text-generation-webui/server.py

import os
import re
import time
import glob
from sys import exit
import torch
import argparse
import gradio as gr
import transformers
from html_generator import *
from transformers import AutoTokenizer
from transformers import GPTJForCausalLM, AutoModelForCausalLM, AutoModelForSeq2SeqLM, OPTForCausalLM, T5Tokenizer, T5ForConditionalGeneration, GPTJModel, AutoModel


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.')
args = parser.parse_args()
loaded_preset = None
available_models = sorted(set(map(lambda x : x.split('/')[-1].replace('.pt', ''), glob.glob("models/*")+ glob.glob("torch-dumps/*"))))
available_models = [item for item in available_models if not item.endswith('.txt')]
#available_models = sorted(set(map(lambda x : x.split('/')[-1].replace('.pt', ''), glob.glob("models/*[!\.][!t][!x][!t]")+ glob.glob("torch-dumps/*[!\.][!t][!x][!t]"))))

def load_model(model_name):
    print(f"Loading {model_name}...")
    t0 = time.time()

    # Loading the model
    if os.path.exists(f"torch-dumps/{model_name}.pt"):
        print("Loading in .pt format...")
        model = torch.load(f"torch-dumps/{model_name}.pt").cuda()
    elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')):
        if any(size in model_name for size in ('13b', '20b', '30b')):
            model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", device_map='auto', load_in_8bit=True)
        else:
            model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
    elif model_name in ['gpt-j-6B']:
        model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
    elif model_name in ['flan-t5', 't5-large']:
        model = T5ForConditionalGeneration.from_pretrained(f"models/{model_name}").cuda()
    else:
        model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()

    # Loading the tokenizer
    if model_name.startswith('gpt4chan'):
        tokenizer = AutoTokenizer.from_pretrained("models/gpt-j-6B/")
    elif model_name in ['flan-t5']:
        tokenizer = T5Tokenizer.from_pretrained(f"models/{model_name}/")
    else:
        tokenizer = AutoTokenizer.from_pretrained(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

def fix_galactica(s):
    s = s.replace(r'\[', r'$')
    s = s.replace(r'\]', r'$')
    return s

def generate_reply(question, temperature, max_length, inference_settings, selected_model):
    global model, tokenizer, model_name, loaded_preset, preset

    if selected_model != model_name:
        model_name = selected_model
        model = None
        tokenier = None
        torch.cuda.empty_cache()
        model, tokenizer = load_model(model_name)
    if inference_settings != loaded_preset:
        with open(f'presets/{inference_settings}.txt', 'r') as infile:
            preset = infile.read()
        loaded_preset = inference_settings

    torch.cuda.empty_cache()
    input_text = question
    input_ids = tokenizer.encode(str(input_text), return_tensors='pt').cuda()

    output = eval(f"model.generate(input_ids, {preset}).cuda()")
    reply = tokenizer.decode(output[0], skip_special_tokens=True)

    if model_name.lower().startswith('galactica'):
        reply = fix_galactica(reply)
        return reply, reply, 'Only applicable for gpt4chan.'
    elif model_name.lower().startswith('gpt4chan'):
        reply = fix_gpt4chan(reply)
        return reply, 'Only applicable for galactica models.', generate_html(reply)
    else:
        return reply, 'Only applicable for galactica models.', 'Only applicable for gpt4chan.'

# 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
    model_name = available_models[i]
model, tokenizer = load_model(model_name)

if model_name.startswith('gpt4chan'):
    default_text = "-----\n--- 865467536\nInput text\n--- 865467537\n"
else:
    default_text = "Common sense questions and answers\n\nQuestion: \nFactual answer:"

if args.notebook:
    with gr.Blocks() as interface:
        gr.Markdown(
        f"""
        # Text generation lab
        Generate text using Large Language Models.
        """
        )

        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():
                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)
            with gr.Column():
                preset_menu = gr.Dropdown(choices=list(map(lambda x : x.split('/')[-1].split('.')[0], glob.glob("presets/*.txt"))), 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=False)
else:
    with gr.Blocks() as interface:
        gr.Markdown(
        f"""
        # Text generation lab
        Generate text using Large Language Models.
        """
        )

        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=list(map(lambda x : x.split('/')[-1].split('.')[0], glob.glob("presets/*.txt"))), value="NovelAI-Sphinx Moth", label='Preset')
                model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
                btn = gr.Button("Generate")
            with gr.Column():
                with gr.Tab('Raw'):
                    output_textbox = gr.Textbox(value=default_text, 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)

interface.launch(share=False, server_name="0.0.0.0")