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Merge pull request #20 from VE-FORBRYDERNE/sp

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Soft prompt support for PyTorch models
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henk717 2021-10-30 00:35:44 +02:00 committed by GitHub
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5 changed files with 377 additions and 28 deletions
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154
aiserver.py
View File

@ -12,7 +12,8 @@ import tkinter as tk
from tkinter import messagebox
import json
import collections
from typing import Union
import zipfile
from typing import Union, Tuple
import requests
import html
@ -103,7 +104,9 @@ class vars:
formatoptns = {'frmttriminc': True, 'frmtrmblln': False, 'frmtrmspch': False, 'frmtadsnsp': False, 'singleline': False} # Container for state of formatting options
importnum = -1 # Selection on import popup list
importjs = {} # Temporary storage for import data
loadselect = "" # Temporary storage for filename to load
loadselect = "" # Temporary storage for story filename to load
spselect = "" # Temporary storage for soft prompt filename to load
sp = None # Current soft prompt tensor (as a NumPy array)
svowname = "" # Filename that was flagged for overwrite confirm
saveow = False # Whether or not overwrite confirm has been displayed
genseqs = [] # Temporary storage for generated sequences
@ -113,6 +116,8 @@ class vars:
bmsupported = False # Whether the breakmodel option is supported (GPT-Neo/GPT-J only, currently)
smandelete = False # Whether stories can be deleted from inside the browser
smanrename = False # Whether stories can be renamed from inside the browser
allowsp = False # Whether we are allowed to use soft prompts (by default enabled if we're using GPT-2, GPT-Neo or GPT-J)
modeldim = -1 # Embedding dimension of your model (e.g. it's 4096 for GPT-J-6B and 2560 for GPT-Neo-2.7B)
laststory = None # Filename (without extension) of most recent story JSON file we loaded
regex_sl = re.compile(r'\n*(?<=.) *\n(.|\n)*') # Pattern for limiting the output to a single line
acregex_ai = re.compile(r'\n* *>(.|\n)*') # Pattern for matching adventure actions from the AI so we can remove them
@ -312,6 +317,7 @@ else:
# If transformers model was selected & GPU available, ask to use CPU or GPU
if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
vars.allowsp = True
# Test for GPU support
import torch
print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="")
@ -506,11 +512,41 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
if(not vars.noai):
print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END))
from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM
# Patch transformers to use our soft prompt
def patch_causallm(cls):
old_forward = cls.forward
def new_causallm_forward(self, *args, **kwargs):
input_ids = kwargs.get('input_ids').to(self.device)
assert input_ids is not None
kwargs['input_ids'] = None
if(vars.sp is not None):
shifted_input_ids = input_ids - self.config.vocab_size
input_ids.clamp_(max=self.config.vocab_size-1)
inputs_embeds = self.transformer.wte(input_ids)
if(vars.sp is not None):
vars.sp = vars.sp.to(inputs_embeds.dtype).to(inputs_embeds.device)
inputs_embeds = torch.where(
(shifted_input_ids >= 0)[:, :, None],
vars.sp[shifted_input_ids.clamp(min=0)],
inputs_embeds,
)
kwargs['inputs_embeds'] = inputs_embeds
return old_forward(self, *args, **kwargs)
cls.forward = new_causallm_forward
for cls in (GPT2LMHeadModel, GPTNeoForCausalLM):
patch_causallm(cls)
try:
from transformers import GPTJForCausalLM
patch_causallm(GPTJForCausalLM)
except:
pass
# If custom GPT Neo model was chosen
if(vars.model == "NeoCustom"):
model_config = open(vars.custmodpth + "/config.json", "r")
js = json.load(model_config)
vars.modeldim = int(js['hidden_size'])
if("model_type" in js):
model = AutoModelForCausalLM.from_pretrained(vars.custmodpth)
else:
@ -519,36 +555,46 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
# Is CUDA available? If so, use GPU, otherwise fall back to CPU
if(vars.hascuda):
if(vars.usegpu):
generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=0)
model = model.to(0)
generator = model.generate
elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
device_config(model)
else:
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
generator = model.generate
else:
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
generator = model.generate
# If custom GPT2 model was chosen
elif(vars.model == "GPT2Custom"):
model_config = open(vars.custmodpth + "/config.json", "r")
js = json.load(model_config)
vars.modeldim = int(js['hidden_size'])
model = GPT2LMHeadModel.from_pretrained(vars.custmodpth)
tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth)
# Is CUDA available? If so, use GPU, otherwise fall back to CPU
if(vars.hascuda and vars.usegpu):
generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=0)
model = model.to(0)
generator = model.generate
else:
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
generator = model.generate
# If base HuggingFace model was chosen
else:
# Is CUDA available? If so, use GPU, otherwise fall back to CPU
tokenizer = GPT2Tokenizer.from_pretrained(vars.model)
if(vars.hascuda):
if(vars.usegpu):
generator = pipeline('text-generation', model=vars.model, device=0)
model = AutoModelForCausalLM.from_pretrained(vars.model, device=0)
vars.modeldim = int(model.transformer.hidden_size)
model = model.to(0)
generator = model.generate
elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
model = AutoModelForCausalLM.from_pretrained(vars.model)
device_config(model)
else:
generator = pipeline('text-generation', model=vars.model)
model = AutoModelForCausalLM.from_pretrained(vars.model)
generator = model.generate
else:
generator = pipeline('text-generation', model=vars.model)
model = AutoModelForCausalLM.from_pretrained(vars.model)
generator = model.generate
# Suppress Author's Note by flagging square brackets (Old implementation)
#vocab = tokenizer.get_vocab()
@ -815,10 +861,16 @@ def get_message(msg):
save()
elif(msg['cmd'] == 'loadlistrequest'):
getloadlist()
elif(msg['cmd'] == 'splistrequest'):
getsplist()
elif(msg['cmd'] == 'loadselect'):
vars.loadselect = msg["data"]
elif(msg['cmd'] == 'spselect'):
vars.spselect = msg["data"]
elif(msg['cmd'] == 'loadrequest'):
loadRequest(fileops.storypath(vars.loadselect))
elif(msg['cmd'] == 'sprequest'):
spRequest(vars.spselect)
elif(msg['cmd'] == 'deletestory'):
deletesave(msg['data'])
elif(msg['cmd'] == 'renamestory'):
@ -854,6 +906,8 @@ def get_message(msg):
#==================================================================#
def setStartState():
txt = "<span>Welcome to <span class=\"color_cyan\">KoboldAI</span>! You are running <span class=\"color_green\">"+getmodelname()+"</span>.<br/>"
if(vars.allowsp):
emit('from_server', {'cmd': 'allowsp', 'data': vars.allowsp}, broadcast=True)
if(not vars.noai):
txt = txt + "Please load a game or enter a prompt below to begin!</span>"
else:
@ -1131,15 +1185,17 @@ def calcsubmit(txt):
anotetkns = tokenizer.encode(anotetxt)
lnanote = len(anotetkns)
if(vars.useprompt):
budget = vars.max_length - lnprompt - lnmem - lnanote - lnwi - vars.genamt
else:
budget = vars.max_length - lnmem - lnanote - lnwi - vars.genamt
lnsp = vars.sp.shape[0] if vars.sp is not None else 0
if(vars.useprompt):
budget = vars.max_length - lnsp - lnprompt - lnmem - lnanote - lnwi - vars.genamt
else:
budget = vars.max_length - lnsp - lnmem - lnanote - lnwi - vars.genamt
if(actionlen == 0):
# First/Prompt action
subtxt = vars.memory + winfo + anotetxt + vars.prompt
lnsub = lnmem + lnwi + lnprompt + lnanote
lnsub = lnsp + lnmem + lnwi + lnprompt + lnanote
if(not vars.model in ["Colab", "OAI"]):
generate(subtxt, lnsub+1, lnsub+vars.genamt)
@ -1295,14 +1351,23 @@ def generate(txt, min, max):
top_k = vars.top_k if vars.top_k > 0 else None
tfs = vars.tfs if vars.tfs > 0.0 else None
# generator() only accepts a torch tensor of tokens (long datatype) as
# its first argument if we're using breakmodel, otherwise a string
# is fine
if(vars.hascuda and vars.breakmodel):
gen_in = tokenizer.encode(txt, return_tensors="pt", truncation=True).long().to(breakmodel.primary_device)
gen_in = tokenizer.encode(txt, return_tensors="pt", truncation=True).long()
if(vars.sp is not None):
soft_tokens = torch.arange(
model.config.vocab_size,
model.config.vocab_size + vars.sp.shape[0],
)
gen_in = torch.cat((soft_tokens[None], gen_in), dim=1)
if(vars.hascuda and vars.usegpu):
gen_in = gen_in.to(0)
elif(vars.hascuda and vars.breakmodel):
gen_in = gen_in.to(breakmodel.primary_device)
elif(vars.hascuda):
gen_in = gen_in.to(0)
else:
gen_in = txt
gen_in = gen_in.to('cpu')
with torch.no_grad():
genout = generator(
gen_in,
@ -1326,8 +1391,7 @@ def generate(txt, min, max):
return
# Need to manually strip and decode tokens if we're not using a pipeline
if(vars.hascuda and vars.breakmodel):
genout = [{"generated_text": tokenizer.decode(tokens[len(gen_in[0])-len(tokens):])} for tokens in genout]
genout = [{"generated_text": tokenizer.decode(tokens[len(gen_in[0])-len(tokens):])} for tokens in genout]
if(len(genout) == 1):
genresult(genout[0]["generated_text"])
@ -2143,11 +2207,18 @@ def saveRequest(savpath):
print("{0}Story saved to {1}!{2}".format(colors.GREEN, path.basename(savpath), colors.END))
#==================================================================#
# Load a saved story via file browser
# Show list of saved stories
#==================================================================#
def getloadlist():
emit('from_server', {'cmd': 'buildload', 'data': fileops.getstoryfiles()})
#==================================================================#
# Show list of soft prompts
#==================================================================#
def getsplist():
if(vars.allowsp):
emit('from_server', {'cmd': 'buildsp', 'data': fileops.getspfiles(vars.modeldim)})
#==================================================================#
# Load a saved story via file browser
#==================================================================#
@ -2233,6 +2304,37 @@ def loadRequest(loadpath):
emit('from_server', {'cmd': 'hidegenseqs', 'data': ''}, broadcast=True)
print("{0}Story loaded from {1}!{2}".format(colors.GREEN, path.basename(loadpath), colors.END))
#==================================================================#
# Load a soft prompt from a file
#==================================================================#
def spRequest(filename):
if(len(filename) == 0):
vars.sp = None
return
global np
if 'np' not in globals():
import numpy as np
z, version, shape, fortran_order, dtype = fileops.checksp(filename, vars.modeldim)
assert isinstance(z, zipfile.ZipFile)
z.close()
with np.load(fileops.sppath(filename), allow_pickle=False) as f:
tensor = f['tensor.npy']
# If the tensor is in bfloat16 format, convert it to float32
if(tensor.dtype == 'V2'):
tensor.dtype = np.uint16
tensor = np.uint32(tensor) << 16
tensor.dtype = np.float32
if(tensor.dtype != np.float16):
tensor = np.float32(tensor)
assert not np.isinf(tensor).any() and not np.isnan(tensor).any()
vars.sp = torch.from_numpy(tensor)
#==================================================================#
# Import an AIDungon game exported with Mimi's tool
#==================================================================#

72
fileops.py
View File

@ -1,8 +1,10 @@
import tkinter as tk
from tkinter import filedialog
from os import getcwd, listdir, path
from typing import Tuple, Union, Optional
import os
import json
import zipfile
#==================================================================#
# Generic Method for prompting for file path
@ -61,6 +63,12 @@ def getdirpath(dir, title):
def storypath(name):
return path.join(path.dirname(path.realpath(__file__)), "stories", name + ".json")
#==================================================================#
# Returns the path (as a string) to the given soft prompt by its filename
#==================================================================#
def sppath(filename):
return path.join(path.dirname(path.realpath(__file__)), "softprompts", filename)
#==================================================================#
# Returns an array of dicts containing story files in /stories
#==================================================================#
@ -86,6 +94,70 @@ def getstoryfiles():
list.append(ob)
return list
#==================================================================#
# Checks if the given soft prompt file is valid
#==================================================================#
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']]:
global np
if 'np' not in globals():
import numpy as np
try:
z = zipfile.ZipFile(path.dirname(path.realpath(__file__))+"/softprompts/"+filename)
with z.open('tensor.npy') as f:
# Read only the header of the npy file, for efficiency reasons
version: Tuple[int, int] = np.lib.format.read_magic(f)
shape: Tuple[int, int]
fortran_order: bool
dtype: np.dtype
shape, fortran_order, dtype = np.lib.format._read_array_header(f, version)
assert len(shape) == 2
except:
z.close()
return 1, None, None, None, None
if dtype not in ('V2', np.float16, np.float32):
z.close()
return 2, version, shape, fortran_order, dtype
if shape[1] != model_dimension:
z.close()
return 3, version, shape, fortran_order, dtype
if shape[0] >= 2048:
z.close()
return 4, version, shape, fortran_order, dtype
return z, version, shape, fortran_order, dtype
#==================================================================#
# Returns an array of dicts containing softprompt files in /softprompts
#==================================================================#
def getspfiles(model_dimension: int):
lst = []
os.makedirs(path.dirname(path.realpath(__file__))+"/softprompts", exist_ok=True)
for file in listdir(path.dirname(path.realpath(__file__))+"/softprompts"):
if not file.endswith(".zip"):
continue
z, version, shape, fortran_order, dtype = checksp(file, model_dimension)
if z == 1:
print(f"Browser SP loading error: {file} is malformed or not a soft prompt ZIP file.")
continue
if z == 2:
print(f"Browser SP loading error: {file} tensor.npy has unsupported dtype '{dtype.name}'.")
continue
if z == 3:
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.")
continue
if z == 4:
print(f"Browser SP loading error: {file} tensor.npy has {shape[0]} tokens but it is supposed to have less than 2048 tokens.")
continue
assert isinstance(z, zipfile.ZipFile)
try:
with z.open('meta.json') as f:
ob = json.load(f)
except:
ob = {}
z.close()
ob["filename"] = file
lst.append(ob)
return lst
#==================================================================#
# Returns True if json file exists with requested save name
#==================================================================#

87
static/application.js
View File

@ -18,6 +18,7 @@ var button_importwi;
var button_impaidg;
var button_settings;
var button_format;
var button_softprompt;
var button_mode;
var button_mode_label;
var button_send;
@ -53,6 +54,10 @@ var loadpopup;
var loadcontent;
var load_accept;
var load_close;
var sppopup;
var spcontent;
var sp_accept;
var sp_close;
var nspopup;
var ns_accept;
var ns_close;
@ -77,6 +82,7 @@ var saved_prompt = "...";
var override_focusout = false;
var sman_allow_delete = false;
var sman_allow_rename = false;
var allowsp = false;
// This is true iff [we're in macOS and the browser is Safari] or [we're in iOS]
var using_webkit_patch = true;
@ -589,6 +595,17 @@ function hideLoadPopup() {
loadcontent.html("");
}
function showSPPopup() {
sppopup.removeClass("hidden");
sppopup.addClass("flex");
}
function hideSPPopup() {
sppopup.removeClass("flex");
sppopup.addClass("hidden");
spcontent.html("");
}
function buildLoadList(ar) {
disableButtons([load_accept]);
loadcontent.html("");
@ -654,11 +671,54 @@ function buildLoadList(ar) {
}
}
function buildSPList(ar) {
disableButtons([sp_accept]);
spcontent.html("");
showSPPopup();
ar.push({filename: '', name: "[None]"})
for(var i = 0; i < ar.length; i++) {
var supported = !ar[i].supported
? ''
: Object.prototype.toString.call(ar[i].supported) === "[object Array]"
? "[" + ar[i].supported.join(', ') + "]"
: "[" + ar[i].supported.toString() + "]";
var filename = ar[i].filename.replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;').replace(/"/g, '&quot;').replace(/'/g, '&#039;').replace(/(?=\r|\n)\r?\n?/g, '<br/>');
var name = ar[i].name || ar[i].filename;
name = name.length > 120 ? name.slice(0, 117) + '...' : name;
name = name.replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;').replace(/"/g, '&quot;').replace(/'/g, '&#039;').replace(/(?=\r|\n)\r?\n?/g, '<br/>');
var desc = ar[i].description || '';
desc = desc.length > 500 ? desc.slice(0, 497) + '...' : desc;
desc = desc.replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;').replace(/"/g, '&quot;').replace(/'/g, '&#039;').replace(/(?=\r|\n)\r?\n?/g, '<br/>');
spcontent.append("<div class=\"flex\">\
<div class=\"splistitem flex-row-container\" id=\"sp"+i+"\" name=\""+ar[i].filename+"\">\
<div class=\"flex-row\">\
<div>"+name+"</div>\
<div class=\"flex-push-right splistitemsub\">"+filename+"</div>\
</div>\
<div class=\"flex-row\">\
<div>"+desc+"</div>\
<div class=\"flex-push-right splistitemsub\">"+supported+"</div>\
</div>\
</div>\
</div>");
$("#sp"+i).on("click", function () {
enableButtons([sp_accept]);
socket.send({'cmd': 'spselect', 'data': $(this).attr("name")});
highlightSPLine($(this));
});
}
}
function highlightLoadLine(ref) {
$("#loadlistcontent > div > div.popuplistselected").removeClass("popuplistselected");
ref.addClass("popuplistselected");
}
function highlightSPLine(ref) {
$("#splistcontent > div > div.popuplistselected").removeClass("popuplistselected");
ref.addClass("popuplistselected");
}
function showNewStoryPopup() {
nspopup.removeClass("hidden");
nspopup.addClass("flex");
@ -1142,6 +1202,7 @@ $(document).ready(function(){
button_impaidg = $("#btn_impaidg");
button_settings = $('#btn_settings');
button_format = $('#btn_format');
button_softprompt = $("#btn_softprompt");
button_mode = $('#btnmode')
button_mode_label = $('#btnmode_label')
button_send = $('#btnsend');
@ -1177,6 +1238,10 @@ $(document).ready(function(){
loadcontent = $("#loadlistcontent");
load_accept = $("#btn_loadaccept");
load_close = $("#btn_loadclose");
sppopup = $("#spcontainer");
spcontent = $("#splistcontent");
sp_accept = $("#btn_spaccept");
sp_close = $("#btn_spclose");
nspopup = $("#newgamecontainer");
ns_accept = $("#btn_nsaccept");
ns_close = $("#btn_nsclose");
@ -1314,6 +1379,13 @@ $(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") {
@ -1483,6 +1555,8 @@ $(document).ready(function(){
} else if(msg.cmd == "buildload") {
// Send array of save files to load UI
buildLoadList(msg.data);
} else if(msg.cmd == "buildsp") {
buildSPList(msg.data);
} else if(msg.cmd == "askforoverwrite") {
// Show overwrite warning
show([$(".saveasoverwrite")]);
@ -1657,6 +1731,10 @@ $(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();
@ -1667,6 +1745,15 @@ $(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();

72
static/custom.css
View File

@ -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;
}

20
templates/index.html
View File

@ -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">