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 transformers import is_torch_xpu_available

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 = []
    if is_torch_xpu_available():
        for i in range(torch.xpu.device_count()):
            total_mem.append(math.floor(torch.xpu.get_device_properties(i).total_memory / (1024 * 1024)))
    else:
        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=lambda: 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['hqq_backend'] = gr.Dropdown(label="hqq_backend", choices=["PYTORCH", "PYTORCH_COMPILE", "ATEN"], value=shared.args.hqq_backend)

                            shared.gradio['n_gpu_layers'] = gr.Slider(label="n-gpu-layers", minimum=0, maximum=256, value=shared.args.n_gpu_layers)
                            shared.gradio['n_ctx'] = gr.Slider(minimum=0, maximum=shared.settings['truncation_length_max'], step=256, label="n_ctx", value=shared.args.n_ctx, info='Context length. Try lowering this if you run out of memory while loading the model.')
                            shared.gradio['threads'] = gr.Slider(label="threads", minimum=0, step=1, maximum=32, value=shared.args.threads)
                            shared.gradio['threads_batch'] = gr.Slider(label="threads_batch", minimum=0, step=1, maximum=32, value=shared.args.threads_batch)
                            shared.gradio['n_batch'] = gr.Slider(label="n_batch", minimum=1, maximum=2048, step=1, value=shared.args.n_batch)

                            shared.gradio['wbits'] = gr.Dropdown(label="wbits", choices=["None", 1, 2, 3, 4, 8], value=shared.args.wbits if shared.args.wbits > 0 else "None")
                            shared.gradio['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128, 1024], value=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('ExLlamav2_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=shared.settings['truncation_length_max'], step=256, info='Context length. Try lowering this if you run out of memory while loading the model.', value=shared.args.max_seq_len)
                            shared.gradio['alpha_value'] = gr.Slider(label='alpha_value', minimum=1, maximum=8, step=0.05, info='Positional embeddings alpha factor for NTK RoPE scaling. Recommended values (NTKv1): 1.75 for 1.5x context, 2.5 for 2x context. 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)
                            shared.gradio['quipsharp_info'] = gr.Markdown('QuIP# has to be installed manually at the moment.')

                        with gr.Column():
                            shared.gradio['tensorcores'] = gr.Checkbox(label="tensorcores", value=shared.args.tensorcores, info='Use llama-cpp-python compiled with tensor cores support. This increases performance on RTX cards. NVIDIA only.')
                            shared.gradio['no_offload_kqv'] = gr.Checkbox(label="no_offload_kqv", value=shared.args.no_offload_kqv, info='Do not offload the  K, Q, V to the GPU. This saves VRAM but reduces the performance.')
                            shared.gradio['row_split'] = gr.Checkbox(label="row_split", value=shared.args.row_split, info='Split model by rows across GPUs. Improves performance on some cards.')
                            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. Fuses layers for AutoAWQ. 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['no_mul_mat_q'] = gr.Checkbox(label="no_mul_mat_q", value=shared.args.no_mul_mat_q, info='Disable the mulmat kernels.')
                            shared.gradio['no_mmap'] = gr.Checkbox(label="no-mmap", value=shared.args.no_mmap)
                            shared.gradio['mlock'] = gr.Checkbox(label="mlock", value=shared.args.mlock)
                            shared.gradio['numa'] = gr.Checkbox(label="numa", value=shared.args.numa, info='NUMA support can help on some systems with non-uniform memory access.')
                            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['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['trust_remote_code'] = gr.Checkbox(label="trust-remote-code", value=shared.args.trust_remote_code, info='To enable this option, start the web UI with the --trust-remote-code flag. It is necessary for some models.', interactive=shared.args.trust_remote_code)
                            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['logits_all'] = gr.Checkbox(label="logits_all", value=shared.args.logits_all, info='Needs to be set for perplexity evaluation to work. Otherwise, ignore it, as it makes prompt processing slower.')
                            shared.gradio['use_flash_attention_2'] = gr.Checkbox(label="use_flash_attention_2", value=shared.args.use_flash_attention_2, info='Set use_flash_attention_2=True while loading the model.')
                            shared.gradio['disable_exllama'] = gr.Checkbox(label="disable_exllama", value=shared.args.disable_exllama, info='Disable ExLlama kernel.')
                            shared.gradio['disable_exllamav2'] = gr.Checkbox(label="disable_exllamav2", value=shared.args.disable_exllamav2, info='Disable ExLlamav2 kernel.')
                            shared.gradio['no_flash_attn'] = gr.Checkbox(label="no_flash_attn", value=shared.args.no_flash_attn, info='Force flash-attention to not be used.')
                            shared.gradio['cache_8bit'] = gr.Checkbox(label="cache_8bit", value=shared.args.cache_8bit, info='Use 8-bit cache to save VRAM.')
                            shared.gradio['no_use_fast'] = gr.Checkbox(label="no_use_fast", value=shared.args.no_use_fast, info='Set use_fast=False while loading the tokenizer.')
                            shared.gradio['num_experts_per_token'] = gr.Number(label="Number of experts per token", value=shared.args.num_experts_per_token, info='Only applies to MoE models like Mixtral.')
                            shared.gradio['gptq_for_llama_info'] = gr.Markdown('Legacy loader for compatibility with older GPUs. ExLlamav2_HF or AutoGPTQ are preferred for GPTQ models when supported.')
                            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 (recommended): place your .gguf in a subfolder of models/ along with these 4 files: special_tokens_map.json, tokenizer_config.json, tokenizer.json, tokenizer.model.\n\nOption 2: download `oobabooga/llama-tokenizer` under "Download model or LoRA". That\'s a default Llama tokenizer that will work for some (but not all) models.')

            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}`..."
            unload_model()
            if selected_model != '':
                shared.model, shared.tokenizer = load_model(selected_model, 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:
        progress(0.0)
        downloader = importlib.import_module("download-model").ModelDownloader()
        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=4, 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 'loader' in 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']

    return current_length