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'

{line}

' for line in s.split('\n')]) s = f'
{s}
' return s def generate_reply(question, tokens, 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") length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_new_tokens', value=200) with gr.Row(): with gr.Column(): model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model') with gr.Column(): preset_menu = gr.Dropdown(choices=available_presets, value="NovelAI-Sphinx Moth", label='Settings preset') btn.click(generate_reply, [textbox, length_slider, preset_menu, model_menu], [textbox, markdown, html], show_progress=True, api_name="textgen") textbox.submit(generate_reply, [textbox, length_slider, preset_menu, model_menu], [textbox, markdown, html], show_progress=True) elif args.chat: history = [] def chatbot_wrapper(text, tokens, inference_settings, selected_model, name1, name2, context, check): text = text.replace('\n', '\n\n') text = re.sub(r"\n{3,}", "\n\n", text) text = text.strip() 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}\n" question += f"{name2}:" if check: reply = generate_reply(question, tokens, inference_settings, selected_model, eos_token='\n')[0] reply = reply[len(question):].split('\n')[0].strip() else: reply = generate_reply(question, tokens, inference_settings, selected_model)[0] reply = reply[len(question):] idx = reply.find(f"\n{name1}:") if idx != -1: reply = reply[:idx] reply = reply.replace('\n', '\n\n') reply = re.sub(r"\n{3,}", "\n\n", reply) reply = reply.strip() history.append((text, reply)) return history def clear(): global history history = [] if 'pygmalion' in model_name.lower(): context_str = "This is a conversation between two people.\n" name1_str = "You" name2_str = "Kawaii" else: context_str = "This is a conversation between two people." name1_str = "Person 1" name2_str = "Person 2" with gr.Blocks(css=css+".h-\[40vh\] {height: 50vh}", analytics_enabled=False) as interface: gr.Markdown(description) with gr.Row(): with gr.Column(): length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_new_tokens', value=200) with gr.Row(): with gr.Column(): model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model') with gr.Column(): preset_menu = gr.Dropdown(choices=available_presets, value="NovelAI-Sphinx Moth", label='Settings preset') name1 = gr.Textbox(value=name1_str, lines=1, label='Your name') name2 = gr.Textbox(value=name2_str, lines=1, label='Bot\'s name') context = gr.Textbox(value=context_str, lines=2, label='Context') with gr.Row(): check = gr.Checkbox(value=True, label='Stop generating at new line character?') 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, length_slider, preset_menu, model_menu, name1, name2, context, check], display1, show_progress=True, api_name="textgen") textbox.submit(chatbot_wrapper, [textbox, length_slider, preset_menu, model_menu, name1, name2, context, check], 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, tokens, inference_settings, selected_model): a, b, c = generate_reply(question, tokens, 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') length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_new_tokens', value=200) preset_menu = gr.Dropdown(choices=available_presets, value="NovelAI-Sphinx Moth", label='Settings 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, length_slider, preset_menu, model_menu], [output_textbox, markdown, html], show_progress=True, api_name="textgen") cont.click(continue_wrapper, [output_textbox, length_slider, preset_menu, model_menu], [output_textbox, textbox, markdown, html], show_progress=True) textbox.submit(generate_reply, [textbox, 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")