import argparse import copy import os 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': 'min_p', 'max_new_tokens': 512, 'max_new_tokens_min': 1, 'max_new_tokens_max': 4096, 'negative_prompt': '', 'seed': -1, 'truncation_length': 2048, 'truncation_length_min': 0, 'truncation_length_max': 200000, 'max_tokens_second': 0, 'max_updates_second': 0, 'prompt_lookup_num_tokens': 0, 'custom_stopping_strings': '', 'custom_token_bans': '', 'auto_max_new_tokens': False, 'ban_eos_token': False, 'add_bos_token': True, 'skip_special_tokens': True, 'stream': True, 'character': 'Assistant', 'name1': 'You', 'user_bio': '', 'custom_system_message': '', 'instruction_template_str': "{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {%- set ns.found = true -%}\n {%- endif -%}\n{%- endfor -%}\n{%- if not ns.found -%}\n {{- '' + 'Below is an instruction that describes a task. Write a response that appropriately completes the request.' + '\\n\\n' -}}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' -%}\n {{- '' + message['content'] + '\\n\\n' -}}\n {%- else -%}\n {%- if message['role'] == 'user' -%}\n {{-'### Instruction:\\n' + message['content'] + '\\n\\n'-}}\n {%- else -%}\n {{-'### Response:\\n' + message['content'] + '\\n\\n' -}}\n {%- endif -%}\n {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n {{-'### Response:\\n'-}}\n{%- endif -%}", 'chat_template_str': "{%- for message in messages %}\n {%- if message['role'] == 'system' -%}\n {%- if message['content'] -%}\n {{- message['content'] + '\\n\\n' -}}\n {%- endif -%}\n {%- if user_bio -%}\n {{- user_bio + '\\n\\n' -}}\n {%- endif -%}\n {%- else -%}\n {%- if message['role'] == 'user' -%}\n {{- name1 + ': ' + message['content'] + '\\n'-}}\n {%- else -%}\n {{- name2 + ': ' + message['content'] + '\\n' -}}\n {%- endif -%}\n {%- endif -%}\n{%- endfor -%}", 'chat-instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>', 'autoload_model': False, 'default_extensions': [], } default_settings = copy.deepcopy(settings) # Parser copied from https://github.com/vladmandic/automatic parser = argparse.ArgumentParser(description="Text generation web UI", conflict_handler='resolve', add_help=True, formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=55, indent_increment=2, width=200)) # Basic settings group = parser.add_argument_group('Basic settings') group.add_argument('--multi-user', action='store_true', help='Multi-user mode. Chat histories are not saved or automatically loaded. Warning: this is likely not safe for sharing publicly.') group.add_argument('--character', type=str, help='The name of the character to load in chat mode by default.') group.add_argument('--model', type=str, help='Name of the model to load by default.') group.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.') group.add_argument('--model-dir', type=str, default='models/', help='Path to directory with all the models.') group.add_argument('--lora-dir', type=str, default='loras/', help='Path to directory with all the loras.') group.add_argument('--model-menu', action='store_true', help='Show a model menu in the terminal when the web UI is first launched.') group.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.') group.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.') group.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.') group.add_argument('--chat-buttons', action='store_true', help='Show buttons on the chat tab instead of a hover menu.') # Model loader group = parser.add_argument_group('Model loader') group.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, llamacpp_HF, ExLlamav2_HF, ExLlamav2, AutoGPTQ, AutoAWQ, GPTQ-for-LLaMa, QuIP#.') # Transformers/Accelerate group = parser.add_argument_group('Transformers/Accelerate') group.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.') group.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.') group.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.') group.add_argument('--cpu-memory', type=str, help='Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.') group.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.') group.add_argument('--disk-cache-dir', type=str, default='cache', help='Directory to save the disk cache to. Defaults to "cache".') group.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision (using bitsandbytes).') group.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.') group.add_argument('--no-cache', action='store_true', help='Set use_cache to False while generating text. This reduces VRAM usage slightly, but it comes at a performance cost.') group.add_argument('--trust-remote-code', action='store_true', help='Set trust_remote_code=True while loading the model. Necessary for some models.') group.add_argument('--force-safetensors', action='store_true', help='Set use_safetensors=True while loading the model. This prevents arbitrary code execution.') group.add_argument('--no_use_fast', action='store_true', help='Set use_fast=False while loading the tokenizer (it\'s True by default). Use this if you have any problems related to use_fast.') group.add_argument('--use_flash_attention_2', action='store_true', help='Set use_flash_attention_2=True while loading the model.') # bitsandbytes 4-bit group = parser.add_argument_group('bitsandbytes 4-bit') group.add_argument('--load-in-4bit', action='store_true', help='Load the model with 4-bit precision (using bitsandbytes).') group.add_argument('--use_double_quant', action='store_true', help='use_double_quant for 4-bit.') group.add_argument('--compute_dtype', type=str, default='float16', help='compute dtype for 4-bit. Valid options: bfloat16, float16, float32.') group.add_argument('--quant_type', type=str, default='nf4', help='quant_type for 4-bit. Valid options: nf4, fp4.') # llama.cpp group = parser.add_argument_group('llama.cpp') group.add_argument('--flash-attn', action='store_true', help='Use flash-attention.') group.add_argument('--tensorcores', action='store_true', help='Use llama-cpp-python compiled with tensor cores support. This increases performance on RTX cards. NVIDIA only.') group.add_argument('--n_ctx', type=int, default=2048, help='Size of the prompt context.') group.add_argument('--threads', type=int, default=0, help='Number of threads to use.') group.add_argument('--threads-batch', type=int, default=0, help='Number of threads to use for batches/prompt processing.') group.add_argument('--no_mul_mat_q', action='store_true', help='Disable the mulmat kernels.') group.add_argument('--n_batch', type=int, default=512, help='Maximum number of prompt tokens to batch together when calling llama_eval.') group.add_argument('--no-mmap', action='store_true', help='Prevent mmap from being used.') group.add_argument('--mlock', action='store_true', help='Force the system to keep the model in RAM.') group.add_argument('--n-gpu-layers', type=int, default=0, help='Number of layers to offload to the GPU.') group.add_argument('--tensor_split', type=str, default=None, help='Split the model across multiple GPUs. Comma-separated list of proportions. Example: 18,17.') group.add_argument('--numa', action='store_true', help='Activate NUMA task allocation for llama.cpp.') group.add_argument('--logits_all', action='store_true', help='Needs to be set for perplexity evaluation to work. Otherwise, ignore it, as it makes prompt processing slower.') group.add_argument('--no_offload_kqv', action='store_true', help='Do not offload the K, Q, V to the GPU. This saves VRAM but reduces the performance.') group.add_argument('--cache-capacity', type=str, help='Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.') group.add_argument('--row_split', action='store_true', help='Split the model by rows across GPUs. This may improve multi-gpu performance.') group.add_argument('--streaming-llm', action='store_true', help='Activate StreamingLLM to avoid re-evaluating the entire prompt when old messages are removed.') group.add_argument('--attention-sink-size', type=int, default=5, help='StreamingLLM: number of sink tokens. Only used if the trimmed prompt does not share a prefix with the old prompt.') # ExLlamaV2 group = parser.add_argument_group('ExLlamaV2') group.add_argument('--gpu-split', type=str, help='Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7.') group.add_argument('--autosplit', action='store_true', help='Autosplit the model tensors across the available GPUs. This causes --gpu-split to be ignored.') group.add_argument('--max_seq_len', type=int, default=2048, help='Maximum sequence length.') group.add_argument('--cfg-cache', action='store_true', help='ExLlamav2_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader.') group.add_argument('--no_flash_attn', action='store_true', help='Force flash-attention to not be used.') group.add_argument('--cache_8bit', action='store_true', help='Use 8-bit cache to save VRAM.') group.add_argument('--cache_4bit', action='store_true', help='Use Q4 cache to save VRAM.') group.add_argument('--num_experts_per_token', type=int, default=2, help='Number of experts to use for generation. Applies to MoE models like Mixtral.') # AutoGPTQ group = parser.add_argument_group('AutoGPTQ') group.add_argument('--triton', action='store_true', help='Use triton.') group.add_argument('--no_inject_fused_attention', action='store_true', help='Disable the use of fused attention, which will use less VRAM at the cost of slower inference.') group.add_argument('--no_inject_fused_mlp', action='store_true', help='Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference.') group.add_argument('--no_use_cuda_fp16', action='store_true', help='This can make models faster on some systems.') group.add_argument('--desc_act', action='store_true', help='For models that do not have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig.') group.add_argument('--disable_exllama', action='store_true', help='Disable ExLlama kernel, which can improve inference speed on some systems.') group.add_argument('--disable_exllamav2', action='store_true', help='Disable ExLlamav2 kernel.') # GPTQ-for-LLaMa group = parser.add_argument_group('GPTQ-for-LLaMa') group.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.') group.add_argument('--model_type', type=str, help='Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.') group.add_argument('--groupsize', type=int, default=-1, help='Group size.') group.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.') group.add_argument('--checkpoint', type=str, help='The path to the quantized checkpoint file. If not specified, it will be automatically detected.') group.add_argument('--monkey-patch', action='store_true', help='Apply the monkey patch for using LoRAs with quantized models.') # HQQ group = parser.add_argument_group('HQQ') group.add_argument('--hqq-backend', type=str, default='PYTORCH_COMPILE', help='Backend for the HQQ loader. Valid options: PYTORCH, PYTORCH_COMPILE, ATEN.') # DeepSpeed group = parser.add_argument_group('DeepSpeed') group.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.') group.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory to use for ZeRO-3 NVME offloading.') group.add_argument('--local_rank', type=int, default=0, help='DeepSpeed: Optional argument for distributed setups.') # RoPE group = parser.add_argument_group('RoPE') group.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.') group.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).') group.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 group = parser.add_argument_group('Gradio') group.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.') group.add_argument('--listen-port', type=int, help='The listening port that the server will use.') group.add_argument('--listen-host', type=str, help='The hostname that the server will use.') group.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.') group.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.') group.add_argument('--gradio-auth', type=str, help='Set Gradio authentication password in the format "username:password". Multiple credentials can also be supplied with "u1:p1,u2:p2,u3:p3".', default=None) group.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 the same format as above.', default=None) group.add_argument('--ssl-keyfile', type=str, help='The path to the SSL certificate key file.', default=None) group.add_argument('--ssl-certfile', type=str, help='The path to the SSL certificate cert file.', default=None) # API group = parser.add_argument_group('API') group.add_argument('--api', action='store_true', help='Enable the API extension.') group.add_argument('--public-api', action='store_true', help='Create a public URL for the API using Cloudfare.') group.add_argument('--public-api-id', type=str, help='Tunnel ID for named Cloudflare Tunnel. Use together with public-api option.', default=None) group.add_argument('--api-port', type=int, default=5000, help='The listening port for the API.') group.add_argument('--api-key', type=str, default='', help='API authentication key.') group.add_argument('--admin-key', type=str, default='', help='API authentication key for admin tasks like loading and unloading models. If not set, will be the same as --api-key.') group.add_argument('--nowebui', action='store_true', help='Do not launch the Gradio UI. Useful for launching the API in standalone mode.') # Multimodal group = parser.add_argument_group('Multimodal') group.add_argument('--multimodal-pipeline', type=str, default=None, help='The multimodal pipeline to use. Examples: llava-7b, llava-13b.') # Deprecated parameters # group = parser.add_argument_group('Deprecated') 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) 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 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): 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 ['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 ['autoawq', 'awq', 'auto-awq']: return 'AutoAWQ' elif name in ['quip#', 'quip-sharp', 'quipsharp', 'quip_sharp']: return 'QuIP#' elif name in ['hqq']: return 'HQQ' def add_extension(name, last=False): if args.extensions is None: args.extensions = [name] elif last: args.extensions = [x for x in args.extensions if x != name] args.extensions.append(name) elif name not in args.extensions: args.extensions.append(name) def is_chat(): return True def load_user_config(): ''' Loads custom model-specific settings ''' if Path(f'{args.model_dir}/config-user.yaml').exists(): file_content = open(f'{args.model_dir}/config-user.yaml', 'r').read().strip() if file_content: user_config = yaml.safe_load(file_content) else: user_config = {} else: user_config = {} return user_config args.loader = fix_loader_name(args.loader) # Activate the multimodal extension if args.multimodal_pipeline is not None: add_extension('multimodal') # Activate the API extension if args.api or args.public_api: add_extension('openai', last=True) # 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 user_config = load_user_config() model_config = OrderedDict(model_config) user_config = OrderedDict(user_config)