Merge branch 'main' into Honkware-main

This commit is contained in:
oobabooga 2023-07-04 18:50:07 -07:00
commit 84d6c93d0d
35 changed files with 821 additions and 453 deletions

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@ -193,6 +193,7 @@ Optionally, you can use the following command-line flags:
| `-h`, `--help` | Show this help message and exit. |
| `--notebook` | Launch the web UI in notebook mode, where the output is written to the same text box as the input. |
| `--chat` | Launch the web UI in chat mode. |
| `--multi-user` | Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental. |
| `--character CHARACTER` | The name of the character to load in chat mode by default. |
| `--model MODEL` | Name of the model to load by default. |
| `--lora LORA [LORA ...]` | The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. |
@ -268,6 +269,7 @@ Optionally, you can use the following command-line flags:
|`--gpu-split` | Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. `20,7,7` |
|`--max_seq_len MAX_SEQ_LEN` | Maximum sequence length. |
|`--compress_pos_emb COMPRESS_POS_EMB` | Positional embeddings compression factor. Should typically be set to max_seq_len / 2048. |
|`--alpha_value ALPHA_VALUE` | Positional embeddings alpha factor for NTK RoPE scaling. Same as above. Use either this or compress_pos_emb, not both. `
#### GPTQ-for-LLaMa

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@ -44,6 +44,7 @@ async def run(user_input, history):
'tfs': 1,
'top_a': 0,
'repetition_penalty': 1.18,
'repetition_penalty_range': 0,
'top_k': 40,
'min_length': 0,
'no_repeat_ngram_size': 0,

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@ -38,6 +38,7 @@ def run(user_input, history):
'tfs': 1,
'top_a': 0,
'repetition_penalty': 1.18,
'repetition_penalty_range': 0,
'top_k': 40,
'min_length': 0,
'no_repeat_ngram_size': 0,

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@ -33,6 +33,7 @@ async def run(context):
'tfs': 1,
'top_a': 0,
'repetition_penalty': 1.18,
'repetition_penalty_range': 0,
'top_k': 40,
'min_length': 0,
'no_repeat_ngram_size': 0,

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@ -25,6 +25,7 @@ def run(prompt):
'tfs': 1,
'top_a': 0,
'repetition_penalty': 1.18,
'repetition_penalty_range': 0,
'top_k': 40,
'min_length': 0,
'no_repeat_ngram_size': 0,

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@ -6,6 +6,12 @@
padding-top: 2.5rem
}
.small-button {
max-width: 171px;
height: 39.594px;
align-self: end;
}
.refresh-button {
max-width: 4.4em;
min-width: 2.2em !important;
@ -50,7 +56,7 @@ ol li p, ul li p {
display: inline-block;
}
#main, #parameters, #chat-settings, #interface-mode, #lora, #training-tab, #model-tab {
#main, #parameters, #chat-settings, #lora, #training-tab, #model-tab, #session-tab {
border: 0;
}
@ -121,10 +127,6 @@ button {
font-size: 14px !important;
}
.small-button {
max-width: 171px;
}
.file-saver {
position: fixed !important;
top: 50%;
@ -148,4 +150,8 @@ button {
.checkboxgroup-table div {
display: grid !important;
}
.markdown ul ol {
font-size: 100% !important;
}

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@ -38,11 +38,11 @@ script.py may define the special functions and variables below.
| `def ui()` | Creates custom gradio elements when the UI is launched. |
| `def custom_css()` | Returns custom CSS as a string. It is applied whenever the web UI is loaded. |
| `def custom_js()` | Same as above but for javascript. |
| `def input_modifier(string)` | Modifies the input string before it enters the model. In chat mode, it is applied to the user message. Otherwise, it is applied to the entire prompt. |
| `def output_modifier(string)` | Modifies the output string before it is presented in the UI. In chat mode, it is applied to the bot's reply. Otherwise, it is applied to the entire output. |
| `def input_modifier(string, state)` | Modifies the input string before it enters the model. In chat mode, it is applied to the user message. Otherwise, it is applied to the entire prompt. |
| `def output_modifier(string, state)` | Modifies the output string before it is presented in the UI. In chat mode, it is applied to the bot's reply. Otherwise, it is applied to the entire output. |
| `def bot_prefix_modifier(string, state)` | Applied in chat mode to the prefix for the bot's reply. |
| `def state_modifier(state)` | Modifies the dictionary containing the UI input parameters before it is used by the text generation functions. |
| `def history_modifier(history)` | Modifies the chat history before the text generation in chat mode begins. |
| `def bot_prefix_modifier(string)` | Applied in chat mode to the prefix for the bot's reply. |
| `def custom_generate_reply(...)` | Overrides the main text generation function. |
| `def custom_generate_chat_prompt(...)` | Overrides the prompt generator in chat mode. |
| `def tokenizer_modifier(state, prompt, input_ids, input_embeds)` | Modifies the `input_ids`/`input_embeds` fed to the model. Should return `prompt`, `input_ids`, `input_embeds`. See the `multimodal` extension for an example. |

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@ -23,6 +23,7 @@ ExLlama only uses the following parameters:
* top_p
* top_k
* repetition_penalty
* repetition_penalty_range
* typical_p
### RWKV

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@ -18,12 +18,16 @@ from pathlib import Path
import requests
import tqdm
from requests.adapters import HTTPAdapter
from tqdm.contrib.concurrent import thread_map
class ModelDownloader:
def __init__(self):
def __init__(self, max_retries):
self.s = requests.Session()
if max_retries:
self.s.mount('https://cdn-lfs.huggingface.co', HTTPAdapter(max_retries=max_retries))
self.s.mount('https://huggingface.co', HTTPAdapter(max_retries=max_retries))
if os.getenv('HF_USER') is not None and os.getenv('HF_PASS') is not None:
self.s.auth = (os.getenv('HF_USER'), os.getenv('HF_PASS'))
@ -212,6 +216,7 @@ if __name__ == '__main__':
parser.add_argument('--output', type=str, default=None, help='The folder where the model should be saved.')
parser.add_argument('--clean', action='store_true', help='Does not resume the previous download.')
parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.')
parser.add_argument('--max-retries', type=int, default=5, help='Max retries count when get error in download time.')
args = parser.parse_args()
branch = args.branch
@ -221,7 +226,7 @@ if __name__ == '__main__':
print("Error: Please specify the model you'd like to download (e.g. 'python download-model.py facebook/opt-1.3b').")
sys.exit()
downloader = ModelDownloader()
downloader = ModelDownloader(max_retries=args.max_retries)
# Cleaning up the model/branch names
try:
model, branch = downloader.sanitize_model_and_branch_names(model, branch)

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@ -72,7 +72,6 @@ class Handler(BaseHTTPRequestHandler):
self.end_headers()
user_input = body['user_input']
history = body['history']
regenerate = body.get('regenerate', False)
_continue = body.get('_continue', False)
@ -80,9 +79,9 @@ class Handler(BaseHTTPRequestHandler):
generate_params['stream'] = False
generator = generate_chat_reply(
user_input, history, generate_params, regenerate=regenerate, _continue=_continue, loading_message=False)
user_input, generate_params, regenerate=regenerate, _continue=_continue, loading_message=False)
answer = history
answer = generate_params['history']
for a in generator:
answer = a

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@ -55,14 +55,13 @@ async def _handle_connection(websocket, path):
body = json.loads(message)
user_input = body['user_input']
history = body['history']
generate_params = build_parameters(body, chat=True)
generate_params['stream'] = True
regenerate = body.get('regenerate', False)
_continue = body.get('_continue', False)
generator = generate_chat_reply(
user_input, history, generate_params, regenerate=regenerate, _continue=_continue, loading_message=False)
user_input, generate_params, regenerate=regenerate, _continue=_continue, loading_message=False)
message_num = 0
for a in generator:

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@ -21,6 +21,7 @@ def build_parameters(body, chat=False):
'tfs': float(body.get('tfs', 1)),
'top_a': float(body.get('top_a', 0)),
'repetition_penalty': float(body.get('repetition_penalty', body.get('rep_pen', 1.1))),
'repetition_penalty_range': int(body.get('repetition_penalty_range', 0)),
'encoder_repetition_penalty': float(body.get('encoder_repetition_penalty', 1.0)),
'top_k': int(body.get('top_k', 0)),
'min_length': int(body.get('min_length', 0)),
@ -64,6 +65,7 @@ def build_parameters(body, chat=False):
'context_instruct': context_instruct,
'turn_template': turn_template,
'chat-instruct_command': str(body.get('chat-instruct_command', shared.settings['chat-instruct_command'])),
'history': body.get('history', {'internal': [], 'visible': []})
})
return generate_params

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@ -3,7 +3,9 @@ from pathlib import Path
import elevenlabs
import gradio as gr
from modules import chat, shared
from modules.utils import gradio
params = {
'activate': True,
@ -35,24 +37,24 @@ def refresh_voices_dd():
return gr.Dropdown.update(value=all_voices[0], choices=all_voices)
def remove_tts_from_history():
for i, entry in enumerate(shared.history['internal']):
shared.history['visible'][i] = [shared.history['visible'][i][0], entry[1]]
def remove_tts_from_history(history):
for i, entry in enumerate(history['internal']):
history['visible'][i] = [history['visible'][i][0], entry[1]]
return history
def toggle_text_in_history():
for i, entry in enumerate(shared.history['visible']):
def toggle_text_in_history(history):
for i, entry in enumerate(history['visible']):
visible_reply = entry[1]
if visible_reply.startswith('<audio'):
if params['show_text']:
reply = shared.history['internal'][i][1]
shared.history['visible'][i] = [
shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>\n\n{reply}"
]
reply = history['internal'][i][1]
history['visible'][i] = [history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>\n\n{reply}"]
else:
shared.history['visible'][i] = [
shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>"
]
history['visible'][i] = [history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>"]
return history
def remove_surrounded_chars(string):
@ -150,25 +152,24 @@ def ui():
convert_cancel = gr.Button('Cancel', visible=False)
convert_confirm = gr.Button('Confirm (cannot be undone)', variant="stop", visible=False)
# Convert history with confirmation
convert_arr = [convert_confirm, convert, convert_cancel]
convert.click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr)
convert_confirm.click(
lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr).then(
remove_tts_from_history, None, None).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
if shared.is_chat():
# Convert history with confirmation
convert_arr = [convert_confirm, convert, convert_cancel]
convert.click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr)
convert_confirm.click(
lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr).then(
remove_tts_from_history, gradio('history'), gradio('history')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
chat.redraw_html, shared.reload_inputs, gradio('display'))
convert_cancel.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
convert_cancel.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
# Toggle message text in history
show_text.change(
lambda x: params.update({"show_text": x}), show_text, None).then(
toggle_text_in_history, None, None).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
convert_cancel.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
# Toggle message text in history
show_text.change(
lambda x: params.update({"show_text": x}), show_text, None).then(
toggle_text_in_history, gradio('history'), gradio('history')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
chat.redraw_html, shared.reload_inputs, gradio('display'))
# Event functions to update the parameters in the backend
activate.change(lambda x: params.update({'activate': x}), activate, None)

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@ -29,6 +29,7 @@ default_req_params = {
'top_p': 1.0,
'top_k': 1,
'repetition_penalty': 1.18,
'repetition_penalty_range': 0,
'encoder_repetition_penalty': 1.0,
'suffix': None,
'stream': False,

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@ -10,7 +10,7 @@ import requests
import torch
from PIL import Image
import modules.shared as shared
from modules import shared
from modules.models import reload_model, unload_model
from modules.ui import create_refresh_button
@ -126,7 +126,7 @@ def input_modifier(string):
return string
# Get and save the Stable Diffusion-generated picture
def get_SD_pictures(description):
def get_SD_pictures(description, character):
global params
@ -160,7 +160,7 @@ def get_SD_pictures(description):
if params['save_img']:
img_data = base64.b64decode(img_str)
variadic = f'{date.today().strftime("%Y_%m_%d")}/{shared.character}_{int(time.time())}'
variadic = f'{date.today().strftime("%Y_%m_%d")}/{character}_{int(time.time())}'
output_file = Path(f'extensions/sd_api_pictures/outputs/{variadic}.png')
output_file.parent.mkdir(parents=True, exist_ok=True)
@ -186,7 +186,7 @@ def get_SD_pictures(description):
# TODO: how do I make the UI history ignore the resulting pictures (I don't want HTML to appear in history)
# and replace it with 'text' for the purposes of logging?
def output_modifier(string):
def output_modifier(string, state):
"""
This function is applied to the model outputs.
"""
@ -213,7 +213,7 @@ def output_modifier(string):
else:
text = string
string = get_SD_pictures(string) + "\n" + text
string = get_SD_pictures(string, state['character_menu']) + "\n" + text
return string

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@ -3,9 +3,10 @@ from pathlib import Path
import gradio as gr
import torch
from modules import chat, shared
from extensions.silero_tts import tts_preprocessor
from modules import chat, shared
from modules.utils import gradio
torch._C._jit_set_profiling_mode(False)
@ -56,20 +57,24 @@ def load_model():
return model
def remove_tts_from_history():
for i, entry in enumerate(shared.history['internal']):
shared.history['visible'][i] = [shared.history['visible'][i][0], entry[1]]
def remove_tts_from_history(history):
for i, entry in enumerate(history['internal']):
history['visible'][i] = [history['visible'][i][0], entry[1]]
return history
def toggle_text_in_history():
for i, entry in enumerate(shared.history['visible']):
def toggle_text_in_history(history):
for i, entry in enumerate(history['visible']):
visible_reply = entry[1]
if visible_reply.startswith('<audio'):
if params['show_text']:
reply = shared.history['internal'][i][1]
shared.history['visible'][i] = [shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>\n\n{reply}"]
reply = history['internal'][i][1]
history['visible'][i] = [history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>\n\n{reply}"]
else:
shared.history['visible'][i] = [shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>"]
history['visible'][i] = [history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>"]
return history
def state_modifier(state):
@ -80,7 +85,7 @@ def state_modifier(state):
return state
def input_modifier(string):
def input_modifier(string, state):
if not params['activate']:
return string
@ -99,7 +104,7 @@ def history_modifier(history):
return history
def output_modifier(string):
def output_modifier(string, state):
global model, current_params, streaming_state
for i in params:
if params[i] != current_params[i]:
@ -116,7 +121,7 @@ def output_modifier(string):
if string == '':
string = '*Empty reply, try regenerating*'
else:
output_file = Path(f'extensions/silero_tts/outputs/{shared.character}_{int(time.time())}.wav')
output_file = Path(f'extensions/silero_tts/outputs/{state["character_menu"]}_{int(time.time())}.wav')
prosody = '<prosody rate="{}" pitch="{}">'.format(params['voice_speed'], params['voice_pitch'])
silero_input = f'<speak>{prosody}{xmlesc(string)}</prosody></speak>'
model.save_wav(ssml_text=silero_input, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file))
@ -155,23 +160,24 @@ def ui():
gr.Markdown('[Click here for Silero audio samples](https://oobabooga.github.io/silero-samples/index.html)')
# Convert history with confirmation
convert_arr = [convert_confirm, convert, convert_cancel]
convert.click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr)
convert_confirm.click(
lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr).then(
remove_tts_from_history, None, None).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
if shared.is_chat():
# Convert history with confirmation
convert_arr = [convert_confirm, convert, convert_cancel]
convert.click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr)
convert_confirm.click(
lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr).then(
remove_tts_from_history, gradio('history'), gradio('history')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
chat.redraw_html, shared.reload_inputs, gradio('display'))
convert_cancel.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
convert_cancel.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
# Toggle message text in history
show_text.change(
lambda x: params.update({"show_text": x}), show_text, None).then(
toggle_text_in_history, None, None).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
# Toggle message text in history
show_text.change(
lambda x: params.update({"show_text": x}), show_text, None).then(
toggle_text_in_history, gradio('history'), gradio('history')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
chat.redraw_html, shared.reload_inputs, gradio('display'))
# Event functions to update the parameters in the backend
activate.change(lambda x: params.update({"activate": x}), activate, None)

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@ -96,6 +96,8 @@ def apply_settings(chunk_count, chunk_count_initial, time_weight):
def custom_generate_chat_prompt(user_input, state, **kwargs):
global chat_collector
history = state['history']
if state['mode'] == 'instruct':
results = collector.get_sorted(user_input, n_results=params['chunk_count'])
additional_context = '\nYour reply should be based on the context below:\n\n' + '\n'.join(results)
@ -104,29 +106,29 @@ def custom_generate_chat_prompt(user_input, state, **kwargs):
def make_single_exchange(id_):
output = ''
output += f"{state['name1']}: {shared.history['internal'][id_][0]}\n"
output += f"{state['name2']}: {shared.history['internal'][id_][1]}\n"
output += f"{state['name1']}: {history['internal'][id_][0]}\n"
output += f"{state['name2']}: {history['internal'][id_][1]}\n"
return output
if len(shared.history['internal']) > params['chunk_count'] and user_input != '':
if len(history['internal']) > params['chunk_count'] and user_input != '':
chunks = []
hist_size = len(shared.history['internal'])
hist_size = len(history['internal'])
for i in range(hist_size-1):
chunks.append(make_single_exchange(i))
add_chunks_to_collector(chunks, chat_collector)
query = '\n'.join(shared.history['internal'][-1] + [user_input])
query = '\n'.join(history['internal'][-1] + [user_input])
try:
best_ids = chat_collector.get_ids_sorted(query, n_results=params['chunk_count'], n_initial=params['chunk_count_initial'], time_weight=params['time_weight'])
additional_context = '\n'
for id_ in best_ids:
if shared.history['internal'][id_][0] != '<|BEGIN-VISIBLE-CHAT|>':
if history['internal'][id_][0] != '<|BEGIN-VISIBLE-CHAT|>':
additional_context += make_single_exchange(id_)
logger.warning(f'Adding the following new context:\n{additional_context}')
state['context'] = state['context'].strip() + '\n' + additional_context
kwargs['history'] = {
'internal': [shared.history['internal'][i] for i in range(hist_size) if i not in best_ids],
'internal': [history['internal'][i] for i in range(hist_size) if i not in best_ids],
'visible': ''
}
except RuntimeError:

View File

@ -106,8 +106,10 @@ def add_lora_transformers(lora_names):
# If any LoRA needs to be removed, start over
if len(removed_set) > 0:
shared.model.disable_adapter()
shared.model = shared.model.base_model.model
# shared.model may no longer be PeftModel
if hasattr(shared.model, 'disable_adapter'):
shared.model.disable_adapter()
shared.model = shared.model.base_model.model
if len(lora_names) > 0:
params = {}

View File

@ -1,12 +1,11 @@
import base64
import copy
import functools
import io
import json
import re
from datetime import datetime
from pathlib import Path
import gradio as gr
import yaml
from PIL import Image
@ -19,7 +18,12 @@ from modules.text_generation import (
get_encoded_length,
get_max_prompt_length
)
from modules.utils import delete_file, replace_all, save_file
from modules.utils import (
delete_file,
get_available_characters,
replace_all,
save_file
)
def get_turn_substrings(state, instruct=False):
@ -53,7 +57,7 @@ def generate_chat_prompt(user_input, state, **kwargs):
impersonate = kwargs.get('impersonate', False)
_continue = kwargs.get('_continue', False)
also_return_rows = kwargs.get('also_return_rows', False)
history = kwargs.get('history', shared.history)['internal']
history = kwargs.get('history', state['history'])['internal']
is_instruct = state['mode'] == 'instruct'
# Find the maximum prompt size
@ -75,10 +79,10 @@ def generate_chat_prompt(user_input, state, **kwargs):
if impersonate:
wrapper += substrings['user_turn_stripped'].rstrip(' ')
elif _continue:
wrapper += apply_extensions("bot_prefix", substrings['bot_turn_stripped'])
wrapper += apply_extensions('bot_prefix', substrings['bot_turn_stripped'], state)
wrapper += history[-1][1]
else:
wrapper += apply_extensions("bot_prefix", substrings['bot_turn_stripped'].rstrip(' '))
wrapper += apply_extensions('bot_prefix', substrings['bot_turn_stripped'].rstrip(' '), state)
else:
wrapper = '<|prompt|>'
@ -112,7 +116,7 @@ def generate_chat_prompt(user_input, state, **kwargs):
# Add the character prefix
if state['mode'] != 'chat-instruct':
rows.append(apply_extensions("bot_prefix", substrings['bot_turn_stripped'].rstrip(' ')))
rows.append(apply_extensions('bot_prefix', substrings['bot_turn_stripped'].rstrip(' '), state))
while len(rows) > min_rows and get_encoded_length(wrapper.replace('<|prompt|>', ''.join(rows))) >= max_length:
rows.pop(1)
@ -152,7 +156,8 @@ def get_stopping_strings(state):
return stopping_strings
def chatbot_wrapper(text, history, state, regenerate=False, _continue=False, loading_message=True):
def chatbot_wrapper(text, state, regenerate=False, _continue=False, loading_message=True):
history = state['history']
output = copy.deepcopy(history)
output = apply_extensions('history', output)
state = apply_extensions('state', state)
@ -173,11 +178,11 @@ def chatbot_wrapper(text, history, state, regenerate=False, _continue=False, loa
if visible_text is None:
visible_text = text
text = apply_extensions('input', text, state)
# *Is typing...*
if loading_message:
yield {'visible': output['visible'] + [[visible_text, shared.processing_message]], 'internal': output['internal']}
text = apply_extensions('input', text)
else:
text, visible_text = output['internal'][-1][0], output['visible'][-1][0]
if regenerate:
@ -214,7 +219,7 @@ def chatbot_wrapper(text, history, state, regenerate=False, _continue=False, loa
# We need this global variable to handle the Stop event,
# otherwise gradio gets confused
if shared.stop_everything:
output['visible'][-1][1] = apply_extensions("output", output['visible'][-1][1])
output['visible'][-1][1] = apply_extensions('output', output['visible'][-1][1], state)
yield output
return
@ -240,7 +245,7 @@ def chatbot_wrapper(text, history, state, regenerate=False, _continue=False, loa
else:
cumulative_reply = reply
output['visible'][-1][1] = apply_extensions("output", output['visible'][-1][1])
output['visible'][-1][1] = apply_extensions('output', output['visible'][-1][1], state)
yield output
@ -273,14 +278,15 @@ def impersonate_wrapper(text, start_with, state):
yield cumulative_reply.lstrip(' ')
def generate_chat_reply(text, history, state, regenerate=False, _continue=False, loading_message=True):
def generate_chat_reply(text, state, regenerate=False, _continue=False, loading_message=True):
history = state['history']
if regenerate or _continue:
text = ''
if (len(history['visible']) == 1 and not history['visible'][0][0]) or len(history['internal']) == 0:
yield history
return
for history in chatbot_wrapper(text, history, state, regenerate=regenerate, _continue=_continue, loading_message=loading_message):
for history in chatbot_wrapper(text, state, regenerate=regenerate, _continue=_continue, loading_message=loading_message):
yield history
@ -288,151 +294,127 @@ def generate_chat_reply(text, history, state, regenerate=False, _continue=False,
def generate_chat_reply_wrapper(text, start_with, state, regenerate=False, _continue=False):
if start_with != '' and not _continue:
if regenerate:
text = remove_last_message()
text, state['history'] = remove_last_message(state['history'])
regenerate = False
_continue = True
send_dummy_message(text)
send_dummy_reply(start_with)
send_dummy_message(text, state)
send_dummy_reply(start_with, state)
for i, history in enumerate(generate_chat_reply(text, shared.history, state, regenerate, _continue, loading_message=True)):
if i != 0:
shared.history = copy.deepcopy(history)
yield chat_html_wrapper(history['visible'], state['name1'], state['name2'], state['mode'], state['chat_style'])
for i, history in enumerate(generate_chat_reply(text, state, regenerate, _continue, loading_message=True)):
yield chat_html_wrapper(history, state['name1'], state['name2'], state['mode'], state['chat_style']), history
def remove_last_message():
if len(shared.history['visible']) > 0 and shared.history['internal'][-1][0] != '<|BEGIN-VISIBLE-CHAT|>':
last = shared.history['visible'].pop()
shared.history['internal'].pop()
def remove_last_message(history):
if len(history['visible']) > 0 and history['internal'][-1][0] != '<|BEGIN-VISIBLE-CHAT|>':
last = history['visible'].pop()
history['internal'].pop()
else:
last = ['', '']
return last[0]
return last[0], history
def send_last_reply_to_input():
if len(shared.history['internal']) > 0:
return shared.history['internal'][-1][1]
def send_last_reply_to_input(history):
if len(history['internal']) > 0:
return history['internal'][-1][1]
else:
return ''
def replace_last_reply(text):
if len(shared.history['visible']) > 0:
shared.history['visible'][-1][1] = text
shared.history['internal'][-1][1] = apply_extensions("input", text)
def send_dummy_message(text):
shared.history['visible'].append([text, ''])
shared.history['internal'].append([apply_extensions("input", text), ''])
def send_dummy_reply(text):
if len(shared.history['visible']) > 0 and not shared.history['visible'][-1][1] == '':
shared.history['visible'].append(['', ''])
shared.history['internal'].append(['', ''])
shared.history['visible'][-1][1] = text
shared.history['internal'][-1][1] = apply_extensions("input", text)
def clear_chat_log(greeting, mode):
shared.history['visible'] = []
shared.history['internal'] = []
if mode != 'instruct':
if greeting != '':
shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]]
shared.history['visible'] += [['', apply_extensions("output", greeting)]]
save_history(mode)
def redraw_html(name1, name2, mode, style, reset_cache=False):
return chat_html_wrapper(shared.history['visible'], name1, name2, mode, style, reset_cache=reset_cache)
def tokenize_dialogue(dialogue, name1, name2):
history = []
messages = []
dialogue = re.sub('<START>', '', dialogue)
dialogue = re.sub('<start>', '', dialogue)
dialogue = re.sub('(\n|^)[Aa]non:', '\\1You:', dialogue)
dialogue = re.sub('(\n|^)\[CHARACTER\]:', f'\\g<1>{name2}:', dialogue)
idx = [m.start() for m in re.finditer(f"(^|\n)({re.escape(name1)}|{re.escape(name2)}):", dialogue)]
if len(idx) == 0:
return history
for i in range(len(idx) - 1):
messages.append(dialogue[idx[i]:idx[i + 1]].strip())
messages.append(dialogue[idx[-1]:].strip())
entry = ['', '']
for i in messages:
if i.startswith(f'{name1}:'):
entry[0] = i[len(f'{name1}:'):].strip()
elif i.startswith(f'{name2}:'):
entry[1] = i[len(f'{name2}:'):].strip()
if not (len(entry[0]) == 0 and len(entry[1]) == 0):
history.append(entry)
entry = ['', '']
print("\033[1;32;1m\nDialogue tokenized to:\033[0;37;0m\n", end='')
for row in history:
for column in row:
print("\n")
for line in column.strip().split('\n'):
print("| " + line + "\n")
print("|\n")
print("------------------------------")
def replace_last_reply(text, state):
history = state['history']
if len(history['visible']) > 0:
history['visible'][-1][1] = text
history['internal'][-1][1] = apply_extensions('input', text, state)
return history
def save_history(mode, timestamp=False, user_request=False):
# Instruct mode histories should not be saved as if
# Alpaca or Vicuna were characters
if mode == 'instruct':
if not timestamp:
return
fname = f"Instruct_{datetime.now().strftime('%Y%m%d-%H%M%S')}.json"
else:
if shared.character == 'None' and not user_request:
return
if timestamp:
fname = f"{shared.character}_{datetime.now().strftime('%Y%m%d-%H%M%S')}.json"
else:
fname = f"{shared.character}_persistent.json"
if not Path('logs').exists():
Path('logs').mkdir()
with open(Path(f'logs/{fname}'), 'w', encoding='utf-8') as f:
f.write(json.dumps({'data': shared.history['internal'], 'data_visible': shared.history['visible']}, indent=2))
return Path(f'logs/{fname}')
def send_dummy_message(text, state):
history = state['history']
history['visible'].append([text, ''])
history['internal'].append([apply_extensions('input', text, state), ''])
return history
def load_history(file, name1, name2):
file = file.decode('utf-8')
def send_dummy_reply(text, state):
history = state['history']
if len(history['visible']) > 0 and not history['visible'][-1][1] == '':
history['visible'].append(['', ''])
history['internal'].append(['', ''])
history['visible'][-1][1] = text
history['internal'][-1][1] = apply_extensions('input', text, state)
return history
def clear_chat_log(state):
greeting = state['greeting']
mode = state['mode']
history = state['history']
history['visible'] = []
history['internal'] = []
if mode != 'instruct':
if greeting != '':
history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]]
history['visible'] += [['', apply_extensions('output', greeting, state)]]
return history
def redraw_html(history, name1, name2, mode, style, reset_cache=False):
return chat_html_wrapper(history, name1, name2, mode, style, reset_cache=reset_cache)
def save_history(history, path=None):
p = path or Path('logs/exported_history.json')
with open(p, 'w', encoding='utf-8') as f:
f.write(json.dumps(history, indent=4))
return p
def load_history(file, history):
try:
file = file.decode('utf-8')
j = json.loads(file)
if 'data' in j:
shared.history['internal'] = j['data']
if 'data_visible' in j:
shared.history['visible'] = j['data_visible']
else:
shared.history['visible'] = copy.deepcopy(shared.history['internal'])
if 'internal' in j and 'visible' in j:
return j
else:
return history
except:
shared.history['internal'] = tokenize_dialogue(file, name1, name2)
shared.history['visible'] = copy.deepcopy(shared.history['internal'])
return history
def save_persistent_history(history, character, mode):
if mode in ['chat', 'chat-instruct'] and character not in ['', 'None', None] and not shared.args.multi_user:
save_history(history, path=Path(f'logs/{character}_persistent.json'))
def load_persistent_history(state):
if state['mode'] == 'instruct':
return state['history']
character = state['character_menu']
greeting = state['greeting']
p = Path(f'logs/{character}_persistent.json')
if not shared.args.multi_user and character not in ['None', '', None] and p.exists():
f = json.loads(open(p, 'rb').read())
if 'internal' in f and 'visible' in f:
history = f
else:
history = {'internal': [], 'visible': []}
history['internal'] = f['data']
history['visible'] = f['data_visible']
else:
history = {'internal': [], 'visible': []}
if greeting != "":
history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]]
history['visible'] += [['', apply_extensions('output', greeting, state)]]
return history
def replace_character_names(text, name1, name2):
@ -467,7 +449,6 @@ def generate_pfp_cache(character):
def load_character(character, name1, name2, instruct=False):
shared.character = character
context = greeting = turn_template = ""
greeting_field = 'greeting'
picture = None
@ -476,7 +457,7 @@ def load_character(character, name1, name2, instruct=False):
if Path("cache/pfp_character.png").exists():
Path("cache/pfp_character.png").unlink()
if character != 'None':
if character not in ['None', '', None]:
folder = 'characters' if not instruct else 'characters/instruction-following'
picture = generate_pfp_cache(character)
for extension in ["yml", "yaml", "json"]:
@ -526,20 +507,6 @@ def load_character(character, name1, name2, instruct=False):
greeting = shared.settings['greeting']
turn_template = shared.settings['turn_template']
if not instruct:
shared.history['internal'] = []
shared.history['visible'] = []
if shared.character != 'None' and Path(f'logs/{shared.character}_persistent.json').exists():
load_history(open(Path(f'logs/{shared.character}_persistent.json'), 'rb').read(), name1, name2)
else:
# Insert greeting if it exists
if greeting != "":
shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]]
shared.history['visible'] += [['', apply_extensions("output", greeting)]]
# Create .json log files since they don't already exist
save_history('instruct' if instruct else 'chat')
return name1, name2, picture, greeting, context, turn_template.replace("\n", r"\n")
@ -567,16 +534,22 @@ def upload_character(json_file, img, tavern=False):
img.save(Path(f'characters/{outfile_name}.png'))
logger.info(f'New character saved to "characters/{outfile_name}.json".')
return outfile_name
return gr.update(value=outfile_name, choices=get_available_characters())
def upload_tavern_character(img, name1, name2):
_img = Image.open(io.BytesIO(img))
_img.getexif()
decoded_string = base64.b64decode(_img.info['chara'])
_json = json.loads(decoded_string)
def upload_tavern_character(img, _json):
_json = {"char_name": _json['name'], "char_persona": _json['description'], "char_greeting": _json["first_mes"], "example_dialogue": _json['mes_example'], "world_scenario": _json['scenario']}
return upload_character(json.dumps(_json), _img, tavern=True)
return upload_character(json.dumps(_json), img, tavern=True)
def check_tavern_character(img):
if "chara" not in img.info:
return "Not a TavernAI card", None, None, gr.update(interactive=False)
decoded_string = base64.b64decode(img.info['chara'])
_json = json.loads(decoded_string)
if "data" in _json:
_json = _json["data"]
return _json['name'], _json['description'], _json, gr.update(interactive=True)
def upload_your_profile_picture(img):

View File

@ -1,6 +1,8 @@
import sys
from pathlib import Path
from torch import version as torch_version
from modules import shared
from modules.logging_colors import logger
@ -52,6 +54,16 @@ class ExllamaModel:
config.set_auto_map(shared.args.gpu_split)
config.gpu_peer_fix = True
if shared.args.alpha_value:
config.alpha_value = shared.args.alpha_value
config.calculate_rotary_embedding_base()
if torch_version.hip:
config.rmsnorm_no_half2 = True
config.rope_no_half2 = True
config.matmul_no_half2 = True
config.silu_no_half2 = True
model = ExLlama(config)
tokenizer = ExLlamaTokenizer(str(tokenizer_model_path))
cache = ExLlamaCache(model)
@ -71,6 +83,7 @@ class ExllamaModel:
self.generator.settings.top_k = state['top_k']
self.generator.settings.typical = state['typical_p']
self.generator.settings.token_repetition_penalty_max = state['repetition_penalty']
self.generator.settings.token_repetition_penalty_sustain = -1 if state['repetition_penalty_range'] <= 0 else state['repetition_penalty_range']
if state['ban_eos_token']:
self.generator.disallow_tokens([self.tokenizer.eos_token_id])
else:

View File

@ -98,6 +98,16 @@ class ExllamaHF(PreTrainedModel):
config.set_auto_map(shared.args.gpu_split)
config.gpu_peer_fix = True
if shared.args.alpha_value:
config.alpha_value = shared.args.alpha_value
config.calculate_rotary_embedding_base()
if torch.version.hip:
config.rmsnorm_no_half2 = True
config.rope_no_half2 = True
config.matmul_no_half2 = True
config.silu_no_half2 = True
# This slowes down a bit but align better with autogptq generation.
# TODO: Should give user choice to tune the exllama config
# config.fused_attn = False

View File

@ -6,6 +6,8 @@ import gradio as gr
import extensions
import modules.shared as shared
from modules.logging_colors import logger
from inspect import signature
state = {}
available_extensions = []
@ -52,10 +54,14 @@ def iterator():
# Extension functions that map string -> string
def _apply_string_extensions(function_name, text):
def _apply_string_extensions(function_name, text, state):
for extension, _ in iterator():
if hasattr(extension, function_name):
text = getattr(extension, function_name)(text)
func = getattr(extension, function_name)
if len(signature(func).parameters) == 2:
text = func(text, state)
else:
text = func(text)
return text

View File

@ -14,16 +14,20 @@ def clone_or_pull_repository(github_url):
# Check if the repository is already cloned
if os.path.exists(repo_path):
yield f"Updating {github_url}..."
# Perform a 'git pull' to update the repository
try:
pull_output = subprocess.check_output(["git", "-C", repo_path, "pull"], stderr=subprocess.STDOUT)
yield "Done."
return pull_output.decode()
except subprocess.CalledProcessError as e:
return str(e)
# Clone the repository
try:
yield f"Cloning {github_url}..."
clone_output = subprocess.check_output(["git", "clone", github_url, repo_path], stderr=subprocess.STDOUT)
yield "Done."
return clone_output.decode()
except subprocess.CalledProcessError as e:
return str(e)

View File

@ -129,7 +129,7 @@ def generate_4chan_html(f):
def make_thumbnail(image):
image = image.resize((350, round(image.size[1] / image.size[0] * 350)), Image.Resampling.LANCZOS)
if image.size[1] > 470:
image = ImageOps.fit(image, (350, 470), Image.ANTIALIAS)
image = ImageOps.fit(image, (350, 470), Image.LANCZOS)
return image
@ -266,8 +266,8 @@ def generate_chat_html(history, name1, name2, reset_cache=False):
def chat_html_wrapper(history, name1, name2, mode, style, reset_cache=False):
if mode == 'instruct':
return generate_instruct_html(history)
return generate_instruct_html(history['visible'])
elif style == 'wpp':
return generate_chat_html(history, name1, name2)
return generate_chat_html(history['visible'], name1, name2)
else:
return generate_cai_chat_html(history, name1, name2, style, reset_cache)
return generate_cai_chat_html(history['visible'], name1, name2, style, reset_cache)

View File

@ -57,12 +57,14 @@ loaders_and_params = {
'gpu_split',
'max_seq_len',
'compress_pos_emb',
'alpha_value',
'exllama_info',
],
'ExLlama_HF' : [
'gpu_split',
'max_seq_len',
'compress_pos_emb',
'alpha_value',
'exllama_HF_info',
]
}

View File

@ -326,6 +326,7 @@ def clear_torch_cache():
def unload_model():
shared.model = shared.tokenizer = None
shared.lora_names = []
clear_torch_cache()

View File

@ -15,6 +15,7 @@ def load_preset(name):
'tfs': 1,
'top_a': 0,
'repetition_penalty': 1,
'repetition_penalty_range': 0,
'encoder_repetition_penalty': 1,
'top_k': 0,
'num_beams': 1,
@ -28,11 +29,12 @@ def load_preset(name):
'mirostat_eta': 0.1,
}
with open(Path(f'presets/{name}.yaml'), 'r') as infile:
preset = yaml.safe_load(infile)
if name not in ['None', None, '']:
with open(Path(f'presets/{name}.yaml'), 'r') as infile:
preset = yaml.safe_load(infile)
for k in preset:
generate_params[k] = preset[k]
for k in preset:
generate_params[k] = preset[k]
generate_params['temperature'] = min(1.99, generate_params['temperature'])
return generate_params
@ -46,9 +48,9 @@ def load_preset_memoized(name):
def load_preset_for_ui(name, state):
generate_params = load_preset(name)
state.update(generate_params)
return state, *[generate_params[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']]
return state, *[generate_params[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']]
def generate_preset_yaml(state):
data = {k: state[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']}
data = {k: state[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']}
return yaml.dump(data, sort_keys=False)

View File

@ -5,6 +5,7 @@ import transformers
from transformers import LogitsWarper
from transformers.generation.logits_process import (
LogitNormalization,
LogitsProcessor,
LogitsProcessorList,
TemperatureLogitsWarper
)
@ -121,6 +122,29 @@ class MirostatLogitsWarper(LogitsWarper):
return scores
class RepetitionPenaltyLogitsProcessorWithRange(LogitsProcessor):
'''
Copied from the transformers library
'''
def __init__(self, penalty: float, _range: int):
if not isinstance(penalty, float) or not (penalty > 0):
raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
self.penalty = penalty
self._range = _range
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
input_ids = input_ids[:, -self._range:]
score = torch.gather(scores, 1, input_ids)
# if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
score = torch.where(score < 0, score * self.penalty, score / self.penalty)
scores.scatter_(1, input_ids, score)
return scores
def get_logits_warper_patch(self, generation_config):
warpers = self._get_logits_warper_old(generation_config)
warpers_to_add = LogitsProcessorList()
@ -146,6 +170,19 @@ def get_logits_warper_patch(self, generation_config):
return warpers
def get_logits_processor_patch(self, **kwargs):
result = self._get_logits_processor_old(**kwargs)
repetition_penalty_range = kwargs['generation_config'].repetition_penalty_range
repetition_penalty = kwargs['generation_config'].repetition_penalty
if repetition_penalty_range > 0:
for i in range(len(result)):
if result[i].__class__.__name__ == 'RepetitionPenaltyLogitsProcessor':
result[i] = RepetitionPenaltyLogitsProcessorWithRange(repetition_penalty, repetition_penalty_range)
return result
def generation_config_init_patch(self, **kwargs):
self.__init___old(**kwargs)
self.tfs = kwargs.pop("tfs", 1.0)
@ -153,11 +190,15 @@ def generation_config_init_patch(self, **kwargs):
self.mirostat_mode = kwargs.pop("mirostat_mode", 0)
self.mirostat_eta = kwargs.pop("mirostat_eta", 0.1)
self.mirostat_tau = kwargs.pop("mirostat_tau", 5)
self.repetition_penalty_range = kwargs.pop("repetition_penalty_range", 0)
def hijack_samplers():
transformers.GenerationMixin._get_logits_warper_old = transformers.GenerationMixin._get_logits_warper
transformers.GenerationMixin._get_logits_warper = get_logits_warper_patch
transformers.GenerationMixin._get_logits_processor_old = transformers.GenerationMixin._get_logits_processor
transformers.GenerationMixin._get_logits_processor = get_logits_processor_patch
transformers.GenerationConfig.__init___old = transformers.GenerationConfig.__init__
transformers.GenerationConfig.__init__ = generation_config_init_patch

View File

@ -14,8 +14,6 @@ model_name = "None"
lora_names = []
# Chat variables
history = {'internal': [], 'visible': []}
character = 'None'
stop_everything = False
processing_message = '*Is typing...*'
@ -83,6 +81,7 @@ parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpForma
# Basic settings
parser.add_argument('--notebook', action='store_true', help='Launch the web UI in notebook mode, where the output is written to the same text box as the input.')
parser.add_argument('--chat', action='store_true', help='Launch the web UI in chat mode with a style similar to the Character.AI website.')
parser.add_argument('--multi-user', action='store_true', help='Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental.')
parser.add_argument('--character', type=str, help='The name of the character to load in chat mode by default.')
parser.add_argument('--model', type=str, help='Name of the model to load by default.')
parser.add_argument('--lora', type=str, nargs="+", help='The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.')
@ -151,6 +150,7 @@ parser.add_argument('--desc_act', action='store_true', help='For models that don
parser.add_argument('--gpu-split', type=str, help="Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. 20,7,7")
parser.add_argument('--max_seq_len', type=int, default=2048, help="Maximum sequence length.")
parser.add_argument('--compress_pos_emb', type=int, default=1, help="Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.")
parser.add_argument('--alpha_value', type=int, default=1, help="Positional embeddings alpha factor for NTK RoPE scaling. Same as above. Use either this or compress_pos_emb, not both.")
# FlexGen
parser.add_argument('--flexgen', action='store_true', help='DEPRECATED')
@ -204,6 +204,8 @@ if args.trust_remote_code:
logger.warning("trust_remote_code is enabled. This is dangerous.")
if args.share:
logger.warning("The gradio \"share link\" feature uses a proprietary executable to create a reverse tunnel. Use it with care.")
if args.multi_user:
logger.warning("The multi-user mode is highly experimental. DO NOT EXPOSE IT TO THE INTERNET.")
def fix_loader_name(name):
@ -246,6 +248,15 @@ def is_chat():
return args.chat
def get_mode():
if args.chat:
return 'chat'
elif args.notebook:
return 'notebook'
else:
return 'default'
# Loading model-specific settings
with Path(f'{args.model_dir}/config.yaml') as p:
if p.exists():

View File

@ -190,7 +190,7 @@ def _generate_reply(question, state, stopping_strings=None, is_chat=False):
original_question = question
if not is_chat:
state = apply_extensions('state', state)
question = apply_extensions('input', question)
question = apply_extensions('input', question, state)
# Finding the stopping strings
all_stop_strings = []
@ -223,14 +223,14 @@ def _generate_reply(question, state, stopping_strings=None, is_chat=False):
break
if not is_chat:
reply = apply_extensions('output', reply)
reply = apply_extensions('output', reply, state)
yield reply
def generate_reply_HF(question, original_question, seed, state, stopping_strings=None, is_chat=False):
generate_params = {}
for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'tfs', 'top_a', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta']:
for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'tfs', 'top_a', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta']:
generate_params[k] = state[k]
for k in ['epsilon_cutoff', 'eta_cutoff']:
@ -262,7 +262,7 @@ def generate_reply_HF(question, original_question, seed, state, stopping_strings
eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
generate_params['eos_token_id'] = eos_token_ids
generate_params['stopping_criteria'] = transformers.StoppingCriteriaList()
generate_params['stopping_criteria'].append(_StopEverythingStoppingCriteria());
generate_params['stopping_criteria'].append(_StopEverythingStoppingCriteria())
t0 = time.time()
try:

View File

@ -240,6 +240,21 @@ def backup_adapter(input_folder):
except Exception as e:
print("An error occurred in backup_adapter:", str(e))
def calc_trainable_parameters(model):
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
num_params = param.numel()
# if using DS Zero 3 and the weights are initialized empty
if num_params == 0 and hasattr(param, "ds_numel"):
num_params = param.ds_numel
all_param += num_params
if param.requires_grad:
trainable_params += num_params
return trainable_params,all_param
def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, overlap_len: int, newline_favor_len: int, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str, stop_at_loss: float):
@ -268,7 +283,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
else:
model_id = "llama"
if model_type == "PeftModelForCausalLM":
if len(shared.args.lora_names) > 0:
if len(shared.lora_names) > 0:
yield "You are trying to train a LoRA while you already have another LoRA loaded. This will work, but may have unexpected effects. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
logger.warning("Training LoRA over top of another LoRA. May have unexpected effects.")
else:
@ -431,6 +446,9 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
if not always_override:
backup_adapter(lora_file_path)
# == get model trainable params
model_trainable_params, model_all_params = calc_trainable_parameters(shared.model)
try:
logger.info("Creating LoRA model...")
lora_model = get_peft_model(shared.model, config)
@ -540,6 +558,12 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
logger.info("Starting training...")
yield "Starting..."
lora_trainable_param, lora_all_param = calc_trainable_parameters(lora_model)
if lora_all_param>0:
print(f"Trainable params: {lora_trainable_param:,d} ({100 * lora_trainable_param / lora_all_param:.4f} %), All params: {lora_all_param:,d} (Model: {model_all_params:,d})")
train_log.update({"base_model_name": shared.model_name})
train_log.update({"base_model_class": shared.model.__class__.__name__})
train_log.update({"base_loaded_in_4bit": getattr(lora_model, "is_loaded_in_4bit", False)})

View File

@ -1,3 +1,4 @@
import json
from pathlib import Path
import gradio as gr
@ -5,6 +6,7 @@ import torch
from modules import shared
with open(Path(__file__).resolve().parent / '../css/main.css', 'r') as f:
css = f.read()
with open(Path(__file__).resolve().parent / '../css/chat.css', 'r') as f:
@ -14,7 +16,7 @@ with open(Path(__file__).resolve().parent / '../css/main.js', 'r') as f:
with open(Path(__file__).resolve().parent / '../css/chat.js', 'r') as f:
chat_js = f.read()
refresh_symbol = '\U0001f504' # 🔄
refresh_symbol = '🔄'
delete_symbol = '🗑️'
save_symbol = '💾'
@ -30,17 +32,105 @@ theme = gr.themes.Default(
def list_model_elements():
elements = ['loader', 'cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'trust_remote_code', 'load_in_4bit', 'compute_dtype', 'quant_type', 'use_double_quant', 'wbits', 'groupsize', 'model_type', 'pre_layer', 'triton', 'desc_act', 'no_inject_fused_attention', 'no_inject_fused_mlp', 'no_use_cuda_fp16', 'threads', 'n_batch', 'no_mmap', 'mlock', 'n_gpu_layers', 'n_ctx', 'llama_cpp_seed', 'gpu_split', 'max_seq_len', 'compress_pos_emb']
elements = [
'loader',
'cpu_memory',
'auto_devices',
'disk',
'cpu',
'bf16',
'load_in_8bit',
'trust_remote_code',
'load_in_4bit',
'compute_dtype',
'quant_type',
'use_double_quant',
'wbits',
'groupsize',
'model_type',
'pre_layer',
'triton',
'desc_act',
'no_inject_fused_attention',
'no_inject_fused_mlp',
'no_use_cuda_fp16',
'threads',
'n_batch',
'no_mmap',
'mlock',
'n_gpu_layers',
'n_ctx',
'llama_cpp_seed',
'gpu_split',
'max_seq_len',
'compress_pos_emb',
'alpha_value'
]
for i in range(torch.cuda.device_count()):
elements.append(f'gpu_memory_{i}')
return elements
def list_interface_input_elements(chat=False):
elements = ['max_new_tokens', 'seed', 'temperature', 'top_p', 'top_k', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'do_sample', 'penalty_alpha', 'num_beams', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'add_bos_token', 'ban_eos_token', 'truncation_length', 'custom_stopping_strings', 'skip_special_tokens', 'preset_menu', 'stream', 'tfs', 'top_a']
if chat:
elements += ['name1', 'name2', 'greeting', 'context', 'chat_generation_attempts', 'stop_at_newline', 'mode', 'instruction_template', 'character_menu', 'name1_instruct', 'name2_instruct', 'context_instruct', 'turn_template', 'chat_style', 'chat-instruct_command']
def list_interface_input_elements():
elements = [
'preset_menu',
'max_new_tokens',
'seed',
'temperature',
'top_p',
'top_k',
'typical_p',
'epsilon_cutoff',
'eta_cutoff',
'repetition_penalty',
'repetition_penalty_range',
'encoder_repetition_penalty',
'no_repeat_ngram_size',
'min_length',
'do_sample',
'penalty_alpha',
'num_beams',
'length_penalty',
'early_stopping',
'mirostat_mode',
'mirostat_tau',
'mirostat_eta',
'add_bos_token',
'ban_eos_token',
'truncation_length',
'custom_stopping_strings',
'skip_special_tokens',
'stream',
'tfs',
'top_a',
]
if shared.args.chat:
elements += [
'character_menu',
'history',
'name1',
'name2',
'greeting',
'context',
'chat_generation_attempts',
'stop_at_newline',
'mode',
'instruction_template',
'name1_instruct',
'name2_instruct',
'context_instruct',
'turn_template',
'chat_style',
'chat-instruct_command',
]
else:
elements.append('textbox')
if not shared.args.notebook:
elements.append('output_textbox')
elements += list_model_elements()
return elements
@ -48,10 +138,15 @@ def list_interface_input_elements(chat=False):
def gather_interface_values(*args):
output = {}
for i, element in enumerate(shared.input_elements):
for i, element in enumerate(list_interface_input_elements()):
output[element] = args[i]
shared.persistent_interface_state = output
if not shared.args.multi_user:
shared.persistent_interface_state = output
Path('logs').mkdir(exist_ok=True)
with open(Path(f'logs/session_{shared.get_mode()}_autosave.json'), 'w') as f:
f.write(json.dumps(output, indent=4))
return output
@ -59,11 +154,12 @@ def apply_interface_values(state, use_persistent=False):
if use_persistent:
state = shared.persistent_interface_state
elements = list_interface_input_elements(chat=shared.is_chat())
elements = list_interface_input_elements()
if len(state) == 0:
return [gr.update() for k in elements] # Dummy, do nothing
else:
return [state[k] if k in state else gr.update() for k in elements]
ans = [state[k] if k in state else gr.update() for k in elements]
return ans
class ToolButton(gr.Button, gr.components.FormComponent):
@ -92,6 +188,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
inputs=[],
outputs=[refresh_component]
)
return refresh_button

View File

@ -7,6 +7,14 @@ from modules import shared
from modules.logging_colors import logger
# Helper function to get multiple values from shared.gradio
def gradio(*keys):
if len(keys) == 1 and type(keys[0]) is list:
keys = keys[0]
return [shared.gradio[k] for k in keys]
def save_file(fname, contents):
if fname == '':
logger.error('File name is empty!')
@ -111,3 +119,8 @@ def get_datasets(path: str, ext: str):
def get_available_chat_styles():
return sorted(set(('-'.join(k.stem.split('-')[1:]) for k in Path('css').glob('chat_style*.css'))), key=natural_keys)
def get_available_sessions():
items = sorted(set(k.stem for k in Path('logs').glob(f'session_{shared.get_mode()}*')), key=natural_keys, reverse=True)
return [item for item in items if 'autosave' in item] + [item for item in items if 'autosave' not in item]

View File

@ -23,5 +23,5 @@ llama-cpp-python==0.1.66; platform_system != "Windows"
https://github.com/abetlen/llama-cpp-python/releases/download/v0.1.66/llama_cpp_python-0.1.66-cp310-cp310-win_amd64.whl; platform_system == "Windows"
https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.2.2/auto_gptq-0.2.2+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.2.2/auto_gptq-0.2.2+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
https://github.com/jllllll/exllama/releases/download/0.0.4/exllama-0.0.4+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
https://github.com/jllllll/exllama/releases/download/0.0.4/exllama-0.0.4+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
https://github.com/jllllll/exllama/releases/download/0.0.5/exllama-0.0.5+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
https://github.com/jllllll/exllama/releases/download/0.0.5/exllama-0.0.5+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"

499
server.py
View File

@ -49,6 +49,7 @@ from modules.text_generation import (
get_encoded_length,
stop_everything_event
)
from modules.utils import gradio
def load_model_wrapper(selected_model, loader, autoload=False):
@ -225,6 +226,7 @@ def create_model_menus():
shared.gradio['gpu_split'] = gr.Textbox(label='gpu-split', info='Comma-separated list of VRAM (in GB) to use per GPU. Example: 20,7,7')
shared.gradio['max_seq_len'] = gr.Slider(label='max_seq_len', minimum=2048, maximum=16384, step=256, info='Maximum sequence length.', value=shared.args.max_seq_len)
shared.gradio['compress_pos_emb'] = gr.Slider(label='compress_pos_emb', minimum=1, maximum=8, step=1, info='Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.', value=shared.args.compress_pos_emb)
shared.gradio['alpha_value'] = gr.Slider(label='alpha_value', minimum=1, maximum=8, step=1, info='Positional embeddings alpha factor for NTK RoPE scaling. Same as above. Use either this or compress_pos_emb, not both.', value=shared.args.alpha_value)
with gr.Column():
shared.gradio['triton'] = gr.Checkbox(label="triton", value=shared.args.triton)
@ -257,40 +259,43 @@ def create_model_menus():
with gr.Row():
shared.gradio['model_status'] = gr.Markdown('No model is loaded' if shared.model_name == 'None' else 'Ready')
shared.gradio['loader'].change(loaders.make_loader_params_visible, shared.gradio['loader'], [shared.gradio[k] for k in loaders.get_all_params()])
shared.gradio['loader'].change(loaders.make_loader_params_visible, gradio('loader'), gradio(loaders.get_all_params()))
# In this event handler, the interface state is read and updated
# with the model defaults (if any), and then the model is loaded
# unless "autoload_model" is unchecked
shared.gradio['model_menu'].change(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
apply_model_settings_to_state, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['interface_state']).then(
ui.apply_interface_values, shared.gradio['interface_state'], [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False).then(
update_model_parameters, shared.gradio['interface_state'], None).then(
load_model_wrapper, [shared.gradio[k] for k in ['model_menu', 'loader', 'autoload_model']], shared.gradio['model_status'], show_progress=False)
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
apply_model_settings_to_state, gradio('model_menu', 'interface_state'), gradio('interface_state')).then(
ui.apply_interface_values, gradio('interface_state'), gradio(ui.list_interface_input_elements()), show_progress=False).then(
update_model_parameters, gradio('interface_state'), None).then(
load_model_wrapper, gradio('model_menu', 'loader', 'autoload_model'), gradio('model_status'), show_progress=False)
load.click(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
update_model_parameters, shared.gradio['interface_state'], None).then(
partial(load_model_wrapper, autoload=True), [shared.gradio[k] for k in ['model_menu', 'loader']], shared.gradio['model_status'], show_progress=False)
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
update_model_parameters, gradio('interface_state'), None).then(
partial(load_model_wrapper, autoload=True), gradio('model_menu', 'loader'), gradio('model_status'), show_progress=False).then(
lambda: shared.lora_names, None, gradio('lora_menu'))
unload.click(
unload_model, None, None).then(
lambda: "Model unloaded", None, shared.gradio['model_status'])
lambda: "Model unloaded", None, gradio('model_status')).then(
lambda: shared.lora_names, None, gradio('lora_menu'))
reload.click(
unload_model, None, None).then(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
update_model_parameters, shared.gradio['interface_state'], None).then(
partial(load_model_wrapper, autoload=True), [shared.gradio[k] for k in ['model_menu', 'loader']], shared.gradio['model_status'], show_progress=False)
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
update_model_parameters, gradio('interface_state'), None).then(
partial(load_model_wrapper, autoload=True), gradio('model_menu', 'loader'), gradio('model_status'), show_progress=False).then(
lambda: shared.lora_names, None, gradio('lora_menu'))
save_settings.click(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
save_model_settings, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['model_status'], show_progress=False)
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
save_model_settings, gradio('model_menu', 'interface_state'), gradio('model_status'), show_progress=False)
shared.gradio['lora_menu_apply'].click(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['model_status'], show_progress=False)
shared.gradio['download_model_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=True)
shared.gradio['autoload_model'].change(lambda x: gr.update(visible=not x), shared.gradio['autoload_model'], load)
shared.gradio['lora_menu_apply'].click(load_lora_wrapper, gradio('lora_menu'), gradio('model_status'), show_progress=False)
shared.gradio['download_model_button'].click(download_model_wrapper, gradio('custom_model_menu'), gradio('model_status'), show_progress=True)
shared.gradio['autoload_model'].change(lambda x: gr.update(visible=not x), gradio('autoload_model'), load)
def create_chat_settings_menus():
@ -327,23 +332,75 @@ def create_settings_menus(default_preset):
gr.Markdown('Main parameters')
with gr.Row():
with gr.Column():
shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature', info='Primary factor to control randomness of outputs. 0 = deterministic (only the most likely token is used). Higher value = more randomness.')
shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=generate_params['top_p'], step=0.01, label='top_p', info='If not set to 1, select tokens with probabilities adding up to less than this number. Higher value = higher range of possible random results.')
shared.gradio['top_k'] = gr.Slider(0, 200, value=generate_params['top_k'], step=1, label='top_k', info='Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.')
shared.gradio['typical_p'] = gr.Slider(0.0, 1.0, value=generate_params['typical_p'], step=0.01, label='typical_p', info='If not set to 1, select only tokens that are at least this much more likely to appear than random tokens, given the prior text.')
shared.gradio['epsilon_cutoff'] = gr.Slider(0, 9, value=generate_params['epsilon_cutoff'], step=0.01, label='epsilon_cutoff', info='In units of 1e-4; a reasonable value is 3. This sets a probability floor below which tokens are excluded from being sampled. Should be used with top_p, top_k, and eta_cutoff set to 0.')
shared.gradio['eta_cutoff'] = gr.Slider(0, 20, value=generate_params['eta_cutoff'], step=0.01, label='eta_cutoff', info='In units of 1e-4; a reasonable value is 3. Should be used with top_p, top_k, and epsilon_cutoff set to 0.')
shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature')
shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=generate_params['top_p'], step=0.01, label='top_p')
shared.gradio['top_k'] = gr.Slider(0, 200, value=generate_params['top_k'], step=1, label='top_k')
shared.gradio['typical_p'] = gr.Slider(0.0, 1.0, value=generate_params['typical_p'], step=0.01, label='typical_p')
shared.gradio['epsilon_cutoff'] = gr.Slider(0, 9, value=generate_params['epsilon_cutoff'], step=0.01, label='epsilon_cutoff')
shared.gradio['eta_cutoff'] = gr.Slider(0, 20, value=generate_params['eta_cutoff'], step=0.01, label='eta_cutoff')
with gr.Column():
shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'], step=0.01, label='repetition_penalty', info='Exponential penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.')
shared.gradio['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'], step=0.01, label='encoder_repetition_penalty', info='Also known as the "Hallucinations filter". Used to penalize tokens that are *not* in the prior text. Higher value = more likely to stay in context, lower value = more likely to diverge.')
shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size', info='If not set to 0, specifies the length of token sets that are completely blocked from repeating at all. Higher values = blocks larger phrases, lower values = blocks words or letters from repeating. Only 0 or high values are a good idea in most cases.')
shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'], label='min_length', info='Minimum generation length in tokens.')
shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'], step=0.01, label='repetition_penalty')
shared.gradio['repetition_penalty_range'] = gr.Slider(0, 4096, step=64, value=generate_params['repetition_penalty_range'], label='repetition_penalty_range')
shared.gradio['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'], step=0.01, label='encoder_repetition_penalty')
shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size')
shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'], label='min_length')
shared.gradio['tfs'] = gr.Slider(0.0, 1.0, value=generate_params['tfs'], step=0.01, label='tfs')
shared.gradio['top_a'] = gr.Slider(0.0, 1.0, value=generate_params['top_a'], step=0.01, label='top_a')
shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
gr.Markdown('[Click here for more information.](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Generation-parameters.md)')
with gr.Accordion("Learn more", open=False):
gr.Markdown("""
Not all parameters are used by all loaders. See [this page](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Generation-parameters.md) for details.
For a technical description of the parameters, the [transformers documentation](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig) is a good reference.
The best presets, according to the [Preset Arena](https://github.com/oobabooga/oobabooga.github.io/blob/main/arena/results.md) experiment, are:
* Instruction following:
1) Divine Intellect
2) Big O
3) simple-1
4) Space Alien
5) StarChat
6) Titanic
7) tfs-with-top-a
8) Asterism
9) Contrastive Search
* Chat:
1) Midnight Enigma
2) Yara
3) Shortwave
4) Kobold-Godlike
### Temperature
Primary factor to control randomness of outputs. 0 = deterministic (only the most likely token is used). Higher value = more randomness.
### top_p
If not set to 1, select tokens with probabilities adding up to less than this number. Higher value = higher range of possible random results.
### top_k
Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.
### typical_p
If not set to 1, select only tokens that are at least this much more likely to appear than random tokens, given the prior text.
### epsilon_cutoff
In units of 1e-4; a reasonable value is 3. This sets a probability floor below which tokens are excluded from being sampled. Should be used with top_p, top_k, and eta_cutoff set to 0.
### eta_cutoff
In units of 1e-4; a reasonable value is 3. Should be used with top_p, top_k, and epsilon_cutoff set to 0.
### repetition_penalty
Exponential penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.
### repetition_penalty_range
The number of most recent tokens to consider for repetition penalty. 0 makes all tokens be used.
### encoder_repetition_penalty
Also known as the "Hallucinations filter". Used to penalize tokens that are *not* in the prior text. Higher value = more likely to stay in context, lower value = more likely to diverge.
### no_repeat_ngram_size
If not set to 0, specifies the length of token sets that are completely blocked from repeating at all. Higher values = blocks larger phrases, lower values = blocks words or letters from repeating. Only 0 or high values are a good idea in most cases.
### min_length
Minimum generation length in tokens.
### penalty_alpha
Contrastive Search is enabled by setting this to greater than zero and unchecking "do_sample". It should be used with a low value of top_k, for instance, top_k = 4.
""", elem_classes="markdown")
with gr.Column():
create_chat_settings_menus()
@ -351,7 +408,7 @@ def create_settings_menus(default_preset):
with gr.Row():
with gr.Column():
gr.Markdown('Contrastive search')
shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha', info='Contrastive Search is enabled by setting this to greater than zero and unchecking "do_sample". It should be used with a low value of top_k, for instance, top_k = 4.')
shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha')
gr.Markdown('Beam search')
shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams')
@ -376,7 +433,7 @@ def create_settings_menus(default_preset):
shared.gradio['skip_special_tokens'] = gr.Checkbox(value=shared.settings['skip_special_tokens'], label='Skip special tokens', info='Some specific models need this unset.')
shared.gradio['stream'] = gr.Checkbox(value=not shared.args.no_stream, label='Activate text streaming')
shared.gradio['preset_menu'].change(presets.load_preset_for_ui, [shared.gradio[k] for k in ['preset_menu', 'interface_state']], [shared.gradio[k] for k in ['interface_state', 'do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']])
shared.gradio['preset_menu'].change(presets.load_preset_for_ui, gradio('preset_menu', 'interface_state'), gradio('interface_state', 'do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a'))
def create_file_saving_menus():
@ -415,39 +472,80 @@ def create_file_saving_menus():
def create_file_saving_event_handlers():
shared.gradio['save_confirm'].click(
lambda x, y, z: utils.save_file(x + y, z), [shared.gradio[k] for k in ['save_root', 'save_filename', 'save_contents']], None).then(
lambda: gr.update(visible=False), None, shared.gradio['file_saver'])
lambda x, y, z: utils.save_file(x + y, z), gradio('save_root', 'save_filename', 'save_contents'), None).then(
lambda: gr.update(visible=False), None, gradio('file_saver'))
shared.gradio['delete_confirm'].click(
lambda x, y: utils.delete_file(x + y), [shared.gradio[k] for k in ['delete_root', 'delete_filename']], None).then(
lambda: gr.update(visible=False), None, shared.gradio['file_deleter'])
lambda x, y: utils.delete_file(x + y), gradio('delete_root', 'delete_filename'), None).then(
lambda: gr.update(visible=False), None, gradio('file_deleter'))
shared.gradio['delete_cancel'].click(lambda: gr.update(visible=False), None, shared.gradio['file_deleter'])
shared.gradio['save_cancel'].click(lambda: gr.update(visible=False), None, shared.gradio['file_saver'])
shared.gradio['delete_cancel'].click(lambda: gr.update(visible=False), None, gradio('file_deleter'))
shared.gradio['save_cancel'].click(lambda: gr.update(visible=False), None, gradio('file_saver'))
if shared.is_chat():
shared.gradio['save_character_confirm'].click(
chat.save_character, [shared.gradio[k] for k in ['name2', 'greeting', 'context', 'character_picture', 'save_character_filename']], None).then(
lambda: gr.update(visible=False), None, shared.gradio['character_saver'])
chat.save_character, gradio('name2', 'greeting', 'context', 'character_picture', 'save_character_filename'), None).then(
lambda: gr.update(visible=False), None, gradio('character_saver'))
shared.gradio['delete_character_confirm'].click(
chat.delete_character, shared.gradio['character_menu'], None).then(
lambda: gr.update(visible=False), None, shared.gradio['character_deleter']).then(
lambda: gr.update(choices=utils.get_available_characters()), outputs=shared.gradio['character_menu'])
chat.delete_character, gradio('character_menu'), None).then(
lambda: gr.update(visible=False), None, gradio('character_deleter')).then(
lambda: gr.update(choices=utils.get_available_characters()), None, gradio('character_menu'))
shared.gradio['save_character_cancel'].click(lambda: gr.update(visible=False), None, shared.gradio['character_saver'])
shared.gradio['delete_character_cancel'].click(lambda: gr.update(visible=False), None, shared.gradio['character_deleter'])
shared.gradio['save_character_cancel'].click(lambda: gr.update(visible=False), None, gradio('character_saver'))
shared.gradio['delete_character_cancel'].click(lambda: gr.update(visible=False), None, gradio('character_deleter'))
shared.gradio['save_preset'].click(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
presets.generate_preset_yaml, shared.gradio['interface_state'], shared.gradio['save_contents']).then(
lambda: 'presets/', None, shared.gradio['save_root']).then(
lambda: 'My Preset.yaml', None, shared.gradio['save_filename']).then(
lambda: gr.update(visible=True), None, shared.gradio['file_saver'])
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
presets.generate_preset_yaml, gradio('interface_state'), gradio('save_contents')).then(
lambda: 'presets/', None, gradio('save_root')).then(
lambda: 'My Preset.yaml', None, gradio('save_filename')).then(
lambda: gr.update(visible=True), None, gradio('file_saver'))
shared.gradio['delete_preset'].click(
lambda x: f'{x}.yaml', shared.gradio['preset_menu'], shared.gradio['delete_filename']).then(
lambda: 'presets/', None, shared.gradio['delete_root']).then(
lambda: gr.update(visible=True), None, shared.gradio['file_deleter'])
lambda x: f'{x}.yaml', gradio('preset_menu'), gradio('delete_filename')).then(
lambda: 'presets/', None, gradio('delete_root')).then(
lambda: gr.update(visible=True), None, gradio('file_deleter'))
if not shared.args.multi_user:
def load_session(session, state):
with open(Path(f'logs/{session}.json'), 'r') as f:
state.update(json.loads(f.read()))
if shared.is_chat():
chat.save_persistent_history(state['history'], state['character_menu'], state['mode'])
return state
if shared.is_chat():
shared.gradio['save_session'].click(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
lambda x: json.dumps(x, indent=4), gradio('interface_state'), gradio('save_contents')).then(
lambda: 'logs/', None, gradio('save_root')).then(
lambda x: f'session_{shared.get_mode()}_{x + "_" if x not in ["None", None, ""] else ""}{utils.current_time()}.json', gradio('character_menu'), gradio('save_filename')).then(
lambda: gr.update(visible=True), None, gradio('file_saver'))
shared.gradio['session_menu'].change(
load_session, gradio('session_menu', 'interface_state'), gradio('interface_state')).then(
ui.apply_interface_values, gradio('interface_state'), gradio(ui.list_interface_input_elements()), show_progress=False).then(
chat.redraw_html, shared.reload_inputs, gradio('display'))
else:
shared.gradio['save_session'].click(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
lambda x: json.dumps(x, indent=4), gradio('interface_state'), gradio('save_contents')).then(
lambda: 'logs/', None, gradio('save_root')).then(
lambda: f'session_{shared.get_mode()}_{utils.current_time()}.json', None, gradio('save_filename')).then(
lambda: gr.update(visible=True), None, gradio('file_saver'))
shared.gradio['session_menu'].change(
load_session, gradio('session_menu', 'interface_state'), gradio('interface_state')).then(
ui.apply_interface_values, gradio('interface_state'), gradio(ui.list_interface_input_elements()), show_progress=False)
shared.gradio['delete_session'].click(
lambda x: f'{x}.json', gradio('session_menu'), gradio('delete_filename')).then(
lambda: 'logs/', None, gradio('delete_root')).then(
lambda: gr.update(visible=True), None, gradio('file_deleter'))
def set_interface_arguments(interface_mode, extensions, bool_active):
@ -511,13 +609,17 @@ def create_interface():
# Create chat mode interface
if shared.is_chat():
shared.input_elements = ui.list_interface_input_elements(chat=True)
shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
shared.gradio['Chat input'] = gr.State()
shared.gradio['dummy'] = gr.State()
shared.input_elements = ui.list_interface_input_elements()
shared.gradio.update({
'interface_state': gr.State({k: None for k in shared.input_elements}),
'Chat input': gr.State(),
'dummy': gr.State(),
'history': gr.State({'internal': [], 'visible': []}),
})
with gr.Tab('Text generation', elem_id='main'):
shared.gradio['display'] = gr.HTML(value=chat_html_wrapper(shared.history['visible'], shared.settings['name1'], shared.settings['name2'], 'chat', 'cai-chat'))
shared.gradio['display'] = gr.HTML(value=chat_html_wrapper({'internal': [], 'visible': []}, shared.settings['name1'], shared.settings['name2'], 'chat', 'cai-chat'))
shared.gradio['textbox'] = gr.Textbox(label='Input')
with gr.Row():
shared.gradio['Stop'] = gr.Button('Stop', elem_id='stop')
@ -553,7 +655,7 @@ def create_interface():
with gr.Row():
with gr.Column(scale=8):
with gr.Row():
shared.gradio['character_menu'] = gr.Dropdown(choices=utils.get_available_characters(), label='Character', elem_id='character-menu', info='Used in chat and chat-instruct modes.', elem_classes='slim-dropdown')
shared.gradio['character_menu'] = gr.Dropdown(value='None', choices=utils.get_available_characters(), label='Character', elem_id='character-menu', info='Used in chat and chat-instruct modes.', elem_classes='slim-dropdown')
ui.create_refresh_button(shared.gradio['character_menu'], lambda: None, lambda: {'choices': utils.get_available_characters()}, 'refresh-button')
shared.gradio['save_character'] = gr.Button('💾', elem_classes='refresh-button')
shared.gradio['delete_character'] = gr.Button('🗑️', elem_classes='refresh-button')
@ -597,18 +699,25 @@ def create_interface():
shared.gradio['upload_json'] = gr.File(type='binary', file_types=['.json'], label='JSON File')
shared.gradio['upload_img_bot'] = gr.Image(type='pil', label='Profile Picture (optional)')
shared.gradio['Upload character'] = gr.Button(value='Submit', interactive=False)
shared.gradio['Submit character'] = gr.Button(value='Submit', interactive=False)
with gr.Tab('TavernAI'):
shared.gradio['upload_img_tavern'] = gr.File(type='binary', file_types=['image'], label='TavernAI PNG File')
shared.gradio['Upload tavern character'] = gr.Button(value='Submit', interactive=False)
with gr.Row():
with gr.Column():
shared.gradio['upload_img_tavern'] = gr.Image(type='pil', label='TavernAI PNG File', elem_id="upload_img_tavern")
shared.gradio['tavern_json'] = gr.State()
with gr.Column():
shared.gradio['tavern_name'] = gr.Textbox(value='', lines=1, label='Name', interactive=False)
shared.gradio['tavern_desc'] = gr.Textbox(value='', lines=4, max_lines=4, label='Description', interactive=False)
shared.gradio['Submit tavern character'] = gr.Button(value='Submit', interactive=False)
with gr.Tab("Parameters", elem_id="parameters"):
create_settings_menus(default_preset)
# Create notebook mode interface
elif shared.args.notebook:
shared.input_elements = ui.list_interface_input_elements(chat=False)
shared.input_elements = ui.list_interface_input_elements()
shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
shared.gradio['last_input'] = gr.State('')
with gr.Tab("Text generation", elem_id="main"):
@ -647,7 +756,7 @@ def create_interface():
# Create default mode interface
else:
shared.input_elements = ui.list_interface_input_elements(chat=False)
shared.input_elements = ui.list_interface_input_elements()
shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
shared.gradio['last_input'] = gr.State('')
with gr.Tab("Text generation", elem_id="main"):
@ -691,8 +800,8 @@ def create_interface():
with gr.Tab("Training", elem_id="training-tab"):
training.create_train_interface()
# Interface mode tab
with gr.Tab("Interface mode", elem_id="interface-mode"):
# Session tab
with gr.Tab("Session", elem_id="session-tab"):
modes = ["default", "notebook", "chat"]
current_mode = "default"
for mode in modes[1:]:
@ -705,223 +814,251 @@ def create_interface():
bool_active = [k for k in bool_list if vars(shared.args)[k]]
with gr.Row():
shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode")
shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface", elem_classes="small-button")
shared.gradio['toggle_dark_mode'] = gr.Button('Toggle dark/light mode', elem_classes="small-button")
with gr.Row():
with gr.Column():
shared.gradio['extensions_menu'] = gr.CheckboxGroup(choices=utils.get_available_extensions(), value=shared.args.extensions, label="Available extensions", info='Note that some of these extensions may require manually installing Python requirements through the command: pip install -r extensions/extension_name/requirements.txt', elem_classes='checkboxgroup-table')
with gr.Column():
shared.gradio['bool_menu'] = gr.CheckboxGroup(choices=bool_list, value=bool_active, label="Boolean command-line flags", elem_classes='checkboxgroup-table')
with gr.Row():
shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode", elem_classes='slim-dropdown')
shared.gradio['reset_interface'] = gr.Button("Apply and restart", elem_classes="small-button", variant="primary")
shared.gradio['toggle_dark_mode'] = gr.Button('Toggle 💡', elem_classes="small-button")
with gr.Row():
extension_name = gr.Textbox(lines=1, label='Install or update an extension', info='Enter the GitHub URL below. For a list of extensions, see: https://github.com/oobabooga/text-generation-webui-extensions ⚠️ WARNING ⚠️ : extensions can execute arbitrary code. Make sure to inspect their source code before activating them.')
extension_install = gr.Button('Install or update', elem_classes="small-button")
with gr.Row():
with gr.Column():
shared.gradio['extensions_menu'] = gr.CheckboxGroup(choices=utils.get_available_extensions(), value=shared.args.extensions, label="Available extensions", info='Note that some of these extensions may require manually installing Python requirements through the command: pip install -r extensions/extension_name/requirements.txt', elem_classes='checkboxgroup-table')
extension_status = gr.Markdown()
with gr.Column():
shared.gradio['bool_menu'] = gr.CheckboxGroup(choices=bool_list, value=bool_active, label="Boolean command-line flags", elem_classes='checkboxgroup-table')
extension_install.click(
with gr.Column():
if not shared.args.multi_user:
with gr.Row():
shared.gradio['session_menu'] = gr.Dropdown(choices=utils.get_available_sessions(), value='None', label='Session', elem_classes='slim-dropdown', info='When saving a session, make sure to keep the initial part of the filename (session_chat, session_notebook, or session_default), otherwise it will not appear on this list afterwards.')
ui.create_refresh_button(shared.gradio['session_menu'], lambda: None, lambda: {'choices': utils.get_available_sessions()}, ['refresh-button'])
shared.gradio['save_session'] = gr.Button('💾', elem_classes=['refresh-button'])
shared.gradio['delete_session'] = gr.Button('🗑️', elem_classes=['refresh-button'])
extension_name = gr.Textbox(lines=1, label='Install or update an extension', info='Enter the GitHub URL below and press Enter. For a list of extensions, see: https://github.com/oobabooga/text-generation-webui-extensions ⚠️ WARNING ⚠️ : extensions can execute arbitrary code. Make sure to inspect their source code before activating them.')
extension_status = gr.Markdown()
extension_name.submit(
clone_or_pull_repository, extension_name, extension_status, show_progress=False).then(
lambda: gr.update(choices=utils.get_available_extensions(), value=shared.args.extensions), outputs=shared.gradio['extensions_menu'])
lambda: gr.update(choices=utils.get_available_extensions(), value=shared.args.extensions), None, gradio('extensions_menu'))
# Reset interface event
shared.gradio['reset_interface'].click(
set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'bool_menu']], None).then(
set_interface_arguments, gradio('interface_modes_menu', 'extensions_menu', 'bool_menu'), None).then(
lambda: None, None, None, _js='() => {document.body.innerHTML=\'<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>\'; setTimeout(function(){location.reload()},2500); return []}')
shared.gradio['toggle_dark_mode'].click(lambda: None, None, None, _js='() => {document.getElementsByTagName("body")[0].classList.toggle("dark")}')
# chat mode event handlers
if shared.is_chat():
shared.input_params = [shared.gradio[k] for k in ['Chat input', 'start_with', 'interface_state']]
clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']]
shared.reload_inputs = [shared.gradio[k] for k in ['name1', 'name2', 'mode', 'chat_style']]
shared.input_params = gradio('Chat input', 'start_with', 'interface_state')
clear_arr = gradio('Clear history-confirm', 'Clear history', 'Clear history-cancel')
shared.reload_inputs = gradio('history', 'name1', 'name2', 'mode', 'chat_style')
gen_events.append(shared.gradio['Generate'].click(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then(
chat.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
lambda x: (x, ''), gradio('textbox'), gradio('Chat input', 'textbox'), show_progress=False).then(
chat.generate_chat_reply_wrapper, shared.input_params, gradio('display', 'history'), show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
)
gen_events.append(shared.gradio['textbox'].submit(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then(
chat.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
lambda x: (x, ''), gradio('textbox'), gradio('Chat input', 'textbox'), show_progress=False).then(
chat.generate_chat_reply_wrapper, shared.input_params, gradio('display', 'history'), show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
)
gen_events.append(shared.gradio['Regenerate'].click(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
partial(chat.generate_chat_reply_wrapper, regenerate=True), shared.input_params, shared.gradio['display'], show_progress=False).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
partial(chat.generate_chat_reply_wrapper, regenerate=True), shared.input_params, gradio('display', 'history'), show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
)
gen_events.append(shared.gradio['Continue'].click(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
partial(chat.generate_chat_reply_wrapper, _continue=True), shared.input_params, shared.gradio['display'], show_progress=False).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
partial(chat.generate_chat_reply_wrapper, _continue=True), shared.input_params, gradio('display', 'history'), show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
)
gen_events.append(shared.gradio['Impersonate'].click(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
lambda x: x, shared.gradio['textbox'], shared.gradio['Chat input'], show_progress=False).then(
chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
lambda x: x, gradio('textbox'), gradio('Chat input'), show_progress=False).then(
chat.impersonate_wrapper, shared.input_params, gradio('textbox'), show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
)
shared.gradio['Replace last reply'].click(
chat.replace_last_reply, shared.gradio['textbox'], None).then(
lambda: '', None, shared.gradio['textbox'], show_progress=False).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
chat.replace_last_reply, gradio('textbox', 'interface_state'), gradio('history')).then(
lambda: '', None, gradio('textbox'), show_progress=False).then(
chat.redraw_html, shared.reload_inputs, gradio('display')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None)
shared.gradio['Send dummy message'].click(
chat.send_dummy_message, shared.gradio['textbox'], None).then(
lambda: '', None, shared.gradio['textbox'], show_progress=False).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
chat.send_dummy_message, gradio('textbox', 'interface_state'), gradio('history')).then(
lambda: '', None, gradio('textbox'), show_progress=False).then(
chat.redraw_html, shared.reload_inputs, gradio('display')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None)
shared.gradio['Send dummy reply'].click(
chat.send_dummy_reply, shared.gradio['textbox'], None).then(
lambda: '', None, shared.gradio['textbox'], show_progress=False).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
chat.send_dummy_reply, gradio('textbox', 'interface_state'), gradio('history')).then(
lambda: '', None, gradio('textbox'), show_progress=False).then(
chat.redraw_html, shared.reload_inputs, gradio('display')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None)
shared.gradio['Clear history'].click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr)
shared.gradio['Clear history-cancel'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
shared.gradio['Clear history-confirm'].click(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr).then(
chat.clear_chat_log, [shared.gradio[k] for k in ['greeting', 'mode']], None).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
chat.clear_chat_log, gradio('interface_state'), gradio('history')).then(
chat.redraw_html, shared.reload_inputs, gradio('display')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None)
shared.gradio['Remove last'].click(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
chat.remove_last_message, gradio('history'), gradio('textbox', 'history'), show_progress=False).then(
chat.redraw_html, shared.reload_inputs, gradio('display')).then(
chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None)
shared.gradio['character_menu'].change(
partial(chat.load_character, instruct=False), gradio('character_menu', 'name1', 'name2'), gradio('name1', 'name2', 'character_picture', 'greeting', 'context', 'dummy')).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
chat.load_persistent_history, gradio('interface_state'), gradio('history')).then(
chat.redraw_html, shared.reload_inputs, gradio('display'))
shared.gradio['Stop'].click(
stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
chat.redraw_html, shared.reload_inputs, gradio('display'))
shared.gradio['mode'].change(
lambda x: gr.update(visible=x != 'instruct'), shared.gradio['mode'], shared.gradio['chat_style'], show_progress=False).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
lambda x: gr.update(visible=x != 'instruct'), gradio('mode'), gradio('chat_style'), show_progress=False).then(
chat.redraw_html, shared.reload_inputs, gradio('display'))
shared.gradio['chat_style'].change(chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
shared.gradio['chat_style'].change(chat.redraw_html, shared.reload_inputs, gradio('display'))
shared.gradio['instruction_template'].change(
partial(chat.load_character, instruct=True), [shared.gradio[k] for k in ['instruction_template', 'name1_instruct', 'name2_instruct']], [shared.gradio[k] for k in ['name1_instruct', 'name2_instruct', 'dummy', 'dummy', 'context_instruct', 'turn_template']])
partial(chat.load_character, instruct=True), gradio('instruction_template', 'name1_instruct', 'name2_instruct'), gradio('name1_instruct', 'name2_instruct', 'dummy', 'dummy', 'context_instruct', 'turn_template'))
shared.gradio['upload_chat_history'].upload(
chat.load_history, [shared.gradio[k] for k in ['upload_chat_history', 'name1', 'name2']], None).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
chat.load_history, gradio('upload_chat_history', 'history'), gradio('history')).then(
chat.redraw_html, shared.reload_inputs, gradio('display'))
shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, None, shared.gradio['textbox'], show_progress=False)
shared.gradio['Clear history'].click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr)
shared.gradio['Clear history-cancel'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
shared.gradio['Remove last'].click(
chat.remove_last_message, None, shared.gradio['textbox'], show_progress=False).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, gradio('history'), gradio('textbox'), show_progress=False)
# Save/delete a character
shared.gradio['save_character'].click(
lambda x: x, shared.gradio['name2'], shared.gradio['save_character_filename']).then(
lambda: gr.update(visible=True), None, shared.gradio['character_saver'])
lambda x: x, gradio('name2'), gradio('save_character_filename')).then(
lambda: gr.update(visible=True), None, gradio('character_saver'))
shared.gradio['delete_character'].click(lambda: gr.update(visible=True), None, shared.gradio['character_deleter'])
shared.gradio['delete_character'].click(lambda: gr.update(visible=True), None, gradio('character_deleter'))
shared.gradio['save_template'].click(
lambda: 'My Template.yaml', None, shared.gradio['save_filename']).then(
lambda: 'characters/instruction-following/', None, shared.gradio['save_root']).then(
chat.generate_instruction_template_yaml, [shared.gradio[k] for k in ['name1_instruct', 'name2_instruct', 'context_instruct', 'turn_template']], shared.gradio['save_contents']).then(
lambda: gr.update(visible=True), None, shared.gradio['file_saver'])
lambda: 'My Template.yaml', None, gradio('save_filename')).then(
lambda: 'characters/instruction-following/', None, gradio('save_root')).then(
chat.generate_instruction_template_yaml, gradio('name1_instruct', 'name2_instruct', 'context_instruct', 'turn_template'), gradio('save_contents')).then(
lambda: gr.update(visible=True), None, gradio('file_saver'))
shared.gradio['delete_template'].click(
lambda x: f'{x}.yaml', shared.gradio['instruction_template'], shared.gradio['delete_filename']).then(
lambda: 'characters/instruction-following/', None, shared.gradio['delete_root']).then(
lambda: gr.update(visible=True), None, shared.gradio['file_deleter'])
lambda x: f'{x}.yaml', gradio('instruction_template'), gradio('delete_filename')).then(
lambda: 'characters/instruction-following/', None, gradio('delete_root')).then(
lambda: gr.update(visible=True), None, gradio('file_deleter'))
shared.gradio['download_button'].click(lambda x: chat.save_history(x, timestamp=True, user_request=True), shared.gradio['mode'], shared.gradio['download'])
shared.gradio['Upload character'].click(chat.upload_character, [shared.gradio['upload_json'], shared.gradio['upload_img_bot']], [shared.gradio['character_menu']])
shared.gradio['upload_json'].upload(lambda: gr.update(interactive=True), None, [shared.gradio['Upload character']])
shared.gradio['upload_json'].clear(lambda: gr.update(interactive=False), None, [shared.gradio['Upload character']])
shared.gradio['download_button'].click(chat.save_history, gradio('history'), gradio('download'))
shared.gradio['Submit character'].click(chat.upload_character, gradio('upload_json', 'upload_img_bot'), gradio('character_menu'))
shared.gradio['upload_json'].upload(lambda: gr.update(interactive=True), None, gradio('Submit character'))
shared.gradio['upload_json'].clear(lambda: gr.update(interactive=False), None, gradio('Submit character'))
shared.gradio['character_menu'].change(
partial(chat.load_character, instruct=False), [shared.gradio[k] for k in ['character_menu', 'name1', 'name2']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'dummy']]).then(
chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
shared.gradio['Upload tavern character'].click(chat.upload_tavern_character, [shared.gradio['upload_img_tavern'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['character_menu']])
shared.gradio['upload_img_tavern'].upload(lambda: gr.update(interactive=True), None, [shared.gradio['Upload tavern character']])
shared.gradio['upload_img_tavern'].clear(lambda: gr.update(interactive=False), None, [shared.gradio['Upload tavern character']])
shared.gradio['Submit tavern character'].click(chat.upload_tavern_character, gradio('upload_img_tavern', 'tavern_json'), gradio('character_menu'))
shared.gradio['upload_img_tavern'].upload(chat.check_tavern_character, gradio('upload_img_tavern'), gradio('tavern_name', 'tavern_desc', 'tavern_json', 'Submit tavern character'), show_progress=False)
shared.gradio['upload_img_tavern'].clear(lambda: (None, None, None, gr.update(interactive=False)), None, gradio('tavern_name', 'tavern_desc', 'tavern_json', 'Submit tavern character'), show_progress=False)
shared.gradio['your_picture'].change(
chat.upload_your_profile_picture, shared.gradio['your_picture'], None).then(
partial(chat.redraw_html, reset_cache=True), shared.reload_inputs, shared.gradio['display'])
chat.upload_your_profile_picture, gradio('your_picture'), None).then(
partial(chat.redraw_html, reset_cache=True), shared.reload_inputs, gradio('display'))
# notebook/default modes event handlers
else:
shared.input_params = [shared.gradio[k] for k in ['textbox', 'interface_state']]
shared.input_params = gradio('textbox', 'interface_state')
if shared.args.notebook:
output_params = [shared.gradio[k] for k in ['textbox', 'html']]
output_params = gradio('textbox', 'html')
else:
output_params = [shared.gradio[k] for k in ['output_textbox', 'html']]
output_params = gradio('output_textbox', 'html')
gen_events.append(shared.gradio['Generate'].click(
lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
lambda x: x, gradio('textbox'), gradio('last_input')).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
generate_reply_wrapper, shared.input_params, output_params, show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
# lambda: None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}")
)
gen_events.append(shared.gradio['textbox'].submit(
lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
lambda x: x, gradio('textbox'), gradio('last_input')).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
generate_reply_wrapper, shared.input_params, output_params, show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
# lambda: None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}")
)
if shared.args.notebook:
shared.gradio['Undo'].click(lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False)
shared.gradio['markdown_render'].click(lambda x: x, shared.gradio['textbox'], shared.gradio['markdown'], queue=False)
shared.gradio['Undo'].click(lambda x: x, gradio('last_input'), gradio('textbox'), show_progress=False)
shared.gradio['markdown_render'].click(lambda x: x, gradio('textbox'), gradio('markdown'), queue=False)
gen_events.append(shared.gradio['Regenerate'].click(
lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False).then(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
lambda x: x, gradio('last_input'), gradio('textbox'), show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
generate_reply_wrapper, shared.input_params, output_params, show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
# lambda: None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}")
)
else:
shared.gradio['markdown_render'].click(lambda x: x, shared.gradio['output_textbox'], shared.gradio['markdown'], queue=False)
shared.gradio['markdown_render'].click(lambda x: x, gradio('output_textbox'), gradio('markdown'), queue=False)
gen_events.append(shared.gradio['Continue'].click(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
generate_reply_wrapper, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=False).then(
ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
# lambda: None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[1]; element.scrollTop = element.scrollHeight}")
)
shared.gradio['Stop'].click(stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None)
shared.gradio['prompt_menu'].change(load_prompt, shared.gradio['prompt_menu'], shared.gradio['textbox'], show_progress=False)
shared.gradio['prompt_menu'].change(load_prompt, gradio('prompt_menu'), gradio('textbox'), show_progress=False)
shared.gradio['save_prompt'].click(
lambda x: x, shared.gradio['textbox'], shared.gradio['save_contents']).then(
lambda: 'prompts/', None, shared.gradio['save_root']).then(
lambda: utils.current_time() + '.txt', None, shared.gradio['save_filename']).then(
lambda: gr.update(visible=True), None, shared.gradio['file_saver'])
lambda x: x, gradio('textbox'), gradio('save_contents')).then(
lambda: 'prompts/', None, gradio('save_root')).then(
lambda: utils.current_time() + '.txt', None, gradio('save_filename')).then(
lambda: gr.update(visible=True), None, gradio('file_saver'))
shared.gradio['delete_prompt'].click(
lambda: 'prompts/', None, shared.gradio['delete_root']).then(
lambda x: x + '.txt', shared.gradio['prompt_menu'], shared.gradio['delete_filename']).then(
lambda: gr.update(visible=True), None, shared.gradio['file_deleter'])
lambda: 'prompts/', None, gradio('delete_root')).then(
lambda x: x + '.txt', gradio('prompt_menu'), gradio('delete_filename')).then(
lambda: gr.update(visible=True), None, gradio('file_deleter'))
shared.gradio['count_tokens'].click(count_tokens, shared.gradio['textbox'], shared.gradio['status'], show_progress=False)
shared.gradio['count_tokens'].click(count_tokens, gradio('textbox'), gradio('status'), show_progress=False)
create_file_saving_event_handlers()
shared.gradio['interface'].load(lambda: None, None, None, _js=f"() => {{{js}}}")
shared.gradio['interface'].load(partial(ui.apply_interface_values, {}, use_persistent=True), None, gradio(ui.list_interface_input_elements()), show_progress=False)
if shared.settings['dark_theme']:
shared.gradio['interface'].load(lambda: None, None, None, _js="() => document.getElementsByTagName('body')[0].classList.add('dark')")
shared.gradio['interface'].load(partial(ui.apply_interface_values, {}, use_persistent=True), None, [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False)
if shared.is_chat():
shared.gradio['interface'].load(chat.redraw_html, shared.reload_inputs, gradio('display'))
# Extensions tabs
extensions_module.create_extensions_tabs()
@ -1018,7 +1155,11 @@ if __name__ == "__main__":
if shared.args.lora:
add_lora_to_model(shared.args.lora)
# Force a character to be loaded
# Forcing some events to be triggered on page load
shared.persistent_interface_state.update({
'loader': shared.args.loader or 'Transformers',
})
if shared.is_chat():
shared.persistent_interface_state.update({
'mode': shared.settings['mode'],
@ -1026,11 +1167,11 @@ if __name__ == "__main__":
'instruction_template': shared.settings['instruction_template']
})
shared.persistent_interface_state.update({
'loader': shared.args.loader or 'Transformers',
})
if Path("cache/pfp_character.png").exists():
Path("cache/pfp_character.png").unlink()
shared.generation_lock = Lock()
# Launch the web UI
create_interface()
while True: