Show message when TPU backend is compiling
This commit is contained in:
Gnome Ann
parent
9a50f8d294
commit
33f9f2dc82
29
aiserver.py
29
aiserver.py
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@ -14,6 +14,7 @@ os.environ['EVENTLET_THREADPOOL_SIZE'] = '50'
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from eventlet import tpool
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from os import path, getcwd
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import time
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import re
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import json
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import collections
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@ -127,6 +128,8 @@ class vars:
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lua_edited = set() # Set of chunk numbers that were edited from a Lua generation modifier
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lua_deleted = set() # Set of chunk numbers that were deleted from a Lua generation modifier
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generated_tkns = 0 # If using a backend that supports Lua generation modifiers, how many tokens have already been generated, otherwise 0
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compiling = False # If using a TPU Colab, this will be set to True when the TPU backend starts compiling and then set to False again
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checking = False # Whether or not we are actively checking to see if TPU backend is compiling or not
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spfilename = "" # Filename of soft prompt to load, or an empty string if not using a soft prompt
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userscripts = [] # List of userscripts to load
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last_userscripts = [] # List of previous userscript filenames from the previous time userscripts were send via usstatitems
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@ -638,7 +641,7 @@ log.setLevel(logging.ERROR)
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# Start flask & SocketIO
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print("{0}Initializing Flask... {1}".format(colors.PURPLE, colors.END), end="")
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from flask import Flask, render_template, Response, request
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from flask import Flask, render_template, Response, request, copy_current_request_context
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from flask_socketio import SocketIO, emit
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app = Flask(__name__)
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app.config['SECRET KEY'] = 'secret!'
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@ -1054,6 +1057,13 @@ else:
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break
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return excluded_world_info, regeneration_required, halt
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def tpumtjgenerate_compiling_callback() -> None:
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print(colors.GREEN + "TPU backend compilation triggered" + colors.END)
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vars.compiling = True
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def tpumtjgenerate_stopped_compiling_callback() -> None:
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vars.compiling = False
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# If we're running Colab or OAI, we still need a tokenizer.
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if(vars.model == "Colab"):
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from transformers import GPT2TokenizerFast
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@ -1068,6 +1078,8 @@ else:
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import tpu_mtj_backend
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tpu_mtj_backend.warper_callback = tpumtjgenerate_warper_callback
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tpu_mtj_backend.stopping_callback = tpumtjgenerate_stopping_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.load_model(vars.custmodpth)
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vars.allowsp = True
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vars.modeldim = int(tpu_mtj_backend.params["d_model"])
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@ -1645,6 +1657,7 @@ def execute_genmod():
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vars.lua_koboldbridge.execute_genmod()
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def execute_outmod():
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emit('from_server', {'cmd': 'hidemsg', 'data': ''}, broadcast=True)
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try:
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tpool.execute(vars.lua_koboldbridge.execute_outmod)
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except lupa.LuaError as e:
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@ -2251,6 +2264,18 @@ def settingschanged():
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#==================================================================#
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# Take input text from SocketIO and decide what to do with it
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#==================================================================#
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def check_for_backend_compilation():
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if(vars.checking):
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return
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vars.checking = True
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for _ in range(31):
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time.sleep(0.06276680299820175)
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if(vars.compiling):
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emit('from_server', {'cmd': 'warnmsg', 'data': 'Compiling TPU backend—this usually takes 1–2 minutes...'}, broadcast=True)
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break
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vars.checking = False
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def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False, disable_recentrng=False):
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# Ignore new submissions if the AI is currently busy
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if(vars.aibusy):
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@ -2966,6 +2991,8 @@ def tpumtjgenerate(txt, minimum, maximum, found_entries=None):
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global past
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socketio.start_background_task(copy_current_request_context(check_for_backend_compilation))
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if(vars.dynamicscan or (not vars.nogenmod and vars.has_genmod)):
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context = np.tile(np.uint32(txt), (vars.numseqs, 1))
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@ -663,9 +663,9 @@ function showMessage(msg) {
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message_text.html(msg);
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}
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function errMessage(msg) {
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function errMessage(msg, type="error") {
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message_text.removeClass();
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message_text.addClass("color_red");
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message_text.addClass(type == "warn" ? "color_orange" : "color_red");
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message_text.html(msg);
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}
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@ -1932,7 +1932,12 @@ $(document).ready(function(){
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}
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} else if(msg.cmd == "errmsg") {
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// Send error message
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errMessage(msg.data);
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errMessage(msg.data, "error");
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} else if(msg.cmd == "warnmsg") {
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// Send warning message
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errMessage(msg.data, "warn");
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} else if(msg.cmd == "hidemsg") {
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hideMessage();
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} else if(msg.cmd == "texteffect") {
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// Apply color highlight to line of text
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newTextHighlight($("#n"+msg.data))
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@ -17,7 +17,7 @@
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<script src="static/bootstrap.min.js"></script>
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<script src="static/bootstrap-toggle.min.js"></script>
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<script src="static/rangy-core.min.js"></script>
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<script src="static/application.js?ver=1.16.4w"></script>
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<script src="static/application.js?ver=1.16.4y"></script>
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</head>
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<body>
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<input type="file" id="remote-save-select" accept="application/json" style="display:none">
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@ -26,6 +26,15 @@ def warper_callback(logits) -> np.array:
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def stopping_callback(generated, n_generated, excluded_world_info) -> Tuple[List[set], bool, bool]:
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raise NotImplementedError("`tpu_mtj_backend.stopping_callback()` needs to be defined")
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def started_compiling_callback() -> None:
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pass
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def stopped_compiling_callback() -> None:
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pass
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def compiling_callback() -> None:
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pass
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def show_spinner():
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bar = progressbar.ProgressBar(max_value=progressbar.UnknownLength, widgets=[progressbar.Timer(), ' ', progressbar.BouncingBar(left='[', right=']', marker='█')])
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@ -358,6 +367,7 @@ class PenalizingCausalTransformer(CausalTransformer):
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# Initialize
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super().__init__(config)
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def generate_static(state, key, ctx, ctx_length, gen_length, numseqs_aux, sampler_options, soft_embeddings=None):
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compiling_callback()
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numseqs = numseqs_aux.shape[0]
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# These are the tokens that we don't want the AI to ever write
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self.badwords = jnp.array([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])
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@ -452,6 +462,7 @@ class PenalizingCausalTransformer(CausalTransformer):
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axis_resources={'shard': 'mp', 'batch': 'dp'},
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)
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def generate_initial(state, key, ctx, ctx_length, numseqs_aux, soft_embeddings=None):
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compiling_callback()
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numseqs = numseqs_aux.shape[0]
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@hk.transform
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def generate_initial_inner(context, ctx_length):
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@ -552,6 +563,7 @@ class PenalizingCausalTransformer(CausalTransformer):
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n_generated = 0
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regeneration_required = False
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halt = False
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started_compiling_callback()
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generate_data, sample_key = self.generate_initial_xmap(self.state, jnp.array(key.take(batch_size)), ctx, ctx_length, numseqs_aux, soft_embeddings)
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sample_key = np.asarray(sample_key[0, 0])
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while True:
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@ -574,13 +586,15 @@ class PenalizingCausalTransformer(CausalTransformer):
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break
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else:
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break
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stopped_compiling_callback()
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return sample_data, n_generated, regeneration_required, halt
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def generate_static(self, ctx, ctx_length, gen_length, numseqs, sampler_options, return_logits=False, soft_embeddings=None):
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assert not return_logits
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key = hk.PRNGSequence(random.randint(0, 2 ** 60))
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batch_size = ctx.shape[0]
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self.batch_size = batch_size
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return self.generate_static_xmap(
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started_compiling_callback()
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result = self.generate_static_xmap(
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self.state,
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jnp.array(key.take(batch_size)),
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ctx,
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@ -590,6 +604,8 @@ class PenalizingCausalTransformer(CausalTransformer):
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sampler_options,
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soft_embeddings,
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)
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stopped_compiling_callback()
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return result
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def infer_dynamic(
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