mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-25 17:29:22 +01:00
259 lines
19 KiB
Python
259 lines
19 KiB
Python
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,
|
|
save_model_settings,
|
|
update_model_parameters
|
|
)
|
|
from modules.utils import gradio
|
|
|
|
|
|
def create_ui():
|
|
# 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')
|
|
ui.create_refresh_button(shared.gradio['model_menu'], lambda: None, lambda: {'choices': utils.get_available_models()}, 'refresh-button')
|
|
shared.gradio['load_model'] = gr.Button("Load", visible=not shared.settings['autoload_model'], elem_classes='refresh-button')
|
|
shared.gradio['unload_model'] = gr.Button("Unload", elem_classes='refresh-button')
|
|
shared.gradio['reload_model'] = gr.Button("Reload", elem_classes='refresh-button')
|
|
shared.gradio['save_model_settings'] = gr.Button("Save settings", elem_classes='refresh-button')
|
|
|
|
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')
|
|
ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': utils.get_available_loras(), 'value': shared.lora_names}, 'refresh-button')
|
|
shared.gradio['lora_menu_apply'] = gr.Button(value='Apply LoRAs', elem_classes='refresh-button')
|
|
|
|
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['n_gqa'] = gr.Slider(minimum=0, maximum=16, step=1, label="n_gqa", value=shared.args.n_gqa, info='GGML only (not used by GGUF): Grouped-Query Attention. Must be 8 for llama-2 70b.')
|
|
shared.gradio['rms_norm_eps'] = gr.Slider(minimum=0, maximum=1e-5, step=1e-6, label="rms_norm_eps", value=shared.args.rms_norm_eps, info='GGML only (not used by GGUF): 5e-6 is a good value for llama-2 models.')
|
|
|
|
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['disable_exllama'] = gr.Checkbox(label="disable_exllama", value=shared.args.disable_exllama, info='Disable ExLlama kernel, which can improve inference speed on some systems.')
|
|
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)
|
|
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['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 is a wrapper that lets you use llama.cpp like a Transformers model, which means it can use the Transformers samplers. To use it, make sure to first download oobabooga/llama-tokenizer under "Download model or LoRA".')
|
|
|
|
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.')
|
|
|
|
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.")
|
|
shared.gradio['download_specific_file'] = gr.Textbox(placeholder="File name (for GGUF/GGML)", show_label=False, max_lines=1)
|
|
with gr.Row():
|
|
shared.gradio['download_model_button'] = gr.Button("Download", variant='primary')
|
|
shared.gradio['get_file_list'] = gr.Button("Get file list")
|
|
|
|
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'))
|
|
|
|
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'))
|
|
|
|
shared.gradio['unload_model'].click(
|
|
unload_model, None, None).then(
|
|
lambda: "Model unloaded", None, gradio('model_status'))
|
|
|
|
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'))
|
|
|
|
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.\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:
|
|
yield f"Successfully loaded {selected_model}"
|
|
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):
|
|
try:
|
|
downloader_module = importlib.import_module("download-model")
|
|
downloader = downloader_module.ModelDownloader()
|
|
repo_id_parts = repo_id.split(":")
|
|
model = repo_id_parts[0] if len(repo_id_parts) > 0 else repo_id
|
|
branch = repo_id_parts[1] if len(repo_id_parts) > 1 else "main"
|
|
check = False
|
|
|
|
progress(0.0)
|
|
yield ("Cleaning up the model/branch names")
|
|
model, branch = downloader.sanitize_model_and_branch_names(model, branch)
|
|
|
|
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'] in ['ExLlama', 'ExLlama_HF']:
|
|
return state['max_seq_len']
|
|
elif state['loader'] in ['llama.cpp', 'llamacpp_HF', 'ctransformers']:
|
|
return state['n_ctx']
|
|
else:
|
|
return current_length
|