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Make the code more like PEP8 for readability (#862)

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oobabooga 2023-04-07 00:15:45 -03:00 committed by GitHub
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28 changed files with 302 additions and 165 deletions
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6
api-example-stream.py
View File

@ -17,6 +17,7 @@ def random_hash():
letters = string.ascii_lowercase + string.digits
return ''.join(random.choice(letters) for i in range(9))
async def run(context):
server = "127.0.0.1"
params = {
@ -41,7 +42,7 @@ async def run(context):
async with websockets.connect(f"ws://{server}:7860/queue/join") as websocket:
while content := json.loads(await websocket.recv()):
#Python3.10 syntax, replace with if elif on older
# Python3.10 syntax, replace with if elif on older
match content["msg"]:
case "send_hash":
await websocket.send(json.dumps({
@ -62,13 +63,14 @@ async def run(context):
pass
case "process_generating" | "process_completed":
yield content["output"]["data"][0]
# You can search for your desired end indicator and
# You can search for your desired end indicator and
# stop generation by closing the websocket here
if (content["msg"] == "process_completed"):
break
prompt = "What I would like to say is the following: "
async def get_result():
async for response in run(prompt):
# Print intermediate steps

9
convert-to-flexgen.py
View File

@ -13,10 +13,11 @@ import torch
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54))
parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54))
parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.")
args = parser.parse_args()
def disable_torch_init():
"""
Disable the redundant torch default initialization to accelerate model creation.
@ -31,20 +32,22 @@ def disable_torch_init():
torch_layer_norm_init_backup = torch.nn.LayerNorm.reset_parameters
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
def restore_torch_init():
"""Rollback the change made by disable_torch_init."""
import torch
setattr(torch.nn.Linear, "reset_parameters", torch_linear_init_backup)
setattr(torch.nn.LayerNorm, "reset_parameters", torch_layer_norm_init_backup)
if __name__ == '__main__':
path = Path(args.MODEL)
model_name = path.name
print(f"Loading {model_name}...")
#disable_torch_init()
# disable_torch_init()
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
#restore_torch_init()
# restore_torch_init()
tokenizer = AutoTokenizer.from_pretrained(path)

2
convert-to-safetensors.py
View File

@ -17,7 +17,7 @@ from pathlib import Path
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54))
parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54))
parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.")
parser.add_argument('--output', type=str, default=None, help='Path to the output folder (default: models/{model_name}_safetensors).')
parser.add_argument("--max-shard-size", type=str, default="2GB", help="Maximum size of a shard in GB or MB (default: %(default)s).")

16
download-model.py
View File

@ -29,6 +29,7 @@ parser.add_argument('--clean', action='store_true', help='Does not resume the pr
parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.')
args = parser.parse_args()
def get_file(url, output_folder):
filename = Path(url.rsplit('/', 1)[1])
output_path = output_folder / filename
@ -54,6 +55,7 @@ def get_file(url, output_folder):
t.update(len(data))
f.write(data)
def sanitize_branch_name(branch_name):
pattern = re.compile(r"^[a-zA-Z0-9._-]+$")
if pattern.match(branch_name):
@ -61,6 +63,7 @@ def sanitize_branch_name(branch_name):
else:
raise ValueError("Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.")
def select_model_from_default_options():
models = {
"OPT 6.7B": ("facebook", "opt-6.7b", "main"),
@ -78,11 +81,11 @@ def select_model_from_default_options():
choices = {}
print("Select the model that you want to download:\n")
for i,name in enumerate(models):
char = chr(ord('A')+i)
for i, name in enumerate(models):
char = chr(ord('A') + i)
choices[char] = name
print(f"{char}) {name}")
char = chr(ord('A')+len(models))
char = chr(ord('A') + len(models))
print(f"{char}) None of the above")
print()
@ -106,6 +109,7 @@ EleutherAI/pythia-1.4b-deduped
return model, branch
def get_download_links_from_huggingface(model, branch):
base = "https://huggingface.co"
page = f"/api/models/{model}/tree/{branch}?cursor="
@ -166,15 +170,17 @@ def get_download_links_from_huggingface(model, branch):
# If both pytorch and safetensors are available, download safetensors only
if (has_pytorch or has_pt) and has_safetensors:
for i in range(len(classifications)-1, -1, -1):
for i in range(len(classifications) - 1, -1, -1):
if classifications[i] in ['pytorch', 'pt']:
links.pop(i)
return links, sha256, is_lora
def download_files(file_list, output_folder, num_threads=8):
thread_map(lambda url: get_file(url, output_folder), file_list, max_workers=num_threads, disable=True)
if __name__ == '__main__':
model = args.MODEL
branch = args.branch
@ -224,7 +230,7 @@ if __name__ == '__main__':
validated = False
else:
print(f'Checksum validated: {sha256[i][0]} {sha256[i][1]}')
if validated:
print('[+] Validated checksums of all model files!')
else:

28
extensions/api/script.py
View File

@ -9,6 +9,7 @@ params = {
'port': 5000,
}
class Handler(BaseHTTPRequestHandler):
def do_GET(self):
if self.path == '/api/v1/model':
@ -32,7 +33,7 @@ class Handler(BaseHTTPRequestHandler):
self.end_headers()
prompt = body['prompt']
prompt_lines = [l.strip() for l in prompt.split('\n')]
prompt_lines = [k.strip() for k in prompt.split('\n')]
max_context = body.get('max_context_length', 2048)
@ -40,18 +41,18 @@ class Handler(BaseHTTPRequestHandler):
prompt_lines.pop(0)
prompt = '\n'.join(prompt_lines)
generate_params = {
'max_new_tokens': int(body.get('max_length', 200)),
generate_params = {
'max_new_tokens': int(body.get('max_length', 200)),
'do_sample': bool(body.get('do_sample', True)),
'temperature': float(body.get('temperature', 0.5)),
'top_p': float(body.get('top_p', 1)),
'typical_p': float(body.get('typical', 1)),
'repetition_penalty': float(body.get('rep_pen', 1.1)),
'temperature': float(body.get('temperature', 0.5)),
'top_p': float(body.get('top_p', 1)),
'typical_p': float(body.get('typical', 1)),
'repetition_penalty': float(body.get('rep_pen', 1.1)),
'encoder_repetition_penalty': 1,
'top_k': int(body.get('top_k', 0)),
'top_k': int(body.get('top_k', 0)),
'min_length': int(body.get('min_length', 0)),
'no_repeat_ngram_size': int(body.get('no_repeat_ngram_size',0)),
'num_beams': int(body.get('num_beams',1)),
'no_repeat_ngram_size': int(body.get('no_repeat_ngram_size', 0)),
'num_beams': int(body.get('num_beams', 1)),
'penalty_alpha': float(body.get('penalty_alpha', 0)),
'length_penalty': float(body.get('length_penalty', 1)),
'early_stopping': bool(body.get('early_stopping', False)),
@ -59,7 +60,7 @@ class Handler(BaseHTTPRequestHandler):
}
generator = generate_reply(
prompt,
prompt,
generate_params,
stopping_strings=body.get('stopping_strings', []),
)
@ -84,9 +85,9 @@ class Handler(BaseHTTPRequestHandler):
def run_server():
server_addr = ('0.0.0.0' if shared.args.listen else '127.0.0.1', params['port'])
server = ThreadingHTTPServer(server_addr, Handler)
if shared.args.share:
if shared.args.share:
try:
from flask_cloudflared import _run_cloudflared
from flask_cloudflared import _run_cloudflared
public_url = _run_cloudflared(params['port'], params['port'] + 1)
print(f'Starting KoboldAI compatible api at {public_url}/api')
except ImportError:
@ -95,5 +96,6 @@ def run_server():
print(f'Starting KoboldAI compatible api at http://{server_addr[0]}:{server_addr[1]}/api')
server.serve_forever()
def setup():
Thread(target=run_server, daemon=True).start()

8
extensions/character_bias/script.py
View File

@ -5,14 +5,16 @@ params = {
"bias string": " *I am so happy*",
}
def input_modifier(string):
"""
This function is applied to your text inputs before
they are fed into the model.
"""
"""
return string
def output_modifier(string):
"""
This function is applied to the model outputs.
@ -20,6 +22,7 @@ def output_modifier(string):
return string
def bot_prefix_modifier(string):
"""
This function is only applied in chat mode. It modifies
@ -27,11 +30,12 @@ def bot_prefix_modifier(string):
behavior.
"""
if params['activate'] == True:
if params['activate']:
return f'{string} {params["bias string"].strip()} '
else:
return string
def ui():
# Gradio elements
activate = gr.Checkbox(value=params['activate'], label='Activate character bias')

34
extensions/elevenlabs_tts/script.py
View File

@ -20,16 +20,18 @@ user_info = None
if not shared.args.no_stream:
print("Please add --no-stream. This extension is not meant to be used with streaming.")
raise ValueError
# Check if the API is valid and refresh the UI accordingly.
def check_valid_api():
global user, user_info, params
user = ElevenLabsUser(params['api_key'])
user_info = user._get_subscription_data()
print('checking api')
if params['activate'] == False:
if not params['activate']:
return gr.update(value='Disconnected')
elif user_info is None:
print('Incorrect API Key')
@ -37,24 +39,28 @@ def check_valid_api():
else:
print('Got an API Key!')
return gr.update(value='Connected')
# Once the API is verified, get the available voices and update the dropdown list
def refresh_voices():
global user, user_info
your_voices = [None]
if user_info is not None:
for voice in user.get_available_voices():
your_voices.append(voice.initialName)
return gr.Dropdown.update(choices=your_voices)
return gr.Dropdown.update(choices=your_voices)
else:
return
def remove_surrounded_chars(string):
# this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
# 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
return re.sub('\*[^\*]*?(\*|$)','',string)
return re.sub('\*[^\*]*?(\*|$)', '', string)
def input_modifier(string):
"""
@ -64,16 +70,17 @@ def input_modifier(string):
return string
def output_modifier(string):
"""
This function is applied to the model outputs.
"""
global params, wav_idx, user, user_info
if params['activate'] == False:
if not params['activate']:
return string
elif user_info == None:
elif user_info is None:
return string
string = remove_surrounded_chars(string)
@ -84,7 +91,7 @@ def output_modifier(string):
if string == '':
string = 'empty reply, try regenerating'
output_file = Path(f'extensions/elevenlabs_tts/outputs/{wav_idx:06d}.wav'.format(wav_idx))
voice = user.get_voices_by_name(params['selected_voice'])[0]
audio_data = voice.generate_audio_bytes(string)
@ -94,6 +101,7 @@ def output_modifier(string):
wav_idx += 1
return string
def ui():
# Gradio elements
@ -110,4 +118,4 @@ def ui():
voice.change(lambda x: params.update({'selected_voice': x}), voice, None)
api_key.change(lambda x: params.update({'api_key': x}), api_key, None)
connect.click(check_valid_api, [], connection_status)
connect.click(refresh_voices, [], voice)
connect.click(refresh_voices, [], voice)

4
extensions/gallery/script.py
View File

@ -85,7 +85,7 @@ def select_character(evt: gr.SelectData):
def ui():
with gr.Accordion("Character gallery", open=False):
update = gr.Button("Refresh")
gr.HTML(value="<style>"+generate_css()+"</style>")
gr.HTML(value="<style>" + generate_css() + "</style>")
gallery = gr.Dataset(components=[gr.HTML(visible=False)],
label="",
samples=generate_html(),
@ -93,4 +93,4 @@ def ui():
samples_per_page=50
)
update.click(generate_html, [], gallery)
gallery.select(select_character, None, gradio['character_menu'])
gallery.select(select_character, None, gradio['character_menu'])

6
extensions/google_translate/script.py
View File

@ -7,14 +7,16 @@ params = {
language_codes = {'Afrikaans': 'af', 'Albanian': 'sq', 'Amharic': 'am', 'Arabic': 'ar', 'Armenian': 'hy', 'Azerbaijani': 'az', 'Basque': 'eu', 'Belarusian': 'be', 'Bengali': 'bn', 'Bosnian': 'bs', 'Bulgarian': 'bg', 'Catalan': 'ca', 'Cebuano': 'ceb', 'Chinese (Simplified)': 'zh-CN', 'Chinese (Traditional)': 'zh-TW', 'Corsican': 'co', 'Croatian': 'hr', 'Czech': 'cs', 'Danish': 'da', 'Dutch': 'nl', 'English': 'en', 'Esperanto': 'eo', 'Estonian': 'et', 'Finnish': 'fi', 'French': 'fr', 'Frisian': 'fy', 'Galician': 'gl', 'Georgian': 'ka', 'German': 'de', 'Greek': 'el', 'Gujarati': 'gu', 'Haitian Creole': 'ht', 'Hausa': 'ha', 'Hawaiian': 'haw', 'Hebrew': 'iw', 'Hindi': 'hi', 'Hmong': 'hmn', 'Hungarian': 'hu', 'Icelandic': 'is', 'Igbo': 'ig', 'Indonesian': 'id', 'Irish': 'ga', 'Italian': 'it', 'Japanese': 'ja', 'Javanese': 'jw', 'Kannada': 'kn', 'Kazakh': 'kk', 'Khmer': 'km', 'Korean': 'ko', 'Kurdish': 'ku', 'Kyrgyz': 'ky', 'Lao': 'lo', 'Latin': 'la', 'Latvian': 'lv', 'Lithuanian': 'lt', 'Luxembourgish': 'lb', 'Macedonian': 'mk', 'Malagasy': 'mg', 'Malay': 'ms', 'Malayalam': 'ml', 'Maltese': 'mt', 'Maori': 'mi', 'Marathi': 'mr', 'Mongolian': 'mn', 'Myanmar (Burmese)': 'my', 'Nepali': 'ne', 'Norwegian': 'no', 'Nyanja (Chichewa)': 'ny', 'Pashto': 'ps', 'Persian': 'fa', 'Polish': 'pl', 'Portuguese (Portugal, Brazil)': 'pt', 'Punjabi': 'pa', 'Romanian': 'ro', 'Russian': 'ru', 'Samoan': 'sm', 'Scots Gaelic': 'gd', 'Serbian': 'sr', 'Sesotho': 'st', 'Shona': 'sn', 'Sindhi': 'sd', 'Sinhala (Sinhalese)': 'si', 'Slovak': 'sk', 'Slovenian': 'sl', 'Somali': 'so', 'Spanish': 'es', 'Sundanese': 'su', 'Swahili': 'sw', 'Swedish': 'sv', 'Tagalog (Filipino)': 'tl', 'Tajik': 'tg', 'Tamil': 'ta', 'Telugu': 'te', 'Thai': 'th', 'Turkish': 'tr', 'Ukrainian': 'uk', 'Urdu': 'ur', 'Uzbek': 'uz', 'Vietnamese': 'vi', 'Welsh': 'cy', 'Xhosa': 'xh', 'Yiddish': 'yi', 'Yoruba': 'yo', 'Zulu': 'zu'}
def input_modifier(string):
"""
This function is applied to your text inputs before
they are fed into the model.
"""
"""
return GoogleTranslator(source=params['language string'], target='en').translate(string)
def output_modifier(string):
"""
This function is applied to the model outputs.
@ -22,6 +24,7 @@ def output_modifier(string):
return GoogleTranslator(source='en', target=params['language string']).translate(string)
def bot_prefix_modifier(string):
"""
This function is only applied in chat mode. It modifies
@ -31,6 +34,7 @@ def bot_prefix_modifier(string):
return string
def ui():
# Finding the language name from the language code to use as the default value
language_name = list(language_codes.keys())[list(language_codes.values()).index(params['language string'])]

2
extensions/llama_prompts/script.py
View File

@ -4,12 +4,14 @@ import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/devbrones/llama-prompts/main/prompts/prompts.csv")
def get_prompt_by_name(name):
if name == 'None':
return ''
else:
return df[df['Prompt name'] == name].iloc[0]['Prompt'].replace('\\n', '\n')
def ui():
if not shared.is_chat():
choices = ['None'] + list(df['Prompt name'])

38
extensions/sd_api_pictures/script.py
View File

@ -12,30 +12,33 @@ from PIL import Image
torch._C._jit_set_profiling_mode(False)
# parameters which can be customized in settings.json of webui
# parameters which can be customized in settings.json of webui
params = {
'enable_SD_api': False,
'address': 'http://127.0.0.1:7860',
'save_img': False,
'SD_model': 'NeverEndingDream', # not really used right now
'SD_model': 'NeverEndingDream', # not really used right now
'prompt_prefix': '(Masterpiece:1.1), (solo:1.3), detailed, intricate, colorful',
'negative_prompt': '(worst quality, low quality:1.3)',
'side_length': 512,
'restore_faces': False
}
SD_models = ['NeverEndingDream'] # TODO: get with http://{address}}/sdapi/v1/sd-models and allow user to select
SD_models = ['NeverEndingDream'] # TODO: get with http://{address}}/sdapi/v1/sd-models and allow user to select
streaming_state = shared.args.no_stream # remember if chat streaming was enabled
picture_response = False # specifies if the next model response should appear as a picture
streaming_state = shared.args.no_stream # remember if chat streaming was enabled
picture_response = False # specifies if the next model response should appear as a picture
pic_id = 0
def remove_surrounded_chars(string):
# this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
# 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
return re.sub('\*[^\*]*?(\*|$)','',string)
return re.sub('\*[^\*]*?(\*|$)', '', string)
# I don't even need input_hijack for this as visible text will be commited to history as the unmodified string
def input_modifier(string):
"""
This function is applied to your text inputs before
@ -51,7 +54,7 @@ def input_modifier(string):
lowstr = string.lower()
# TODO: refactor out to separate handler and also replace detection with a regexp
if any(command in lowstr for command in commands) and any(case in lowstr for case in mediums): # trigger the generation if a command signature and a medium signature is found
if any(command in lowstr for command in commands) and any(case in lowstr for case in mediums): # trigger the generation if a command signature and a medium signature is found
picture_response = True
shared.args.no_stream = True # Disable streaming cause otherwise the SD-generated picture would return as a dud
shared.processing_message = "*Is sending a picture...*"
@ -62,6 +65,8 @@ def input_modifier(string):
return string
# Get and save the Stable Diffusion-generated picture
def get_SD_pictures(description):
global params, pic_id
@ -77,13 +82,13 @@ def get_SD_pictures(description):
"restore_faces": params['restore_faces'],
"negative_prompt": params['negative_prompt']
}
response = requests.post(url=f'{params["address"]}/sdapi/v1/txt2img', json=payload)
r = response.json()
visible_result = ""
for img_str in r['images']:
image = Image.open(io.BytesIO(base64.b64decode(img_str.split(",",1)[0])))
image = Image.open(io.BytesIO(base64.b64decode(img_str.split(",", 1)[0])))
if params['save_img']:
output_file = Path(f'extensions/sd_api_pictures/outputs/{pic_id:06d}.png')
image.save(output_file.as_posix())
@ -96,11 +101,13 @@ def get_SD_pictures(description):
image_bytes = buffered.getvalue()
img_str = "data:image/jpeg;base64," + base64.b64encode(image_bytes).decode()
visible_result = visible_result + f'<img src="{img_str}" alt="{description}">\n'
return visible_result
# 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):
"""
This function is applied to the model outputs.
@ -130,6 +137,7 @@ def output_modifier(string):
shared.args.no_stream = streaming_state
return image + "\n" + text
def bot_prefix_modifier(string):
"""
This function is only applied in chat mode. It modifies
@ -139,10 +147,12 @@ def bot_prefix_modifier(string):
return string
def force_pic():
global picture_response
picture_response = True
def ui():
# Gradio elements
@ -153,7 +163,7 @@ def ui():
save_img = gr.Checkbox(value=params['save_img'], label='Keep original received images in the outputs subdir')
with gr.Column():
address = gr.Textbox(placeholder=params['address'], value=params['address'], label='Stable Diffusion host address')
with gr.Row():
force_btn = gr.Button("Force the next response to be a picture")
generate_now_btn = gr.Button("Generate an image response to the input")
@ -162,9 +172,9 @@ def ui():
prompt_prefix = gr.Textbox(placeholder=params['prompt_prefix'], value=params['prompt_prefix'], label='Prompt Prefix (best used to describe the look of the character)')
with gr.Row():
negative_prompt = gr.Textbox(placeholder=params['negative_prompt'], value=params['negative_prompt'], label='Negative Prompt')
dimensions = gr.Slider(256,702,value=params['side_length'],step=64,label='Image dimensions')
dimensions = gr.Slider(256, 702, value=params['side_length'], step=64, label='Image dimensions')
# model = gr.Dropdown(value=SD_models[0], choices=SD_models, label='Model')
# Event functions to update the parameters in the backend
enable.change(lambda x: params.update({"enable_SD_api": x}), enable, None)
save_img.change(lambda x: params.update({"save_img": x}), save_img, None)
@ -176,4 +186,4 @@ def ui():
force_btn.click(force_pic)
generate_now_btn.click(force_pic)
generate_now_btn.click(chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)
generate_now_btn.click(chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)

5
extensions/send_pictures/script.py
View File

@ -17,11 +17,13 @@ input_hijack = {
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float32).to("cpu")
def caption_image(raw_image):
inputs = processor(raw_image.convert('RGB'), return_tensors="pt").to("cpu", torch.float32)
out = model.generate(**inputs, max_new_tokens=100)
return processor.decode(out[0], skip_special_tokens=True)
def generate_chat_picture(picture, name1, name2):
text = f'*{name1} sends {name2} a picture that contains the following: "{caption_image(picture)}"*'
# lower the resolution of sent images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history
@ -32,6 +34,7 @@ def generate_chat_picture(picture, name1, name2):
visible_text = f'<img src="data:image/jpeg;base64,{img_str}" alt="{text}">'
return text, visible_text
def ui():
picture_select = gr.Image(label='Send a picture', type='pil')
@ -42,4 +45,4 @@ def ui():
picture_select.upload(chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)
# Clear the picture from the upload field
picture_select.upload(lambda : None, [], [picture_select], show_progress=False)
picture_select.upload(lambda: None, [], [picture_select], show_progress=False)

19
modules/GPTQ_loader.py
View File

@ -17,9 +17,11 @@ from quant import make_quant
def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=['lm_head'], kernel_switch_threshold=128):
config = AutoConfig.from_pretrained(model)
def noop(*args, **kwargs):
pass
config = AutoConfig.from_pretrained(model)
torch.nn.init.kaiming_uniform_ = noop
torch.nn.init.uniform_ = noop
torch.nn.init.normal_ = noop
@ -34,11 +36,11 @@ def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exc
for name in exclude_layers:
if name in layers:
del layers[name]
gptq_args = inspect.getfullargspec(make_quant).args
make_quant_kwargs = {
'module': model,
'module': model,
'names': layers,
'bits': wbits,
}
@ -48,7 +50,7 @@ def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exc
make_quant_kwargs['faster'] = faster_kernel
if 'kernel_switch_threshold' in gptq_args:
make_quant_kwargs['kernel_switch_threshold'] = kernel_switch_threshold
make_quant(**make_quant_kwargs)
del layers
@ -56,14 +58,15 @@ def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exc
print('Loading model ...')
if checkpoint.endswith('.safetensors'):
from safetensors.torch import load_file as safe_load
model.load_state_dict(safe_load(checkpoint), strict = False)
model.load_state_dict(safe_load(checkpoint), strict=False)
else:
model.load_state_dict(torch.load(checkpoint), strict = False)
model.load_state_dict(torch.load(checkpoint), strict=False)
model.seqlen = 2048
print('Done.')
return model
def load_quantized(model_name):
if not shared.args.model_type:
# Try to determine model type from model name
@ -114,7 +117,7 @@ def load_quantized(model_name):
pt_model = f'{model_name}-{shared.args.wbits}bit'
# Try to find the .safetensors or .pt both in the model dir and in the subfolder
for path in [Path(p+ext) for ext in ['.safetensors', '.pt'] for p in [f"{shared.args.model_dir}/{pt_model}", f"{path_to_model}/{pt_model}"]]:
for path in [Path(p + ext) for ext in ['.safetensors', '.pt'] for p in [f"{shared.args.model_dir}/{pt_model}", f"{path_to_model}/{pt_model}"]]:
if path.exists():
print(f"Found {path}")
pt_path = path
@ -133,7 +136,7 @@ def load_quantized(model_name):
# accelerate offload (doesn't work properly)
if shared.args.gpu_memory:
memory_map = list(map(lambda x : x.strip(), shared.args.gpu_memory))
memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory))
max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
max_memory = {}
for i in range(len(memory_map)):

5
modules/LoRA.py
View File

@ -13,6 +13,7 @@ def reload_model():
clear_torch_cache()
shared.model, shared.tokenizer = load_model(shared.model_name)
def add_lora_to_model(lora_name):
# If a LoRA had been previously loaded, or if we want
@ -27,10 +28,10 @@ def add_lora_to_model(lora_name):
if not shared.args.cpu:
params['dtype'] = shared.model.dtype
if hasattr(shared.model, "hf_device_map"):
params['device_map'] = {"base_model.model."+k: v for k, v in shared.model.hf_device_map.items()}
params['device_map'] = {"base_model.model." + k: v for k, v in shared.model.hf_device_map.items()}
elif shared.args.load_in_8bit:
params['device_map'] = {'': 0}
shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_name}"), **params)
if not shared.args.load_in_8bit and not shared.args.cpu:
shared.model.half()

17
modules/RWKV.py
View File

@ -10,7 +10,7 @@ from modules.callbacks import Iteratorize
np.set_printoptions(precision=4, suppress=True, linewidth=200)
os.environ['RWKV_JIT_ON'] = '1'
os.environ["RWKV_CUDA_ON"] = '1' if shared.args.rwkv_cuda_on else '0' # use CUDA kernel for seq mode (much faster)
os.environ["RWKV_CUDA_ON"] = '1' if shared.args.rwkv_cuda_on else '0' # use CUDA kernel for seq mode (much faster)
from rwkv.model import RWKV
from rwkv.utils import PIPELINE, PIPELINE_ARGS
@ -36,13 +36,13 @@ class RWKVModel:
def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=None, alpha_frequency=0.1, alpha_presence=0.1, token_ban=[0], token_stop=[], callback=None):
args = PIPELINE_ARGS(
temperature = temperature,
top_p = top_p,
top_k = top_k,
alpha_frequency = alpha_frequency, # Frequency Penalty (as in GPT-3)
alpha_presence = alpha_presence, # Presence Penalty (as in GPT-3)
token_ban = token_ban, # ban the generation of some tokens
token_stop = token_stop
temperature=temperature,
top_p=top_p,
top_k=top_k,
alpha_frequency=alpha_frequency, # Frequency Penalty (as in GPT-3)
alpha_presence=alpha_presence, # Presence Penalty (as in GPT-3)
token_ban=token_ban, # ban the generation of some tokens
token_stop=token_stop
)
return self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
@ -54,6 +54,7 @@ class RWKVModel:
reply += token
yield reply
class RWKVTokenizer:
def __init__(self):
pass

1
modules/api.py
View File

@ -28,6 +28,7 @@ def generate_reply_wrapper(string):
for i in generate_reply(params[0], generate_params):
yield i
def create_apis():
t1 = gr.Textbox(visible=False)
t2 = gr.Textbox(visible=False)

9
modules/callbacks.py
View File

@ -30,6 +30,7 @@ class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
return True
return False
class Stream(transformers.StoppingCriteria):
def __init__(self, callback_func=None):
self.callback_func = callback_func
@ -39,6 +40,7 @@ class Stream(transformers.StoppingCriteria):
self.callback_func(input_ids[0])
return False
class Iteratorize:
"""
@ -47,8 +49,8 @@ class Iteratorize:
"""
def __init__(self, func, kwargs={}, callback=None):
self.mfunc=func
self.c_callback=callback
self.mfunc = func
self.c_callback = callback
self.q = Queue()
self.sentinel = object()
self.kwargs = kwargs
@ -80,7 +82,7 @@ class Iteratorize:
return self
def __next__(self):
obj = self.q.get(True,None)
obj = self.q.get(True, None)
if obj is self.sentinel:
raise StopIteration
else:
@ -96,6 +98,7 @@ class Iteratorize:
self.stop_now = True
clear_torch_cache()
def clear_torch_cache():
gc.collect()
if not shared.args.cpu:

57
modules/chat.py
View File

@ -23,12 +23,11 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
end_of_turn = kwargs['end_of_turn'] if 'end_of_turn' in kwargs else ''
impersonate = kwargs['impersonate'] if 'impersonate' in kwargs else False
also_return_rows = kwargs['also_return_rows'] if 'also_return_rows' in kwargs else False
rows = [f"{context.strip()}\n"]
# Finding the maximum prompt size
if shared.soft_prompt:
chat_prompt_size -= shared.soft_prompt_tensor.shape[1]
chat_prompt_size -= shared.soft_prompt_tensor.shape[1]
max_length = min(get_max_prompt_length(max_new_tokens), chat_prompt_size)
if is_instruct:
@ -38,7 +37,7 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
prefix1 = f"{name1}: "
prefix2 = f"{name2}: "
i = len(shared.history['internal'])-1
i = len(shared.history['internal']) - 1
while i >= 0 and len(encode(''.join(rows), max_new_tokens)[0]) < max_length:
rows.insert(1, f"{prefix2}{shared.history['internal'][i][1].strip()}{end_of_turn}\n")
string = shared.history['internal'][i][0]
@ -68,6 +67,7 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
else:
return prompt
def extract_message_from_reply(reply, name1, name2, stop_at_newline):
next_character_found = False
@ -87,7 +87,7 @@ def extract_message_from_reply(reply, name1, name2, stop_at_newline):
# is completed, trim it
if not next_character_found:
for string in [f"\n{name1}:", f"\n{name2}:"]:
for j in range(len(string)-1, 0, -1):
for j in range(len(string) - 1, 0, -1):
if reply[-j:] == string[:j]:
reply = reply[:-j]
break
@ -98,12 +98,13 @@ def extract_message_from_reply(reply, name1, name2, stop_at_newline):
reply = fix_newlines(reply)
return reply, next_character_found
def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn, regenerate=False):
if mode == 'instruct':
stopping_strings = [f"\n{name1}", f"\n{name2}"]
else:
stopping_strings = [f"\n{name1}:", f"\n{name2}:"]
eos_token = '\n' if generate_state['stop_at_newline'] else None
name1_original = name1
if 'pygmalion' in shared.model_name.lower():
@ -113,7 +114,7 @@ def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_tu
visible_text = None
custom_generate_chat_prompt = None
for extension, _ in extensions_module.iterator():
if hasattr(extension, 'input_hijack') and extension.input_hijack['state'] == True:
if hasattr(extension, 'input_hijack') and extension.input_hijack['state']:
extension.input_hijack['state'] = False
text, visible_text = extension.input_hijack['value']
if custom_generate_chat_prompt is None and hasattr(extension, 'custom_generate_chat_prompt'):
@ -131,7 +132,7 @@ def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_tu
# Yield *Is typing...*
if not regenerate:
yield shared.history['visible']+[[visible_text, shared.processing_message]]
yield shared.history['visible'] + [[visible_text, shared.processing_message]]
# Generate
cumulative_reply = ''
@ -167,12 +168,13 @@ def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_tu
yield shared.history['visible']
def impersonate_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
if mode == 'instruct':
stopping_strings = [f"\n{name1}", f"\n{name2}"]
else:
stopping_strings = [f"\n{name1}:", f"\n{name2}:"]
eos_token = '\n' if generate_state['stop_at_newline'] else None
if 'pygmalion' in shared.model_name.lower():
name1 = "You"
@ -197,10 +199,12 @@ def impersonate_wrapper(text, generate_state, name1, name2, context, mode, end_o
yield reply
def cai_chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
for history in chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
yield chat_html_wrapper(history, name1, name2, mode)
def regenerate_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
if (shared.character != 'None' and len(shared.history['visible']) == 1) or len(shared.history['internal']) == 0:
yield chat_html_wrapper(shared.history['visible'], name1, name2, mode)
@ -208,11 +212,12 @@ def regenerate_wrapper(text, generate_state, name1, name2, context, mode, end_of
last_visible = shared.history['visible'].pop()
last_internal = shared.history['internal'].pop()
# Yield '*Is typing...*'
yield chat_html_wrapper(shared.history['visible']+[[last_visible[0], shared.processing_message]], name1, name2, mode)
yield chat_html_wrapper(shared.history['visible'] + [[last_visible[0], shared.processing_message]], name1, name2, mode)
for history in chatbot_wrapper(last_internal[0], generate_state, name1, name2, context, mode, end_of_turn, regenerate=True):
shared.history['visible'][-1] = [last_visible[0], history[-1][1]]
yield chat_html_wrapper(shared.history['visible'], name1, name2, mode)
def remove_last_message(name1, name2, mode):
if len(shared.history['visible']) > 0 and shared.history['internal'][-1][0] != '<|BEGIN-VISIBLE-CHAT|>':
last = shared.history['visible'].pop()
@ -222,12 +227,14 @@ def remove_last_message(name1, name2, mode):
return chat_html_wrapper(shared.history['visible'], name1, name2, mode), last[0]
def send_last_reply_to_input():
if len(shared.history['internal']) > 0:
return shared.history['internal'][-1][1]
else:
return ''
def replace_last_reply(text, name1, name2, mode):
if len(shared.history['visible']) > 0:
shared.history['visible'][-1][1] = text
@ -235,9 +242,11 @@ def replace_last_reply(text, name1, name2, mode):
return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
def clear_html():
return chat_html_wrapper([], "", "")
def clear_chat_log(name1, name2, greeting, mode):
shared.history['visible'] = []
shared.history['internal'] = []
@ -248,9 +257,11 @@ def clear_chat_log(name1, name2, greeting, mode):
return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
def redraw_html(name1, name2, mode):
return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
def tokenize_dialogue(dialogue, name1, name2, mode):
history = []
@ -263,8 +274,8 @@ def tokenize_dialogue(dialogue, name1, name2, mode):
return history
messages = []
for i in range(len(idx)-1):
messages.append(dialogue[idx[i]:idx[i+1]].strip())
for i in range(len(idx) - 1):
messages.append(dialogue[idx[i]:idx[i + 1]].strip())
messages.append(dialogue[idx[-1]:].strip())
entry = ['', '']
@ -282,12 +293,13 @@ def tokenize_dialogue(dialogue, name1, name2, mode):
for column in row:
print("\n")
for line in column.strip().split('\n'):
print("| "+line+"\n")
print("| " + line + "\n")
print("|\n")
print("------------------------------")
return history
def save_history(timestamp=True):
if timestamp:
fname = f"{shared.character}_{datetime.now().strftime('%Y%m%d-%H%M%S')}.json"
@ -299,6 +311,7 @@ def save_history(timestamp=True):
f.write(json.dumps({'data': shared.history['internal'], 'data_visible': shared.history['visible']}, indent=2))
return Path(f'logs/{fname}')
def load_history(file, name1, name2):
file = file.decode('utf-8')
try:
@ -313,20 +326,22 @@ def load_history(file, name1, name2):
elif 'chat' in j:
shared.history['internal'] = [':'.join(x.split(':')[1:]).strip() for x in j['chat']]
if len(j['chat']) > 0 and j['chat'][0].startswith(f'{name2}:'):
shared.history['internal'] = [['<|BEGIN-VISIBLE-CHAT|>', shared.history['internal'][0]]] + [[shared.history['internal'][i], shared.history['internal'][i+1]] for i in range(1, len(shared.history['internal'])-1, 2)]
shared.history['internal'] = [['<|BEGIN-VISIBLE-CHAT|>', shared.history['internal'][0]]] + [[shared.history['internal'][i], shared.history['internal'][i + 1]] for i in range(1, len(shared.history['internal']) - 1, 2)]
shared.history['visible'] = copy.deepcopy(shared.history['internal'])
shared.history['visible'][0][0] = ''
else:
shared.history['internal'] = [[shared.history['internal'][i], shared.history['internal'][i+1]] for i in range(0, len(shared.history['internal'])-1, 2)]
shared.history['internal'] = [[shared.history['internal'][i], shared.history['internal'][i + 1]] for i in range(0, len(shared.history['internal']) - 1, 2)]
shared.history['visible'] = copy.deepcopy(shared.history['internal'])
except:
shared.history['internal'] = tokenize_dialogue(file, name1, name2)
shared.history['visible'] = copy.deepcopy(shared.history['internal'])
def replace_character_names(text, name1, name2):
text = text.replace('{{user}}', name1).replace('{{char}}', name2)
return text.replace('<USER>', name1).replace('<BOT>', name2)
def build_pygmalion_style_context(data):
context = ""
if 'char_persona' in data and data['char_persona'] != '':
@ -336,6 +351,7 @@ def build_pygmalion_style_context(data):
context = f"{context.strip()}\n<START>\n"
return context
def generate_pfp_cache(character):
cache_folder = Path("cache")
if not cache_folder.exists():
@ -348,6 +364,7 @@ def generate_pfp_cache(character):
return img
return None
def load_character(character, name1, name2, mode):
shared.character = character
shared.history['internal'] = []
@ -387,13 +404,13 @@ def load_character(character, name1, name2, mode):
if 'example_dialogue' in data:
context += f"{data['example_dialogue'].strip()}\n"
if greeting_field in data:
greeting = data[greeting_field]
greeting = data[greeting_field]
if 'end_of_turn' in data:
end_of_turn = data['end_of_turn']
end_of_turn = data['end_of_turn']
else:
context = shared.settings['context']
name2 = shared.settings['name2']
greeting = shared.settings['greeting']
greeting = shared.settings['greeting']
end_of_turn = shared.settings['end_of_turn']
if Path(f'logs/{shared.character}_persistent.json').exists():
@ -404,9 +421,11 @@ def load_character(character, name1, name2, mode):
return name1, name2, picture, greeting, context, end_of_turn, chat_html_wrapper(shared.history['visible'], name1, name2, mode, reset_cache=True)
def load_default_history(name1, name2):
load_character("None", name1, name2, "chat")
def upload_character(json_file, img, tavern=False):
json_file = json_file if type(json_file) == str else json_file.decode('utf-8')
data = json.loads(json_file)
@ -425,6 +444,7 @@ def upload_character(json_file, img, tavern=False):
print(f'New character saved to "characters/{outfile_name}.json".')
return outfile_name
def upload_tavern_character(img, name1, name2):
_img = Image.open(io.BytesIO(img))
_img.getexif()
@ -433,12 +453,13 @@ def upload_tavern_character(img, name1, name2):
_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)
def upload_your_profile_picture(img, name1, name2, mode):
cache_folder = Path("cache")
if not cache_folder.exists():
cache_folder.mkdir()
if img == None:
if img is None:
if Path("cache/pfp_me.png").exists():
Path("cache/pfp_me.png").unlink()
else:

8
modules/extensions.py
View File

@ -9,6 +9,7 @@ state = {}
available_extensions = []
setup_called = set()
def load_extensions():
global state
for i, name in enumerate(shared.args.extensions):
@ -23,12 +24,16 @@ def load_extensions():
traceback.print_exc()
# This iterator returns the extensions in the order specified in the command-line
def iterator():
for name in sorted(state, key=lambda x : state[x][1]):
for name in sorted(state, key=lambda x: state[x][1]):
if state[name][0] == True:
yield eval(f"extensions.{name}.script"), name
# Extension functions that map string -> string
def apply_extensions(text, typ):
for extension, _ in iterator():
if typ == "input" and hasattr(extension, "input_modifier"):
@ -39,6 +44,7 @@ def apply_extensions(text, typ):
text = extension.bot_prefix_modifier(text)
return text
def create_extensions_block():
global setup_called

26
modules/html_generator.py
View File

@ -24,6 +24,7 @@ with open(Path(__file__).resolve().parent / '../css/html_cai_style.css', 'r') as
with open(Path(__file__).resolve().parent / '../css/html_instruct_style.css', 'r') as f:
instruct_css = f.read()
def fix_newlines(string):
string = string.replace('\n', '\n\n')
string = re.sub(r"\n{3,}", "\n\n", string)
@ -31,6 +32,8 @@ def fix_newlines(string):
return string
# This could probably be generalized and improved
def convert_to_markdown(string):
string = string.replace('\\begin{code}', '```')
string = string.replace('\\end{code}', '```')
@ -38,13 +41,15 @@ def convert_to_markdown(string):
string = string.replace('\\end{blockquote}', '')
string = re.sub(r"(.)```", r"\1\n```", string)
string = fix_newlines(string)
return markdown.markdown(string, extensions=['fenced_code'])
return markdown.markdown(string, extensions=['fenced_code'])
def generate_basic_html(string):
string = convert_to_markdown(string)
string = f'<style>{readable_css}</style><div class="container">{string}</div>'
return string
def process_post(post, c):
t = post.split('\n')
number = t[0].split(' ')[1]
@ -59,6 +64,7 @@ def process_post(post, c):
src = f'<span class="name">Anonymous </span> <span class="number">No.{number}</span>\n{src}'
return src
def generate_4chan_html(f):
posts = []
post = ''
@ -84,7 +90,7 @@ def generate_4chan_html(f):
posts[i] = f'<div class="op">{posts[i]}</div>\n'
else:
posts[i] = f'<div class="reply">{posts[i]}</div>\n'
output = ''
output += f'<style>{_4chan_css}</style><div id="parent"><div id="container">'
for post in posts:
@ -98,13 +104,15 @@ def generate_4chan_html(f):
return output
def make_thumbnail(image):
image = image.resize((350, round(image.size[1]/image.size[0]*350)), Image.Resampling.LANCZOS)
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)
return image
def get_image_cache(path):
cache_folder = Path("cache")
if not cache_folder.exists():
@ -119,9 +127,10 @@ def get_image_cache(path):
return image_cache[path][1]
def generate_instruct_html(history):
output = f'<style>{instruct_css}</style><div class="chat" id="chat">'
for i,_row in enumerate(history[::-1]):
for i, _row in enumerate(history[::-1]):
row = [convert_to_markdown(entry) for entry in _row]
output += f"""
@ -134,7 +143,7 @@ def generate_instruct_html(history):
</div>
"""
if len(row[0]) == 0: # don't display empty user messages
if len(row[0]) == 0: # don't display empty user messages
continue
output += f"""
@ -151,6 +160,7 @@ def generate_instruct_html(history):
return output
def generate_cai_chat_html(history, name1, name2, reset_cache=False):
output = f'<style>{cai_css}</style><div class="chat" id="chat">'
@ -159,7 +169,7 @@ def generate_cai_chat_html(history, name1, name2, reset_cache=False):
img_bot = f'<img src="file/cache/pfp_character.png{suffix}">' if Path("cache/pfp_character.png").exists() else ''
img_me = f'<img src="file/cache/pfp_me.png{suffix}">' if Path("cache/pfp_me.png").exists() else ''
for i,_row in enumerate(history[::-1]):
for i, _row in enumerate(history[::-1]):
row = [convert_to_markdown(entry) for entry in _row]
output += f"""
@ -178,7 +188,7 @@ def generate_cai_chat_html(history, name1, name2, reset_cache=False):
</div>
"""
if len(row[0]) == 0: # don't display empty user messages
if len(row[0]) == 0: # don't display empty user messages
continue
output += f"""
@ -200,9 +210,11 @@ def generate_cai_chat_html(history, name1, name2, reset_cache=False):
output += "</div>"
return output
def generate_chat_html(history, name1, name2):
return generate_cai_chat_html(history, name1, name2)
def chat_html_wrapper(history, name1, name2, mode, reset_cache=False):
if mode == "cai-chat":
return generate_cai_chat_html(history, name1, name2, reset_cache)

4
modules/llamacpp_model.py
View File

@ -50,9 +50,9 @@ class LlamaCppModel:
params.top_k = top_k
params.temp = temperature
params.repeat_penalty = repetition_penalty
#params.repeat_last_n = repeat_last_n
# params.repeat_last_n = repeat_last_n
#self.model.params = params
# self.model.params = params
self.model.add_bos()
self.model.update_input(context)

6
modules/llamacpp_model_alternative.py
View File

@ -1,13 +1,11 @@
'''
Based on
Based on
https://github.com/abetlen/llama-cpp-python
Documentation:
https://abetlen.github.io/llama-cpp-python/
'''
import multiprocessing
from llama_cpp import Llama
from modules import shared
@ -31,7 +29,7 @@ class LlamaCppModel:
self.model = Llama(**params)
# This is ugly, but the model and the tokenizer are the same object in this library.
return result, result
return result, result
def encode(self, string):
if type(string) is str:

21
modules/models.py
View File

@ -34,7 +34,7 @@ if shared.args.deepspeed:
torch.cuda.set_device(local_rank)
deepspeed.init_distributed()
ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir)
dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
def load_model(model_name):
@ -83,7 +83,7 @@ def load_model(model_name):
elif shared.args.deepspeed:
model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
model.module.eval() # Inference
model.module.eval() # Inference
print(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
# RMKV model (not on HuggingFace)
@ -132,7 +132,7 @@ def load_model(model_name):
params["torch_dtype"] = torch.float16
if shared.args.gpu_memory:
memory_map = list(map(lambda x : x.strip(), shared.args.gpu_memory))
memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory))
max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
max_memory = {}
for i in range(len(memory_map)):
@ -140,13 +140,13 @@ def load_model(model_name):
max_memory['cpu'] = max_cpu_memory
params['max_memory'] = max_memory
elif shared.args.auto_devices:
total_mem = (torch.cuda.get_device_properties(0).total_memory / (1024*1024))
suggestion = round((total_mem-1000) / 1000) * 1000
total_mem = (torch.cuda.get_device_properties(0).total_memory / (1024 * 1024))
suggestion = round((total_mem - 1000) / 1000) * 1000
if total_mem - suggestion < 800:
suggestion -= 1000
suggestion = int(round(suggestion/1000))
suggestion = int(round(suggestion / 1000))
print(f"\033[1;32;1mAuto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors.\nYou can manually set other values.\033[0;37;0m")
max_memory = {0: f'{suggestion}GiB', 'cpu': f'{shared.args.cpu_memory or 99}GiB'}
params['max_memory'] = max_memory
@ -161,10 +161,10 @@ def load_model(model_name):
model = AutoModelForCausalLM.from_config(config)
model.tie_weights()
params['device_map'] = infer_auto_device_map(
model,
dtype=torch.int8,
model,
dtype=torch.int8,
max_memory=params['max_memory'],
no_split_module_classes = model._no_split_modules
no_split_module_classes=model._no_split_modules
)
model = AutoModelForCausalLM.from_pretrained(checkpoint, **params)
@ -181,6 +181,7 @@ def load_model(model_name):
print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
return model, tokenizer
def load_soft_prompt(name):
if name == 'None':
shared.soft_prompt = False

5
modules/shared.py
View File

@ -61,6 +61,7 @@ settings = {
}
}
def str2bool(v):
if isinstance(v, bool):
return v
@ -71,7 +72,8 @@ def str2bool(v):
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54))
parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54))
# 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.')
@ -145,5 +147,6 @@ if args.cai_chat:
print("Warning: --cai-chat is deprecated. Use --chat instead.")
args.chat = True
def is_chat():
return args.chat

26
modules/text_generation.py
View File

@ -16,11 +16,12 @@ from modules.models import local_rank
def get_max_prompt_length(tokens):
max_length = 2048-tokens
max_length = 2048 - tokens
if shared.soft_prompt:
max_length -= shared.soft_prompt_tensor.shape[1]
return max_length
def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
if any((shared.is_RWKV, shared.is_llamacpp)):
input_ids = shared.tokenizer.encode(str(prompt))
@ -30,7 +31,7 @@ def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens)
if type(shared.tokenizer) is transformers.LlamaTokenizer and input_ids[0][0] == 29871:
input_ids = input_ids[:,1:]
input_ids = input_ids[:, 1:]
if shared.args.cpu:
return input_ids
@ -44,6 +45,7 @@ def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
else:
return input_ids.cuda()
def decode(output_ids):
# Open Assistant relies on special tokens like <|endoftext|>
if re.match('.*(oasst|galactica)-*', shared.model_name.lower()):
@ -53,14 +55,17 @@ def decode(output_ids):
reply = reply.replace(r'<|endoftext|>', '')
return reply
def generate_softprompt_input_tensors(input_ids):
inputs_embeds = shared.model.transformer.wte(input_ids)
inputs_embeds = torch.cat((shared.soft_prompt_tensor, inputs_embeds), dim=1)
filler_input_ids = torch.zeros((1, inputs_embeds.shape[1]), dtype=input_ids.dtype).to(shared.model.device)
#filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens
# filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens
return inputs_embeds, filler_input_ids
# Removes empty replies from gpt4chan outputs
def fix_gpt4chan(s):
for i in range(10):
s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
@ -69,6 +74,8 @@ def fix_gpt4chan(s):
return s
# Fix the LaTeX equations in galactica
def fix_galactica(s):
s = s.replace(r'\[', r'$')
s = s.replace(r'\]', r'$')
@ -79,6 +86,7 @@ def fix_galactica(s):
s = re.sub(r"\n{3,}", "\n\n", s)
return s
def formatted_outputs(reply, model_name):
if not shared.is_chat():
if 'galactica' in model_name.lower():
@ -92,20 +100,24 @@ def formatted_outputs(reply, model_name):
else:
return reply
def clear_torch_cache():
gc.collect()
if not shared.args.cpu:
torch.cuda.empty_cache()
def set_manual_seed(seed):
if seed != -1:
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def stop_everything_event():
shared.stop_everything = True
def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]):
clear_torch_cache()
set_manual_seed(generate_state['seed'])
@ -128,7 +140,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
try:
if shared.args.no_stream:
reply = shared.model.generate(context=question, **generate_params)
output = original_question+reply
output = original_question + reply
if not shared.is_chat():
reply = original_question + apply_extensions(reply, "output")
yield formatted_outputs(reply, shared.model_name)
@ -139,7 +151,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
# RWKV has proper streaming, which is very nice.
# No need to generate 8 tokens at a time.
for reply in shared.model.generate_with_streaming(context=question, **generate_params):
output = original_question+reply
output = original_question + reply
if not shared.is_chat():
reply = original_question + apply_extensions(reply, "output")
yield formatted_outputs(reply, shared.model_name)
@ -240,7 +252,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
else:
for i in range(generate_state['max_new_tokens']//8+1):
for i in range(generate_state['max_new_tokens'] // 8 + 1):
clear_torch_cache()
with torch.no_grad():
output = shared.model.generate(**generate_params)[0]
@ -271,6 +283,6 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
finally:
t1 = time.time()
original_tokens = len(original_input_ids[0])
new_tokens = len(output)-original_tokens
new_tokens = len(output) - original_tokens
print(f"Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})")
return

30
modules/training.py
View File

@ -19,9 +19,11 @@ CURRENT_STEPS = 0
MAX_STEPS = 0
CURRENT_GRADIENT_ACCUM = 1
def get_dataset(path: str, ext: str):
return ['None'] + sorted(set([k.stem for k in Path(path).glob(f'*.{ext}') if k.stem != 'put-trainer-datasets-here']), key=str.lower)
def create_train_interface():
with gr.Tab('Train LoRA', elem_id='lora-train-tab'):
lora_name = gr.Textbox(label="Name", info="The name of your new LoRA file")
@ -44,16 +46,16 @@ def create_train_interface():
with gr.Tab(label="Formatted Dataset"):
with gr.Row():
dataset = gr.Dropdown(choices=get_dataset('training/datasets', 'json'), value='None', label='Dataset', info='The dataset file to use for training.')
ui.create_refresh_button(dataset, lambda : None, lambda : {'choices': get_dataset('training/datasets', 'json')}, 'refresh-button')
ui.create_refresh_button(dataset, lambda: None, lambda: {'choices': get_dataset('training/datasets', 'json')}, 'refresh-button')
eval_dataset = gr.Dropdown(choices=get_dataset('training/datasets', 'json'), value='None', label='Evaluation Dataset', info='The (optional) dataset file used to evaluate the model after training.')
ui.create_refresh_button(eval_dataset, lambda : None, lambda : {'choices': get_dataset('training/datasets', 'json')}, 'refresh-button')
ui.create_refresh_button(eval_dataset, lambda: None, lambda: {'choices': get_dataset('training/datasets', 'json')}, 'refresh-button')
format = gr.Dropdown(choices=get_dataset('training/formats', 'json'), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.')
ui.create_refresh_button(format, lambda : None, lambda : {'choices': get_dataset('training/formats', 'json')}, 'refresh-button')
ui.create_refresh_button(format, lambda: None, lambda: {'choices': get_dataset('training/formats', 'json')}, 'refresh-button')
with gr.Tab(label="Raw Text File"):
with gr.Row():
raw_text_file = gr.Dropdown(choices=get_dataset('training/datasets', 'txt'), value='None', label='Text File', info='The raw text file to use for training.')
ui.create_refresh_button(raw_text_file, lambda : None, lambda : {'choices': get_dataset('training/datasets', 'txt')}, 'refresh-button')
ui.create_refresh_button(raw_text_file, lambda: None, lambda: {'choices': get_dataset('training/datasets', 'txt')}, 'refresh-button')
with gr.Row():
overlap_len = gr.Slider(label='Overlap Length', minimum=0, maximum=512, value=128, step=16, info='Overlap length - ie how many tokens from the prior chunk of text to include into the next chunk. (The chunks themselves will be of a size determined by Cutoff Length below). Setting overlap to exactly half the cutoff length may be ideal.')
newline_favor_len = gr.Slider(label='Prefer Newline Cut Length', minimum=0, maximum=512, value=128, step=16, info='Length (in characters, not tokens) of the maximum distance to shift an overlap cut by to ensure chunks cut at newlines. If too low, cuts may occur in the middle of lines.')
@ -67,10 +69,12 @@ def create_train_interface():
cutoff_len, dataset, eval_dataset, format, raw_text_file, overlap_len, newline_favor_len], [output])
stop_button.click(do_interrupt, [], [], cancels=[], queue=False)
def do_interrupt():
global WANT_INTERRUPT
WANT_INTERRUPT = True
class Callbacks(transformers.TrainerCallback):
def on_step_begin(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs):
global CURRENT_STEPS, MAX_STEPS
@ -79,6 +83,7 @@ class Callbacks(transformers.TrainerCallback):
if WANT_INTERRUPT:
control.should_epoch_stop = True
control.should_training_stop = True
def on_substep_end(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs):
global CURRENT_STEPS
CURRENT_STEPS += 1
@ -86,6 +91,7 @@ class Callbacks(transformers.TrainerCallback):
control.should_epoch_stop = True
control.should_training_stop = True
def clean_path(base_path: str, path: str):
""""Strips unusual symbols and forcibly builds a path as relative to the intended directory."""
# TODO: Probably could do with a security audit to guarantee there's no ways this can be bypassed to target an unwanted path.
@ -95,6 +101,7 @@ def clean_path(base_path: str, path: str):
return path
return f'{Path(base_path).absolute()}/{path}'
def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lora_rank: int, lora_alpha: int, lora_dropout: float,
cutoff_len: int, dataset: str, eval_dataset: str, format: str, raw_text_file: str, overlap_len: int, newline_favor_len: int):
global WANT_INTERRUPT, CURRENT_STEPS, MAX_STEPS, CURRENT_GRADIENT_ACCUM
@ -124,7 +131,7 @@ def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int
elif not shared.args.load_in_8bit:
yield "It is highly recommended you use `--load-in-8bit` for LoRA training. *(Will continue anyway in 2 seconds, press `Interrupt` to stop.)*"
print("Warning: It is highly recommended you use `--load-in-8bit` for LoRA training.")
time.sleep(2) # Give it a moment for the message to show in UI before continuing
time.sleep(2) # Give it a moment for the message to show in UI before continuing
if cutoff_len <= 0 or micro_batch_size <= 0 or batch_size <= 0 or actual_lr <= 0 or lora_rank <= 0 or lora_alpha <= 0:
yield "Cannot input zeroes."
@ -148,7 +155,7 @@ def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int
with open(clean_path('training/datasets', f'{raw_text_file}.txt'), 'r') as file:
raw_text = file.read()
tokens = shared.tokenizer.encode(raw_text)
del raw_text # Note: could be a gig for a large dataset, so delete redundant data as we go to be safe on RAM
del raw_text # Note: could be a gig for a large dataset, so delete redundant data as we go to be safe on RAM
tokens = list(split_chunks(tokens, cutoff_len - overlap_len))
for i in range(1, len(tokens)):
@ -197,18 +204,18 @@ def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int
else:
eval_data = load_dataset("json", data_files=clean_path('training/datasets', f'{eval_dataset}.json'))
eval_data = eval_data['train'].shuffle().map(generate_and_tokenize_prompt)
# == Start prepping the model itself ==
if not hasattr(shared.model, 'lm_head') or hasattr(shared.model.lm_head, 'weight'):
print("Getting model ready...")
prepare_model_for_int8_training(shared.model)
print("Prepping for training...")
config = LoraConfig(
r=lora_rank,
lora_alpha=lora_alpha,
# TODO: Should target_modules be configurable?
target_modules=[ "q_proj", "v_proj" ],
target_modules=["q_proj", "v_proj"],
lora_dropout=lora_dropout,
bias="none",
task_type="CAUSAL_LM"
@ -289,7 +296,7 @@ def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int
timer_info = f"`{its:.2f}` it/s"
else:
timer_info = f"`{1.0/its:.2f}` s/it"
total_time_estimate = (1.0/its) * (MAX_STEPS)
total_time_estimate = (1.0 / its) * (MAX_STEPS)
yield f"Running... **{CURRENT_STEPS}** / **{MAX_STEPS}** ... {timer_info}, {format_time(time_elapsed)} / {format_time(total_time_estimate)} ... {format_time(total_time_estimate - time_elapsed)} remaining"
print("Training complete, saving...")
@ -302,10 +309,12 @@ def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int
print("Training complete!")
yield f"Done! LoRA saved to `{lora_name}`"
def split_chunks(arr, step):
for i in range(0, len(arr), step):
yield arr[i:i + step]
def cut_chunk_for_newline(chunk: str, max_length: int):
if '\n' not in chunk:
return chunk
@ -319,6 +328,7 @@ def cut_chunk_for_newline(chunk: str, max_length: int):
chunk = chunk[:last_newline]
return chunk
def format_time(seconds: float):
if seconds < 120:
return f"`{seconds:.0f}` seconds"

2
modules/ui.py
View File

@ -13,6 +13,7 @@ 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()
class ToolButton(gr.Button, gr.components.FormComponent):
"""Small button with single emoji as text, fits inside gradio forms"""
@ -22,6 +23,7 @@ class ToolButton(gr.Button, gr.components.FormComponent):
def get_block_name(self):
return "button"
def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
def refresh():
refresh_method()

73
server.py
View File

@ -34,15 +34,18 @@ if settings_file is not None:
for item in new_settings:
shared.settings[item] = new_settings[item]
def get_available_models():
if shared.args.flexgen:
return sorted([re.sub('-np$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if item.name.endswith('-np')], key=str.lower)
else:
return sorted([re.sub('.pth$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
def get_available_presets():
return sorted(set((k.stem for k in Path('presets').glob('*.txt'))), key=str.lower)
def get_available_prompts():
prompts = []
prompts += sorted(set((k.stem for k in Path('prompts').glob('[0-9]*.txt'))), key=str.lower, reverse=True)
@ -50,10 +53,12 @@ def get_available_prompts():
prompts += ['None']
return prompts
def get_available_characters():
paths = (x for x in Path('characters').iterdir() if x.suffix in ('.json', '.yaml', '.yml'))
return ['None'] + sorted(set((k.stem for k in paths if k.stem != "instruction-following")), key=str.lower)
def get_available_instruction_templates():
path = "characters/instruction-following"
paths = []
@ -61,19 +66,24 @@ def get_available_instruction_templates():
paths = (x for x in Path(path).iterdir() if x.suffix in ('.json', '.yaml', '.yml'))
return ['None'] + sorted(set((k.stem for k in paths)), key=str.lower)
def get_available_extensions():
return sorted(set(map(lambda x : x.parts[1], Path('extensions').glob('*/script.py'))), key=str.lower)
return sorted(set(map(lambda x: x.parts[1], Path('extensions').glob('*/script.py'))), key=str.lower)
def get_available_softprompts():
return ['None'] + sorted(set((k.stem for k in Path('softprompts').glob('*.zip'))), key=str.lower)
def get_available_loras():
return ['None'] + sorted([item.name for item in list(Path(shared.args.lora_dir).glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
def unload_model():
shared.model = shared.tokenizer = None
clear_torch_cache()
def load_model_wrapper(selected_model):
if selected_model != shared.model_name:
shared.model_name = selected_model
@ -84,10 +94,12 @@ def load_model_wrapper(selected_model):
return selected_model
def load_lora_wrapper(selected_lora):
add_lora_to_model(selected_lora)
return selected_lora
def load_preset_values(preset_menu, state, return_dict=False):
generate_params = {
'do_sample': True,
@ -118,6 +130,7 @@ def load_preset_values(preset_menu, state, return_dict=False):
state.update(generate_params)
return state, *[generate_params[k] for k in ['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']]
def upload_soft_prompt(file):
with zipfile.ZipFile(io.BytesIO(file)) as zf:
zf.extract('meta.json')
@ -130,12 +143,14 @@ def upload_soft_prompt(file):
return name
def save_prompt(text):
fname = f"{datetime.now().strftime('%Y-%m-%d-%H%M%S')}.txt"
with open(Path(f'prompts/{fname}'), 'w', encoding='utf-8') as f:
f.write(text)
return f"Saved to prompts/{fname}"
def load_prompt(fname):
if fname in ['None', '']:
return ''
@ -146,12 +161,13 @@ def load_prompt(fname):
text = text[:-1]
return text
def create_prompt_menus():
with gr.Row():
with gr.Column():
with gr.Row():
shared.gradio['prompt_menu'] = gr.Dropdown(choices=get_available_prompts(), value='None', label='Prompt')
ui.create_refresh_button(shared.gradio['prompt_menu'], lambda : None, lambda : {'choices': get_available_prompts()}, 'refresh-button')
ui.create_refresh_button(shared.gradio['prompt_menu'], lambda: None, lambda: {'choices': get_available_prompts()}, 'refresh-button')
with gr.Column():
with gr.Column():
@ -161,20 +177,22 @@ def create_prompt_menus():
shared.gradio['prompt_menu'].change(load_prompt, [shared.gradio['prompt_menu']], [shared.gradio['textbox']], show_progress=False)
shared.gradio['save_prompt'].click(save_prompt, [shared.gradio['textbox']], [shared.gradio['status']], show_progress=False)
def create_model_menus():
with gr.Row():
with gr.Column():
with gr.Row():
shared.gradio['model_menu'] = gr.Dropdown(choices=available_models, value=shared.model_name, label='Model')
ui.create_refresh_button(shared.gradio['model_menu'], lambda : None, lambda : {'choices': get_available_models()}, 'refresh-button')
ui.create_refresh_button(shared.gradio['model_menu'], lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button')
with gr.Column():
with gr.Row():
shared.gradio['lora_menu'] = gr.Dropdown(choices=available_loras, value=shared.lora_name, label='LoRA')
ui.create_refresh_button(shared.gradio['lora_menu'], lambda : None, lambda : {'choices': get_available_loras()}, 'refresh-button')
ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': get_available_loras()}, 'refresh-button')
shared.gradio['model_menu'].change(load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_menu'], show_progress=True)
shared.gradio['lora_menu'].change(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['lora_menu'], show_progress=True)
def create_settings_menus(default_preset):
generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', {}, return_dict=True)
for k in ['max_new_tokens', 'seed', 'stop_at_newline', 'chat_prompt_size', 'chat_generation_attempts']:
@ -185,7 +203,7 @@ def create_settings_menus(default_preset):
with gr.Column():
with gr.Row():
shared.gradio['preset_menu'] = gr.Dropdown(choices=available_presets, value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
ui.create_refresh_button(shared.gradio['preset_menu'], lambda : None, lambda : {'choices': get_available_presets()}, 'refresh-button')
ui.create_refresh_button(shared.gradio['preset_menu'], lambda: None, lambda: {'choices': get_available_presets()}, 'refresh-button')
with gr.Column():
shared.gradio['seed'] = gr.Number(value=shared.settings['seed'], label='Seed (-1 for random)')
@ -196,12 +214,12 @@ def create_settings_menus(default_preset):
with gr.Row():
with gr.Column():
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['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')
with gr.Column():
shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'],step=0.01,label='repetition_penalty')
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['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'], step=0.01, label='repetition_penalty')
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'] if shared.args.no_stream else 0, label='min_length', interactive=shared.args.no_stream)
shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
@ -209,7 +227,6 @@ def create_settings_menus(default_preset):
with gr.Box():
gr.Markdown('Contrastive search')
shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha')
with gr.Box():
gr.Markdown('Beam search (uses a lot of VRAM)')
with gr.Row():
@ -219,11 +236,10 @@ def create_settings_menus(default_preset):
shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty')
shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
with gr.Accordion('Soft prompt', open=False):
with gr.Row():
shared.gradio['softprompts_menu'] = gr.Dropdown(choices=available_softprompts, value='None', label='Soft prompt')
ui.create_refresh_button(shared.gradio['softprompts_menu'], lambda : None, lambda : {'choices': get_available_softprompts()}, 'refresh-button')
ui.create_refresh_button(shared.gradio['softprompts_menu'], lambda: None, lambda: {'choices': get_available_softprompts()}, 'refresh-button')
gr.Markdown('Upload a soft prompt (.zip format):')
with gr.Row():
@ -233,6 +249,7 @@ def create_settings_menus(default_preset):
shared.gradio['softprompts_menu'].change(load_soft_prompt, shared.gradio['softprompts_menu'], shared.gradio['softprompts_menu'], show_progress=True)
shared.gradio['upload_softprompt'].upload(upload_soft_prompt, shared.gradio['upload_softprompt'], shared.gradio['softprompts_menu'])
def set_interface_arguments(interface_mode, extensions, bool_active):
modes = ["default", "notebook", "chat", "cai_chat"]
cmd_list = vars(shared.args)
@ -251,6 +268,7 @@ def set_interface_arguments(interface_mode, extensions, bool_active):
shared.need_restart = True
available_models = get_available_models()
available_presets = get_available_presets()
available_characters = get_available_characters()
@ -284,7 +302,7 @@ else:
for i, model in enumerate(available_models):
print(f'{i+1}. {model}')
print(f'\nWhich one do you want to load? 1-{len(available_models)}\n')
i = int(input())-1
i = int(input()) - 1
print()
shared.model_name = available_models[i]
shared.model, shared.tokenizer = load_model(shared.model_name)
@ -297,15 +315,15 @@ if shared.lora_name != "None":
default_text = load_prompt(shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')])
else:
default_text = load_prompt(shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')])
title ='Text generation web UI'
title = 'Text generation web UI'
def create_interface():
gen_events = []
if shared.args.extensions is not None and len(shared.args.extensions) > 0:
extensions_module.load_extensions()
with gr.Blocks(css=ui.css if not shared.is_chat() else ui.css+ui.chat_css, analytics_enabled=False, title=title) as shared.gradio['interface']:
with gr.Blocks(css=ui.css if not shared.is_chat() else ui.css + ui.chat_css, analytics_enabled=False, title=title) as shared.gradio['interface']:
if shared.is_chat():
shared.gradio['Chat input'] = gr.State()
with gr.Tab("Text generation", elem_id="main"):
@ -342,7 +360,7 @@ def create_interface():
shared.gradio['your_picture'] = gr.Image(label='Your picture', type="pil", value=Image.open(Path("cache/pfp_me.png")) if Path("cache/pfp_me.png").exists() else None)
with gr.Row():
shared.gradio['character_menu'] = gr.Dropdown(choices=available_characters, value='None', label='Character', elem_id='character-menu')
ui.create_refresh_button(shared.gradio['character_menu'], lambda : None, lambda : {'choices': get_available_characters()}, 'refresh-button')
ui.create_refresh_button(shared.gradio['character_menu'], lambda: None, lambda: {'choices': get_available_characters()}, 'refresh-button')
with gr.Row():
with gr.Tab('Chat history'):
@ -399,11 +417,11 @@ def create_interface():
# Clear history with confirmation
clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']]
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-confirm'].click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
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-confirm'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
shared.gradio['Clear history-confirm'].click(chat.clear_chat_log, [shared.gradio[k] for k in ['name1', 'name2', 'greeting', 'Chat mode']], shared.gradio['display'])
shared.gradio['Clear history-cancel'].click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
shared.gradio['Chat mode'].change(lambda x : gr.update(visible= x=='instruct'), shared.gradio['Chat mode'], shared.gradio['Instruction templates'])
shared.gradio['Clear history-cancel'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
shared.gradio['Chat mode'].change(lambda x: gr.update(visible=x == 'instruct'), shared.gradio['Chat mode'], shared.gradio['Instruction templates'])
shared.gradio['Remove last'].click(chat.remove_last_message, [shared.gradio[k] for k in ['name1', 'name2', 'Chat mode']], [shared.gradio['display'], shared.gradio['textbox']], show_progress=False)
shared.gradio['download_button'].click(chat.save_history, inputs=[], outputs=[shared.gradio['download']])
@ -412,10 +430,10 @@ def create_interface():
# Clearing stuff and saving the history
for i in ['Generate', 'Regenerate', 'Replace last reply']:
shared.gradio[i].click(lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False)
shared.gradio[i].click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
shared.gradio['Clear history-confirm'].click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
shared.gradio[i].click(lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
shared.gradio['Clear history-confirm'].click(lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
shared.gradio['textbox'].submit(lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False)
shared.gradio['textbox'].submit(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
shared.gradio['textbox'].submit(lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
shared.gradio['character_menu'].change(chat.load_character, [shared.gradio[k] for k in ['character_menu', 'name1', 'name2', 'Chat mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'end_of_turn', 'display']])
shared.gradio['Instruction templates'].change(lambda character, name1, name2, mode: chat.load_character(character, name1, name2, mode), [shared.gradio[k] for k in ['Instruction templates', 'name1', 'name2', 'Chat mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'end_of_turn', 'display']])
@ -430,7 +448,7 @@ def create_interface():
shared.gradio['Chat mode'].change(chat.redraw_html, reload_inputs, [shared.gradio['display']])
shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js+ui.chat_js}}}")
shared.gradio['interface'].load(lambda : chat.load_default_history(shared.settings['name1'], shared.settings['name2']), None, None)
shared.gradio['interface'].load(lambda: chat.load_default_history(shared.settings['name1'], shared.settings['name2']), None, None)
shared.gradio['interface'].load(chat.redraw_html, reload_inputs, [shared.gradio['display']], show_progress=True)
elif shared.args.notebook:
@ -526,7 +544,7 @@ def create_interface():
shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface", type="primary")
shared.gradio['reset_interface'].click(set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'bool_menu']], None)
shared.gradio['reset_interface'].click(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['reset_interface'].click(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 []}')
if shared.args.extensions is not None:
extensions_module.create_extensions_block()
@ -562,6 +580,7 @@ def create_interface():
else:
shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth)
create_interface()
while True: