mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
403 lines
18 KiB
Python
403 lines
18 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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import json
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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, AutoModelForCausalLM
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import warnings
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import gc
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transformers.logging.set_verbosity_error()
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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 web UI 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 web UI in chat mode.')
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parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI in chat mode with a style similar to Character.AI\'s. If the file profile.png or profile.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture.')
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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('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
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parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
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parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
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parser.add_argument('--max-gpu-memory', type=int, help='Maximum memory in GiB to allocate to the GPU when loading the model. This is useful if you get out of memory errors while trying to generate text. Must be an integer number.')
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parser.add_argument('--no-listen', action='store_true', help='Make the web UI unreachable from your local network.')
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parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time. This slightly improves the text generation performance.')
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parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example.')
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args = parser.parse_args()
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loaded_preset = None
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available_models = sorted(set([item.replace('.pt', '') for item in map(lambda x : str(x.name), list(Path('models/').glob('*'))+list(Path('torch-dumps/').glob('*'))) if not item.endswith('.txt')]), key=str.lower)
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available_presets = sorted(set(map(lambda x : str(x.name).split('.')[0], Path('presets').glob('*.txt'))), key=str.lower)
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settings = {
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'max_new_tokens': 200,
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'max_new_tokens_min': 1,
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'max_new_tokens_max': 2000,
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'preset': 'NovelAI-Sphinx Moth',
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'name1': 'Person 1',
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'name2': 'Person 2',
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'name1_pygmalion': 'You',
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'name2_pygmalion': 'Kawaii',
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'context': 'This is a conversation between two people.',
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'context_pygmalion': 'This is a conversation between two people.\n<START>',
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'prompt': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
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'prompt_gpt4chan': '-----\n--- 865467536\nInput text\n--- 865467537\n',
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'stop_at_newline': True,
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}
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if args.settings is not None and Path(args.settings).exists():
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with open(Path(args.settings), 'r') as f:
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new_settings = json.load(f)
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for item in new_settings:
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if item in settings:
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settings[item] = new_settings[item]
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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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# Default settings
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if not (args.cpu or args.auto_devices or args.load_in_8bit or args.max_gpu_memory is not None):
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if 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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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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# Custom
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else:
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settings = ["low_cpu_mem_usage=True"]
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command = "AutoModelForCausalLM.from_pretrained"
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if args.cpu:
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settings.append("torch_dtype=torch.float32")
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else:
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settings.append("device_map='auto'")
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if args.max_gpu_memory is not None:
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settings.append(f"max_memory={{0: '{args.max_gpu_memory}GiB', 'cpu': '99GiB'}}")
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if args.disk:
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settings.append("offload_folder='cache'")
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if args.load_in_8bit:
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settings.append("load_in_8bit=True")
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else:
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settings.append("torch_dtype=torch.float16")
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settings = ', '.join(set(settings))
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command = f"{command}(Path(f'models/{model_name}'), {settings})"
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model = eval(command)
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# Loading the tokenizer
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if model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) 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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else:
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tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{model_name}/"))
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tokenizer.truncation_side = 'left'
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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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# Fix the LaTeX equations in galactica
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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 encode(prompt, tokens):
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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(prompt), return_tensors='pt', truncation=True, max_length=2048-tokens).cuda()
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else:
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input_ids = tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=2048-tokens)
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return input_ids
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def decode(output_ids):
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reply = tokenizer.decode(output_ids, skip_special_tokens=True)
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reply = reply.replace(r'<|endoftext|>', '')
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return reply
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def formatted_outputs(reply, model_name):
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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, generate_basic_html(reply)
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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_4chan_html(reply)
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else:
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return reply, 'Only applicable for GALACTICA models.', generate_basic_html(reply)
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def generate_reply(question, tokens, 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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gc.collect()
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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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cuda = "" if args.cpu else ".cuda()"
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n = None if eos_token is None else tokenizer.encode(eos_token, return_tensors='pt')[0][-1]
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# Generate the entire reply at once
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if args.no_stream:
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input_ids = encode(question, tokens)
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output = eval(f"model.generate(input_ids, eos_token_id={n}, {preset}){cuda}")
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reply = decode(output[0])
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yield formatted_outputs(reply, model_name)
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# Generate the reply 1 token at a time
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else:
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yield formatted_outputs(question, model_name)
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input_ids = encode(question, 1)
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preset = preset.replace('max_new_tokens=tokens', 'max_new_tokens=1')
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for i in range(tokens):
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output = eval(f"model.generate(input_ids, {preset}){cuda}")
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reply = decode(output[0])
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if eos_token is not None and reply[-1] == eos_token:
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break
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yield formatted_outputs(reply, model_name)
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input_ids = output
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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 = settings['prompt_gpt4chan']
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else:
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default_text = settings['prompt']
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description = f"\n\n# Text generation lab\nGenerate text using Large Language Models.\n"
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css = ".my-4 {margin-top: 0} .py-6 {padding-top: 2.5rem}"
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if args.chat or args.cai_chat:
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history = []
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# This gets the new line characters right.
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def clean_chat_message(text):
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text = text.replace('\n', '\n\n')
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text = re.sub(r"\n{3,}", "\n\n", text)
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text = text.strip()
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return text
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def generate_chat_prompt(text, tokens, name1, name2, context):
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text = clean_chat_message(text)
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rows = [f"{context}\n\n"]
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i = len(history)-1
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while i >= 0 and len(encode(''.join(rows), tokens)[0]) < 2048-tokens:
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rows.insert(1, f"{name2}: {history[i][1].strip()}\n")
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rows.insert(1, f"{name1}: {history[i][0].strip()}\n")
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i -= 1
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rows.append(f"{name1}: {text}\n")
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rows.append(f"{name2}:")
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while len(rows) > 3 and len(encode(''.join(rows), tokens)[0]) >= 2048-tokens:
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rows.pop(1)
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rows.pop(1)
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question = ''.join(rows)
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return question
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def chatbot_wrapper(text, tokens, inference_settings, selected_model, name1, name2, context, check):
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question = generate_chat_prompt(text, tokens, name1, name2, context)
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history.append(['', ''])
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eos_token = '\n' if check else None
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for i in generate_reply(question, tokens, inference_settings, selected_model, eos_token=eos_token):
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reply = i[0]
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next_character_found = False
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if check:
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idx = reply.rfind(question[-1024:])
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reply = reply[idx+min(1024, len(question)):].split('\n')[0].strip()
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else:
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idx = reply.rfind(question[-1024:])
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reply = reply[idx+min(1024, len(question)):]
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idx = reply.find(f"\n{name1}:")
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if idx != -1:
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reply = reply[:idx]
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next_character_found = True
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reply = clean_chat_message(reply)
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history[-1] = [text, reply]
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if next_character_found:
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break
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# Prevent the chat log from flashing if something like "\nYo" is generated just
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# before "\nYou:" is completed
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tmp = f"\n{name1}:"
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next_character_substring_found = False
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for j in range(1, len(tmp)):
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if reply[-j:] == tmp[:j]:
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next_character_substring_found = True
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if not next_character_substring_found:
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yield history
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yield history
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def cai_chatbot_wrapper(text, tokens, inference_settings, selected_model, name1, name2, context, check):
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for history in chatbot_wrapper(text, tokens, inference_settings, selected_model, name1, name2, context, check):
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yield generate_chat_html(history, name1, name2)
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def remove_last_message(name1, name2):
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history.pop()
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if args.cai_chat:
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return generate_chat_html(history, name1, name2)
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else:
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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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def clear_html():
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return generate_chat_html([], "", "")
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if 'pygmalion' in model_name.lower():
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context_str = settings['context_pygmalion']
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name1_str = settings['name1_pygmalion']
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name2_str = settings['name2_pygmalion']
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else:
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context_str = settings['context']
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name1_str = settings['name1']
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name2_str = settings['name2']
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with gr.Blocks(css=css+".h-\[40vh\] {height: 66.67vh} .gradio-container {max-width: 800px; margin-left: auto; margin-right: auto}", analytics_enabled=False) as interface:
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if args.cai_chat:
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display1 = gr.HTML(value=generate_chat_html([], "", ""))
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else:
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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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with gr.Row():
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btn2 = gr.Button("Clear history")
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stop = gr.Button("Stop")
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btn3 = gr.Button("Remove last message")
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length_slider = gr.Slider(minimum=settings['max_new_tokens_min'], maximum=settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=settings['max_new_tokens'])
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with gr.Row():
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with gr.Column():
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model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
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with gr.Column():
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preset_menu = gr.Dropdown(choices=available_presets, value=settings['preset'], label='Settings preset')
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name1 = gr.Textbox(value=name1_str, lines=1, label='Your name')
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name2 = gr.Textbox(value=name2_str, lines=1, label='Bot\'s name')
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context = gr.Textbox(value=context_str, lines=2, label='Context')
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with gr.Row():
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check = gr.Checkbox(value=settings['stop_at_newline'], label='Stop generating at new line character?')
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if args.cai_chat:
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gen_event = btn.click(cai_chatbot_wrapper, [textbox, length_slider, preset_menu, model_menu, name1, name2, context, check], display1, show_progress=args.no_stream, api_name="textgen")
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gen_event2 = textbox.submit(cai_chatbot_wrapper, [textbox, length_slider, preset_menu, model_menu, name1, name2, context, check], display1, show_progress=args.no_stream)
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btn2.click(clear_html, [], display1, show_progress=False)
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else:
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gen_event = btn.click(chatbot_wrapper, [textbox, length_slider, preset_menu, model_menu, name1, name2, context, check], display1, show_progress=args.no_stream, api_name="textgen")
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gen_event2 = textbox.submit(chatbot_wrapper, [textbox, length_slider, preset_menu, model_menu, name1, name2, context, check], display1, show_progress=args.no_stream)
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btn2.click(lambda x: "", display1, display1, show_progress=False)
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btn2.click(clear)
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btn3.click(remove_last_message, [name1, name2], display1, show_progress=False)
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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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stop.click(None, None, None, cancels=[gen_event, gen_event2])
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elif 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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stop = gr.Button("Stop")
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length_slider = gr.Slider(minimum=settings['max_new_tokens_min'], maximum=settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=settings['max_new_tokens'])
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with gr.Row():
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with gr.Column():
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model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
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with gr.Column():
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preset_menu = gr.Dropdown(choices=available_presets, value=settings['preset'], label='Settings preset')
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gen_event = btn.click(generate_reply, [textbox, length_slider, preset_menu, model_menu], [textbox, markdown, html], show_progress=args.no_stream, api_name="textgen")
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gen_event2 = textbox.submit(generate_reply, [textbox, length_slider, preset_menu, model_menu], [textbox, markdown, html], show_progress=args.no_stream)
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stop.click(None, None, None, cancels=[gen_event, gen_event2])
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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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length_slider = gr.Slider(minimum=settings['max_new_tokens_min'], maximum=settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=settings['max_new_tokens'])
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preset_menu = gr.Dropdown(choices=available_presets, value=settings['preset'], label='Settings 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.Row():
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with gr.Column():
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cont = gr.Button("Continue")
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with gr.Column():
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stop = gr.Button("Stop")
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with gr.Column():
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with gr.Tab('Raw'):
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output_textbox = gr.Textbox(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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gen_event = btn.click(generate_reply, [textbox, length_slider, preset_menu, model_menu], [output_textbox, markdown, html], show_progress=args.no_stream, api_name="textgen")
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gen_event2 = textbox.submit(generate_reply, [textbox, length_slider, preset_menu, model_menu], [output_textbox, markdown, html], show_progress=args.no_stream)
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cont_event = cont.click(generate_reply, [output_textbox, length_slider, preset_menu, model_menu], [output_textbox, markdown, html], show_progress=args.no_stream)
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stop.click(None, None, None, cancels=[gen_event, gen_event2, cont_event])
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interface.queue()
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if args.no_listen:
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|
interface.launch(share=False)
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|
else:
|
|
interface.launch(share=False, server_name="0.0.0.0")
|