mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
Make the code more like PEP8 for readability (#862)
This commit is contained in:
oobabooga
GitHub
parent
848c4edfd5
commit
ea6e77df72
@ -17,6 +17,7 @@ def random_hash():
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letters = string.ascii_lowercase + string.digits
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return ''.join(random.choice(letters) for i in range(9))
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async def run(context):
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server = "127.0.0.1"
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params = {
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@ -69,6 +70,7 @@ async def run(context):
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prompt = "What I would like to say is the following: "
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async def get_result():
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async for response in run(prompt):
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# Print intermediate steps
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@ -17,6 +17,7 @@ parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpForma
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parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.")
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args = parser.parse_args()
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def disable_torch_init():
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"""
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Disable the redundant torch default initialization to accelerate model creation.
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@ -31,12 +32,14 @@ def disable_torch_init():
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torch_layer_norm_init_backup = torch.nn.LayerNorm.reset_parameters
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setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
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def restore_torch_init():
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"""Rollback the change made by disable_torch_init."""
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import torch
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setattr(torch.nn.Linear, "reset_parameters", torch_linear_init_backup)
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setattr(torch.nn.LayerNorm, "reset_parameters", torch_layer_norm_init_backup)
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if __name__ == '__main__':
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path = Path(args.MODEL)
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model_name = path.name
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@ -29,6 +29,7 @@ parser.add_argument('--clean', action='store_true', help='Does not resume the pr
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parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.')
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args = parser.parse_args()
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def get_file(url, output_folder):
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filename = Path(url.rsplit('/', 1)[1])
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output_path = output_folder / filename
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@ -54,6 +55,7 @@ def get_file(url, output_folder):
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t.update(len(data))
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f.write(data)
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def sanitize_branch_name(branch_name):
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pattern = re.compile(r"^[a-zA-Z0-9._-]+$")
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if pattern.match(branch_name):
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@ -61,6 +63,7 @@ def sanitize_branch_name(branch_name):
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else:
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raise ValueError("Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.")
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def select_model_from_default_options():
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models = {
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"OPT 6.7B": ("facebook", "opt-6.7b", "main"),
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@ -106,6 +109,7 @@ EleutherAI/pythia-1.4b-deduped
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return model, branch
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def get_download_links_from_huggingface(model, branch):
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base = "https://huggingface.co"
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page = f"/api/models/{model}/tree/{branch}?cursor="
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@ -172,9 +176,11 @@ def get_download_links_from_huggingface(model, branch):
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return links, sha256, is_lora
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def download_files(file_list, output_folder, num_threads=8):
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thread_map(lambda url: get_file(url, output_folder), file_list, max_workers=num_threads, disable=True)
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if __name__ == '__main__':
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model = args.MODEL
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branch = args.branch
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@ -9,6 +9,7 @@ params = {
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'port': 5000,
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}
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class Handler(BaseHTTPRequestHandler):
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def do_GET(self):
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if self.path == '/api/v1/model':
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@ -32,7 +33,7 @@ class Handler(BaseHTTPRequestHandler):
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self.end_headers()
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prompt = body['prompt']
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prompt_lines = [l.strip() for l in prompt.split('\n')]
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prompt_lines = [k.strip() for k in prompt.split('\n')]
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max_context = body.get('max_context_length', 2048)
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@ -95,5 +96,6 @@ def run_server():
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print(f'Starting KoboldAI compatible api at http://{server_addr[0]}:{server_addr[1]}/api')
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server.serve_forever()
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def setup():
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Thread(target=run_server, daemon=True).start()
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@ -5,6 +5,7 @@ params = {
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"bias string": " *I am so happy*",
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}
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def input_modifier(string):
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"""
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This function is applied to your text inputs before
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@ -13,6 +14,7 @@ def input_modifier(string):
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return string
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def output_modifier(string):
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"""
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This function is applied to the model outputs.
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@ -20,6 +22,7 @@ def output_modifier(string):
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return string
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def bot_prefix_modifier(string):
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"""
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This function is only applied in chat mode. It modifies
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@ -27,11 +30,12 @@ def bot_prefix_modifier(string):
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behavior.
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"""
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if params['activate'] == True:
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if params['activate']:
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return f'{string} {params["bias string"].strip()} '
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else:
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return string
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def ui():
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# Gradio elements
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activate = gr.Checkbox(value=params['activate'], label='Activate character bias')
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@ -22,6 +22,8 @@ if not shared.args.no_stream:
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raise ValueError
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# Check if the API is valid and refresh the UI accordingly.
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def check_valid_api():
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global user, user_info, params
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@ -29,7 +31,7 @@ def check_valid_api():
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user = ElevenLabsUser(params['api_key'])
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user_info = user._get_subscription_data()
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print('checking api')
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if params['activate'] == False:
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if not params['activate']:
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return gr.update(value='Disconnected')
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elif user_info is None:
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print('Incorrect API Key')
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@ -39,6 +41,8 @@ def check_valid_api():
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return gr.update(value='Connected')
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# Once the API is verified, get the available voices and update the dropdown list
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def refresh_voices():
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global user, user_info
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@ -51,11 +55,13 @@ def refresh_voices():
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else:
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return
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def remove_surrounded_chars(string):
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# this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
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# 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
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return re.sub('\*[^\*]*?(\*|$)', '', string)
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def input_modifier(string):
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"""
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This function is applied to your text inputs before
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@ -64,6 +70,7 @@ def input_modifier(string):
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return string
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def output_modifier(string):
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"""
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This function is applied to the model outputs.
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@ -71,9 +78,9 @@ def output_modifier(string):
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global params, wav_idx, user, user_info
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if params['activate'] == False:
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if not params['activate']:
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return string
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elif user_info == None:
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elif user_info is None:
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return string
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string = remove_surrounded_chars(string)
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@ -94,6 +101,7 @@ def output_modifier(string):
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wav_idx += 1
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return string
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def ui():
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# Gradio elements
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@ -7,6 +7,7 @@ params = {
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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'}
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def input_modifier(string):
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"""
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This function is applied to your text inputs before
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@ -15,6 +16,7 @@ def input_modifier(string):
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return GoogleTranslator(source=params['language string'], target='en').translate(string)
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def output_modifier(string):
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"""
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This function is applied to the model outputs.
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@ -22,6 +24,7 @@ def output_modifier(string):
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return GoogleTranslator(source='en', target=params['language string']).translate(string)
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def bot_prefix_modifier(string):
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"""
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This function is only applied in chat mode. It modifies
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@ -31,6 +34,7 @@ def bot_prefix_modifier(string):
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return string
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def ui():
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# Finding the language name from the language code to use as the default value
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language_name = list(language_codes.keys())[list(language_codes.values()).index(params['language string'])]
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@ -4,12 +4,14 @@ import pandas as pd
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df = pd.read_csv("https://raw.githubusercontent.com/devbrones/llama-prompts/main/prompts/prompts.csv")
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def get_prompt_by_name(name):
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if name == 'None':
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return ''
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else:
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return df[df['Prompt name'] == name].iloc[0]['Prompt'].replace('\\n', '\n')
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def ui():
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if not shared.is_chat():
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choices = ['None'] + list(df['Prompt name'])
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@ -30,12 +30,15 @@ streaming_state = shared.args.no_stream # remember if chat streaming was enabled
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picture_response = False # specifies if the next model response should appear as a picture
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pic_id = 0
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def remove_surrounded_chars(string):
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# this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
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# 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
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return re.sub('\*[^\*]*?(\*|$)', '', string)
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# I don't even need input_hijack for this as visible text will be commited to history as the unmodified string
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def input_modifier(string):
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"""
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This function is applied to your text inputs before
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@ -62,6 +65,8 @@ def input_modifier(string):
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return string
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# Get and save the Stable Diffusion-generated picture
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def get_SD_pictures(description):
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global params, pic_id
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@ -101,6 +106,8 @@ def get_SD_pictures(description):
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# TODO: how do I make the UI history ignore the resulting pictures (I don't want HTML to appear in history)
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# and replace it with 'text' for the purposes of logging?
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def output_modifier(string):
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"""
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This function is applied to the model outputs.
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@ -130,6 +137,7 @@ def output_modifier(string):
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shared.args.no_stream = streaming_state
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return image + "\n" + text
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def bot_prefix_modifier(string):
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"""
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This function is only applied in chat mode. It modifies
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@ -139,10 +147,12 @@ def bot_prefix_modifier(string):
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return string
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def force_pic():
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global picture_response
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picture_response = True
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def ui():
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# Gradio elements
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@ -17,11 +17,13 @@ input_hijack = {
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processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
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model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float32).to("cpu")
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def caption_image(raw_image):
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inputs = processor(raw_image.convert('RGB'), return_tensors="pt").to("cpu", torch.float32)
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out = model.generate(**inputs, max_new_tokens=100)
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return processor.decode(out[0], skip_special_tokens=True)
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def generate_chat_picture(picture, name1, name2):
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text = f'*{name1} sends {name2} a picture that contains the following: "{caption_image(picture)}"*'
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# 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
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@ -32,6 +34,7 @@ def generate_chat_picture(picture, name1, name2):
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visible_text = f'<img src="data:image/jpeg;base64,{img_str}" alt="{text}">'
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return text, visible_text
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def ui():
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picture_select = gr.Image(label='Send a picture', type='pil')
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@ -17,9 +17,11 @@ from quant import make_quant
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def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=['lm_head'], kernel_switch_threshold=128):
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config = AutoConfig.from_pretrained(model)
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def noop(*args, **kwargs):
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pass
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config = AutoConfig.from_pretrained(model)
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torch.nn.init.kaiming_uniform_ = noop
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torch.nn.init.uniform_ = noop
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torch.nn.init.normal_ = noop
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@ -64,6 +66,7 @@ def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exc
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return model
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def load_quantized(model_name):
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if not shared.args.model_type:
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# Try to determine model type from model name
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@ -13,6 +13,7 @@ def reload_model():
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clear_torch_cache()
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shared.model, shared.tokenizer = load_model(shared.model_name)
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def add_lora_to_model(lora_name):
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# If a LoRA had been previously loaded, or if we want
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@ -54,6 +54,7 @@ class RWKVModel:
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reply += token
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yield reply
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class RWKVTokenizer:
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def __init__(self):
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pass
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@ -28,6 +28,7 @@ def generate_reply_wrapper(string):
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for i in generate_reply(params[0], generate_params):
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yield i
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def create_apis():
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t1 = gr.Textbox(visible=False)
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t2 = gr.Textbox(visible=False)
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@ -30,6 +30,7 @@ class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
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return True
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return False
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class Stream(transformers.StoppingCriteria):
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def __init__(self, callback_func=None):
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self.callback_func = callback_func
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@ -39,6 +40,7 @@ class Stream(transformers.StoppingCriteria):
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self.callback_func(input_ids[0])
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return False
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class Iteratorize:
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"""
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@ -96,6 +98,7 @@ class Iteratorize:
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self.stop_now = True
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clear_torch_cache()
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def clear_torch_cache():
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gc.collect()
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if not shared.args.cpu:
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@ -23,7 +23,6 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
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end_of_turn = kwargs['end_of_turn'] if 'end_of_turn' in kwargs else ''
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impersonate = kwargs['impersonate'] if 'impersonate' in kwargs else False
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also_return_rows = kwargs['also_return_rows'] if 'also_return_rows' in kwargs else False
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rows = [f"{context.strip()}\n"]
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# Finding the maximum prompt size
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@ -68,6 +67,7 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
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else:
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return prompt
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def extract_message_from_reply(reply, name1, name2, stop_at_newline):
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next_character_found = False
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@ -98,6 +98,7 @@ def extract_message_from_reply(reply, name1, name2, stop_at_newline):
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reply = fix_newlines(reply)
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return reply, next_character_found
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def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn, regenerate=False):
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if mode == 'instruct':
|
||||
stopping_strings = [f"\n{name1}", f"\n{name2}"]
|
||||
@ -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'):
|
||||
@ -167,6 +168,7 @@ 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}"]
|
||||
@ -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)
|
||||
@ -213,6 +217,7 @@ def regenerate_wrapper(text, generate_state, name1, name2, context, mode, end_of
|
||||
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 = []
|
||||
|
||||
@ -288,6 +299,7 @@ def tokenize_dialogue(dialogue, name1, name2, mode):
|
||||
|
||||
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:
|
||||
@ -323,10 +336,12 @@ def load_history(file, name1, name2):
|
||||
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'] = []
|
||||
@ -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:
|
||||
|
||||
@ -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]):
|
||||
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
|
||||
|
||||
|
||||
@ -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}', '```')
|
||||
@ -40,11 +43,13 @@ def convert_to_markdown(string):
|
||||
string = fix_newlines(string)
|
||||
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 = ''
|
||||
@ -98,6 +104,7 @@ 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)
|
||||
if image.size[1] > 470:
|
||||
@ -105,6 +112,7 @@ def make_thumbnail(image):
|
||||
|
||||
return image
|
||||
|
||||
|
||||
def get_image_cache(path):
|
||||
cache_folder = Path("cache")
|
||||
if not cache_folder.exists():
|
||||
@ -119,6 +127,7 @@ 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]):
|
||||
@ -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">'
|
||||
|
||||
@ -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)
|
||||
|
||||
@ -6,8 +6,6 @@ Documentation:
|
||||
https://abetlen.github.io/llama-cpp-python/
|
||||
'''
|
||||
|
||||
import multiprocessing
|
||||
|
||||
from llama_cpp import Llama
|
||||
|
||||
from modules import shared
|
||||
|
||||
@ -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
|
||||
|
||||
@ -61,6 +61,7 @@ settings = {
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
def str2bool(v):
|
||||
if isinstance(v, bool):
|
||||
return v
|
||||
@ -71,6 +72,7 @@ def str2bool(v):
|
||||
else:
|
||||
raise argparse.ArgumentTypeError('Boolean value expected.')
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54))
|
||||
|
||||
# Basic settings
|
||||
@ -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
|
||||
|
||||
@ -21,6 +21,7 @@ def get_max_prompt_length(tokens):
|
||||
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))
|
||||
@ -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()):
|
||||
@ -53,6 +55,7 @@ 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)
|
||||
@ -61,6 +64,8 @@ def generate_softprompt_input_tensors(input_ids):
|
||||
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'])
|
||||
|
||||
@ -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")
|
||||
@ -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
|
||||
@ -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"
|
||||
|
||||
@ -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()
|
||||
|
||||
25
server.py
25
server.py
@ -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)
|
||||
|
||||
|
||||
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 ''
|
||||
@ -146,6 +161,7 @@ def load_prompt(fname):
|
||||
text = text[:-1]
|
||||
return text
|
||||
|
||||
|
||||
def create_prompt_menus():
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
@ -161,6 +177,7 @@ 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():
|
||||
@ -175,6 +192,7 @@ def create_model_menus():
|
||||
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']:
|
||||
@ -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():
|
||||
@ -219,7 +236,6 @@ 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')
|
||||
@ -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()
|
||||
@ -299,8 +317,8 @@ 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'
|
||||
|
||||
def create_interface():
|
||||
|
||||
def create_interface():
|
||||
gen_events = []
|
||||
if shared.args.extensions is not None and len(shared.args.extensions) > 0:
|
||||
extensions_module.load_extensions()
|
||||
@ -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:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user