mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-25 17:29:22 +01:00
267 lines
20 KiB
Python
267 lines
20 KiB
Python
import importlib
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import math
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import re
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import traceback
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from functools import partial
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from pathlib import Path
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import gradio as gr
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import psutil
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import torch
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from modules import loaders, shared, ui, utils
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from modules.logging_colors import logger
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from modules.LoRA import add_lora_to_model
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from modules.models import load_model, unload_model
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from modules.models_settings import (
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apply_model_settings_to_state,
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get_model_metadata,
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save_model_settings,
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update_model_parameters
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)
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from modules.utils import gradio
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def create_ui():
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mu = shared.args.multi_user
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# Finding the default values for the GPU and CPU memories
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total_mem = []
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for i in range(torch.cuda.device_count()):
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total_mem.append(math.floor(torch.cuda.get_device_properties(i).total_memory / (1024 * 1024)))
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default_gpu_mem = []
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if shared.args.gpu_memory is not None and len(shared.args.gpu_memory) > 0:
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for i in shared.args.gpu_memory:
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if 'mib' in i.lower():
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default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i)))
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else:
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default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i)) * 1000)
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while len(default_gpu_mem) < len(total_mem):
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default_gpu_mem.append(0)
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total_cpu_mem = math.floor(psutil.virtual_memory().total / (1024 * 1024))
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if shared.args.cpu_memory is not None:
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default_cpu_mem = re.sub('[a-zA-Z ]', '', shared.args.cpu_memory)
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else:
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default_cpu_mem = 0
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with gr.Tab("Model", elem_id="model-tab"):
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with gr.Row():
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with gr.Column():
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with gr.Row():
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with gr.Column():
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with gr.Row():
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shared.gradio['model_menu'] = gr.Dropdown(choices=utils.get_available_models(), value=shared.model_name, label='Model', elem_classes='slim-dropdown', interactive=not mu)
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ui.create_refresh_button(shared.gradio['model_menu'], lambda: None, lambda: {'choices': utils.get_available_models()}, 'refresh-button', interactive=not mu)
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shared.gradio['load_model'] = gr.Button("Load", visible=not shared.settings['autoload_model'], elem_classes='refresh-button', interactive=not mu)
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shared.gradio['unload_model'] = gr.Button("Unload", elem_classes='refresh-button', interactive=not mu)
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shared.gradio['reload_model'] = gr.Button("Reload", elem_classes='refresh-button', interactive=not mu)
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shared.gradio['save_model_settings'] = gr.Button("Save settings", elem_classes='refresh-button', interactive=not mu)
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with gr.Column():
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with gr.Row():
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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)
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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)
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shared.gradio['lora_menu_apply'] = gr.Button(value='Apply LoRAs', elem_classes='refresh-button', interactive=not mu)
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with gr.Row():
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with gr.Column():
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shared.gradio['loader'] = gr.Dropdown(label="Model loader", choices=loaders.loaders_and_params.keys(), value=None)
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with gr.Box():
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with gr.Row():
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with gr.Column():
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for i in range(len(total_mem)):
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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])
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shared.gradio['cpu_memory'] = gr.Slider(label="cpu-memory in MiB", maximum=total_cpu_mem, value=default_cpu_mem)
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shared.gradio['transformers_info'] = gr.Markdown('load-in-4bit params:')
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shared.gradio['compute_dtype'] = gr.Dropdown(label="compute_dtype", choices=["bfloat16", "float16", "float32"], value=shared.args.compute_dtype)
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shared.gradio['quant_type'] = gr.Dropdown(label="quant_type", choices=["nf4", "fp4"], value=shared.args.quant_type)
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shared.gradio['n_gpu_layers'] = gr.Slider(label="n-gpu-layers", minimum=0, maximum=128, value=shared.args.n_gpu_layers)
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shared.gradio['n_ctx'] = gr.Slider(minimum=0, maximum=32768, step=256, label="n_ctx", value=shared.args.n_ctx)
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shared.gradio['threads'] = gr.Slider(label="threads", minimum=0, step=1, maximum=32, value=shared.args.threads)
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shared.gradio['n_batch'] = gr.Slider(label="n_batch", minimum=1, maximum=2048, value=shared.args.n_batch)
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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")
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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")
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shared.gradio['model_type'] = gr.Dropdown(label="model_type", choices=["None"], value=shared.args.model_type or "None")
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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)
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shared.gradio['autogptq_info'] = gr.Markdown('* ExLlama_HF is recommended over AutoGPTQ for models derived from LLaMA.')
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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')
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shared.gradio['max_seq_len'] = gr.Slider(label='max_seq_len', minimum=0, maximum=32768, step=256, info='Maximum sequence length.', value=shared.args.max_seq_len)
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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)
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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)
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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)
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with gr.Column():
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shared.gradio['triton'] = gr.Checkbox(label="triton", value=shared.args.triton)
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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.')
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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.')
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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.')
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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.')
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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%.')
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shared.gradio['cfg_cache'] = gr.Checkbox(label="cfg-cache", value=shared.args.cfg_cache, info='Create an additional cache for CFG negative prompts.')
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shared.gradio['no_mmap'] = gr.Checkbox(label="no-mmap", value=shared.args.no_mmap)
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shared.gradio['mlock'] = gr.Checkbox(label="mlock", value=shared.args.mlock)
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shared.gradio['numa'] = gr.Checkbox(label="numa", value=shared.args.numa, info='NUMA support can help on some systems with non-uniform memory access.')
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shared.gradio['low_vram'] = gr.Checkbox(label="low-vram", value=shared.args.low_vram)
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shared.gradio['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu)
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shared.gradio['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit)
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shared.gradio['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16)
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shared.gradio['auto_devices'] = gr.Checkbox(label="auto-devices", value=shared.args.auto_devices)
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shared.gradio['disk'] = gr.Checkbox(label="disk", value=shared.args.disk)
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shared.gradio['load_in_4bit'] = gr.Checkbox(label="load-in-4bit", value=shared.args.load_in_4bit)
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shared.gradio['use_double_quant'] = gr.Checkbox(label="use_double_quant", value=shared.args.use_double_quant)
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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')
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shared.gradio['llama_cpp_seed'] = gr.Number(label='Seed (0 for random)', value=shared.args.llama_cpp_seed)
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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.')
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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.')
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shared.gradio['disable_exllama'] = gr.Checkbox(label="disable_exllama", value=shared.args.disable_exllama, info='Disable ExLlama kernel.')
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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).')
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shared.gradio['exllama_info'] = gr.Markdown('For more information, consult the [docs](https://github.com/oobabooga/text-generation-webui/blob/main/docs/ExLlama.md).')
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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.')
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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.')
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with gr.Column():
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with gr.Row():
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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)
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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)
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shared.gradio['download_specific_file'] = gr.Textbox(placeholder="File name (for GGUF models)", show_label=False, max_lines=1, interactive=not mu)
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with gr.Row():
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shared.gradio['download_model_button'] = gr.Button("Download", variant='primary', interactive=not mu)
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shared.gradio['get_file_list'] = gr.Button("Get file list", interactive=not mu)
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with gr.Row():
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shared.gradio['model_status'] = gr.Markdown('No model is loaded' if shared.model_name == 'None' else 'Ready')
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def create_event_handlers():
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shared.gradio['loader'].change(
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loaders.make_loader_params_visible, gradio('loader'), gradio(loaders.get_all_params())).then(
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lambda value: gr.update(choices=loaders.get_model_types(value)), gradio('loader'), gradio('model_type'))
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# In this event handler, the interface state is read and updated
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# with the model defaults (if any), and then the model is loaded
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# unless "autoload_model" is unchecked
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shared.gradio['model_menu'].change(
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ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
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apply_model_settings_to_state, gradio('model_menu', 'interface_state'), gradio('interface_state')).then(
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ui.apply_interface_values, gradio('interface_state'), gradio(ui.list_interface_input_elements()), show_progress=False).then(
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update_model_parameters, gradio('interface_state'), None).then(
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load_model_wrapper, gradio('model_menu', 'loader', 'autoload_model'), gradio('model_status'), show_progress=False).success(
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update_truncation_length, gradio('truncation_length', 'interface_state'), gradio('truncation_length')).then(
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lambda x: x, gradio('loader'), gradio('filter_by_loader'))
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shared.gradio['load_model'].click(
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ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
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update_model_parameters, gradio('interface_state'), None).then(
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partial(load_model_wrapper, autoload=True), gradio('model_menu', 'loader'), gradio('model_status'), show_progress=False).success(
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update_truncation_length, gradio('truncation_length', 'interface_state'), gradio('truncation_length')).then(
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lambda x: x, gradio('loader'), gradio('filter_by_loader'))
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shared.gradio['reload_model'].click(
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unload_model, None, None).then(
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ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
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update_model_parameters, gradio('interface_state'), None).then(
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partial(load_model_wrapper, autoload=True), gradio('model_menu', 'loader'), gradio('model_status'), show_progress=False).success(
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update_truncation_length, gradio('truncation_length', 'interface_state'), gradio('truncation_length')).then(
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lambda x: x, gradio('loader'), gradio('filter_by_loader'))
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shared.gradio['unload_model'].click(
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unload_model, None, None).then(
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lambda: "Model unloaded", None, gradio('model_status'))
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shared.gradio['save_model_settings'].click(
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ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
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save_model_settings, gradio('model_menu', 'interface_state'), gradio('model_status'), show_progress=False)
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shared.gradio['lora_menu_apply'].click(load_lora_wrapper, gradio('lora_menu'), gradio('model_status'), show_progress=False)
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shared.gradio['download_model_button'].click(download_model_wrapper, gradio('custom_model_menu', 'download_specific_file'), gradio('model_status'), show_progress=True)
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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)
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shared.gradio['autoload_model'].change(lambda x: gr.update(visible=not x), gradio('autoload_model'), gradio('load_model'))
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def load_model_wrapper(selected_model, loader, autoload=False):
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if not autoload:
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yield f"The settings for `{selected_model}` have been updated.\n\nClick on \"Load\" to load it."
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return
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if selected_model == 'None':
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yield "No model selected"
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else:
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try:
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yield f"Loading `{selected_model}`..."
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shared.model_name = selected_model
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unload_model()
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if selected_model != '':
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shared.model, shared.tokenizer = load_model(shared.model_name, loader)
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if shared.model is not None:
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output = f"Successfully loaded `{selected_model}`."
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settings = get_model_metadata(selected_model)
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if 'instruction_template' in settings:
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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'])
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yield output
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else:
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yield f"Failed to load `{selected_model}`."
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except:
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exc = traceback.format_exc()
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logger.error('Failed to load the model.')
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print(exc)
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yield exc.replace('\n', '\n\n')
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def load_lora_wrapper(selected_loras):
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yield ("Applying the following LoRAs to {}:\n\n{}".format(shared.model_name, '\n'.join(selected_loras)))
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add_lora_to_model(selected_loras)
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yield ("Successfuly applied the LoRAs")
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def download_model_wrapper(repo_id, specific_file, progress=gr.Progress(), return_links=False, check=False):
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try:
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downloader_module = importlib.import_module("download-model")
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downloader = downloader_module.ModelDownloader()
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progress(0.0)
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yield ("Cleaning up the model/branch names")
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model, branch = downloader.sanitize_model_and_branch_names(repo_id, None)
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yield ("Getting the download links from Hugging Face")
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links, sha256, is_lora, is_llamacpp = downloader.get_download_links_from_huggingface(model, branch, text_only=False, specific_file=specific_file)
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if return_links:
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yield '\n\n'.join([f"`{Path(link).name}`" for link in links])
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return
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yield ("Getting the output folder")
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base_folder = shared.args.lora_dir if is_lora else shared.args.model_dir
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output_folder = downloader.get_output_folder(model, branch, is_lora, is_llamacpp=is_llamacpp, base_folder=base_folder)
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if check:
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progress(0.5)
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yield ("Checking previously downloaded files")
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downloader.check_model_files(model, branch, links, sha256, output_folder)
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progress(1.0)
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else:
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yield (f"Downloading file{'s' if len(links) > 1 else ''} to `{output_folder}/`")
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downloader.download_model_files(model, branch, links, sha256, output_folder, progress_bar=progress, threads=1, is_llamacpp=is_llamacpp)
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yield ("Done!")
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except:
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progress(1.0)
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yield traceback.format_exc().replace('\n', '\n\n')
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def update_truncation_length(current_length, state):
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if state['loader'].lower().startswith('exllama'):
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return state['max_seq_len']
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elif state['loader'] in ['llama.cpp', 'llamacpp_HF', 'ctransformers']:
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return state['n_ctx']
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else:
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return current_length
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