text-generation-webui/modules/shared.py

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)')
parser.add_argument('--numa', action='store_true', help='Activate NUMA task allocation 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, 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 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 ['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)