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| 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=128, 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, 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('* 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=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# only works on Linux.') | |
| 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['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. ExLlama_HF or AutoGPTQ are preferred for GPTQ models when supported.') | |
| shared.gradio['exllama_info'] = gr.Markdown("ExLlama_HF is recommended over ExLlama for better integration with extensions and more consistent sampling behavior across loaders.") | |
| shared.gradio['exllamav2_info'] = gr.Markdown("ExLlamav2_HF is recommended over ExLlamav2 for better integration with extensions and more consistent sampling behavior across loaders.") | |
| 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 | |