import argparse from pathlib import Path import yaml model = None tokenizer = None model_name = "None" model_type = None lora_names = [] soft_prompt_tensor = None soft_prompt = False # Chat variables history = {'internal': [], 'visible': []} character = 'None' stop_everything = False processing_message = '*Is typing...*' # UI elements (buttons, sliders, HTML, etc) gradio = {} # For keeping the values of UI elements on page reload persistent_interface_state = {} # Generation input parameters input_params = [] # For restarting the interface need_restart = False settings = { 'max_new_tokens': 200, 'max_new_tokens_min': 1, 'max_new_tokens_max': 2000, 'seed': -1, 'character': 'None', 'name1': 'You', 'name2': 'Assistant', 'context': 'This is a conversation with your Assistant. The Assistant is very helpful and is eager to chat with you and answer your questions.', 'greeting': '', 'turn_template': '', 'custom_stopping_strings': '', 'stop_at_newline': False, 'add_bos_token': True, 'ban_eos_token': False, 'skip_special_tokens': True, 'truncation_length': 2048, 'truncation_length_min': 0, 'truncation_length_max': 8192, 'mode': 'cai-chat', 'instruction_template': 'None', 'chat_prompt_size': 2048, 'chat_prompt_size_min': 0, 'chat_prompt_size_max': 2048, 'chat_generation_attempts': 1, 'chat_generation_attempts_min': 1, 'chat_generation_attempts_max': 5, 'default_extensions': [], 'chat_default_extensions': ["gallery"], 'presets': { 'default': 'Default', '.*(alpaca|llama|llava)': "LLaMA-Precise", '.*pygmalion': 'NovelAI-Storywriter', '.*RWKV': 'Naive', }, 'prompts': { 'default': 'QA', '.*(gpt4chan|gpt-4chan|4chan)': 'GPT-4chan', '.*oasst': 'Open Assistant', '.*alpaca': "Alpaca", }, 'lora_prompts': { 'default': 'QA', '.*alpaca': "Alpaca", } } def str2bool(v): if isinstance(v, bool): return v if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') 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.') parser.add_argument('--chat', action='store_true', help='Launch the web UI in chat mode with a style similar to the Character.AI website.') parser.add_argument('--cai-chat', action='store_true', help='DEPRECATED: use --chat instead.') parser.add_argument('--character', type=str, help='The name of the character to load in chat mode by default.') parser.add_argument('--model', type=str, help='Name of the model to load by default.') parser.add_argument('--lora', type=str, nargs="+", help='The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.') parser.add_argument("--model-dir", type=str, default='models/', help="Path to directory with all the models") parser.add_argument("--lora-dir", type=str, default='loras/', help="Path to directory with all the loras") parser.add_argument('--model-menu', action='store_true', help='Show a model menu in the terminal when the web UI is first launched.') parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time.') parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example. If you create a file called settings.json, this file will be loaded by default without the need to use the --settings flag.') parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.') parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.') # Accelerate/transformers parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.') parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.') parser.add_argument('--gpu-memory', type=str, nargs="+", help='Maxmimum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB.') parser.add_argument('--cpu-memory', type=str, help='Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.') parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.') parser.add_argument('--disk-cache-dir', type=str, default="cache", help='Directory to save the disk cache to. Defaults to "cache".') parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.') parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.') parser.add_argument('--no-cache', action='store_true', help='Set use_cache to False while generating text. This reduces the VRAM usage a bit at a performance cost.') parser.add_argument('--xformers', action='store_true', help="Use xformer's memory efficient attention. This should increase your tokens/s.") parser.add_argument('--sdp-attention', action='store_true', help="Use torch 2.0's sdp attention.") parser.add_argument('--trust-remote-code', action='store_true', help="Set trust_remote_code=True while loading a model. Necessary for ChatGLM.") # llama.cpp parser.add_argument('--threads', type=int, default=0, help='Number of threads to use in llama.cpp.') parser.add_argument('--n_batch', type=int, default=8, help='Processing batch size for llama.cpp.') # GPTQ parser.add_argument('--wbits', type=int, default=0, help='Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.') parser.add_argument('--model_type', type=str, help='Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.') parser.add_argument('--groupsize', type=int, default=-1, help='Group size.') parser.add_argument('--pre_layer', type=int, default=0, help='The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models.') parser.add_argument('--monkey-patch', action='store_true', help='Apply the monkey patch for using LoRAs with quantized models.') parser.add_argument('--quant_attn', action='store_true', help='(triton) Enable quant attention.') parser.add_argument('--warmup_autotune', action='store_true', help='(triton) Enable warmup autotune.') parser.add_argument('--fused_mlp', action='store_true', help='(triton) Enable fused mlp.') # FlexGen parser.add_argument('--flexgen', action='store_true', help='Enable the use of FlexGen offloading.') parser.add_argument('--percent', type=int, nargs="+", default=[0, 100, 100, 0, 100, 0], help='FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0).') parser.add_argument("--compress-weight", action="store_true", help="FlexGen: activate weight compression.") parser.add_argument("--pin-weight", type=str2bool, nargs="?", const=True, default=True, help="FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%%).") # DeepSpeed parser.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.') parser.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory to use for ZeRO-3 NVME offloading.') parser.add_argument('--local_rank', type=int, default=0, help='DeepSpeed: Optional argument for distributed setups.') # RWKV parser.add_argument('--rwkv-strategy', type=str, default=None, help='RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".') parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile the CUDA kernel for better performance.') # Gradio parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.') parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.') parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.') parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.') parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.') parser.add_argument("--gradio-auth-path", type=str, help='Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3"', default=None) # API parser.add_argument('--api', action='store_true', help='Enable the API extension.') parser.add_argument('--public-api', action='store_true', help='Create a public URL for the API using Cloudfare.') args = parser.parse_args() args_defaults = parser.parse_args([]) # Deprecation warnings for parameters that have been renamed deprecated_dict = {} for k in deprecated_dict: if getattr(args, k) != deprecated_dict[k][1]: print(f"Warning: --{k} is deprecated and will be removed. Use --{deprecated_dict[k][0]} instead.\n") setattr(args, deprecated_dict[k][0], getattr(args, k)) # Deprecation warnings for parameters that have been removed if args.cai_chat: print("Warning: --cai-chat is deprecated. Use --chat instead.\n") args.chat = True # Security warnings if args.trust_remote_code: print("Warning: trust_remote_code is enabled. This is dangerous.\n") if args.share: print("Warning: the gradio \"share link\" feature downloads a proprietary and\nunaudited blob to create a reverse tunnel. This is potentially dangerous.\n") # Activating the API extension if args.api or args.public_api: if args.extensions is None: args.extensions = ['api'] elif 'api' not in args.extensions: args.extensions.append('api') def is_chat(): return args.chat # Loading model-specific settings (default) with Path(f'{args.model_dir}/config.yaml') as p: if p.exists(): model_config = yaml.safe_load(open(p, 'r').read()) else: model_config = {} # Applying user-defined model settings with Path(f'{args.model_dir}/config-user.yaml') as p: if p.exists(): user_config = yaml.safe_load(open(p, 'r').read()) for k in user_config: if k in model_config: model_config[k].update(user_config[k]) else: model_config[k] = user_config[k]