text-generation-webui/modules/ui_model_menu.py

import importlib
import math
import re
import traceback
from functools import partial
from pathlib import Path

import gradio as gr
import psutil
import torch

from modules import loaders, shared, ui, utils
from modules.logging_colors import logger
from modules.LoRA import add_lora_to_model
from modules.models import load_model, unload_model
from modules.models_settings import (
    apply_model_settings_to_state,
    get_model_metadata,
    save_model_settings,
    update_model_parameters
)
from modules.utils import gradio


def create_ui():
    mu = shared.args.multi_user

    # Finding the default values for the GPU and CPU memories
    total_mem = []
    for i in range(torch.cuda.device_count()):
        total_mem.append(math.floor(torch.cuda.get_device_properties(i).total_memory / (1024 * 1024)))

    default_gpu_mem = []
    if shared.args.gpu_memory is not None and len(shared.args.gpu_memory) > 0:
        for i in shared.args.gpu_memory:
            if 'mib' in i.lower():
                default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i)))
            else:
                default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i)) * 1000)

    while len(default_gpu_mem) < len(total_mem):
        default_gpu_mem.append(0)

    total_cpu_mem = math.floor(psutil.virtual_memory().total / (1024 * 1024))
    if shared.args.cpu_memory is not None:
        default_cpu_mem = re.sub('[a-zA-Z ]', '', shared.args.cpu_memory)
    else:
        default_cpu_mem = 0

    with gr.Tab("Model", elem_id="model-tab"):
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    with gr.Column():
                        with gr.Row():
                            shared.gradio['model_menu'] = gr.Dropdown(choices=utils.get_available_models(), value=shared.model_name, label='Model', elem_classes='slim-dropdown', interactive=not mu)
                            ui.create_refresh_button(shared.gradio['model_menu'], lambda: None, lambda: {'choices': utils.get_available_models()}, 'refresh-button', interactive=not mu)
                            shared.gradio['load_model'] = gr.Button("Load", visible=not shared.settings['autoload_model'], elem_classes='refresh-button', interactive=not mu)
                            shared.gradio['unload_model'] = gr.Button("Unload", elem_classes='refresh-button', interactive=not mu)
                            shared.gradio['reload_model'] = gr.Button("Reload", elem_classes='refresh-button', interactive=not mu)
                            shared.gradio['save_model_settings'] = gr.Button("Save settings", elem_classes='refresh-button', interactive=not mu)

                    with gr.Column():
                        with gr.Row():
                            shared.gradio['lora_menu'] = gr.Dropdown(multiselect=True, choices=utils.get_available_loras(), value=shared.lora_names, label='LoRA(s)', elem_classes='slim-dropdown', interactive=not mu)
                            ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': utils.get_available_loras(), 'value': shared.lora_names}, 'refresh-button', interactive=not mu)
                            shared.gradio['lora_menu_apply'] = gr.Button(value='Apply LoRAs', elem_classes='refresh-button', interactive=not mu)

        with gr.Row():
            with gr.Column():
                shared.gradio['loader'] = gr.Dropdown(label="Model loader", choices=loaders.loaders_and_params.keys(), value=None)
                with gr.Box():
                    with gr.Row():
                        with gr.Column():
                            for i in range(len(total_mem)):
                                shared.gradio[f'gpu_memory_{i}'] = gr.Slider(label=f"gpu-memory in MiB for device :{i}", maximum=total_mem[i], value=default_gpu_mem[i])

                            shared.gradio['cpu_memory'] = gr.Slider(label="cpu-memory in MiB", maximum=total_cpu_mem, value=default_cpu_mem)
                            shared.gradio['transformers_info'] = gr.Markdown('load-in-4bit params:')
                            shared.gradio['compute_dtype'] = gr.Dropdown(label="compute_dtype", choices=["bfloat16", "float16", "float32"], value=shared.args.compute_dtype)
                            shared.gradio['quant_type'] = gr.Dropdown(label="quant_type", choices=["nf4", "fp4"], value=shared.args.quant_type)

                            shared.gradio['n_gpu_layers'] = gr.Slider(label="n-gpu-layers", minimum=0, maximum=128, value=shared.args.n_gpu_layers)
                            shared.gradio['n_ctx'] = gr.Slider(minimum=0, maximum=16384, step=256, label="n_ctx", value=shared.args.n_ctx)
                            shared.gradio['threads'] = gr.Slider(label="threads", minimum=0, step=1, maximum=32, value=shared.args.threads)
                            shared.gradio['n_batch'] = gr.Slider(label="n_batch", minimum=1, maximum=2048, value=shared.args.n_batch)

                            shared.gradio['wbits'] = gr.Dropdown(label="wbits", choices=["None", 1, 2, 3, 4, 8], value=str(shared.args.wbits) if shared.args.wbits > 0 else "None")
                            shared.gradio['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128, 1024], value=str(shared.args.groupsize) if shared.args.groupsize > 0 else "None")
                            shared.gradio['model_type'] = gr.Dropdown(label="model_type", choices=["None"], value=shared.args.model_type or "None")
                            shared.gradio['pre_layer'] = gr.Slider(label="pre_layer", minimum=0, maximum=100, value=shared.args.pre_layer[0] if shared.args.pre_layer is not None else 0)
                            shared.gradio['autogptq_info'] = gr.Markdown('* ExLlama_HF is recommended over AutoGPTQ for models derived from LLaMA.')
                            shared.gradio['gpu_split'] = gr.Textbox(label='gpu-split', info='Comma-separated list of VRAM (in GB) to use per GPU. Example: 20,7,7')
                            shared.gradio['max_seq_len'] = gr.Slider(label='max_seq_len', minimum=0, maximum=16384, step=256, info='Maximum sequence length.', value=shared.args.max_seq_len)
                            shared.gradio['alpha_value'] = gr.Slider(label='alpha_value', minimum=1, maximum=8, step=0.1, info='Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both.', value=shared.args.alpha_value)
                            shared.gradio['rope_freq_base'] = gr.Slider(label='rope_freq_base', minimum=0, maximum=1000000, step=1000, info='If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63)', value=shared.args.rope_freq_base)
                            shared.gradio['compress_pos_emb'] = gr.Slider(label='compress_pos_emb', minimum=1, maximum=8, step=1, info='Positional embeddings compression factor. Should be set to (context length) / (model\'s original context length). Equal to 1/rope_freq_scale.', value=shared.args.compress_pos_emb)

                        with gr.Column():
                            shared.gradio['triton'] = gr.Checkbox(label="triton", value=shared.args.triton)
                            shared.gradio['no_inject_fused_attention'] = gr.Checkbox(label="no_inject_fused_attention", value=shared.args.no_inject_fused_attention, info='Disable fused attention. Fused attention improves inference performance but uses more VRAM. Disable if running low on VRAM.')
                            shared.gradio['no_inject_fused_mlp'] = gr.Checkbox(label="no_inject_fused_mlp", value=shared.args.no_inject_fused_mlp, info='Affects Triton only. Disable fused MLP. Fused MLP improves performance but uses more VRAM. Disable if running low on VRAM.')
                            shared.gradio['no_use_cuda_fp16'] = gr.Checkbox(label="no_use_cuda_fp16", value=shared.args.no_use_cuda_fp16, info='This can make models faster on some systems.')
                            shared.gradio['desc_act'] = gr.Checkbox(label="desc_act", value=shared.args.desc_act, info='\'desc_act\', \'wbits\', and \'groupsize\' are used for old models without a quantize_config.json.')
                            shared.gradio['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu)
                            shared.gradio['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit)
                            shared.gradio['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16)
                            shared.gradio['auto_devices'] = gr.Checkbox(label="auto-devices", value=shared.args.auto_devices)
                            shared.gradio['disk'] = gr.Checkbox(label="disk", value=shared.args.disk)
                            shared.gradio['load_in_4bit'] = gr.Checkbox(label="load-in-4bit", value=shared.args.load_in_4bit)
                            shared.gradio['use_double_quant'] = gr.Checkbox(label="use_double_quant", value=shared.args.use_double_quant)
                            shared.gradio['no_mmap'] = gr.Checkbox(label="no-mmap", value=shared.args.no_mmap)
                            shared.gradio['low_vram'] = gr.Checkbox(label="low-vram", value=shared.args.low_vram)
                            shared.gradio['mlock'] = gr.Checkbox(label="mlock", value=shared.args.mlock)
                            shared.gradio['mul_mat_q'] = gr.Checkbox(label="mul_mat_q", value=shared.args.mul_mat_q, info='Recommended in most cases. Improves generation speed by 10-20%.')
                            shared.gradio['cfg_cache'] = gr.Checkbox(label="cfg-cache", value=shared.args.cfg_cache, info='Create an additional cache for CFG negative prompts.')
                            shared.gradio['tensor_split'] = gr.Textbox(label='tensor_split', info='Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17')
                            shared.gradio['llama_cpp_seed'] = gr.Number(label='Seed (0 for random)', value=shared.args.llama_cpp_seed)
                            shared.gradio['trust_remote_code'] = gr.Checkbox(label="trust-remote-code", value=shared.args.trust_remote_code, info='Make sure to inspect the .py files inside the model folder before loading it with this option enabled.')
                            shared.gradio['use_fast'] = gr.Checkbox(label="use_fast", value=shared.args.use_fast, info='Set use_fast=True while loading the tokenizer. May trigger a conversion that takes several minutes.')
                            shared.gradio['disable_exllama'] = gr.Checkbox(label="disable_exllama", value=shared.args.disable_exllama, info='Disable ExLlama kernel.')
                            shared.gradio['gptq_for_llama_info'] = gr.Markdown('GPTQ-for-LLaMa support is currently only kept for compatibility with older GPUs. AutoGPTQ or ExLlama is preferred when compatible. GPTQ-for-LLaMa is installed by default with the webui on supported systems. Otherwise, it has to be installed manually following the instructions here: [instructions](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#installation-1).')
                            shared.gradio['exllama_info'] = gr.Markdown('For more information, consult the [docs](https://github.com/oobabooga/text-generation-webui/blob/main/docs/ExLlama.md).')
                            shared.gradio['exllama_HF_info'] = gr.Markdown('ExLlama_HF is a wrapper that lets you use ExLlama like a Transformers model, which means it can use the Transformers samplers. It\'s a bit slower than the regular ExLlama.')
                            shared.gradio['llamacpp_HF_info'] = gr.Markdown('llamacpp_HF loads llama.cpp as a Transformers model. To use it, you need to download a tokenizer.\n\nOption 1: download `oobabooga/llama-tokenizer` under "Download model or LoRA". That\'s a default Llama tokenizer.\n\nOption 2: place your .gguf in a subfolder of models/ along with these 3 files: tokenizer.model, tokenizer_config.json, and special_tokens_map.json. This takes precedence over Option 1.')

            with gr.Column():
                with gr.Row():
                    shared.gradio['autoload_model'] = gr.Checkbox(value=shared.settings['autoload_model'], label='Autoload the model', info='Whether to load the model as soon as it is selected in the Model dropdown.', interactive=not mu)

                shared.gradio['custom_model_menu'] = gr.Textbox(label="Download model or LoRA", info="Enter the Hugging Face username/model path, for instance: facebook/galactica-125m. To specify a branch, add it at the end after a \":\" character like this: facebook/galactica-125m:main. To download a single file, enter its name in the second box.", interactive=not mu)
                shared.gradio['download_specific_file'] = gr.Textbox(placeholder="File name (for GGUF models)", show_label=False, max_lines=1, interactive=not mu)
                with gr.Row():
                    shared.gradio['download_model_button'] = gr.Button("Download", variant='primary', interactive=not mu)
                    shared.gradio['get_file_list'] = gr.Button("Get file list", interactive=not mu)

                with gr.Row():
                    shared.gradio['model_status'] = gr.Markdown('No model is loaded' if shared.model_name == 'None' else 'Ready')


def create_event_handlers():
    shared.gradio['loader'].change(
        loaders.make_loader_params_visible, gradio('loader'), gradio(loaders.get_all_params())).then(
        lambda value: gr.update(choices=loaders.get_model_types(value)), gradio('loader'), gradio('model_type'))

    # In this event handler, the interface state is read and updated
    # with the model defaults (if any), and then the model is loaded
    # unless "autoload_model" is unchecked
    shared.gradio['model_menu'].change(
        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
        apply_model_settings_to_state, gradio('model_menu', 'interface_state'), gradio('interface_state')).then(
        ui.apply_interface_values, gradio('interface_state'), gradio(ui.list_interface_input_elements()), show_progress=False).then(
        update_model_parameters, gradio('interface_state'), None).then(
        load_model_wrapper, gradio('model_menu', 'loader', 'autoload_model'), gradio('model_status'), show_progress=False).success(
        update_truncation_length, gradio('truncation_length', 'interface_state'), gradio('truncation_length')).then(
        lambda x: x, gradio('loader'), gradio('filter_by_loader'))

    shared.gradio['load_model'].click(
        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
        update_model_parameters, gradio('interface_state'), None).then(
        partial(load_model_wrapper, autoload=True), gradio('model_menu', 'loader'), gradio('model_status'), show_progress=False).success(
        update_truncation_length, gradio('truncation_length', 'interface_state'), gradio('truncation_length')).then(
        lambda x: x, gradio('loader'), gradio('filter_by_loader'))

    shared.gradio['reload_model'].click(
        unload_model, None, None).then(
        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
        update_model_parameters, gradio('interface_state'), None).then(
        partial(load_model_wrapper, autoload=True), gradio('model_menu', 'loader'), gradio('model_status'), show_progress=False).success(
        update_truncation_length, gradio('truncation_length', 'interface_state'), gradio('truncation_length')).then(
        lambda x: x, gradio('loader'), gradio('filter_by_loader'))

    shared.gradio['unload_model'].click(
        unload_model, None, None).then(
        lambda: "Model unloaded", None, gradio('model_status'))

    shared.gradio['save_model_settings'].click(
        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
        save_model_settings, gradio('model_menu', 'interface_state'), gradio('model_status'), show_progress=False)

    shared.gradio['lora_menu_apply'].click(load_lora_wrapper, gradio('lora_menu'), gradio('model_status'), show_progress=False)
    shared.gradio['download_model_button'].click(download_model_wrapper, gradio('custom_model_menu', 'download_specific_file'), gradio('model_status'), show_progress=True)
    shared.gradio['get_file_list'].click(partial(download_model_wrapper, return_links=True), gradio('custom_model_menu', 'download_specific_file'), gradio('model_status'), show_progress=True)
    shared.gradio['autoload_model'].change(lambda x: gr.update(visible=not x), gradio('autoload_model'), gradio('load_model'))


def load_model_wrapper(selected_model, loader, autoload=False):
    if not autoload:
        yield f"The settings for `{selected_model}` have been updated.\n\nClick on \"Load\" to load it."
        return

    if selected_model == 'None':
        yield "No model selected"
    else:
        try:
            yield f"Loading `{selected_model}`..."
            shared.model_name = selected_model
            unload_model()
            if selected_model != '':
                shared.model, shared.tokenizer = load_model(shared.model_name, loader)

            if shared.model is not None:
                output = f"Successfully loaded `{selected_model}`."

                settings = get_model_metadata(selected_model)
                if 'instruction_template' in settings:
                    output += '\n\nIt seems to be an instruction-following model with template "{}". In the chat tab, instruct or chat-instruct modes should be used.'.format(settings['instruction_template'])

                yield output
            else:
                yield f"Failed to load `{selected_model}`."
        except:
            exc = traceback.format_exc()
            logger.error('Failed to load the model.')
            print(exc)
            yield exc.replace('\n', '\n\n')


def load_lora_wrapper(selected_loras):
    yield ("Applying the following LoRAs to {}:\n\n{}".format(shared.model_name, '\n'.join(selected_loras)))
    add_lora_to_model(selected_loras)
    yield ("Successfuly applied the LoRAs")


def download_model_wrapper(repo_id, specific_file, progress=gr.Progress(), return_links=False, check=False):
    try:
        downloader_module = importlib.import_module("download-model")
        downloader = downloader_module.ModelDownloader()

        progress(0.0)
        yield ("Cleaning up the model/branch names")
        model, branch = downloader.sanitize_model_and_branch_names(repo_id, None)

        yield ("Getting the download links from Hugging Face")
        links, sha256, is_lora, is_llamacpp = downloader.get_download_links_from_huggingface(model, branch, text_only=False, specific_file=specific_file)

        if return_links:
            yield '\n\n'.join([f"`{Path(link).name}`" for link in links])
            return

        yield ("Getting the output folder")
        base_folder = shared.args.lora_dir if is_lora else shared.args.model_dir
        output_folder = downloader.get_output_folder(model, branch, is_lora, is_llamacpp=is_llamacpp, base_folder=base_folder)

        if check:
            progress(0.5)
            yield ("Checking previously downloaded files")
            downloader.check_model_files(model, branch, links, sha256, output_folder)
            progress(1.0)
        else:
            yield (f"Downloading file{'s' if len(links) > 1 else ''} to `{output_folder}/`")
            downloader.download_model_files(model, branch, links, sha256, output_folder, progress_bar=progress, threads=1, is_llamacpp=is_llamacpp)
            yield ("Done!")
    except:
        progress(1.0)
        yield traceback.format_exc().replace('\n', '\n\n')


def update_truncation_length(current_length, state):
    if state['loader'].lower().startswith('exllama'):
        return state['max_seq_len']
    elif state['loader'] in ['llama.cpp', 'llamacpp_HF', 'ctransformers']:
        return state['n_ctx']
    else:
        return current_length