diff --git a/modules/GPTQ_loader.py b/modules/GPTQ_loader.py index 7dc20b0a..601c58f3 100644 --- a/modules/GPTQ_loader.py +++ b/modules/GPTQ_loader.py @@ -126,7 +126,7 @@ def load_quantized(model_name): path_to_model = Path(f'{shared.args.model_dir}/{model_name}') pt_path = find_quantized_model_file(model_name) if not pt_path: - logger.error("Could not find the quantized model in .pt or .safetensors format, exiting...") + logger.error("Could not find the quantized model in .pt or .safetensors format. Exiting.") exit() else: logger.info(f"Found the following quantized model: {pt_path}") diff --git a/modules/LoRA.py b/modules/LoRA.py index dea476ad..97027eb4 100644 --- a/modules/LoRA.py +++ b/modules/LoRA.py @@ -138,7 +138,7 @@ def add_lora_transformers(lora_names): # Add a LoRA when another LoRA is already present if len(removed_set) == 0 and len(prior_set) > 0 and "__merged" not in shared.model.peft_config.keys(): - logger.info(f"Adding the LoRA(s) named {added_set} to the model...") + logger.info(f"Adding the LoRA(s) named {added_set} to the model") for lora in added_set: shared.model.load_adapter(get_lora_path(lora), lora) diff --git a/modules/extensions.py b/modules/extensions.py index 25fcc0a3..2a3b0bb1 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -31,7 +31,7 @@ def load_extensions(): for i, name in enumerate(shared.args.extensions): if name in available_extensions: if name != 'api': - logger.info(f'Loading the extension "{name}"...') + logger.info(f'Loading the extension "{name}"') try: try: exec(f"import extensions.{name}.script") diff --git a/modules/models.py b/modules/models.py index 5a23f743..f37f3d60 100644 --- a/modules/models.py +++ b/modules/models.py @@ -54,7 +54,7 @@ sampler_hijack.hijack_samplers() def load_model(model_name, loader=None): - logger.info(f"Loading {model_name}...") + logger.info(f"Loading {model_name}") t0 = time.time() shared.is_seq2seq = False diff --git a/modules/shared.py b/modules/shared.py index e0e77362..bcb20905 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -204,22 +204,26 @@ for arg in sys.argv[1:]: if hasattr(args, arg): provided_arguments.append(arg) -# Deprecation warnings deprecated_args = ['notebook', 'chat', 'no_stream', 'mul_mat_q', 'use_fast'] -for k in deprecated_args: - if getattr(args, k): - logger.warning(f'The --{k} flag has been deprecated and will be removed soon. Please remove that flag.') -# Security warnings -if args.trust_remote_code: - logger.warning('trust_remote_code is enabled. This is dangerous.') -if 'COLAB_GPU' not in os.environ and not args.nowebui: - if args.share: - logger.warning("The gradio \"share link\" feature uses a proprietary executable to create a reverse tunnel. Use it with care.") - if any((args.listen, args.share)) and not any((args.gradio_auth, args.gradio_auth_path)): - logger.warning("\nYou are potentially exposing the web UI to the entire internet without any access password.\nYou can create one with the \"--gradio-auth\" flag like this:\n\n--gradio-auth username:password\n\nMake sure to replace username:password with your own.") - if args.multi_user: - logger.warning('\nThe multi-user mode is highly experimental and should not be shared publicly.') + +def do_cmd_flags_warnings(): + + # Deprecation warnings + for k in deprecated_args: + if getattr(args, k): + logger.warning(f'The --{k} flag has been deprecated and will be removed soon. Please remove that flag.') + + # Security warnings + if args.trust_remote_code: + logger.warning('trust_remote_code is enabled. This is dangerous.') + if 'COLAB_GPU' not in os.environ and not args.nowebui: + if args.share: + logger.warning("The gradio \"share link\" feature uses a proprietary executable to create a reverse tunnel. Use it with care.") + if any((args.listen, args.share)) and not any((args.gradio_auth, args.gradio_auth_path)): + logger.warning("\nYou are potentially exposing the web UI to the entire internet without any access password.\nYou can create one with the \"--gradio-auth\" flag like this:\n\n--gradio-auth username:password\n\nMake sure to replace username:password with your own.") + if args.multi_user: + logger.warning('\nThe multi-user mode is highly experimental and should not be shared publicly.') def fix_loader_name(name): diff --git a/modules/training.py b/modules/training.py index ca1fffb3..b0e02400 100644 --- a/modules/training.py +++ b/modules/training.py @@ -249,7 +249,7 @@ def backup_adapter(input_folder): adapter_file = Path(f"{input_folder}/adapter_model.bin") if adapter_file.is_file(): - logger.info("Backing up existing LoRA adapter...") + logger.info("Backing up existing LoRA adapter") creation_date = datetime.fromtimestamp(adapter_file.stat().st_ctime) creation_date_str = creation_date.strftime("Backup-%Y-%m-%d") @@ -406,7 +406,7 @@ def do_train(lora_name: str, always_override: bool, q_proj_en: bool, v_proj_en: # == Prep the dataset, format, etc == if raw_text_file not in ['None', '']: train_template["template_type"] = "raw_text" - logger.info("Loading raw text file dataset...") + logger.info("Loading raw text file dataset") fullpath = clean_path('training/datasets', f'{raw_text_file}') fullpath = Path(fullpath) if fullpath.is_dir(): @@ -486,7 +486,7 @@ def do_train(lora_name: str, always_override: bool, q_proj_en: bool, v_proj_en: prompt = generate_prompt(data_point) return tokenize(prompt, add_eos_token) - logger.info("Loading JSON datasets...") + logger.info("Loading JSON datasets") data = load_dataset("json", data_files=clean_path('training/datasets', f'{dataset}.json')) train_data = data['train'].map(generate_and_tokenize_prompt, new_fingerprint='%030x' % random.randrange(16**30)) @@ -516,13 +516,13 @@ def do_train(lora_name: str, always_override: bool, q_proj_en: bool, v_proj_en: # == Start prepping the model itself == if not hasattr(shared.model, 'lm_head') or hasattr(shared.model.lm_head, 'weight'): - logger.info("Getting model ready...") + logger.info("Getting model ready") prepare_model_for_kbit_training(shared.model) # base model is now frozen and should not be reused for any other LoRA training than this one shared.model_dirty_from_training = True - logger.info("Preparing for training...") + logger.info("Preparing for training") config = LoraConfig( r=lora_rank, lora_alpha=lora_alpha, @@ -540,10 +540,10 @@ def do_train(lora_name: str, always_override: bool, q_proj_en: bool, v_proj_en: model_trainable_params, model_all_params = calc_trainable_parameters(shared.model) try: - logger.info("Creating LoRA model...") + logger.info("Creating LoRA model") lora_model = get_peft_model(shared.model, config) if not always_override and Path(f"{lora_file_path}/adapter_model.bin").is_file(): - logger.info("Loading existing LoRA data...") + logger.info("Loading existing LoRA data") state_dict_peft = torch.load(f"{lora_file_path}/adapter_model.bin", weights_only=True) set_peft_model_state_dict(lora_model, state_dict_peft) except: @@ -648,7 +648,7 @@ def do_train(lora_name: str, always_override: bool, q_proj_en: bool, v_proj_en: json.dump(train_template, file, indent=2) # == Main run and monitor loop == - logger.info("Starting training...") + logger.info("Starting training") yield "Starting..." lora_trainable_param, lora_all_param = calc_trainable_parameters(lora_model) @@ -730,7 +730,7 @@ def do_train(lora_name: str, always_override: bool, q_proj_en: bool, v_proj_en: # Saving in the train thread might fail if an error occurs, so save here if so. if not tracked.did_save: - logger.info("Training complete, saving...") + logger.info("Training complete, saving") lora_model.save_pretrained(lora_file_path) if WANT_INTERRUPT: diff --git a/server.py b/server.py index ae0aed09..d5d11bc4 100644 --- a/server.py +++ b/server.py @@ -12,6 +12,7 @@ os.environ['BITSANDBYTES_NOWELCOME'] = '1' warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated') warnings.filterwarnings('ignore', category=UserWarning, message='Using the update method is deprecated') warnings.filterwarnings('ignore', category=UserWarning, message='Field "model_name" has conflict') +warnings.filterwarnings('ignore', category=UserWarning, message='The value passed into gr.Dropdown()') with RequestBlocker(): import gradio as gr @@ -54,6 +55,7 @@ from modules.models_settings import ( get_model_metadata, update_model_parameters ) +from modules.shared import do_cmd_flags_warnings from modules.utils import gradio @@ -170,6 +172,9 @@ def create_interface(): if __name__ == "__main__": + logger.info("Starting Text generation web UI") + do_cmd_flags_warnings() + # Load custom settings settings_file = None if shared.args.settings is not None and Path(shared.args.settings).exists(): @@ -180,7 +185,7 @@ if __name__ == "__main__": settings_file = Path('settings.json') if settings_file is not None: - logger.info(f"Loading settings from {settings_file}...") + logger.info(f"Loading settings from {settings_file}") file_contents = open(settings_file, 'r', encoding='utf-8').read() new_settings = json.loads(file_contents) if settings_file.suffix == "json" else yaml.safe_load(file_contents) shared.settings.update(new_settings)