mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-26 01:30:20 +01:00
240 lines
11 KiB
Python
240 lines
11 KiB
Python
import re
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import time
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import glob
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from sys import exit
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import torch
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import argparse
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from pathlib import Path
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import gradio as gr
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import transformers
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from html_generator import *
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from transformers import AutoTokenizer, T5Tokenizer
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from transformers import AutoModelForCausalLM, T5ForConditionalGeneration
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', type=str, help='Name of the model to load by default.')
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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.')
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parser.add_argument('--chat', action='store_true', help='Launch the webui in chat mode.')
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parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
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parser.add_argument('--listen', action='store_true', help='Makes the webui reachable from your local network.')
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args = parser.parse_args()
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loaded_preset = None
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available_models = sorted(set(map(lambda x : str(x.name).replace('.pt', ''), list(Path('models/').glob('*'))+list(Path('torch-dumps/').glob('*')))))
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available_models = [item for item in available_models if not item.endswith('.txt')]
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available_presets = sorted(set(map(lambda x : str(x.name).split('.')[0], list(Path('presets').glob('*.txt')))))
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def load_model(model_name):
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print(f"Loading {model_name}...")
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t0 = time.time()
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# Loading the model
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if not args.cpu and Path(f"torch-dumps/{model_name}.pt").exists():
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print("Loading in .pt format...")
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model = torch.load(Path(f"torch-dumps/{model_name}.pt"))
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elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')) and any(size in model_name.lower() for size in ('13b', '20b', '30b')):
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model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), device_map='auto', load_in_8bit=True)
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elif model_name in ['flan-t5', 't5-large']:
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if args.cpu:
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model = T5ForConditionalGeneration.from_pretrained(Path(f"models/{model_name}"))
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else:
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model = T5ForConditionalGeneration.from_pretrained(Path(f"models/{model_name}")).cuda()
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else:
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if args.cpu:
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model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float32)
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else:
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model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
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# Loading the tokenizer
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if model_name.lower().startswith('gpt4chan') and Path(f"models/gpt-j-6B/").exists():
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tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
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elif model_name in ['flan-t5', 't5-large']:
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tokenizer = T5Tokenizer.from_pretrained(Path(f"models/{model_name}/"))
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else:
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tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{model_name}/"))
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print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
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return model, tokenizer
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# Removes empty replies from gpt4chan outputs
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def fix_gpt4chan(s):
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for i in range(10):
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s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
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s = re.sub("--- [0-9]*\n *\n---", "---", s)
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s = re.sub("--- [0-9]*\n\n\n---", "---", s)
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return s
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def fix_galactica(s):
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s = s.replace(r'\[', r'$')
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s = s.replace(r'\]', r'$')
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s = s.replace(r'\(', r'$')
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s = s.replace(r'\)', r'$')
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s = s.replace(r'$$', r'$')
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return s
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def generate_reply(question, temperature, max_length, inference_settings, selected_model, eos_token=None):
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global model, tokenizer, model_name, loaded_preset, preset
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if selected_model != model_name:
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model_name = selected_model
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model = None
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tokenizer = None
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if not args.cpu:
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torch.cuda.empty_cache()
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model, tokenizer = load_model(model_name)
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if inference_settings != loaded_preset:
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with open(Path(f'presets/{inference_settings}.txt'), 'r') as infile:
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preset = infile.read()
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loaded_preset = inference_settings
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if not args.cpu:
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torch.cuda.empty_cache()
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input_ids = tokenizer.encode(str(question), return_tensors='pt').cuda()
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cuda = ".cuda()"
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else:
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input_ids = tokenizer.encode(str(question), return_tensors='pt')
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cuda = ""
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if eos_token is None:
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output = eval(f"model.generate(input_ids, {preset}){cuda}")
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else:
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n = tokenizer.encode(eos_token, return_tensors='pt')[0][-1]
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output = eval(f"model.generate(input_ids, eos_token_id={n}, {preset}){cuda}")
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reply = tokenizer.decode(output[0], skip_special_tokens=True)
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if model_name.lower().startswith('galactica'):
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reply = fix_galactica(reply)
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return reply, reply, 'Only applicable for gpt4chan.'
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elif model_name.lower().startswith('gpt4chan'):
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reply = fix_gpt4chan(reply)
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return reply, 'Only applicable for galactica models.', generate_html(reply)
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else:
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return reply, 'Only applicable for galactica models.', 'Only applicable for gpt4chan.'
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# Choosing the default model
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if args.model is not None:
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model_name = args.model
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else:
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if len(available_models) == 0:
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print("No models are available! Please download at least one.")
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exit(0)
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elif len(available_models) == 1:
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i = 0
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else:
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print("The following models are available:\n")
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for i,model in enumerate(available_models):
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print(f"{i+1}. {model}")
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print(f"\nWhich one do you want to load? 1-{len(available_models)}\n")
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i = int(input())-1
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print()
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model_name = available_models[i]
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model, tokenizer = load_model(model_name)
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# UI settings
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if model_name.lower().startswith('gpt4chan'):
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default_text = "-----\n--- 865467536\nInput text\n--- 865467537\n"
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else:
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default_text = "Common sense questions and answers\n\nQuestion: \nFactual answer:"
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description = f"""
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# Text generation lab
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Generate text using Large Language Models.
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"""
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css=".my-4 {margin-top: 0} .py-6 {padding-top: 2.5rem}"
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if args.notebook:
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with gr.Blocks(css=css, analytics_enabled=False) as interface:
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gr.Markdown(description)
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with gr.Tab('Raw'):
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textbox = gr.Textbox(value=default_text, lines=23)
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with gr.Tab('Markdown'):
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markdown = gr.Markdown()
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with gr.Tab('HTML'):
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html = gr.HTML()
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btn = gr.Button("Generate")
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with gr.Row():
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with gr.Column():
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length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200)
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temp_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7)
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with gr.Column():
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preset_menu = gr.Dropdown(choices=available_presets, value="NovelAI-Sphinx Moth", label='Preset')
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model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
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btn.click(generate_reply, [textbox, temp_slider, length_slider, preset_menu, model_menu], [textbox, markdown, html], show_progress=True)
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textbox.submit(generate_reply, [textbox, temp_slider, length_slider, preset_menu, model_menu], [textbox, markdown, html], show_progress=True)
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elif args.chat:
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history = []
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def chatbot(text, temperature, max_length, inference_settings, selected_model, name1, name2, context):
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question = context+'\n\n'
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for i in range(len(history)):
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question += f"{name1}: {history[i][0][3:-5].strip()}\n"
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question += f"{name2}: {history[i][1][3:-5].strip()}\n"
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question += f"{name1}: {text.strip()}\n"
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question += f"{name2}:"
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reply = generate_reply(question, temperature, max_length, inference_settings, selected_model, eos_token='\n')[0]
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reply = reply[len(question):].split('\n')[0].strip()
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history.append((text, reply))
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return history
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def clear():
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global history
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history = []
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with gr.Blocks(css=css+".h-\[40vh\] {height: 50vh}", analytics_enabled=False) as interface:
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gr.Markdown(description)
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with gr.Row():
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with gr.Column():
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with gr.Row():
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with gr.Column():
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length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200)
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preset_menu = gr.Dropdown(choices=available_presets, value="NovelAI-Sphinx Moth", label='Preset')
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with gr.Column():
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temp_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7)
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model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
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name1 = gr.Textbox(value='Person 1', lines=1, label='Your name')
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name2 = gr.Textbox(value='Person 2', lines=1, label='Bot\'s name')
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context = gr.Textbox(value='This is a conversation between two people.', lines=2, label='Context')
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with gr.Column():
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display1 = gr.Chatbot()
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textbox = gr.Textbox(lines=2, label='Input')
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btn = gr.Button("Generate")
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btn2 = gr.Button("Clear history")
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btn.click(chatbot, [textbox, temp_slider, length_slider, preset_menu, model_menu, name1, name2, context], display1, show_progress=True)
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textbox.submit(chatbot, [textbox, temp_slider, length_slider, preset_menu, model_menu, name1, name2, context], display1, show_progress=True)
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btn2.click(clear)
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btn.click(lambda x: "", textbox, textbox, show_progress=False)
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textbox.submit(lambda x: "", textbox, textbox, show_progress=False)
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btn2.click(lambda x: "", display1, display1)
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else:
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with gr.Blocks(css=css, analytics_enabled=False) as interface:
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gr.Markdown(description)
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with gr.Row():
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with gr.Column():
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textbox = gr.Textbox(value=default_text, lines=15, label='Input')
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temp_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7)
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length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200)
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preset_menu = gr.Dropdown(choices=available_presets, value="NovelAI-Sphinx Moth", label='Preset')
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model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
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btn = gr.Button("Generate")
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with gr.Column():
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with gr.Tab('Raw'):
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output_textbox = gr.Textbox(value=default_text, lines=15, label='Output')
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with gr.Tab('Markdown'):
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markdown = gr.Markdown()
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with gr.Tab('HTML'):
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html = gr.HTML()
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btn.click(generate_reply, [textbox, temp_slider, length_slider, preset_menu, model_menu], [output_textbox, markdown, html], show_progress=True)
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textbox.submit(generate_reply, [textbox, temp_slider, length_slider, preset_menu, model_menu], [output_textbox, markdown, html], show_progress=True)
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if args.listen:
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interface.launch(share=False, server_name="0.0.0.0")
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else:
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interface.launch(share=False)
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