"):
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model_config = open(vars.custmodpth + "/config.json", "r")
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js = json.load(model_config)
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vars.modeldim = int(js['hidden_size'])
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if("model_type" in js):
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model = AutoModelForCausalLM.from_pretrained(vars.custmodpth)
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else:
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@ -525,6 +551,9 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
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generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
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# If custom GPT2 model was chosen
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elif(vars.model == "GPT2Custom"):
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model_config = open(vars.custmodpth + "/config.json", "r")
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js = json.load(model_config)
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vars.modeldim = int(js['hidden_size'])
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model = GPT2LMHeadModel.from_pretrained(vars.custmodpth)
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tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth)
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# Is CUDA available? If so, use GPU, otherwise fall back to CPU
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@ -538,13 +567,17 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
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tokenizer = GPT2Tokenizer.from_pretrained(vars.model)
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if(vars.hascuda):
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if(vars.usegpu):
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generator = pipeline('text-generation', model=vars.model, device=0)
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model = AutoModelForCausalLM.from_pretrained(vars.model, device=0)
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vars.modeldim = int(model.transformer.hidden_size)
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generator = pipeline('text-generation', model=model, device=0)
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elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
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model = AutoModelForCausalLM.from_pretrained(vars.model)
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device_config(model)
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else:
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model = AutoModelForCausalLM.from_pretrained(vars.model)
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generator = pipeline('text-generation', model=vars.model)
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else:
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model = AutoModelForCausalLM.from_pretrained(vars.model)
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generator = pipeline('text-generation', model=vars.model)
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# Suppress Author's Note by flagging square brackets (Old implementation)
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@ -807,10 +840,16 @@ def get_message(msg):
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save()
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elif(msg['cmd'] == 'loadlistrequest'):
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getloadlist()
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elif(msg['cmd'] == 'splistrequest'):
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getsplist()
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elif(msg['cmd'] == 'loadselect'):
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vars.loadselect = msg["data"]
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elif(msg['cmd'] == 'spselect'):
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vars.spselect = msg["data"]
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elif(msg['cmd'] == 'loadrequest'):
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loadRequest(fileops.storypath(vars.loadselect))
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elif(msg['cmd'] == 'sprequest'):
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spRequest(vars.spselect)
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elif(msg['cmd'] == 'deletestory'):
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deletesave(msg['data'])
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elif(msg['cmd'] == 'renamestory'):
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@ -846,6 +885,8 @@ def get_message(msg):
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#==================================================================#
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def setStartState():
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txt = "<span>Welcome to <span class=\"color_cyan\">KoboldAI</span>! You are running <span class=\"color_green\">"+getmodelname()+"</span>.<br/>"
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if(vars.allowsp):
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emit('from_server', {'cmd': 'allowsp', 'data': vars.allowsp}, broadcast=True)
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if(not vars.noai):
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txt = txt + "Please load a game or enter a prompt below to begin!</span>"
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else:
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@ -1123,10 +1164,12 @@ def calcsubmit(txt):
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anotetkns = tokenizer.encode(anotetxt)
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lnanote = len(anotetkns)
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lnsp = vars.sp.shape[0] if vars.sp is not None else 0
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if(vars.useprompt):
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budget = vars.max_length - lnprompt - lnmem - lnanote - lnwi - vars.genamt
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budget = vars.max_length - lnsp - lnprompt - lnmem - lnanote - lnwi - vars.genamt
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else:
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budget = vars.max_length - lnmem - lnanote - lnwi - vars.genamt
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budget = vars.max_length - lnsp - lnmem - lnanote - lnwi - vars.genamt
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if(actionlen == 0):
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# First/Prompt action
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@ -2131,11 +2174,18 @@ def saveRequest(savpath):
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print("{0}Story saved to {1}!{2}".format(colors.GREEN, path.basename(savpath), colors.END))
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#==================================================================#
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# Load a saved story via file browser
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# Show list of saved stories
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#==================================================================#
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def getloadlist():
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emit('from_server', {'cmd': 'buildload', 'data': fileops.getstoryfiles()})
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#==================================================================#
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# Show list of soft prompts
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#==================================================================#
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def getsplist():
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if(vars.allowsp):
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emit('from_server', {'cmd': 'buildsp', 'data': fileops.getspfiles(vars.modeldim)})
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#==================================================================#
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# Load a saved story via file browser
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#==================================================================#
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@ -2221,6 +2271,35 @@ def loadRequest(loadpath):
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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, path.basename(loadpath), 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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if(len(filename) == 0):
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vars.sp = None
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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('tensor.npy') as f:
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tensor = np.load(f, allow_pickle=False)
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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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tensor = np.float16(tensor)
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assert not np.isinf(tensor).any() and not np.isnan(tensor).any()
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vars.sp = torch.from_numpy(tensor)
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#==================================================================#
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# Import an AIDungon game exported with Mimi's tool
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#==================================================================#
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72
fileops.py
72
fileops.py
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@ -1,8 +1,10 @@
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import tkinter as tk
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from tkinter import filedialog
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from os import getcwd, listdir, path
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from typing import Tuple, Union, Optional
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import os
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import json
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import zipfile
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#==================================================================#
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# Generic Method for prompting for file path
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@ -61,6 +63,12 @@ def getdirpath(dir, title):
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def storypath(name):
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return path.join(path.dirname(path.realpath(__file__)), "stories", name + ".json")
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#==================================================================#
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# Returns the path (as a string) to the given soft prompt by its filename
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#==================================================================#
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def sppath(filename):
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return path.join(path.dirname(path.realpath(__file__)), "softprompts", filename)
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#==================================================================#
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# Returns an array of dicts containing story files in /stories
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#==================================================================#
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@ -86,6 +94,70 @@ def getstoryfiles():
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list.append(ob)
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return list
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#==================================================================#
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# Checks if the given soft prompt file is valid
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#==================================================================#
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def checksp(filename: str, model_dimension: int) -> Tuple[Union[zipfile.ZipFile, int], Optional[Tuple[int, int]], Optional[Tuple[int, int]], Optional[bool], Optional['np.dtype']]:
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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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try:
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z = zipfile.ZipFile(path.dirname(path.realpath(__file__))+"/softprompts/"+filename)
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with z.open('tensor.npy') as f:
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# Read only the header of the npy file, for efficiency reasons
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version: Tuple[int, int] = np.lib.format.read_magic(f)
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shape: Tuple[int, int]
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fortran_order: bool
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dtype: np.dtype
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shape, fortran_order, dtype = np.lib.format._read_array_header(f, version)
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assert len(shape) == 2
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except:
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z.close()
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return 1, None, None, None, None
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if dtype not in ('V2', np.float16, np.float32):
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z.close()
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return 2, version, shape, fortran_order, dtype
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if shape[1] != model_dimension:
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z.close()
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return 3, version, shape, fortran_order, dtype
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if shape[0] >= 2048:
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z.close()
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return 4, version, shape, fortran_order, dtype
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return z, version, shape, fortran_order, dtype
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#==================================================================#
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# Returns an array of dicts containing softprompt files in /softprompts
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#==================================================================#
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def getspfiles(model_dimension: int):
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lst = []
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os.makedirs(path.dirname(path.realpath(__file__))+"/softprompts", exist_ok=True)
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for file in listdir(path.dirname(path.realpath(__file__))+"/softprompts"):
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if not file.endswith(".zip"):
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continue
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z, version, shape, fortran_order, dtype = checksp(file, model_dimension)
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if z == 1:
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print(f"Browser SP loading error: {file} is malformed or not a soft prompt ZIP file.")
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continue
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if z == 2:
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print(f"Browser SP loading error: {file} tensor.npy has unsupported dtype '{dtype.name}'.")
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continue
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if z == 3:
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print(f"Browser SP loading error: {file} tensor.npy has model dimension {shape[1]} which does not match your model's model dimension of {model_dimension}. This usually means this soft prompt is not compatible with your model.")
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continue
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if z == 4:
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print(f"Browser SP loading error: {file} tensor.npy has {shape[0]} tokens but it is supposed to have less than 2048 tokens.")
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continue
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assert isinstance(z, zipfile.ZipFile)
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try:
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with z.open('meta.json') as f:
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ob = json.load(f)
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except:
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ob = {}
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z.close()
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ob["filename"] = file
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lst.append(ob)
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return lst
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#==================================================================#
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# Returns True if json file exists with requested save name
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#==================================================================#
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@ -18,6 +18,7 @@ var button_importwi;
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var button_impaidg;
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var button_settings;
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var button_format;
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var button_softprompt;
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var button_mode;
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var button_mode_label;
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var button_send;
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@ -53,6 +54,10 @@ var loadpopup;
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var loadcontent;
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var load_accept;
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var load_close;
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var sppopup;
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var spcontent;
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var sp_accept;
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var sp_close;
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var nspopup;
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var ns_accept;
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var ns_close;
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@ -77,6 +82,7 @@ var saved_prompt = "...";
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var override_focusout = false;
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var sman_allow_delete = false;
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var sman_allow_rename = false;
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var allowsp = false;
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// This is true iff [we're in macOS and the browser is Safari] or [we're in iOS]
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var using_webkit_patch = true;
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@ -589,6 +595,17 @@ function hideLoadPopup() {
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loadcontent.html("");
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}
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function showSPPopup() {
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sppopup.removeClass("hidden");
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sppopup.addClass("flex");
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}
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function hideSPPopup() {
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sppopup.removeClass("flex");
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sppopup.addClass("hidden");
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spcontent.html("");
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}
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function buildLoadList(ar) {
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disableButtons([load_accept]);
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loadcontent.html("");
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@ -654,11 +671,51 @@ function buildLoadList(ar) {
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}
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}
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function buildSPList(ar) {
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disableButtons([sp_accept]);
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spcontent.html("");
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showSPPopup();
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ar.push({filename: '', name: "[None]"})
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for(var i = 0; i < ar.length; i++) {
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var supported = !ar[i].supported
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? ''
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: Object.prototype.toString.call(ar[i].supported) === "[object Array]"
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? "[" + ar[i].supported.join(', ') + "]"
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: "[" + ar[i].supported.toString() + "]";
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var name = ar[i].name || ar[i].filename;
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name = name.length > 120 ? name.slice(0, 117) + '...' : name;
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var desc = ar[i].description || '';
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desc = desc.length > 500 ? desc.slice(0, 497) + '...' : desc;
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spcontent.append("<div class=\"flex\">\
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<div class=\"splistitem flex-row-container\" id=\"sp"+i+"\" name=\""+ar[i].filename+"\">\
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<div class=\"flex-row\">\
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<div>"+name+"</div>\
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<div class=\"flex-push-right splistitemsub\">"+ar[i].filename+"</div>\
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</div>\
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<div class=\"flex-row\">\
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<div>"+desc+"</div>\
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<div class=\"flex-push-right splistitemsub\">"+supported+"</div>\
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</div>\
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</div>\
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</div>");
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$("#sp"+i).on("click", function () {
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enableButtons([sp_accept]);
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socket.send({'cmd': 'spselect', 'data': $(this).attr("name")});
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highlightSPLine($(this));
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});
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}
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}
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function highlightLoadLine(ref) {
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$("#loadlistcontent > div > div.popuplistselected").removeClass("popuplistselected");
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ref.addClass("popuplistselected");
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}
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function highlightSPLine(ref) {
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$("#splistcontent > div > div.popuplistselected").removeClass("popuplistselected");
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ref.addClass("popuplistselected");
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}
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function showNewStoryPopup() {
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nspopup.removeClass("hidden");
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nspopup.addClass("flex");
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@ -1142,6 +1199,7 @@ $(document).ready(function(){
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button_impaidg = $("#btn_impaidg");
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button_settings = $('#btn_settings');
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button_format = $('#btn_format');
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button_softprompt = $("#btn_softprompt");
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button_mode = $('#btnmode')
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button_mode_label = $('#btnmode_label')
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button_send = $('#btnsend');
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@ -1177,6 +1235,10 @@ $(document).ready(function(){
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loadcontent = $("#loadlistcontent");
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load_accept = $("#btn_loadaccept");
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load_close = $("#btn_loadclose");
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sppopup = $("#spcontainer");
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spcontent = $("#splistcontent");
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sp_accept = $("#btn_spaccept");
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sp_close = $("#btn_spclose");
|
||||
nspopup = $("#newgamecontainer");
|
||||
ns_accept = $("#btn_nsaccept");
|
||||
ns_close = $("#btn_nsclose");
|
||||
|
|
@ -1314,6 +1376,13 @@ $(document).ready(function(){
|
|||
} else if(msg.data == "start") {
|
||||
setStartState();
|
||||
}
|
||||
} else if(msg.cmd == "allowsp") {
|
||||
allowsp = !!msg.data;
|
||||
if(allowsp) {
|
||||
button_softprompt.removeClass("hidden");
|
||||
} else {
|
||||
button_softprompt.addClass("hidden");
|
||||
}
|
||||
} else if(msg.cmd == "setstoryname") {
|
||||
storyname = msg.data;
|
||||
} else if(msg.cmd == "editmode") {
|
||||
|
|
@ -1480,6 +1549,8 @@ $(document).ready(function(){
|
|||
} else if(msg.cmd == "buildload") {
|
||||
// Send array of save files to load UI
|
||||
buildLoadList(msg.data);
|
||||
} else if(msg.cmd == "buildsp") {
|
||||
buildSPList(msg.data);
|
||||
} else if(msg.cmd == "askforoverwrite") {
|
||||
// Show overwrite warning
|
||||
show([$(".saveasoverwrite")]);
|
||||
|
|
@ -1654,6 +1725,10 @@ $(document).ready(function(){
|
|||
button_load.on("click", function(ev) {
|
||||
socket.send({'cmd': 'loadlistrequest', 'data': ''});
|
||||
});
|
||||
|
||||
button_softprompt.on("click", function(ev) {
|
||||
socket.send({'cmd': 'splistrequest', 'data': ''});
|
||||
});
|
||||
|
||||
load_close.on("click", function(ev) {
|
||||
hideLoadPopup();
|
||||
|
|
@ -1664,6 +1739,15 @@ $(document).ready(function(){
|
|||
socket.send({'cmd': 'loadrequest', 'data': ''});
|
||||
hideLoadPopup();
|
||||
});
|
||||
|
||||
sp_close.on("click", function(ev) {
|
||||
hideSPPopup();
|
||||
});
|
||||
|
||||
sp_accept.on("click", function(ev) {
|
||||
socket.send({'cmd': 'sprequest', 'data': ''});
|
||||
hideSPPopup();
|
||||
});
|
||||
|
||||
button_newgame.on("click", function(ev) {
|
||||
showNewStoryPopup();
|
||||
|
|
|
|||
|
|
@ -307,6 +307,38 @@ chunk.editing, chunk.editing * {
|
|||
overflow-y: scroll;
|
||||
}
|
||||
|
||||
#sppopup {
|
||||
width: 500px;
|
||||
background-color: #262626;
|
||||
margin-top: 100px;
|
||||
}
|
||||
|
||||
@media (max-width: 768px) {
|
||||
#loadpopup {
|
||||
width: 100%;
|
||||
background-color: #262626;
|
||||
margin-top: 100px;
|
||||
}
|
||||
}
|
||||
|
||||
#sppopupdelete {
|
||||
width: 350px;
|
||||
background-color: #262626;
|
||||
margin-top: 200px;
|
||||
}
|
||||
|
||||
#sppopuprename {
|
||||
width: 350px;
|
||||
background-color: #262626;
|
||||
margin-top: 200px;
|
||||
}
|
||||
|
||||
#splistcontent {
|
||||
height: 325px;
|
||||
overflow-y: scroll;
|
||||
overflow-wrap: anywhere;
|
||||
}
|
||||
|
||||
#nspopup {
|
||||
width: 350px;
|
||||
background-color: #262626;
|
||||
|
|
@ -423,6 +455,18 @@ chunk.editing, chunk.editing * {
|
|||
align-items: center;
|
||||
}
|
||||
|
||||
.flex-row-container {
|
||||
display: flex;
|
||||
flex-flow: wrap;
|
||||
}
|
||||
|
||||
.flex-row {
|
||||
display: flex;
|
||||
flex-flow: row;
|
||||
flex-grow: 1;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
.flex-push-right {
|
||||
margin-left: auto;
|
||||
}
|
||||
|
|
@ -805,6 +849,34 @@ chunk.editing, chunk.editing * {
|
|||
width: 50px;
|
||||
}
|
||||
|
||||
.splistheader {
|
||||
padding-left: 68px;
|
||||
padding-right: 20px;
|
||||
display: flex;
|
||||
color: #737373;
|
||||
}
|
||||
|
||||
.splistitem {
|
||||
padding: 12px 10px 12px 10px;
|
||||
display: flex;
|
||||
flex-grow: 1;
|
||||
color: #ffffff;
|
||||
|
||||
-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;
|
||||
}
|
||||
|
||||
.splistitemsub {
|
||||
color: #ba9;
|
||||
}
|
||||
|
||||
.splistitem:hover {
|
||||
cursor: pointer;
|
||||
background-color: #688f1f;
|
||||
}
|
||||
|
||||
.width-auto {
|
||||
width: auto;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -6,14 +6,14 @@
|
|||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<script src="static/jquery-3.6.0.min.js"></script>
|
||||
<script src="static/socket.io.min.js"></script>
|
||||
<script src="static/application.js?ver=1.16.2j"></script>
|
||||
<script src="static/application.js?ver=1.16.3ua"></script>
|
||||
<script src="static/bootstrap.min.js"></script>
|
||||
<script src="static/bootstrap-toggle.min.js"></script>
|
||||
<script src="static/rangy-core.min.js"></script>
|
||||
|
||||
<link rel="stylesheet" href="static/bootstrap.min.css">
|
||||
<link rel="stylesheet" href="static/bootstrap-toggle.min.css">
|
||||
<link rel="stylesheet" href="static/custom.css?ver=1.16.2a">
|
||||
<link rel="stylesheet" href="static/custom.css?ver=1.16.3ua">
|
||||
<link rel="stylesheet" href="static/open-iconic-bootstrap.min.css">
|
||||
</head>
|
||||
<body>
|
||||
|
|
@ -67,6 +67,9 @@
|
|||
<li class="nav-item">
|
||||
<a class="nav-link" href="#" id="btn_format">Formatting</a>
|
||||
</li>
|
||||
<li class="nav-item">
|
||||
<a class="nav-link hidden" href="#" id="btn_softprompt">Soft Prompt</a>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</nav>
|
||||
|
|
@ -225,6 +228,19 @@
|
|||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="popupcontainer hidden" id="spcontainer">
|
||||
<div id="sppopup">
|
||||
<div class="popuptitlebar">
|
||||
<div class="popuptitletext">Select A Soft Prompt To Use</div>
|
||||
</div>
|
||||
<div id="splistcontent">
|
||||
</div>
|
||||
<div class="popupfooter">
|
||||
<button type="button" class="btn btn-primary" id="btn_spaccept">Load</button>
|
||||
<button type="button" class="btn btn-primary" id="btn_spclose">Cancel</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="popupcontainer hidden" id="loadcontainerdelete">
|
||||
<div id="loadpopupdelete">
|
||||
<div class="popuptitlebar">
|
||||
|
|
|
|||
Loading…
Reference in New Issue