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, T5Tokenizer from transformers import AutoModelForCausalLM, T5ForConditionalGeneration 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.') 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_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() # Loading the model if Path(f"torch-dumps/{model_name}.pt").exists(): print("Loading in .pt format...") model = torch.load(Path(f"torch-dumps/{model_name}.pt")).cuda() elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')): if 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() elif model_name in ['gpt-j-6B']: model = AutoModelForCausalLM.from_pretrained(Path(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(Path(f"models/{model_name}")).cuda() else: model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda() # Loading the tokenizer if model_name.lower().startswith('gpt4chan'): tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/")) elif model_name in ['flan-t5']: tokenizer = T5Tokenizer.from_pretrained(Path(f"models/{model_name}/")) 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 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_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(Path(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.lower().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(css=".my-4 {margin-top: 0} .py-6 {padding-top: 2.5rem}") 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=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=False) elif args.chat: history = [] def chatbot(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)[0] reply = reply[len(question):].split('\n')[0].strip() history.append((text, reply)) return history def clear(): global history history = [] with gr.Blocks(css=".my-4 {margin-top: 0} .py-6 {padding-top: 2.5rem}") as interface: gr.Markdown( f""" # Text generation lab Generate text using Large Language Models. """ ) with gr.Row(equal_height=True): with gr.Column(): with gr.Row(equal_height=True): 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, [textbox, temp_slider, length_slider, preset_menu, model_menu, name1, name2, context], display1, show_progress=True) btn2.click(clear) else: with gr.Blocks(css=".my-4 {margin-top: 0} .py-6 {padding-top: 2.5rem}") 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=available_presets, 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")