import argparse import sys from collections import OrderedDict from pathlib import Path import yaml from modules.logging_colors import logger # Model variables model = None tokenizer = None model_name = "None" is_seq2seq = False model_dirty_from_training = False lora_names = [] # Generation variables stop_everything = False generation_lock = None processing_message = '*Is typing...*' # UI variables gradio = {} persistent_interface_state = {} need_restart = False # UI defaults settings = { 'dark_theme': True, 'show_controls': True, 'start_with': '', 'mode': 'chat', 'chat_style': 'cai-chat', 'prompt-default': 'QA', 'prompt-notebook': 'QA', 'preset': 'simple-1', 'max_new_tokens': 200, 'max_new_tokens_min': 1, 'max_new_tokens_max': 4096, 'seed': -1, 'negative_prompt': '', 'truncation_length': 2048, 'truncation_length_min': 0, 'truncation_length_max': 16384, 'custom_stopping_strings': '', 'auto_max_new_tokens': False, 'max_tokens_second': 0, 'ban_eos_token': False, 'custom_token_bans': '', 'add_bos_token': True, 'skip_special_tokens': True, 'stream': True, 'name1': 'You', 'character': 'Assistant', 'instruction_template': 'Alpaca', 'chat-instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>', 'autoload_model': False, 'default_extensions': ['gallery'], } 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='DEPRECATED') parser.add_argument('--chat', action='store_true', help='DEPRECATED') parser.add_argument('--multi-user', action='store_true', help='Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental.') 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='DEPRECATED') parser.add_argument('--settings', type=str, help='Load the default interface settings from this yaml file. See settings-template.yaml for an example. If you create a file called settings.yaml, 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.') parser.add_argument('--chat-buttons', action='store_true', help='Show buttons on chat tab instead of hover menu.') # Model loader parser.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv') # 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='Maximum 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 (using bitsandbytes).') 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 and Falcon.") parser.add_argument('--use_fast', action='store_true', help="Set use_fast=True while loading a tokenizer.") # Accelerate 4-bit parser.add_argument('--load-in-4bit', action='store_true', help='Load the model with 4-bit precision (using bitsandbytes).') parser.add_argument('--compute_dtype', type=str, default="float16", help="compute dtype for 4-bit. Valid options: bfloat16, float16, float32.") parser.add_argument('--quant_type', type=str, default="nf4", help='quant_type for 4-bit. Valid options: nf4, fp4.') parser.add_argument('--use_double_quant', action='store_true', help='use_double_quant for 4-bit.') # llama.cpp parser.add_argument('--threads', type=int, default=0, help='Number of threads to use.') parser.add_argument('--n_batch', type=int, default=512, help='Maximum number of prompt tokens to batch together when calling llama_eval.') parser.add_argument('--no-mmap', action='store_true', help='Prevent mmap from being used.') parser.add_argument('--low-vram', action='store_true', help='Low VRAM Mode') parser.add_argument('--mlock', action='store_true', help='Force the system to keep the model in RAM.') parser.add_argument('--mul_mat_q', action='store_true', help='Activate new mulmat kernels.') parser.add_argument('--cache-capacity', type=str, help='Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.') parser.add_argument('--n-gpu-layers', type=int, default=0, help='Number of layers to offload to the GPU.') parser.add_argument('--tensor_split', type=str, default=None, help="Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17") parser.add_argument('--n_ctx', type=int, default=2048, help='Size of the prompt context.') parser.add_argument('--llama_cpp_seed', type=int, default=0, help='Seed for llama-cpp models. Default 0 (random)') # 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, nargs="+", help='The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60.') parser.add_argument('--checkpoint', type=str, help='The path to the quantized checkpoint file. If not specified, it will be automatically detected.') parser.add_argument('--monkey-patch', action='store_true', help='Apply the monkey patch for using LoRAs with quantized models.') # AutoGPTQ parser.add_argument('--triton', action='store_true', help='Use triton.') parser.add_argument('--no_inject_fused_attention', action='store_true', help='Do not use fused attention (lowers VRAM requirements).') parser.add_argument('--no_inject_fused_mlp', action='store_true', help='Triton mode only: Do not use fused MLP (lowers VRAM requirements).') parser.add_argument('--no_use_cuda_fp16', action='store_true', help='This can make models faster on some systems.') parser.add_argument('--desc_act', action='store_true', help='For models that don\'t have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig.') parser.add_argument('--disable_exllama', action='store_true', help='Disable ExLlama kernel, which can improve inference speed on some systems.') # ExLlama parser.add_argument('--gpu-split', type=str, help="Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. 20,7,7") parser.add_argument('--max_seq_len', type=int, default=2048, help="Maximum sequence length.") parser.add_argument('--cfg-cache', action='store_true', help="ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama.") # 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.') # RoPE parser.add_argument('--alpha_value', type=float, default=1, help="Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both.") parser.add_argument('--rope_freq_base', type=int, default=0, help="If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63).") parser.add_argument('--compress_pos_emb', type=int, default=1, help="Positional embeddings compression factor. Should be set to (context length) / (model\'s original context length). Equal to 1/rope_freq_scale.") # 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", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) 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) parser.add_argument("--ssl-keyfile", type=str, help='The path to the SSL certificate key file.', default=None) parser.add_argument("--ssl-certfile", type=str, help='The path to the SSL certificate cert file.', default=None) # API parser.add_argument('--api', action='store_true', help='Enable the API extension.') parser.add_argument('--api-blocking-port', type=int, default=5000, help='The listening port for the blocking API.') parser.add_argument('--api-streaming-port', type=int, default=5005, help='The listening port for the streaming API.') parser.add_argument('--public-api', action='store_true', help='Create a public URL for the API using Cloudfare.') parser.add_argument('--public-api-id', type=str, help='Tunnel ID for named Cloudflare Tunnel. Use together with public-api option.', default=None) # Multimodal parser.add_argument('--multimodal-pipeline', type=str, default=None, help='The multimodal pipeline to use. Examples: llava-7b, llava-13b.') args = parser.parse_args() args_defaults = parser.parse_args([]) provided_arguments = [] for arg in sys.argv[1:]: arg = arg.lstrip('-').replace('-', '_') if hasattr(args, arg): provided_arguments.append(arg) # Deprecation warnings for k in ['chat', 'notebook', 'no_stream']: 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 args.share: logger.warning("The gradio \"share link\" feature uses a proprietary executable to create a reverse tunnel. Use it with care.") if args.multi_user: logger.warning("The multi-user mode is highly experimental. DO NOT EXPOSE IT TO THE INTERNET.") def fix_loader_name(name): if not name: return name name = name.lower() if name in ['llamacpp', 'llama.cpp', 'llama-cpp', 'llama cpp']: return 'llama.cpp' if name in ['llamacpp_hf', 'llama.cpp_hf', 'llama-cpp-hf', 'llamacpp-hf', 'llama.cpp-hf']: return 'llamacpp_HF' elif name in ['transformers', 'huggingface', 'hf', 'hugging_face', 'hugging face']: return 'Transformers' elif name in ['autogptq', 'auto-gptq', 'auto_gptq', 'auto gptq']: return 'AutoGPTQ' elif name in ['gptq-for-llama', 'gptqforllama', 'gptqllama', 'gptq for llama', 'gptq_for_llama']: return 'GPTQ-for-LLaMa' elif name in ['exllama', 'ex-llama', 'ex_llama', 'exlama']: return 'ExLlama' elif name in ['exllama-hf', 'exllama_hf', 'exllama hf', 'ex-llama-hf', 'ex_llama_hf']: return 'ExLlama_HF' elif name in ['exllamav2', 'exllama-v2', 'ex_llama-v2', 'exlamav2', 'exlama-v2', 'exllama2', 'exllama-2']: return 'ExLlamav2' elif name in ['exllamav2-hf', 'exllamav2_hf', 'exllama-v2-hf', 'exllama_v2_hf', 'exllama-v2_hf', 'exllama2-hf', 'exllama2_hf', 'exllama-2-hf', 'exllama_2_hf', 'exllama-2_hf']: return 'ExLlamav2_HF' elif name in ['ctransformers', 'ctranforemrs', 'ctransformer']: return 'ctransformers' def add_extension(name): if args.extensions is None: args.extensions = [name] elif 'api' not in args.extensions: args.extensions.append(name) def is_chat(): return True args.loader = fix_loader_name(args.loader) # Activate the API extension if args.api or args.public_api: add_extension('api') # Activate the multimodal extension if args.multimodal_pipeline is not None: add_extension('multimodal') # Load model-specific settings 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 = {} # Load custom model-specific settings with Path(f'{args.model_dir}/config-user.yaml') as p: if p.exists(): user_config = yaml.safe_load(open(p, 'r').read()) else: user_config = {} model_config = OrderedDict(model_config) user_config = OrderedDict(user_config)