mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
268 lines
12 KiB
Python
268 lines
12 KiB
Python
import re
|
|
import time
|
|
import glob
|
|
from sys import exit
|
|
import torch
|
|
import argparse
|
|
from pathlib import Path
|
|
import gradio as gr
|
|
import transformers
|
|
from html_generator import *
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--model', type=str, help='Name of the model to load by default.')
|
|
parser.add_argument('--notebook', action='store_true', help='Launch the webui in notebook mode, where the output is written to the same text box as the input.')
|
|
parser.add_argument('--chat', action='store_true', help='Launch the webui in chat mode.')
|
|
parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
|
|
parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
|
|
parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
|
|
parser.add_argument('--no-listen', action='store_true', help='Make the webui unreachable from your local network.')
|
|
args = parser.parse_args()
|
|
loaded_preset = None
|
|
available_models = sorted(set(map(lambda x : str(x.name).replace('.pt', ''), list(Path('models/').glob('*'))+list(Path('torch-dumps/').glob('*')))))
|
|
available_models = [item for item in available_models if not item.endswith('.txt')]
|
|
available_models = sorted(available_models, key=str.lower)
|
|
available_presets = sorted(set(map(lambda x : str(x.name).split('.')[0], list(Path('presets').glob('*.txt')))))
|
|
|
|
def load_model(model_name):
|
|
print(f"Loading {model_name}...")
|
|
t0 = time.time()
|
|
|
|
# Default settings
|
|
if not (args.cpu or args.auto_devices or args.load_in_8bit):
|
|
if Path(f"torch-dumps/{model_name}.pt").exists():
|
|
print("Loading in .pt format...")
|
|
model = torch.load(Path(f"torch-dumps/{model_name}.pt"))
|
|
elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')) and any(size in model_name.lower() for size in ('13b', '20b', '30b')):
|
|
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), device_map='auto', load_in_8bit=True)
|
|
else:
|
|
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
|
|
|
|
# Custom
|
|
else:
|
|
settings = ["low_cpu_mem_usage=True"]
|
|
cuda = ""
|
|
command = "AutoModelForCausalLM.from_pretrained"
|
|
|
|
if args.cpu:
|
|
settings.append("torch_dtype=torch.float32")
|
|
else:
|
|
if args.load_in_8bit:
|
|
settings.append("device_map='auto'")
|
|
settings.append("load_in_8bit=True")
|
|
else:
|
|
settings.append("torch_dtype=torch.float16")
|
|
if args.auto_devices:
|
|
settings.append("device_map='auto'")
|
|
else:
|
|
cuda = ".cuda()"
|
|
|
|
settings = ', '.join(settings)
|
|
command = f"{command}(Path(f'models/{model_name}'), {settings}){cuda}"
|
|
model = eval(command)
|
|
|
|
# Loading the tokenizer
|
|
if model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path(f"models/gpt-j-6B/").exists():
|
|
tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
|
|
else:
|
|
tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{model_name}/"))
|
|
|
|
print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
|
|
return model, tokenizer
|
|
|
|
# Removes empty replies from gpt4chan outputs
|
|
def fix_gpt4chan(s):
|
|
for i in range(10):
|
|
s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
|
|
s = re.sub("--- [0-9]*\n *\n---", "---", s)
|
|
s = re.sub("--- [0-9]*\n\n\n---", "---", s)
|
|
return s
|
|
|
|
# Fix the LaTeX equations in GALACTICA
|
|
def fix_galactica(s):
|
|
s = s.replace(r'\[', r'$')
|
|
s = s.replace(r'\]', r'$')
|
|
s = s.replace(r'\(', r'$')
|
|
s = s.replace(r'\)', r'$')
|
|
s = s.replace(r'$$', r'$')
|
|
return s
|
|
|
|
def generate_html(s):
|
|
s = '\n'.join([f'<p style="margin-bottom: 20px">{line}</p>' for line in s.split('\n')])
|
|
s = f'<div style="max-width: 600px; margin-left: auto; margin-right: auto; background-color:#eef2ff; color:#0b0f19; padding:3em; font-size:1.2em;">{s}</div>'
|
|
return s
|
|
|
|
def generate_reply(question, temperature, max_length, inference_settings, selected_model, eos_token=None):
|
|
global model, tokenizer, model_name, loaded_preset, preset
|
|
|
|
if selected_model != model_name:
|
|
model_name = selected_model
|
|
model = None
|
|
tokenizer = None
|
|
if not args.cpu:
|
|
torch.cuda.empty_cache()
|
|
model, tokenizer = load_model(model_name)
|
|
if inference_settings != loaded_preset:
|
|
with open(Path(f'presets/{inference_settings}.txt'), 'r') as infile:
|
|
preset = infile.read()
|
|
loaded_preset = inference_settings
|
|
|
|
if not args.cpu:
|
|
torch.cuda.empty_cache()
|
|
input_ids = tokenizer.encode(str(question), return_tensors='pt').cuda()
|
|
cuda = ".cuda()"
|
|
else:
|
|
input_ids = tokenizer.encode(str(question), return_tensors='pt')
|
|
cuda = ""
|
|
|
|
if eos_token is None:
|
|
output = eval(f"model.generate(input_ids, {preset}){cuda}")
|
|
else:
|
|
n = tokenizer.encode(eos_token, return_tensors='pt')[0][-1]
|
|
output = eval(f"model.generate(input_ids, eos_token_id={n}, {preset}){cuda}")
|
|
|
|
reply = tokenizer.decode(output[0], skip_special_tokens=True)
|
|
reply = reply.replace(r'<|endoftext|>', '')
|
|
if model_name.lower().startswith('galactica'):
|
|
reply = fix_galactica(reply)
|
|
return reply, reply, generate_html(reply)
|
|
elif model_name.lower().startswith('gpt4chan'):
|
|
reply = fix_gpt4chan(reply)
|
|
return reply, 'Only applicable for galactica models.', generate_4chan_html(reply)
|
|
else:
|
|
return reply, 'Only applicable for galactica models.', generate_html(reply)
|
|
|
|
# Choosing the default model
|
|
if args.model is not None:
|
|
model_name = args.model
|
|
else:
|
|
if len(available_models) == 0:
|
|
print("No models are available! Please download at least one.")
|
|
exit(0)
|
|
elif len(available_models) == 1:
|
|
i = 0
|
|
else:
|
|
print("The following models are available:\n")
|
|
for i,model in enumerate(available_models):
|
|
print(f"{i+1}. {model}")
|
|
print(f"\nWhich one do you want to load? 1-{len(available_models)}\n")
|
|
i = int(input())-1
|
|
print()
|
|
model_name = available_models[i]
|
|
model, tokenizer = load_model(model_name)
|
|
|
|
# UI settings
|
|
if model_name.lower().startswith('gpt4chan'):
|
|
default_text = "-----\n--- 865467536\nInput text\n--- 865467537\n"
|
|
else:
|
|
default_text = "Common sense questions and answers\n\nQuestion: \nFactual answer:"
|
|
description = f"""
|
|
|
|
# Text generation lab
|
|
Generate text using Large Language Models.
|
|
"""
|
|
css=".my-4 {margin-top: 0} .py-6 {padding-top: 2.5rem}"
|
|
|
|
if args.notebook:
|
|
with gr.Blocks(css=css, analytics_enabled=False) as interface:
|
|
gr.Markdown(description)
|
|
with gr.Tab('Raw'):
|
|
textbox = gr.Textbox(value=default_text, lines=23)
|
|
with gr.Tab('Markdown'):
|
|
markdown = gr.Markdown()
|
|
with gr.Tab('HTML'):
|
|
html = gr.HTML()
|
|
btn = gr.Button("Generate")
|
|
|
|
with gr.Row():
|
|
with gr.Column():
|
|
length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200)
|
|
temp_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7)
|
|
with gr.Column():
|
|
preset_menu = gr.Dropdown(choices=available_presets, value="NovelAI-Sphinx Moth", label='Preset')
|
|
model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
|
|
|
|
btn.click(generate_reply, [textbox, temp_slider, length_slider, preset_menu, model_menu], [textbox, markdown, html], show_progress=True, api_name="textgen")
|
|
textbox.submit(generate_reply, [textbox, temp_slider, length_slider, preset_menu, model_menu], [textbox, markdown, html], show_progress=True)
|
|
elif args.chat:
|
|
history = []
|
|
|
|
def chatbot_wrapper(text, temperature, max_length, inference_settings, selected_model, name1, name2, context):
|
|
question = context+'\n\n'
|
|
for i in range(len(history)):
|
|
question += f"{name1}: {history[i][0][3:-5].strip()}\n"
|
|
question += f"{name2}: {history[i][1][3:-5].strip()}\n"
|
|
question += f"{name1}: {text.strip()}\n"
|
|
question += f"{name2}:"
|
|
|
|
reply = generate_reply(question, temperature, max_length, inference_settings, selected_model, eos_token='\n')[0]
|
|
reply = reply[len(question):].split('\n')[0].strip()
|
|
history.append((text, reply))
|
|
return history
|
|
|
|
def clear():
|
|
global history
|
|
history = []
|
|
|
|
with gr.Blocks(css=css+".h-\[40vh\] {height: 50vh}", analytics_enabled=False) as interface:
|
|
gr.Markdown(description)
|
|
with gr.Row():
|
|
with gr.Column():
|
|
with gr.Row():
|
|
with gr.Column():
|
|
length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200)
|
|
preset_menu = gr.Dropdown(choices=available_presets, value="NovelAI-Sphinx Moth", label='Preset')
|
|
with gr.Column():
|
|
temp_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7)
|
|
model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
|
|
name1 = gr.Textbox(value='Person 1', lines=1, label='Your name')
|
|
name2 = gr.Textbox(value='Person 2', lines=1, label='Bot\'s name')
|
|
context = gr.Textbox(value='This is a conversation between two people.', lines=2, label='Context')
|
|
with gr.Column():
|
|
display1 = gr.Chatbot()
|
|
textbox = gr.Textbox(lines=2, label='Input')
|
|
btn = gr.Button("Generate")
|
|
btn2 = gr.Button("Clear history")
|
|
|
|
btn.click(chatbot_wrapper, [textbox, temp_slider, length_slider, preset_menu, model_menu, name1, name2, context], display1, show_progress=True, api_name="textgen")
|
|
textbox.submit(chatbot_wrapper, [textbox, temp_slider, length_slider, preset_menu, model_menu, name1, name2, context], display1, show_progress=True)
|
|
btn2.click(clear)
|
|
btn.click(lambda x: "", textbox, textbox, show_progress=False)
|
|
textbox.submit(lambda x: "", textbox, textbox, show_progress=False)
|
|
btn2.click(lambda x: "", display1, display1)
|
|
else:
|
|
|
|
def continue_wrapper(question, temperature, max_length, inference_settings, selected_model):
|
|
a, b, c = generate_reply(question, temperature, max_length, inference_settings, selected_model)
|
|
return a, a, b, c
|
|
|
|
with gr.Blocks(css=css, analytics_enabled=False) as interface:
|
|
gr.Markdown(description)
|
|
with gr.Row():
|
|
with gr.Column():
|
|
textbox = gr.Textbox(value=default_text, lines=15, label='Input')
|
|
temp_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7)
|
|
length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200)
|
|
preset_menu = gr.Dropdown(choices=available_presets, value="NovelAI-Sphinx Moth", label='Preset')
|
|
model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
|
|
btn = gr.Button("Generate")
|
|
cont = gr.Button("Continue")
|
|
with gr.Column():
|
|
with gr.Tab('Raw'):
|
|
output_textbox = gr.Textbox(lines=15, label='Output')
|
|
with gr.Tab('Markdown'):
|
|
markdown = gr.Markdown()
|
|
with gr.Tab('HTML'):
|
|
html = gr.HTML()
|
|
|
|
btn.click(generate_reply, [textbox, temp_slider, length_slider, preset_menu, model_menu], [output_textbox, markdown, html], show_progress=True, api_name="textgen")
|
|
cont.click(continue_wrapper, [output_textbox, temp_slider, length_slider, preset_menu, model_menu], [output_textbox, textbox, markdown, html], show_progress=True)
|
|
textbox.submit(generate_reply, [textbox, temp_slider, length_slider, preset_menu, model_menu], [output_textbox, markdown, html], show_progress=True)
|
|
|
|
if args.no_listen:
|
|
interface.launch(share=False)
|
|
else:
|
|
interface.launch(share=False, server_name="0.0.0.0")
|