text-generation-webui/server.py

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import argparse
import base64
import copy
import gc
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import glob
import io
import json
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import os
import re
import sys
import time
import warnings
from datetime import datetime
from pathlib import Path
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import gradio as gr
import torch
import transformers
from PIL import Image
from tqdm import tqdm
from transformers import AutoConfig
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
from modules.html_generator import *
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from modules.stopping_criteria import _SentinelTokenStoppingCriteria
from modules.ui import *
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transformers.logging.set_verbosity_error()
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parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54))
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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.')
parser.add_argument('--chat', action='store_true', help='Launch the web UI in chat mode.')
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 img_bot.png or img_bot.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture. Similarly, img_me.png or img_me.jpg will be used as your profile picture.')
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parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
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parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
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.')
parser.add_argument('--disk-cache-dir', type=str, help='Directory to save the disk cache to. Defaults to "cache/".')
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parser.add_argument('--gpu-memory', type=int, help='Maximum GPU memory in GiB to allocate. 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('--cpu-memory', type=int, help='Maximum CPU memory in GiB to allocate for offloaded weights. Must be an integer number. Defaults to 99.')
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parser.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.')
parser.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory to use for ZeRO-3 NVME offloading.')
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parser.add_argument('--local_rank', type=int, default=0, help='DeepSpeed: Optional argument for distributed setups.')
parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time. This 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.')
parser.add_argument('--extensions', type=str, help='The list of extensions to load. If you want to load more than one extension, write the names separated by commas and between quotation marks, "like,this".')
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parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')
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parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
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parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.')
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parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
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args = parser.parse_args()
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if (args.chat or args.cai_chat) and not args.no_stream:
print("Warning: chat mode currently becomes somewhat slower with text streaming on.\nConsider starting the web UI with the --no-stream option.\n")
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settings = {
'max_new_tokens': 200,
'max_new_tokens_min': 1,
'max_new_tokens_max': 2000,
'preset': 'NovelAI-Sphinx Moth',
'name1': 'Person 1',
'name2': 'Person 2',
'context': 'This is a conversation between two people.',
'prompt': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
'prompt_gpt4chan': '-----\n--- 865467536\nInput text\n--- 865467537\n',
'stop_at_newline': True,
'history_size': 0,
'history_size_min': 0,
'history_size_max': 64,
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'preset_pygmalion': 'Pygmalion',
'name1_pygmalion': 'You',
'name2_pygmalion': 'Kawaii',
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'context_pygmalion': "Kawaii's persona: Kawaii is a cheerful person who loves to make others smile. She is an optimist who loves to spread happiness and positivity wherever she goes.\n<START>",
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'stop_at_newline_pygmalion': False,
}
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if args.settings is not None and Path(args.settings).exists():
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new_settings = json.loads(open(Path(args.settings), 'r').read())
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for item in new_settings:
settings[item] = new_settings[item]
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if args.deepspeed:
import deepspeed
from transformers.deepspeed import HfDeepSpeedConfig, is_deepspeed_zero3_enabled
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from modules.deepspeed_parameters import generate_ds_config
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# Distributed setup
local_rank = args.local_rank if args.local_rank is not None else int(os.getenv("LOCAL_RANK", "0"))
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world_size = int(os.getenv("WORLD_SIZE", "1"))
torch.cuda.set_device(local_rank)
deepspeed.init_distributed()
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ds_config = generate_ds_config(args.bf16, 1 * world_size, args.nvme_offload_dir)
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dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
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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.load_in_8bit or args.auto_devices or args.disk or args.gpu_memory is not None or args.cpu_memory is not None or args.deepspeed):
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:
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model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if args.bf16 else torch.float16).cuda()
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# DeepSpeed ZeRO-3
elif args.deepspeed:
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), torch_dtype=torch.bfloat16 if args.bf16 else torch.float16)
model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
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model.module.eval() # Inference
print(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
# Custom
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else:
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command = "AutoModelForCausalLM.from_pretrained"
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params = ["low_cpu_mem_usage=True"]
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if args.cpu:
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params.append("low_cpu_mem_usage=True")
params.append("torch_dtype=torch.float32")
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else:
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params.append("device_map='auto'")
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params.append("load_in_8bit=True" if args.load_in_8bit else "torch_dtype=torch.bfloat16" if args.bf16 else "torch_dtype=torch.float16")
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if args.gpu_memory:
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params.append(f"max_memory={{0: '{args.gpu_memory or '99'}GiB', 'cpu': '{args.cpu_memory or '99'}GiB'}}")
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elif (args.gpu_memory or args.cpu_memory) and not args.load_in_8bit:
total_mem = (torch.cuda.get_device_properties(0).total_memory/(1024*1024))
suggestion = round((total_mem-1000)/1000)*1000
if total_mem-suggestion < 800:
suggestion -= 1000
suggestion = int(round(suggestion/1000))
print(f"\033[1;32;1mAuto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors.\nYou can manually set other values.\033[0;37;0m")
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params.append(f"max_memory={{0: '{suggestion}GiB', 'cpu': '{args.cpu_memory or '99'}GiB'}}")
if args.disk:
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params.append(f"offload_folder='{args.disk_cache_dir or 'cache'}'")
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command = f"{command}(Path(f'models/{model_name}'), {','.join(set(params))})"
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():
tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
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else:
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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def load_model_wrapper(selected_model):
global model_name, model, tokenizer
if selected_model != model_name:
model_name = selected_model
model = tokenizer = None
if not args.cpu:
gc.collect()
torch.cuda.empty_cache()
model, tokenizer = load_model(model_name)
def load_preset_values(preset_menu, return_dict=False):
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generate_params = {
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'do_sample': True,
'temperature': 1,
'top_p': 1,
'typical_p': 1,
'repetition_penalty': 1,
'top_k': 50,
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'num_beams': 1,
'min_length': 0,
'length_penalty': 1,
'no_repeat_ngram_size': 0,
'early_stopping': False,
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}
with open(Path(f'presets/{preset_menu}.txt'), 'r') as infile:
preset = infile.read()
for i in preset.split(','):
i = i.strip().split('=')
if len(i) == 2 and i[0].strip() != 'tokens':
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generate_params[i[0].strip()] = eval(i[1].strip())
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generate_params['temperature'] = min(1.99, generate_params['temperature'])
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if return_dict:
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return generate_params
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else:
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return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['length_penalty'], generate_params['early_stopping']
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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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# Fix the LaTeX equations in galactica
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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 encode(prompt, tokens_to_generate=0, add_special_tokens=True):
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input_ids = tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=2048-tokens_to_generate, add_special_tokens=add_special_tokens)
if args.cpu:
return input_ids
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elif args.deepspeed:
return input_ids.to(device=local_rank)
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else:
return input_ids.cuda()
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def decode(output_ids):
reply = tokenizer.decode(output_ids, skip_special_tokens=True)
reply = reply.replace(r'<|endoftext|>', '')
return reply
def formatted_outputs(reply, model_name):
if not (args.chat or args.cai_chat):
if model_name.lower().startswith('galactica'):
reply = fix_galactica(reply)
return reply, reply, generate_basic_html(reply)
elif model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')):
reply = fix_gpt4chan(reply)
return reply, 'Only applicable for GALACTICA models.', generate_4chan_html(reply)
else:
return reply, 'Only applicable for GALACTICA models.', generate_basic_html(reply)
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else:
return reply
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def generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, eos_token=None, stopping_string=None):
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global model_name, model, tokenizer
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original_question = question
if not (args.chat or args.cai_chat):
question = apply_extensions(question, "input")
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if args.verbose:
print(f"\n\n{question}\n--------------------\n")
input_ids = encode(question, tokens)
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cuda = "" if (args.cpu or args.deepspeed) else ".cuda()"
n = tokenizer.eos_token_id if eos_token is None else tokenizer.encode(eos_token, return_tensors='pt')[0][-1]
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if stopping_string is not None:
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# The stopping_criteria code below was copied from
# https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
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t = encode(stopping_string, 0, add_special_tokens=False)
stopping_criteria_list = transformers.StoppingCriteriaList([
_SentinelTokenStoppingCriteria(
sentinel_token_ids=t,
starting_idx=len(input_ids[0])
)
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])
else:
stopping_criteria_list = None
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generate_params = [
f"eos_token_id={n}",
f"stopping_criteria=stopping_criteria_list",
f"do_sample={do_sample}",
f"temperature={temperature}",
f"top_p={top_p}",
f"typical_p={typical_p}",
f"repetition_penalty={repetition_penalty}",
f"top_k={top_k}",
f"min_length={min_length if args.no_stream else 0}",
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f"no_repeat_ngram_size={no_repeat_ngram_size}",
f"num_beams={num_beams}",
f"length_penalty={length_penalty}",
f"early_stopping={early_stopping}",
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]
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if args.deepspeed:
generate_params.append("synced_gpus=True")
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if args.no_stream:
generate_params.append(f"max_new_tokens=tokens")
else:
generate_params.append(f"max_new_tokens=8")
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# Generate the entire reply at once
if args.no_stream:
t0 = time.time()
with torch.no_grad():
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output = eval(f"model.generate(input_ids, {','.join(generate_params)}){cuda}")
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reply = decode(output[0])
t1 = time.time()
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output[0])-len(input_ids[0]))/(t1-t0)/8:.2f} it/s, {len(output[0])-len(input_ids[0])} tokens)")
if not (args.chat or args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
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yield formatted_outputs(reply, model_name)
# Generate the reply 1 token at a time
else:
yield formatted_outputs(original_question, model_name)
for i in tqdm(range(tokens//8+1)):
with torch.no_grad():
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output = eval(f"model.generate(input_ids, {','.join(generate_params)}){cuda}")
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reply = decode(output[0])
if not (args.chat or args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
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yield formatted_outputs(reply, model_name)
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input_ids = output
if output[0][-1] == n:
break
def apply_extensions(text, typ):
global available_extensions, extension_state
for ext in sorted(extension_state, key=lambda x : extension_state[x][1]):
if extension_state[ext][0] == True:
ext_string = f"extensions.{ext}.script"
if typ == "input" and hasattr(eval(ext_string), "input_modifier"):
text = eval(f"{ext_string}.input_modifier(text)")
elif typ == "output" and hasattr(eval(ext_string), "output_modifier"):
text = eval(f"{ext_string}.output_modifier(text)")
elif typ == "bot_prefix" and hasattr(eval(ext_string), "bot_prefix_modifier"):
text = eval(f"{ext_string}.bot_prefix_modifier(text)")
return text
def update_extensions_parameters(*kwargs):
i = 0
for ext in sorted(extension_state, key=lambda x : extension_state[x][1]):
if extension_state[ext][0] == True:
params = eval(f"extensions.{ext}.script.params")
for param in params:
if len(kwargs) >= i+1:
params[param] = eval(f"kwargs[{i}]")
i += 1
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def get_available_models():
return 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)
def get_available_presets():
return sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('presets').glob('*.txt'))), key=str.lower)
def get_available_characters():
return ["None"] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('characters').glob('*.json'))), key=str.lower)
def get_available_extensions():
return sorted(set(map(lambda x : x.parts[1], Path('extensions').glob('*/script.py'))), key=str.lower)
def create_extensions_block():
extensions_ui_elements = []
default_values = []
gr.Markdown('## Extensions parameters')
for ext in sorted(extension_state, key=lambda x : extension_state[x][1]):
if extension_state[ext][0] == True:
params = eval(f"extensions.{ext}.script.params")
for param in params:
_id = f"{ext}-{param}"
default_value = settings[_id] if _id in settings else params[param]
default_values.append(default_value)
if type(params[param]) == str:
extensions_ui_elements.append(gr.Textbox(value=default_value, label=f"{ext}-{param}"))
elif type(params[param]) in [int, float]:
extensions_ui_elements.append(gr.Number(value=default_value, label=f"{ext}-{param}"))
elif type(params[param]) == bool:
extensions_ui_elements.append(gr.Checkbox(value=default_value, label=f"{ext}-{param}"))
update_extensions_parameters(*default_values)
btn_extensions = gr.Button("Apply")
btn_extensions.click(update_extensions_parameters, [*extensions_ui_elements], [])
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def create_settings_menus():
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generate_params = load_preset_values(settings[f'preset{suffix}'], return_dict=True)
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with gr.Row():
with gr.Column():
with gr.Row():
model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
create_refresh_button(model_menu, lambda : None, lambda : {"choices": get_available_models()}, "refresh-button")
with gr.Column():
with gr.Row():
preset_menu = gr.Dropdown(choices=available_presets, value=settings[f'preset{suffix}'], label='Generation parameters preset')
create_refresh_button(preset_menu, lambda : None, lambda : {"choices": get_available_presets()}, "refresh-button")
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with gr.Accordion("Custom generation parameters", open=False):
with gr.Row():
with gr.Column():
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do_sample = gr.Checkbox(value=generate_params['do_sample'], label="do_sample")
temperature = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label="temperature")
top_p = gr.Slider(0.0,1.0,value=generate_params['top_p'],step=0.01,label="top_p")
typical_p = gr.Slider(0.0,1.0,value=generate_params['typical_p'],step=0.01,label="typical_p")
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with gr.Column():
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repetition_penalty = gr.Slider(1.0,4.99,value=generate_params['repetition_penalty'],step=0.01,label="repetition_penalty")
top_k = gr.Slider(0,200,value=generate_params['top_k'],step=1,label="top_k")
no_repeat_ngram_size = gr.Slider(0, 20, step=1, value=generate_params["no_repeat_ngram_size"], label="no_repeat_ngram_size")
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gr.Markdown("Special parameters (only use them if you really need them):")
with gr.Row():
with gr.Column():
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num_beams = gr.Slider(0, 20, step=1, value=generate_params["num_beams"], label="num_beams")
length_penalty = gr.Slider(-5, 5, value=generate_params["length_penalty"], label="length_penalty")
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with gr.Column():
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min_length = gr.Slider(0, 2000, step=1, value=generate_params["min_length"] if args.no_stream else 0, label="min_length", interactive=args.no_stream)
early_stopping = gr.Checkbox(value=generate_params["early_stopping"], label="early_stopping")
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model_menu.change(load_model_wrapper, [model_menu], [])
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preset_menu.change(load_preset_values, [preset_menu], [do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping])
return preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping
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# This gets the new line characters right.
def clean_chat_message(text):
text = text.replace('\n', '\n\n')
text = re.sub(r"\n{3,}", "\n\n", text)
text = text.strip()
return text
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def generate_chat_prompt(text, tokens, name1, name2, context, history_size, impersonate=False):
text = clean_chat_message(text)
rows = [f"{context.strip()}\n"]
i = len(history['internal'])-1
count = 0
while i >= 0 and len(encode(''.join(rows), tokens)[0]) < 2048-tokens:
rows.insert(1, f"{name2}: {history['internal'][i][1].strip()}\n")
count += 1
if not (history['internal'][i][0] == '<|BEGIN-VISIBLE-CHAT|>'):
rows.insert(1, f"{name1}: {history['internal'][i][0].strip()}\n")
count += 1
i -= 1
if history_size != 0 and count >= history_size:
break
if not impersonate:
rows.append(f"{name1}: {text}\n")
rows.append(apply_extensions(f"{name2}:", "bot_prefix"))
limit = 3
else:
rows.append(f"{name1}:")
limit = 2
while len(rows) > limit and len(encode(''.join(rows), tokens)[0]) >= 2048-tokens:
rows.pop(1)
rows.pop(1)
question = ''.join(rows)
return question
def extract_message_from_reply(question, reply, current, other, check, extensions=False):
next_character_found = False
substring_found = False
previous_idx = [m.start() for m in re.finditer(f"(^|\n){current}:", question)]
idx = [m.start() for m in re.finditer(f"(^|\n){current}:", reply)]
idx = idx[len(previous_idx)-1]
if extensions:
reply = reply[idx + 1 + len(apply_extensions(f"{current}:", "bot_prefix")):]
else:
reply = reply[idx + 1 + len(f"{current}:"):]
if check:
reply = reply.split('\n')[0].strip()
else:
idx = reply.find(f"\n{other}:")
if idx != -1:
reply = reply[:idx]
next_character_found = True
reply = clean_chat_message(reply)
# Detect if something like "\nYo" is generated just before
# "\nYou:" is completed
tmp = f"\n{other}:"
for j in range(1, len(tmp)):
if reply[-j:] == tmp[:j]:
substring_found = True
return reply, next_character_found, substring_found
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def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
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original_text = text
text = apply_extensions(text, "input")
question = generate_chat_prompt(text, tokens, name1, name2, context, history_size)
history['internal'].append(['', ''])
history['visible'].append(['', ''])
eos_token = '\n' if check else None
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for reply in generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name1}:"):
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reply, next_character_found, substring_found = extract_message_from_reply(question, reply, name2, name1, check, extensions=True)
history['internal'][-1] = [text, reply]
history['visible'][-1] = [original_text, apply_extensions(reply, "output")]
if not substring_found:
yield history['visible']
if next_character_found:
break
yield history['visible']
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def impersonate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
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question = generate_chat_prompt(text, tokens, name1, name2, context, history_size, impersonate=True)
eos_token = '\n' if check else None
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for reply in generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"):
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reply, next_character_found, substring_found = extract_message_from_reply(question, reply, name1, name2, check, extensions=False)
if not substring_found:
yield apply_extensions(reply, "output")
if next_character_found:
break
yield apply_extensions(reply, "output")
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def cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
for _history in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
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yield generate_chat_html(_history, name1, name2, character)
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def regenerate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
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last = history['visible'].pop()
history['internal'].pop()
text = last[0]
if args.cai_chat:
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for i in cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
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yield i
else:
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for i in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
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yield i
def remove_last_message(name1, name2):
if not history['internal'][-1][0] == '<|BEGIN-VISIBLE-CHAT|>':
last = history['visible'].pop()
history['internal'].pop()
else:
last = ['', '']
if args.cai_chat:
return generate_chat_html(history['visible'], name1, name2, character), last[0]
else:
return history['visible'], last[0]
def send_last_reply_to_input():
if len(history['visible']) > 0:
return history['visible'][-1][1]
else:
return ''
def replace_last_reply(text, name1, name2):
if len(history['visible']) > 0:
history['visible'][-1][1] = text
history['internal'][-1][1] = apply_extensions(text, "input")
if args.cai_chat:
return generate_chat_html(history['visible'], name1, name2, character)
else:
return history['visible']
def clear_html():
return generate_chat_html([], "", "", character)
def clear_chat_log(_character, name1, name2):
global history
if _character != 'None':
for i in range(len(history['internal'])):
if '<|BEGIN-VISIBLE-CHAT|>' in history['internal'][i][0]:
history['visible'] = [['', history['internal'][i][1]]]
history['internal'] = history['internal'][:i+1]
break
else:
history['internal'] = []
history['visible'] = []
if args.cai_chat:
return generate_chat_html(history['visible'], name1, name2, character)
else:
return history['visible']
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def redraw_html(name1, name2):
global history
return generate_chat_html(history['visible'], name1, name2, character)
def tokenize_dialogue(dialogue, name1, name2):
_history = []
dialogue = re.sub('<START>', '', dialogue)
dialogue = re.sub('<start>', '', dialogue)
dialogue = re.sub('(\n|^)[Aa]non:', '\\1You:', dialogue)
dialogue = re.sub('(\n|^)\[CHARACTER\]:', f'\\g<1>{name2}:', dialogue)
idx = [m.start() for m in re.finditer(f"(^|\n)({name1}|{name2}):", dialogue)]
if len(idx) == 0:
return _history
messages = []
for i in range(len(idx)-1):
messages.append(dialogue[idx[i]:idx[i+1]].strip())
messages.append(dialogue[idx[-1]:].strip())
entry = ['', '']
for i in messages:
if i.startswith(f'{name1}:'):
entry[0] = i[len(f'{name1}:'):].strip()
elif i.startswith(f'{name2}:'):
entry[1] = i[len(f'{name2}:'):].strip()
if not (len(entry[0]) == 0 and len(entry[1]) == 0):
_history.append(entry)
entry = ['', '']
print(f"\033[1;32;1m\nDialogue tokenized to:\033[0;37;0m\n", end='')
for row in _history:
for column in row:
print("\n")
for line in column.strip().split('\n'):
print("| "+line+"\n")
print("|\n")
print("------------------------------")
return _history
def save_history():
fname = f"{character or ''}{'_' if character else ''}{datetime.now().strftime('%Y%m%d-%H%M%S')}.json"
if not Path('logs').exists():
Path('logs').mkdir()
with open(Path(f'logs/{fname}'), 'w') as f:
f.write(json.dumps({'data': history['internal'], 'data_visible': history['visible']}))
return Path(f'logs/{fname}')
def load_history(file, name1, name2):
global history
file = file.decode('utf-8')
try:
j = json.loads(file)
if 'data' in j:
history['internal'] = j['data']
if 'data_visible' in j:
history['visible'] = j['data_visible']
else:
history['visible'] = copy.deepcopy(history['internal'])
# Compatibility with Pygmalion AI's official web UI
elif 'chat' in j:
history['internal'] = [':'.join(x.split(':')[1:]).strip() for x in j['chat']]
if len(j['chat']) > 0 and j['chat'][0].startswith(f'{name2}:'):
history['internal'] = [['<|BEGIN-VISIBLE-CHAT|>', history['internal'][0]]] + [[history['internal'][i], history['internal'][i+1]] for i in range(1, len(history['internal'])-1, 2)]
history['visible'] = copy.deepcopy(history['internal'])
history['visible'][0][0] = ''
else:
history['internal'] = [[history['internal'][i], history['internal'][i+1]] for i in range(0, len(history['internal'])-1, 2)]
history['visible'] = copy.deepcopy(history['internal'])
except:
history['internal'] = tokenize_dialogue(file, name1, name2)
history['visible'] = copy.deepcopy(history['internal'])
def load_character(_character, name1, name2):
global history, character
context = ""
history['internal'] = []
history['visible'] = []
if _character != 'None':
character = _character
data = json.loads(open(Path(f'characters/{_character}.json'), 'r').read())
name2 = data['char_name']
if 'char_persona' in data and data['char_persona'] != '':
context += f"{data['char_name']}'s Persona: {data['char_persona']}\n"
if 'world_scenario' in data and data['world_scenario'] != '':
context += f"Scenario: {data['world_scenario']}\n"
context = f"{context.strip()}\n<START>\n"
if 'example_dialogue' in data and data['example_dialogue'] != '':
history['internal'] = tokenize_dialogue(data['example_dialogue'], name1, name2)
if 'char_greeting' in data and len(data['char_greeting'].strip()) > 0:
history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', data['char_greeting']]]
history['visible'] += [['', apply_extensions(data['char_greeting'], "output")]]
else:
history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', "Hello there!"]]
history['visible'] += [['', "Hello there!"]]
else:
character = None
context = settings['context_pygmalion']
name2 = settings['name2_pygmalion']
if args.cai_chat:
return name2, context, generate_chat_html(history['visible'], name1, name2, character)
else:
return name2, context, history['visible']
def upload_character(json_file, img, tavern=False):
json_file = json_file if type(json_file) == str else json_file.decode('utf-8')
data = json.loads(json_file)
outfile_name = data["char_name"]
i = 1
while Path(f'characters/{outfile_name}.json').exists():
outfile_name = f'{data["char_name"]}_{i:03d}'
i += 1
if tavern:
outfile_name = f'TavernAI-{outfile_name}'
with open(Path(f'characters/{outfile_name}.json'), 'w') as f:
f.write(json_file)
if img is not None:
img = Image.open(io.BytesIO(img))
img.save(Path(f'characters/{outfile_name}.png'))
print(f'New character saved to "characters/{outfile_name}.json".')
return outfile_name
def upload_tavern_character(img, name1, name2):
_img = Image.open(io.BytesIO(img))
_img.getexif()
decoded_string = base64.b64decode(_img.info['chara'])
_json = json.loads(decoded_string)
_json = {"char_name": _json['name'], "char_persona": _json['description'], "char_greeting": _json["first_mes"], "example_dialogue": _json['mes_example'], "world_scenario": _json['scenario']}
_json['example_dialogue'] = _json['example_dialogue'].replace('{{user}}', name1).replace('{{char}}', _json['char_name'])
return upload_character(json.dumps(_json), img, tavern=True)
def upload_your_profile_picture(img):
img = Image.open(io.BytesIO(img))
img.save(Path(f'img_me.png'))
print(f'Profile picture saved to "img_me.png"')
# Global variables
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available_models = get_available_models()
available_presets = get_available_presets()
available_characters = get_available_characters()
available_extensions = get_available_extensions()
extension_state = {}
if args.extensions is not None:
for i,ext in enumerate(args.extensions.split(',')):
if ext in available_extensions:
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print(f'Loading the extension "{ext}"... ', end='')
ext_string = f"extensions.{ext}.script"
exec(f"import {ext_string}")
extension_state[ext] = [True, i]
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print(f'Ok.')
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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.")
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sys.exit(0)
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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
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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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loaded_preset = None
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# UI settings
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default_text = settings['prompt_gpt4chan'] if model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) else settings['prompt']
description = f"\n\n# Text generation lab\nGenerate text using Large Language Models.\n"
css = ".my-4 {margin-top: 0} .py-6 {padding-top: 2.5rem} #refresh-button {flex: none; margin: 0; padding: 0; min-width: 50px; border: none; box-shadow: none; border-radius: 0} #download-label, #upload-label {min-height: 0}"
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suffix = '_pygmalion' if 'pygmalion' in model_name.lower() else ''
buttons = {}
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gen_events = []
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history = {'internal': [], 'visible': []}
character = None
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if args.chat or args.cai_chat:
with gr.Blocks(css=css+".h-\[40vh\] {height: 66.67vh} .gradio-container {max-width: 800px; margin-left: auto; margin-right: auto} .w-screen {width: unset}", analytics_enabled=False) as interface:
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if args.cai_chat:
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display = gr.HTML(value=generate_chat_html([], "", "", character))
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else:
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display = gr.Chatbot()
textbox = gr.Textbox(label='Input')
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with gr.Row():
buttons["Stop"] = gr.Button("Stop")
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buttons["Generate"] = gr.Button("Generate")
buttons["Regenerate"] = gr.Button("Regenerate")
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with gr.Row():
buttons["Impersonate"] = gr.Button("Impersonate")
buttons["Remove last"] = gr.Button("Remove last")
buttons["Clear"] = gr.Button("Clear history")
with gr.Row():
buttons["Send last reply to input"] = gr.Button("Send last reply to input")
buttons["Replace last reply"] = gr.Button("Replace last reply")
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with gr.Row():
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with gr.Column():
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max_new_tokens = 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.Column():
history_size_slider = gr.Slider(minimum=settings['history_size_min'], maximum=settings['history_size_max'], step=1, label='Chat history size in prompt (0 for no limit)', value=settings['history_size'])
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preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping = create_settings_menus()
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name1 = gr.Textbox(value=settings[f'name1{suffix}'], lines=1, label='Your name')
name2 = gr.Textbox(value=settings[f'name2{suffix}'], lines=1, label='Bot\'s name')
context = gr.Textbox(value=settings[f'context{suffix}'], lines=2, label='Context')
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with gr.Row():
character_menu = gr.Dropdown(choices=available_characters, value="None", label='Character')
create_refresh_button(character_menu, lambda : None, lambda : {"choices": get_available_characters()}, "refresh-button")
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with gr.Row():
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check = gr.Checkbox(value=settings[f'stop_at_newline{suffix}'], label='Stop generating at new line character?')
with gr.Row():
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with gr.Tab('Chat history'):
with gr.Row():
with gr.Column():
gr.Markdown('Upload')
upload = gr.File(type='binary')
with gr.Column():
gr.Markdown('Download')
download = gr.File()
buttons["Download"] = gr.Button(value="Click me")
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with gr.Tab('Upload character'):
with gr.Row():
with gr.Column():
gr.Markdown('1. Select the JSON file')
upload_char = gr.File(type='binary')
with gr.Column():
gr.Markdown('2. Select your character\'s profile picture (optional)')
upload_img = gr.File(type='binary')
buttons["Upload character"] = gr.Button(value="Submit")
with gr.Tab('Upload your profile picture'):
upload_img_me = gr.File(type='binary')
with gr.Tab('Upload TavernAI Character Card'):
upload_img_tavern = gr.File(type='binary')
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if args.extensions is not None:
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create_extensions_block()
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input_params = [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size_slider]
if args.cai_chat:
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gen_events.append(buttons["Generate"].click(cai_chatbot_wrapper, input_params, display, show_progress=args.no_stream, api_name="textgen"))
gen_events.append(textbox.submit(cai_chatbot_wrapper, input_params, display, show_progress=args.no_stream))
else:
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gen_events.append(buttons["Generate"].click(chatbot_wrapper, input_params, display, show_progress=args.no_stream, api_name="textgen"))
gen_events.append(textbox.submit(chatbot_wrapper, input_params, display, show_progress=args.no_stream))
gen_events.append(buttons["Regenerate"].click(regenerate_wrapper, input_params, display, show_progress=args.no_stream))
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gen_events.append(buttons["Impersonate"].click(impersonate_wrapper, input_params, textbox, show_progress=args.no_stream))
buttons["Send last reply to input"].click(send_last_reply_to_input, [], textbox, show_progress=args.no_stream)
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buttons["Replace last reply"].click(replace_last_reply, [textbox, name1, name2], display, show_progress=args.no_stream)
buttons["Clear"].click(clear_chat_log, [character_menu, name1, name2], display)
buttons["Remove last"].click(remove_last_message, [name1, name2], [display, textbox], show_progress=False)
buttons["Stop"].click(None, None, None, cancels=gen_events)
buttons["Download"].click(save_history, inputs=[], outputs=[download])
buttons["Upload character"].click(upload_character, [upload_char, upload_img], [character_menu])
for i in ["Generate", "Regenerate", "Replace last reply"]:
buttons[i].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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character_menu.change(load_character, [character_menu, name1, name2], [name2, context, display])
upload_img_tavern.upload(upload_tavern_character, [upload_img_tavern, name1, name2], [character_menu])
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upload.upload(load_history, [upload, name1, name2], [])
upload_img_me.upload(upload_your_profile_picture, [upload_img_me], [])
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if args.cai_chat:
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upload.upload(redraw_html, [name1, name2], [display])
upload_img_me.upload(redraw_html, [name1, name2], [display])
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else:
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upload.upload(lambda : history['visible'], [], [display])
upload_img_me.upload(lambda : history['visible'], [], [display])
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elif 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()
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buttons["Generate"] = gr.Button("Generate")
buttons["Stop"] = gr.Button("Stop")
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max_new_tokens = 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, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping = create_settings_menus()
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if args.extensions is not None:
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create_extensions_block()
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gen_events.append(buttons["Generate"].click(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [textbox, markdown, html], show_progress=args.no_stream, api_name="textgen"))
gen_events.append(textbox.submit(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [textbox, markdown, html], show_progress=args.no_stream))
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buttons["Stop"].click(None, None, None, cancels=gen_events)
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else:
with gr.Blocks(css=css, analytics_enabled=False) as interface:
gr.Markdown(description)
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with gr.Row():
with gr.Column():
textbox = gr.Textbox(value=default_text, lines=15, label='Input')
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max_new_tokens = 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'])
buttons["Generate"] = gr.Button("Generate")
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with gr.Row():
with gr.Column():
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buttons["Continue"] = gr.Button("Continue")
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with gr.Column():
buttons["Stop"] = gr.Button("Stop")
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preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping = create_settings_menus()
if args.extensions is not None:
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create_extensions_block()
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with gr.Column():
with gr.Tab('Raw'):
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output_textbox = gr.Textbox(lines=15, label='Output')
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with gr.Tab('Markdown'):
markdown = gr.Markdown()
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with gr.Tab('HTML'):
html = gr.HTML()
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gen_events.append(buttons["Generate"].click(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream, api_name="textgen"))
gen_events.append(textbox.submit(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream))
gen_events.append(buttons["Continue"].click(generate_reply, [output_textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream))
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buttons["Stop"].click(None, None, None, cancels=gen_events)
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interface.queue()
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if args.listen:
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interface.launch(prevent_thread_lock=True, share=args.share, server_name="0.0.0.0", server_port=args.listen_port)
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else:
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interface.launch(prevent_thread_lock=True, share=args.share, server_port=args.listen_port)
# I think that I will need this later
while True:
time.sleep(0.5)