Improve several log messages

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}")

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)
 

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")

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

modules/shared.py

@@ -204,8 +204,12 @@ 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']
+
+
+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.')

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:

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)