text-generation-webui/server.py

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import re
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import time
import glob
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from sys import exit
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import torch
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import argparse
from pathlib import Path
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import gradio as gr
import transformers
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from html_generator import *
from transformers import AutoTokenizer, T5Tokenizer
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.')
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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args = parser.parse_args()
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loaded_preset = None
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')]
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
if Path(f"torch-dumps/{model_name}.pt").exists():
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print("Loading in .pt format...")
model = torch.load(Path(f"torch-dumps/{model_name}.pt")).cuda()
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elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')):
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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)
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else:
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
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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()
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elif model_name in ['flan-t5', 't5-large']:
model = T5ForConditionalGeneration.from_pretrained(Path(f"models/{model_name}")).cuda()
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else:
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
if model_name.startswith('gpt4chan'):
tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
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elif model_name in ['flan-t5']:
tokenizer = T5Tokenizer.from_pretrained(Path(f"models/{model_name}/"))
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else:
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):
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
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def fix_galactica(s):
s = s.replace(r'\[', r'$')
s = s.replace(r'\]', r'$')
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s = s.replace(r'\(', r'$')
s = s.replace(r'\)', r'$')
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):
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global model, tokenizer, model_name, loaded_preset, preset
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if selected_model != model_name:
model_name = selected_model
model = None
tokenier = None
torch.cuda.empty_cache()
model, tokenizer = load_model(model_name)
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if inference_settings != loaded_preset:
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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torch.cuda.empty_cache()
input_text = question
input_ids = tokenizer.encode(str(input_text), return_tensors='pt').cuda()
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output = eval(f"model.generate(input_ids, {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.'
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
if args.model is not None:
model_name = args.model
else:
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if len(available_models) == 0:
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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]
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model, tokenizer = load_model(model_name)
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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:"
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if args.notebook:
with gr.Blocks() as interface:
gr.Markdown(
f"""
# Text generation lab
Generate text using Large Language Models.
"""
)
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with gr.Tab('Raw'):
textbox = gr.Textbox(value=default_text, lines=23)
with gr.Tab('Markdown'):
markdown = gr.Markdown()
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with gr.Tab('HTML'):
html = gr.HTML()
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btn = gr.Button("Generate")
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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')
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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=False)
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else:
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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=available_presets, value="NovelAI-Sphinx Moth", label='Preset')
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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()
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with gr.Tab('HTML'):
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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interface.launch(share=False, server_name="0.0.0.0")