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Merge commit 'refs/pull/82/head' of https://github.com/ebolam/KoboldAI into UI2

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ebolam
2022-08-30 14:50:28 -04:00
parent 1a7451d45d 7c01933743
commit e9678ceea3
9 changed files with 123 additions and 68 deletions
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74
aiserver.py
View File

@@ -821,8 +821,10 @@ def loadmodelsettings():
if("nobreakmodel" in js):
koboldai_vars.nobreakmodel = js["nobreakmodel"]
if("sampler_order" in js):
koboldai_vars.sampler_order = js["sampler_order"]
koboldai_vars.default_preset['sampler_order'] = js["sampler_order"]
sampler_order = koboldai_vars.sampler_order
if(len(sampler_order) < 7):
sampler_order = [6] + sampler_order
koboldai_vars.sampler_order = sampler_order
if("temp" in js):
koboldai_vars.temp = js["temp"]
koboldai_vars.default_preset['temp'] = js["temp"]
@@ -965,7 +967,10 @@ def processsettings(js):
if("andepth" in js):
koboldai_vars.andepth = js["andepth"]
if("sampler_order" in js):
koboldai_vars.sampler_order = js["sampler_order"]
sampler_order = koboldai_vars.sampler_order
if(len(sampler_order) < 7):
sampler_order = [6] + sampler_order
koboldai_vars.sampler_order = sampler_order
if("temp" in js):
koboldai_vars.temp = js["temp"]
if("top_p" in js):
@@ -1363,23 +1368,25 @@ def get_model_info(model, directory=""):
def get_layer_count(model, directory=""):
if(model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ"]):
if(koboldai_vars.model == "GPT2Custom"):
model_config = open(directory + "/config.json", "r")
if(model not in ["InferKit", "Colab", "API", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ"]):
if(model == "GPT2Custom"):
with open(os.path.join(directory, "config.json"), "r") as f:
model_config = json.load(f)
# Get the model_type from the config or assume a model type if it isn't present
else:
if(directory):
model = directory
from transformers import AutoConfig
if directory == "":
model_config = AutoConfig.from_pretrained(model, revision=koboldai_vars.revision, cache_dir="cache")
if(os.path.isdir(model.replace('/', '_'))):
model_config = AutoConfig.from_pretrained(model.replace('/', '_'), revision=koboldai_vars.revision, cache_dir="cache")
elif(os.path.isdir("models/{}".format(model.replace('/', '_')))):
model_config = AutoConfig.from_pretrained("models/{}".format(model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
elif(os.path.isdir(directory)):
model_config = AutoConfig.from_pretrained(directory, revision=koboldai_vars.revision, cache_dir="cache")
elif(os.path.isdir(koboldai_vars.custmodpth.replace('/', '_'))):
model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth.replace('/', '_'), revision=koboldai_vars.revision, cache_dir="cache")
else:
model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
model_config = AutoConfig.from_pretrained(model, revision=koboldai_vars.revision, cache_dir="cache")
return utils.num_layers(model_config)
else:
return None
@@ -1623,8 +1630,6 @@ def patch_transformers():
dynamic_processor_wrap(TailFreeLogitsWarper, "tfs", "tfs", cond=lambda x: x < 1.0)
dynamic_processor_wrap(TypicalLogitsWarper, "typical", "typical", cond=lambda x: x < 1.0)
dynamic_processor_wrap(TemperatureLogitsWarper, "temperature", "temp", cond=lambda x: x != 1.0)
RepetitionPenaltyLogitsProcessor.__init__ = AdvancedRepetitionPenaltyLogitsProcessor.__init__
RepetitionPenaltyLogitsProcessor.__call__ = AdvancedRepetitionPenaltyLogitsProcessor.__call__
class PhraseBiasLogitsProcessor(LogitsProcessor):
def __init__(self):
@@ -1768,9 +1773,13 @@ def patch_transformers():
self.__warper_list.append(TailFreeLogitsWarper(tfs=0.5, min_tokens_to_keep=1 + (beams > 1)))
self.__warper_list.append(TypicalLogitsWarper(typical=0.5, min_tokens_to_keep=1 + (beams > 1)))
self.__warper_list.append(TemperatureLogitsWarper(temperature=0.5))
self.__warper_list.append(AdvancedRepetitionPenaltyLogitsProcessor())
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, *args, **kwargs):
for k in koboldai_vars.sampler_order:
sampler_order = koboldai_vars.sampler_order[:]
if len(sampler_order) < 7: # Add repetition penalty at beginning if it's not present
sampler_order = [6] + sampler_order
for k in sampler_order:
scores = self.__warper_list[k](input_ids, scores, *args, **kwargs)
return scores
@@ -2525,6 +2534,9 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
koboldai_vars.compiling = False
def tpumtjgenerate_settings_callback() -> dict:
sampler_order = vars.sampler_order[:]
if len(sampler_order) < 7: # Add repetition penalty at beginning if it's not present
sampler_order = [6] + sampler_order
return {
"sampler_order": koboldai_vars.sampler_order,
"top_p": float(koboldai_vars.top_p),
@@ -3685,12 +3697,16 @@ def get_message(msg):
sendUSStatItems()
elif(msg['cmd'] == 'samplers'):
sampler_order = msg["data"]
sampler_order_min_length = 6
sampler_order_max_length = 7
if(not isinstance(sampler_order, list)):
raise ValueError(f"Sampler order must be a list, but got a {type(sampler_order)}")
if(len(sampler_order) != len(koboldai_vars.sampler_order)):
raise ValueError(f"Sampler order must be a list of length {len(koboldai_vars.sampler_order)}, but got a list of length {len(sampler_order)}")
if(not (sampler_order_min_length <= len(sampler_order) <= sampler_order_max_length)):
raise ValueError(f"Sampler order must be a list of length greater than or equal to {sampler_order_min_length} and less than or equal to {sampler_order_max_length}, but got a list of length {len(sampler_order)}")
if(not all(isinstance(e, int) for e in sampler_order)):
raise ValueError(f"Sampler order must be a list of ints, but got a list with at least one non-int element")
if(min(sampler_order) != 0 or max(sampler_order) != len(sampler_order) - 1 or len(set(sampler_order)) != len(sampler_order)):
raise ValueError(f"Sampler order list of length {len(sampler_order)} must be a permutation of the first {len(sampler_order)} nonnegative integers")
koboldai_vars.sampler_order = sampler_order
settingschanged()
elif(msg['cmd'] == 'list_model'):
@@ -3701,8 +3717,8 @@ def get_message(msg):
changed = True
if not utils.HAS_ACCELERATE:
msg['disk_layers'] = "0"
if os.path.exists("settings/" + koboldai_vars.model.replace('/', '_') + ".breakmodel"):
with open("settings/" + koboldai_vars.model.replace('/', '_') + ".breakmodel", "r") as file:
if os.path.exists("settings/" + koboldai_vars.model_selected.replace('/', '_') + ".breakmodel"):
with open("settings/" + koboldai_vars.model_selected.replace('/', '_') + ".breakmodel", "r") as file:
data = file.read().split('\n')[:2]
if len(data) < 2:
data.append("0")
@@ -3710,14 +3726,15 @@ def get_message(msg):
if gpu_layers == msg['gpu_layers'] and disk_layers == msg['disk_layers']:
changed = False
if changed:
if koboldai_vars.model in ["NeoCustom", "GPT2Custom"]:
if koboldai_vars.model_selected in ["NeoCustom", "GPT2Custom"]:
filename = "settings/{}.breakmodel".format(os.path.basename(os.path.normpath(koboldai_vars.custmodpth)))
else:
filename = "settings/{}.breakmodel".format(koboldai_vars.model.replace('/', '_'))
filename = "settings/{}.breakmodel".format(koboldai_vars.model_selected.replace('/', '_'))
f = open(filename, "w")
f.write(str(msg['gpu_layers']) + '\n' + str(msg['disk_layers']))
f.close()
koboldai_vars.colaburl = msg['url'] + "/request"
vars.model = vars.model_selected
load_model(use_gpu=msg['use_gpu'], gpu_layers=msg['gpu_layers'], disk_layers=msg['disk_layers'], online_model=msg['online_model'])
elif(msg['cmd'] == 'show_model'):
print("Model Name: {}".format(getmodelname()))
@@ -3742,18 +3759,18 @@ def get_message(msg):
elif msg['data'] in ('NeoCustom', 'GPT2Custom') and 'path_modelname' in msg:
#Here the user entered custom text in the text box. This could be either a model name or a path.
if check_if_dir_is_model(msg['path_modelname']):
koboldai_vars.model = msg['data']
koboldai_vars.model_selected = msg['data']
koboldai_vars.custmodpth = msg['path_modelname']
get_model_info(msg['data'], directory=msg['path'])
else:
koboldai_vars.model = msg['path_modelname']
koboldai_vars.model_selected = msg['path_modelname']
try:
get_model_info(koboldai_vars.model)
get_model_info(koboldai_vars.model_selected)
except:
emit('from_server', {'cmd': 'errmsg', 'data': "The model entered doesn't exist."}, room="UI_1")
elif msg['data'] in ('NeoCustom', 'GPT2Custom'):
if check_if_dir_is_model(msg['path']):
koboldai_vars.model = msg['data']
koboldai_vars.model_selected = msg['data']
koboldai_vars.custmodpth = msg['path']
get_model_info(msg['data'], directory=msg['path'])
else:
@@ -3762,12 +3779,12 @@ def get_message(msg):
else:
sendModelSelection(menu=msg['data'], folder=msg['path'])
else:
koboldai_vars.model = msg['data']
koboldai_vars.model_selected = msg['data']
if 'path' in msg:
koboldai_vars.custmodpth = msg['path']
get_model_info(msg['data'], directory=msg['path'])
else:
get_model_info(koboldai_vars.model)
get_model_info(koboldai_vars.model_selected)
elif(msg['cmd'] == 'delete_model'):
if "{}/models".format(os.getcwd()) in os.path.abspath(msg['data']) or "{}\\models".format(os.getcwd()) in os.path.abspath(msg['data']):
if check_if_dir_is_model(msg['data']):
@@ -3873,7 +3890,6 @@ def get_message(msg):
emit(
'from_server',
{'cmd': 'showfieldbudget', 'data': {"length": None, "max": None, "field": field}},
broadcast=True
)
return
@@ -4612,7 +4628,7 @@ def _generate(txt, minimum, maximum, found_entries):
gen_in,
do_sample=True,
max_length=int(2e9),
repetition_penalty=1.1,
repetition_penalty=1.0,
bad_words_ids=koboldai_vars.badwordsids,
use_cache=True,
num_return_sequences=numseqs

6
colab/TPU.ipynb
View File

@@ -66,7 +66,7 @@
"#@title <b><-- Select your model below and then click this to start KoboldAI</b>\n",
"#@markdown You can find a description of the models below along with instructions on how to start KoboldAI.\n",
"\n",
"Model = \"Nerys 13B V2\" #@param [\"Nerys 13B V2\", \"Janeway 13B\", \"Shinen 13B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Shinen 6B\", \"Lit 6B\", \"NeoX 20B\", \"OPT 13B\", \"Fairseq Dense 13B\", \"GPT-J-6B\"] {allow-input: true}\n",
"Model = \"Nerys 13B V2\" #@param [\"Nerys 13B V2\", \"Janeway 13B\", \"Shinen 13B\", \"Skein 20B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Shinen 6B\", \"Lit 6B\", \"NeoX 20B\", \"OPT 13B\", \"Fairseq Dense 13B\", \"GPT-J-6B\"] {allow-input: true}\n",
"Version = \"Official\" #@param [\"Official\", \"United\"] {allow-input: true}\n",
"Provider = \"Cloudflare\" #@param [\"Localtunnel\", \"Cloudflare\"]\n",
"\n",
@@ -93,6 +93,10 @@
" Model = \"KoboldAI/fairseq-dense-13B-Shinen\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"Skein 20B\":\n",
" Model = \"KoboldAI/GPT-NeoX-20B-Skein\"\n",
" path = \"\"\n",
" download = \"\"\n",
"elif Model == \"NeoX 20B\":\n",
" Model = \"EleutherAI/gpt-neox-20b\"\n",
" path = \"\"\n",

11
gensettings.py
View File

@@ -495,6 +495,17 @@ gensettingstf = [
"classname": "story",
"name": "andepth"
},
{
"uitype": "toggle",
"unit": "bool",
"label": "Show Field Budget",
"id": "setshowbudget",
"min": 0,
"max": 1,
"step": 1,
"default": 0,
"tooltip": "Shows token usage when typing in relevant text boxes. <b>May lag slower devices.</b>"
},
]
gensettingsik =[{

31
static/application.js
View File

@@ -241,8 +241,27 @@ function addSetting(ob) {
if(ob.id == "setadventure"){
setadventure($(this).prop('checked'));
}
});
}
if (ob.id === "setshowbudget") {
$("#setshowbudget").on("change", function () {
for (const el of document.getElementsByClassName("input-token-usage")) {
if (this.checked) {
el.classList.remove("hidden");
} else {
el.classList.add("hidden");
}
}
});
if (!$("#setshowbudget")[0].checked) {
for (const el of document.getElementsByClassName("input-token-usage")) {
el.classList.add("hidden");
}
}
}
}
function refreshTitle() {
@@ -1287,12 +1306,13 @@ function buildSamplerList(samplers) {
"Tail-free Sampling",
"Typical Sampling",
"Temperature",
"Repetition Penalty",
]
for(i=0; i<samplers.length; i++) {
samplerslist.append("<div class=\"flex\">\
<div class=\"samplerslistitem flex-row-container\" sid=\""+samplers[i]+"\">\
<div class=\"flex-row\">\
<div>"+samplers_lookup_table[samplers[i]]+"</div>\
<div>"+(samplers[i] < samplers_lookup_table.length ? samplers_lookup_table[samplers[i]] : "Unknown sampler #" + samplers[i])+"</div>\
</div>\
</div>\
</div>");
@@ -2165,6 +2185,9 @@ function interpolateRGB(color0, color1, t) {
}
function updateInputBudget(inputElement) {
let budgetElement = document.getElementById("setshowbudget");
if (budgetElement && !budgetElement.checked) return;
let data = {"unencoded": inputElement.value, "field": inputElement.id};
if (inputElement.id === "anoteinput") {
@@ -2182,7 +2205,6 @@ function registerTokenCounters() {
let span = document.createElement("span");
span.classList.add("input-token-usage");
span.innerText = "?/? Tokens";
el.appendChild(span);
let inputElement = el.querySelector("input, textarea");
@@ -2446,10 +2468,6 @@ $(document).ready(function(){
} else if(msg.cmd == "updatechunk") {
hideMessage();
game_text.attr('contenteditable', allowedit);
if (typeof submit_start !== 'undefined') {
$("#runtime")[0].innerHTML = `Generation time: ${Math.round((Date.now() - submit_start)/1000)} sec`;
delete submit_start;
}
var index = msg.data.index;
var html = msg.data.html;
var existingChunk = game_text.children('#n' + index);
@@ -2963,6 +2981,7 @@ $(document).ready(function(){
$("#showmodelnamecontainer").removeClass("hidden");
} else if(msg.cmd == 'hide_model_name') {
$("#showmodelnamecontainer").addClass("hidden");
$(window).off('beforeunload');
location.reload();
//console.log("Closing window");
} else if(msg.cmd == 'model_load_status') {

2
static/custom.css
View File

@@ -473,7 +473,7 @@ body.connected #popupfooter, #popupfooter.always-available {
}
#samplerslist {
height: 300px;
height: 310px;
overflow-y: scroll;
overflow-wrap: anywhere;
}

6
static/favicon.js
View File

@@ -2,6 +2,7 @@
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48
tpu_mtj_backend.py
View File

@@ -176,7 +176,7 @@ def apply_repetition_penalty_dynamic(logits, tokens, repetition_penalty, generat
logits[tokens] = penalty_logits
return logits
def kobold_sample_dynamic(key, logits, sampler_order: Optional[np.ndarray] = None, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typical=1.0, top_a=0.0):
def kobold_sample_dynamic(key, logits, rpargs, sampler_order: Optional[np.ndarray] = None, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typical=1.0, top_a=0.0):
'''
This gets called by generate_loop_fn to apply a series of 6 filters
to the logits (top-k, then top-a, then top-p, then TFS, then typical, then temperature)
@@ -312,6 +312,7 @@ def kobold_sample_dynamic(key, logits, sampler_order: Optional[np.ndarray] = Non
if k == 3 and tfs < 1.0: logits = tail_free_filter(logits)
if k == 4 and typical < 1.0: logits = typical_filter(logits)
if k == 5 and temp != 1.0: logits = temp_filter(logits)
if k == 6 and rpargs[1] != 1.0: logits = apply_repetition_penalty_dynamic(logits, *rpargs)
# Finally, pick one token using the softmax thingy again (it gives
# an array whose elements sum to 1 so it can be used nicely as a
# probability distribution)
@@ -362,7 +363,7 @@ def apply_repetition_penalty_static(logits, tokens, repetition_penalty, generate
# positions in the logits array
return logits.at[tokens].set(penalty_logits)
def kobold_sample_static(key, logits, sampler_order: Optional[np.ndarray] = None, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typical=1.0, top_a=0.0):
def kobold_sample_static(key, logits, rpargs, sampler_order: Optional[np.ndarray] = None, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, typical=1.0, top_a=0.0):
'''
This gets called by generate_loop_fn to apply a series of 6 filters
to the logits (top-k, then top-a, then top-p, then TFS, then typical, then temperature)
@@ -497,6 +498,7 @@ def kobold_sample_static(key, logits, sampler_order: Optional[np.ndarray] = None
logits = jax.lax.cond(jnp.logical_and(k == 3, tfs < 1.0), tail_free_filter, lambda x: x, logits)
logits = jax.lax.cond(jnp.logical_and(k == 4, typical < 1.0), typical_filter, lambda x: x, logits)
logits = jax.lax.cond(jnp.logical_and(k == 5, temp != 1.0), temp_filter, lambda x: x, logits)
logits = jax.lax.cond(jnp.logical_and(k == 6, rpargs[1] != 1.0), lambda x: apply_repetition_penalty_static(*x), lambda x: x[0], (logits, *rpargs))
# Finally, pick one token using the softmax thingy again (it gives
# an array whose elements sum to 1 so it can be used nicely as a
# probability distribution)
@@ -513,17 +515,6 @@ def sample_func(data, key, numseqs_aux, badwords, repetition_penalty, generated_
# Get the pseudo-random number generator key that will
# be used by kobold_sample_dynamic to randomly pick a token
sample_key, new_key = jax.random.split(sample_key, num=2)
# Apply repetition penalty to all tokens that are
# currently inside the "generated" array
logits = apply_repetition_penalty_dynamic(
logits,
generated,
repetition_penalty,
generated_index,
gen_length,
rpslope,
rprange,
)
# Remove any tokens in the badwords list by setting
# their logits to negative infinity which effectively
# makes their probabilities of being chosen zero
@@ -535,6 +526,14 @@ def sample_func(data, key, numseqs_aux, badwords, repetition_penalty, generated_
next_token = kobold_sample_dynamic(
sample_key,
logits,
(
generated,
repetition_penalty,
generated_index,
gen_length,
rpslope,
rprange,
),
**sampler_options,
)
# Remember what token was picked
@@ -606,18 +605,6 @@ class PenalizingCausalTransformer(CausalTransformer):
assert logits.shape == (1, config["n_vocab"])
# Flatten it into a 1D array to make it easier to use
logits = logits[0]
# Apply repetition penalty to all tokens that are
# currently inside the "generated" array
if repetition_penalty is not None:
logits = apply_repetition_penalty_static(
logits,
generated,
repetition_penalty,
generated_index,
gen_length,
rpslope,
rprange,
)
# Remove any tokens in the badwords list by setting
# their logits to negative infinity which effectively
# makes their probabilities of being chosen zero
@@ -629,6 +616,14 @@ class PenalizingCausalTransformer(CausalTransformer):
next_token = kobold_sample_static(
sample_key,
logits,
(
generated,
repetition_penalty,
generated_index,
gen_length,
rpslope,
rprange,
),
**sampler_options,
)
# Remember what token was picked
@@ -863,6 +858,9 @@ def infer_static(
maps.thread_resources.env = thread_resources_env
if sampler_order is None:
sampler_order = utils.default_sampler_order.copy()
sampler_order = sampler_order[:]
if len(sampler_order) < 7: # Add repetition penalty at beginning if it's not present
sampler_order = [6] + sampler_order
sampler_order = np.uint32(sampler_order)
total_batch = 1
tokens = context

5
utils.py
View File

@@ -33,7 +33,7 @@ layers_module_names: Optional[List[str]] = None
module_names: Optional[List[str]] = None
named_buffers: Optional[List[tuple]] = None
default_sampler_order = [0, 1, 2, 3, 4, 5]
default_sampler_order = [6, 0, 1, 2, 3, 4, 5]
#==================================================================#
# Decorator to prevent a function's actions from being run until
@@ -167,7 +167,7 @@ def decodenewlines(txt):
# Returns number of layers given an HF model config
#==================================================================#
def num_layers(config):
return config.num_layers if hasattr(config, "num_layers") else config.n_layer if hasattr(config, "n_layer") else config.num_hidden_layers if hasattr(config, 'num_hidden_layers') else None
return config["n_layer"] if isinstance(config, dict) else config.num_layers if hasattr(config, "num_layers") else config.n_layer if hasattr(config, "n_layer") else config.num_hidden_layers if hasattr(config, 'num_hidden_layers') else None
#==================================================================#
# Downloads huggingface checkpoints using aria2c if possible
@@ -177,6 +177,7 @@ class Send_to_socketio(object):
def write(self, bar):
time.sleep(0.01)
try:
print(bar)
emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", "&nbsp;")}, broadcast=True)
except:
pass

4
warpers.py
View File

@@ -28,10 +28,10 @@ SOFTWARE.
'''
import torch
from transformers import LogitsWarper, LogitsProcessor
from transformers import LogitsWarper
class AdvancedRepetitionPenaltyLogitsProcessor(LogitsProcessor):
class AdvancedRepetitionPenaltyLogitsProcessor(LogitsWarper):
def __init__(self, *args, **kwargs):
pass