Merge pull request #5011 from oobabooga/dev

Merge dev branch

extensions/coqui_tts/script.py

@@ -6,27 +6,13 @@ import time
 from pathlib import Path
 
 import gradio as gr
+from TTS.api import TTS
+from TTS.utils.synthesizer import Synthesizer
 
 from modules import chat, shared, ui_chat
-from modules.logging_colors import logger
 from modules.ui import create_refresh_button
 from modules.utils import gradio
 
-try:
-    from TTS.api import TTS
-    from TTS.utils.synthesizer import Synthesizer
-except ModuleNotFoundError:
-    logger.error(
-        "Could not find the TTS module. Make sure to install the requirements for the coqui_tts extension."
-        "\n"
-        "\nLinux / Mac:\npip install -r extensions/coqui_tts/requirements.txt\n"
-        "\nWindows:\npip install -r extensions\\coqui_tts\\requirements.txt\n"
-        "\n"
-        "If you used the one-click installer, paste the command above in the terminal window launched after running the \"cmd_\" script. On Windows, that's \"cmd_windows.bat\"."
-    )
-
-    raise
-
 os.environ["COQUI_TOS_AGREED"] = "1"
 
 params = {

extensions/openai/script.py

@@ -1,5 +1,6 @@
 import asyncio
 import json
+import logging
 import os
 import traceback
 from threading import Thread
@@ -367,6 +368,7 @@ def run_server():
     if shared.args.admin_key and shared.args.admin_key != shared.args.api_key:
         logger.info(f'OpenAI API admin key (for loading/unloading models):\n\n{shared.args.admin_key}\n')
 
+    logging.getLogger("uvicorn.error").propagate = False
     uvicorn.run(app, host=server_addr, port=port, ssl_certfile=ssl_certfile, ssl_keyfile=ssl_keyfile)
 
 

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/chat.py

@@ -95,7 +95,8 @@ def generate_chat_prompt(user_input, state, **kwargs):
     else:
         renderer = chat_renderer
         if state['context'].strip() != '':
-            messages.append({"role": "system", "content": state['context']})
+            context = replace_character_names(state['context'], state['name1'], state['name2'])
+            messages.append({"role": "system", "content": context})
 
     insert_pos = len(messages)
     for user_msg, assistant_msg in reversed(history):

modules/extensions.py

@@ -31,9 +31,14 @@ 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")
+                except ModuleNotFoundError:
+                    logger.error(f"Could not import the requirements for '{name}'. Make sure to install the requirements for the extension.\n\nLinux / Mac:\n\npip install -r extensions/{name}/requirements.txt --upgrade\n\nWindows:\n\npip install -r extensions\\{name}\\requirements.txt --upgrade\n\nIf you used the one-click installer, paste the command above in the terminal window opened after launching the cmd script for your OS.")
+                    raise
+
                 extension = getattr(extensions, name).script
                 apply_settings(extension, name)
                 if extension not in setup_called and hasattr(extension, "setup"):

modules/logging_colors.py

@@ -1,117 +1,67 @@
-# Copied from https://stackoverflow.com/a/1336640
-
 import logging
-import platform
-
-logging.basicConfig(
-    format='%(asctime)s %(levelname)s:%(message)s',
-    datefmt='%Y-%m-%d %H:%M:%S',
-)
-
-
-def add_coloring_to_emit_windows(fn):
-    # add methods we need to the class
-    def _out_handle(self):
-        import ctypes
-        return ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE)
-    out_handle = property(_out_handle)
-
-    def _set_color(self, code):
-        import ctypes
-
-        # Constants from the Windows API
-        self.STD_OUTPUT_HANDLE = -11
-        hdl = ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE)
-        ctypes.windll.kernel32.SetConsoleTextAttribute(hdl, code)
-
-    setattr(logging.StreamHandler, '_set_color', _set_color)
-
-    def new(*args):
-        FOREGROUND_BLUE = 0x0001  # text color contains blue.
-        FOREGROUND_GREEN = 0x0002  # text color contains green.
-        FOREGROUND_RED = 0x0004  # text color contains red.
-        FOREGROUND_INTENSITY = 0x0008  # text color is intensified.
-        FOREGROUND_WHITE = FOREGROUND_BLUE | FOREGROUND_GREEN | FOREGROUND_RED
-        # winbase.h
-        # STD_INPUT_HANDLE = -10
-        # STD_OUTPUT_HANDLE = -11
-        # STD_ERROR_HANDLE = -12
-
-        # wincon.h
-        # FOREGROUND_BLACK = 0x0000
-        FOREGROUND_BLUE = 0x0001
-        FOREGROUND_GREEN = 0x0002
-        # FOREGROUND_CYAN = 0x0003
-        FOREGROUND_RED = 0x0004
-        FOREGROUND_MAGENTA = 0x0005
-        FOREGROUND_YELLOW = 0x0006
-        # FOREGROUND_GREY = 0x0007
-        FOREGROUND_INTENSITY = 0x0008  # foreground color is intensified.
-
-        # BACKGROUND_BLACK = 0x0000
-        # BACKGROUND_BLUE = 0x0010
-        # BACKGROUND_GREEN = 0x0020
-        # BACKGROUND_CYAN = 0x0030
-        # BACKGROUND_RED = 0x0040
-        # BACKGROUND_MAGENTA = 0x0050
-        BACKGROUND_YELLOW = 0x0060
-        # BACKGROUND_GREY = 0x0070
-        BACKGROUND_INTENSITY = 0x0080  # background color is intensified.
-
-        levelno = args[1].levelno
-        if (levelno >= 50):
-            color = BACKGROUND_YELLOW | FOREGROUND_RED | FOREGROUND_INTENSITY | BACKGROUND_INTENSITY
-        elif (levelno >= 40):
-            color = FOREGROUND_RED | FOREGROUND_INTENSITY
-        elif (levelno >= 30):
-            color = FOREGROUND_YELLOW | FOREGROUND_INTENSITY
-        elif (levelno >= 20):
-            color = FOREGROUND_GREEN
-        elif (levelno >= 10):
-            color = FOREGROUND_MAGENTA
-        else:
-            color = FOREGROUND_WHITE
-        args[0]._set_color(color)
-
-        ret = fn(*args)
-        args[0]._set_color(FOREGROUND_WHITE)
-        # print "after"
-        return ret
-    return new
-
-
-def add_coloring_to_emit_ansi(fn):
-    # add methods we need to the class
-    def new(*args):
-        levelno = args[1].levelno
-        if (levelno >= 50):
-            color = '\x1b[31m'  # red
-        elif (levelno >= 40):
-            color = '\x1b[31m'  # red
-        elif (levelno >= 30):
-            color = '\x1b[33m'  # yellow
-        elif (levelno >= 20):
-            color = '\x1b[32m'  # green
-        elif (levelno >= 10):
-            color = '\x1b[35m'  # pink
-        else:
-            color = '\x1b[0m'  # normal
-        args[1].msg = color + args[1].msg + '\x1b[0m'  # normal
-        # print "after"
-        return fn(*args)
-    return new
-
-
-if platform.system() == 'Windows':
-    # Windows does not support ANSI escapes and we are using API calls to set the console color
-    logging.StreamHandler.emit = add_coloring_to_emit_windows(logging.StreamHandler.emit)
-else:
-    # all non-Windows platforms are supporting ANSI escapes so we use them
-    logging.StreamHandler.emit = add_coloring_to_emit_ansi(logging.StreamHandler.emit)
-    # log = logging.getLogger()
-    # log.addFilter(log_filter())
-    # //hdlr = logging.StreamHandler()
-    # //hdlr.setFormatter(formatter())
 
 logger = logging.getLogger('text-generation-webui')
-logger.setLevel(logging.DEBUG)
+
+
+def setup_logging():
+    '''
+    Copied from: https://github.com/vladmandic/automatic
+
+    All credits to vladmandic.
+    '''
+
+    class RingBuffer(logging.StreamHandler):
+        def __init__(self, capacity):
+            super().__init__()
+            self.capacity = capacity
+            self.buffer = []
+            self.formatter = logging.Formatter('{ "asctime":"%(asctime)s", "created":%(created)f, "facility":"%(name)s", "pid":%(process)d, "tid":%(thread)d, "level":"%(levelname)s", "module":"%(module)s", "func":"%(funcName)s", "msg":"%(message)s" }')
+
+        def emit(self, record):
+            msg = self.format(record)
+            # self.buffer.append(json.loads(msg))
+            self.buffer.append(msg)
+            if len(self.buffer) > self.capacity:
+                self.buffer.pop(0)
+
+        def get(self):
+            return self.buffer
+
+    from rich.console import Console
+    from rich.logging import RichHandler
+    from rich.pretty import install as pretty_install
+    from rich.theme import Theme
+    from rich.traceback import install as traceback_install
+
+    level = logging.DEBUG
+    logger.setLevel(logging.DEBUG)  # log to file is always at level debug for facility `sd`
+    console = Console(log_time=True, log_time_format='%H:%M:%S-%f', theme=Theme({
+        "traceback.border": "black",
+        "traceback.border.syntax_error": "black",
+        "inspect.value.border": "black",
+    }))
+    logging.basicConfig(level=logging.ERROR, format='%(asctime)s | %(name)s | %(levelname)s | %(module)s | %(message)s', handlers=[logging.NullHandler()])  # redirect default logger to null
+    pretty_install(console=console)
+    traceback_install(console=console, extra_lines=1, max_frames=10, width=console.width, word_wrap=False, indent_guides=False, suppress=[])
+    while logger.hasHandlers() and len(logger.handlers) > 0:
+        logger.removeHandler(logger.handlers[0])
+
+    # handlers
+    rh = RichHandler(show_time=True, omit_repeated_times=False, show_level=True, show_path=False, markup=False, rich_tracebacks=True, log_time_format='%H:%M:%S-%f', level=level, console=console)
+    rh.setLevel(level)
+    logger.addHandler(rh)
+
+    rb = RingBuffer(100)  # 100 entries default in log ring buffer
+    rb.setLevel(level)
+    logger.addHandler(rb)
+    logger.buffer = rb.buffer
+
+    # overrides
+    logging.getLogger("urllib3").setLevel(logging.ERROR)
+    logging.getLogger("httpx").setLevel(logging.ERROR)
+    logging.getLogger("diffusers").setLevel(logging.ERROR)
+    logging.getLogger("torch").setLevel(logging.ERROR)
+    logging.getLogger("lycoris").handlers = logger.handlers
+
+
+setup_logging()

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
@@ -413,8 +413,12 @@ def ExLlamav2_HF_loader(model_name):
 
 
 def HQQ_loader(model_name):
+    try:
+        from hqq.core.quantize import HQQBackend, HQQLinear
         from hqq.engine.hf import HQQModelForCausalLM
-    from hqq.core.quantize import HQQLinear, HQQBackend
+    except ModuleNotFoundError:
+        logger.error("HQQ is not installed. You can install it with:\n\npip install hqq")
+        return None
 
     logger.info(f"Loading HQQ model with backend: {shared.args.hqq_backend}")
 

modules/shared.py

@@ -204,16 +204,20 @@ 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:
+
+
+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
+    # Security warnings
-if args.trust_remote_code:
+    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 '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)):

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:

requirements.txt

@@ -4,7 +4,6 @@ datasets
 einops
 exllamav2==0.0.11; platform_system != "Darwin" and platform_machine != "x86_64"
 gradio==3.50.*
-hqq==0.1.1
 markdown
 numpy==1.24.*
 optimum==1.16.*

requirements_amd.txt

@@ -4,7 +4,6 @@ datasets
 einops
 exllamav2==0.0.11; platform_system == "Windows" or python_version < "3.10" or python_version > "3.11" or platform_machine != "x86_64"
 gradio==3.50.*
-hqq==0.1.1
 markdown
 numpy==1.24.*
 optimum==1.16.*

requirements_amd_noavx2.txt

@@ -4,7 +4,6 @@ datasets
 einops
 exllamav2==0.0.11; platform_system == "Windows" or python_version < "3.10" or python_version > "3.11" or platform_machine != "x86_64"
 gradio==3.50.*
-hqq==0.1.1
 markdown
 numpy==1.24.*
 optimum==1.16.*

requirements_apple_intel.txt

@@ -4,7 +4,6 @@ datasets
 einops
 exllamav2==0.0.11
 gradio==3.50.*
-hqq==0.1.1
 markdown
 numpy==1.24.*
 optimum==1.16.*

requirements_apple_silicon.txt

@@ -4,7 +4,6 @@ datasets
 einops
 exllamav2==0.0.11
 gradio==3.50.*
-hqq==0.1.1
 markdown
 numpy==1.24.*
 optimum==1.16.*

requirements_cpu_only.txt

@@ -4,7 +4,6 @@ datasets
 einops
 exllamav2==0.0.11
 gradio==3.50.*
-hqq==0.1.1
 markdown
 numpy==1.24.*
 optimum==1.16.*

requirements_cpu_only_noavx2.txt

@@ -4,7 +4,6 @@ datasets
 einops
 exllamav2==0.0.11
 gradio==3.50.*
-hqq==0.1.1
 markdown
 numpy==1.24.*
 optimum==1.16.*

requirements_noavx2.txt

@@ -4,7 +4,6 @@ datasets
 einops
 exllamav2==0.0.11; platform_system != "Darwin" and platform_machine != "x86_64"
 gradio==3.50.*
-hqq==0.1.1
 markdown
 numpy==1.24.*
 optimum==1.16.*

requirements_nowheels.txt

@@ -4,7 +4,6 @@ datasets
 einops
 exllamav2==0.0.11
 gradio==3.50.*
-hqq==0.1.1
 markdown
 numpy==1.24.*
 optimum==1.16.*

server.py

@@ -12,6 +12,8 @@ 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()')
+warnings.filterwarnings('ignore', category=UserWarning, message='Field "model_names" has conflict')
 
 with RequestBlocker():
     import gradio as gr
@@ -54,6 +56,7 @@ 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 +173,9 @@ 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 +186,7 @@ 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)