import copy from pathlib import Path import gradio as gr import torch import yaml from transformers import is_torch_xpu_available from modules import shared with open(Path(__file__).resolve().parent / '../css/NotoSans/stylesheet.css', 'r') as f: css = f.read() with open(Path(__file__).resolve().parent / '../css/main.css', 'r') as f: css += f.read() with open(Path(__file__).resolve().parent / '../js/main.js', 'r') as f: js = f.read() with open(Path(__file__).resolve().parent / '../js/save_files.js', 'r') as f: save_files_js = f.read() with open(Path(__file__).resolve().parent / '../js/switch_tabs.js', 'r') as f: switch_tabs_js = f.read() with open(Path(__file__).resolve().parent / '../js/show_controls.js', 'r') as f: show_controls_js = f.read() refresh_symbol = '🔄' delete_symbol = '🗑️' save_symbol = '💾' theme = gr.themes.Default( font=['Noto Sans', 'Helvetica', 'ui-sans-serif', 'system-ui', 'sans-serif'], font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'], ).set( border_color_primary='#c5c5d2', button_large_padding='6px 12px', body_text_color_subdued='#484848', background_fill_secondary='#eaeaea' ) if Path("notification.mp3").exists(): audio_notification_js = "document.querySelector('#audio_notification audio')?.play();" else: audio_notification_js = "" def list_model_elements(): elements = [ 'loader', 'filter_by_loader', 'cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'trust_remote_code', 'use_fast', 'use_flash_attention_2', 'load_in_4bit', 'compute_dtype', 'quant_type', 'use_double_quant', 'wbits', 'groupsize', 'model_type', 'pre_layer', 'triton', 'desc_act', 'no_inject_fused_attention', 'no_inject_fused_mlp', 'no_use_cuda_fp16', 'disable_exllama', 'cfg_cache', 'no_flash_attn', 'cache_8bit', 'threads', 'threads_batch', 'n_batch', 'no_mmap', 'mlock', 'no_mul_mat_q', 'n_gpu_layers', 'tensor_split', 'n_ctx', 'llama_cpp_seed', 'gpu_split', 'max_seq_len', 'compress_pos_emb', 'alpha_value', 'rope_freq_base', 'numa', ] if is_torch_xpu_available(): for i in range(torch.xpu.device_count()): elements.append(f'gpu_memory_{i}') else: for i in range(torch.cuda.device_count()): elements.append(f'gpu_memory_{i}') return elements def list_interface_input_elements(): elements = [ 'max_new_tokens', 'auto_max_new_tokens', 'max_tokens_second', 'seed', 'temperature', 'temperature_last', 'top_p', 'min_p', 'top_k', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'do_sample', 'penalty_alpha', 'num_beams', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'grammar_string', 'negative_prompt', 'guidance_scale', 'add_bos_token', 'ban_eos_token', 'custom_token_bans', 'truncation_length', 'custom_stopping_strings', 'skip_special_tokens', 'stream', 'tfs', 'top_a', ] # Chat elements elements += [ 'textbox', 'start_with', 'character_menu', 'history', 'name1', 'name2', 'greeting', 'context', 'mode', 'instruction_template', 'name1_instruct', 'name2_instruct', 'context_instruct', 'turn_template', 'chat_style', 'chat-instruct_command', ] # Notebook/default elements elements += [ 'textbox-notebook', 'textbox-default', 'output_textbox', 'prompt_menu-default', 'prompt_menu-notebook', ] # Model elements elements += list_model_elements() return elements def gather_interface_values(*args): output = {} for i, element in enumerate(list_interface_input_elements()): output[element] = args[i] if not shared.args.multi_user: shared.persistent_interface_state = output return output def apply_interface_values(state, use_persistent=False): if use_persistent: state = shared.persistent_interface_state elements = list_interface_input_elements() if len(state) == 0: return [gr.update() for k in elements] # Dummy, do nothing else: return [state[k] if k in state else gr.update() for k in elements] def save_settings(state, preset, instruction_template, extensions, show_controls): output = copy.deepcopy(shared.settings) exclude = ['name2', 'greeting', 'context', 'turn_template'] for k in state: if k in shared.settings and k not in exclude: output[k] = state[k] output['preset'] = preset output['prompt-default'] = state['prompt_menu-default'] output['prompt-notebook'] = state['prompt_menu-notebook'] output['character'] = state['character_menu'] output['instruction_template'] = instruction_template output['default_extensions'] = extensions output['seed'] = int(output['seed']) output['show_controls'] = show_controls return yaml.dump(output, sort_keys=False, width=float("inf")) def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_class, interactive=True): """ Copied from https://github.com/AUTOMATIC1111/stable-diffusion-webui """ def refresh(): refresh_method() args = refreshed_args() if callable(refreshed_args) else refreshed_args for k, v in args.items(): setattr(refresh_component, k, v) return gr.update(**(args or {})) refresh_button = gr.Button(refresh_symbol, elem_classes=elem_class, interactive=interactive) refresh_button.click( fn=refresh, inputs=[], outputs=[refresh_component] ) return refresh_button