text-generation-webui/modules/ui_model_menu.py
cal066 7a4fcee069
Add ctransformers support (#3313)
---------

Co-authored-by: cal066 <cal066@users.noreply.github.com>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
Co-authored-by: randoentity <137087500+randoentity@users.noreply.github.com>
2023-08-11 14:41:33 -03:00

232 lines
16 KiB
Python

import importlib
import math
import re
import traceback
from functools import partial
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='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='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['compress_pos_emb'] = gr.Slider(label='compress_pos_emb', minimum=1, maximum=8, step=1, info='Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.', 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['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['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 custom 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 custom 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")
shared.gradio['download_model_button'] = gr.Button("Download")
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)
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)
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)
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'), 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, progress=gr.Progress()):
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 = downloader.get_download_links_from_huggingface(model, branch, text_only=False)
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, 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 files to {output_folder}")
downloader.download_model_files(model, branch, links, sha256, output_folder, progress_bar=progress, threads=1)
yield ("Done!")
except:
progress(1.0)
yield traceback.format_exc().replace('\n', '\n\n')