mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-09-20 18:45:09 +02:00
oobabooga
GitHub
commit
682f7bdbba
12 changed files with 948 additions and 920 deletions
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@ -10,7 +10,6 @@ Optionally, you can also add the --share flag to generate a public gradio URL,
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allowing you to use the API remotely.
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'''
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import requests
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# Server address
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@ -6,14 +6,12 @@ Converts a transformers model to a format compatible with flexgen.
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import argparse
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import os
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import numpy as np
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from pathlib import Path
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from sys import argv
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import numpy as np
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import torch
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from tqdm import tqdm
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from transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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from transformers import AutoModelForCausalLM, AutoTokenizer
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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, default=None, nargs='?', help="Path to the input model.")
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@ -33,7 +31,6 @@ def disable_torch_init():
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torch_layer_norm_init_backup = torch.nn.LayerNorm.reset_parameters
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setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
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def restore_torch_init():
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"""Rollback the change made by disable_torch_init."""
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import torch
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@ -13,11 +13,9 @@ https://gist.github.com/81300/fe5b08bff1cba45296a829b9d6b0f303
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import argparse
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from pathlib import Path
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from sys import argv
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import torch
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from transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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from transformers import AutoModelForCausalLM, AutoTokenizer
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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, default=None, nargs='?', help="Path to the input model.")
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@ -1,8 +1,5 @@
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import requests
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import torch
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from PIL import Image
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from transformers import BlipForConditionalGeneration
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from transformers import BlipProcessor
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from transformers import BlipForConditionalGeneration, BlipProcessor
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processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
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model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float32).to("cpu")
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366
modules/chat.py
Normal file
366
modules/chat.py
Normal file
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@ -0,0 +1,366 @@
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import base64
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import copy
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import io
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import json
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import re
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from datetime import datetime
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from io import BytesIO
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from pathlib import Path
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from PIL import Image
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import modules.shared as shared
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from modules.extensions import apply_extensions
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from modules.html_generator import generate_chat_html
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from modules.text_generation import encode, generate_reply, get_max_prompt_length
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if shared.args.picture and (shared.args.cai_chat or shared.args.chat):
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import modules.bot_picture as bot_picture
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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, chat_prompt_size, impersonate=False):
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text = clean_chat_message(text)
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rows = [f"{context.strip()}\n"]
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i = len(shared.history['internal'])-1
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count = 0
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if shared.soft_prompt:
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chat_prompt_size -= shared.soft_prompt_tensor.shape[1]
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max_length = min(get_max_prompt_length(tokens), chat_prompt_size)
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while i >= 0 and len(encode(''.join(rows), tokens)[0]) < max_length:
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rows.insert(1, f"{name2}: {shared.history['internal'][i][1].strip()}\n")
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count += 1
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if not (shared.history['internal'][i][0] == '<|BEGIN-VISIBLE-CHAT|>'):
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rows.insert(1, f"{name1}: {shared.history['internal'][i][0].strip()}\n")
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count += 1
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i -= 1
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if not impersonate:
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rows.append(f"{name1}: {text}\n")
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rows.append(apply_extensions(f"{name2}:", "bot_prefix"))
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limit = 3
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else:
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rows.append(f"{name1}:")
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limit = 2
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while len(rows) > limit and len(encode(''.join(rows), tokens)[0]) >= max_length:
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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 extract_message_from_reply(question, reply, current, other, check, extensions=False):
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next_character_found = False
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substring_found = False
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previous_idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(current)}:", question)]
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idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(current)}:", reply)]
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idx = idx[len(previous_idx)-1]
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if extensions:
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reply = reply[idx + 1 + len(apply_extensions(f"{current}:", "bot_prefix")):]
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else:
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reply = reply[idx + 1 + len(f"{current}:"):]
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if check:
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reply = reply.split('\n')[0].strip()
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else:
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idx = reply.find(f"\n{other}:")
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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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# Detect if something like "\nYo" is generated just before
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# "\nYou:" is completed
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tmp = f"\n{other}:"
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for j in range(1, len(tmp)):
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if reply[-j:] == tmp[:j]:
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substring_found = True
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return reply, next_character_found, substring_found
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def generate_chat_picture(picture, name1, name2):
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text = f'*{name1} sends {name2} a picture that contains the following: "{bot_picture.caption_image(picture)}"*'
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buffer = BytesIO()
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picture.save(buffer, format="JPEG")
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img_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
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visible_text = f'<img src="data:image/jpeg;base64,{img_str}">'
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return text, visible_text
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def stop_everything_event():
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shared.stop_everything = True
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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, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture=None):
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shared.stop_everything = False
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if 'pygmalion' in shared.model_name.lower():
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name1 = "You"
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if shared.args.picture and picture is not None:
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text, visible_text = generate_chat_picture(picture, name1, name2)
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else:
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visible_text = text
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if shared.args.chat:
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visible_text = visible_text.replace('\n', '<br>')
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text = apply_extensions(text, "input")
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question = generate_chat_prompt(text, tokens, name1, name2, context, chat_prompt_size)
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eos_token = '\n' if check else None
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first = True
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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, penalty_alpha, 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)
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visible_reply = apply_extensions(reply, "output")
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if shared.args.chat:
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visible_reply = visible_reply.replace('\n', '<br>')
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# We need this global variable to handle the Stop event,
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# otherwise gradio gets confused
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if shared.stop_everything:
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return shared.history['visible']
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if first:
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first = False
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shared.history['internal'].append(['', ''])
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shared.history['visible'].append(['', ''])
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shared.history['internal'][-1] = [text, reply]
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shared.history['visible'][-1] = [visible_text, visible_reply]
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if not substring_found:
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yield shared.history['visible']
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if next_character_found:
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break
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yield shared.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, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture=None):
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if 'pygmalion' in shared.model_name.lower():
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name1 = "You"
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question = generate_chat_prompt(text, tokens, name1, name2, context, chat_prompt_size, impersonate=True)
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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, penalty_alpha, 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)
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if not substring_found:
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yield reply
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if next_character_found:
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break
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yield reply
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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, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture=None):
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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, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture):
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yield generate_chat_html(_history, name1, name2, shared.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, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture=None):
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if shared.character is not None and len(shared.history['visible']) == 1:
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if shared.args.cai_chat:
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yield generate_chat_html(shared.history['visible'], name1, name2, shared.character)
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else:
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yield shared.history['visible']
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else:
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last_visible = shared.history['visible'].pop()
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last_internal = shared.history['internal'].pop()
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for _history in chatbot_wrapper(last_internal[0], tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture):
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if shared.args.cai_chat:
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shared.history['visible'][-1] = [last_visible[0], _history[-1][1]]
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yield generate_chat_html(shared.history['visible'], name1, name2, shared.character)
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else:
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shared.history['visible'][-1] = (last_visible[0], _history[-1][1])
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yield shared.history['visible']
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def remove_last_message(name1, name2):
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if not shared.history['internal'][-1][0] == '<|BEGIN-VISIBLE-CHAT|>':
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last = shared.history['visible'].pop()
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shared.history['internal'].pop()
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else:
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last = ['', '']
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if shared.args.cai_chat:
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return generate_chat_html(shared.history['visible'], name1, name2, shared.character), last[0]
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else:
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return shared.history['visible'], last[0]
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def send_last_reply_to_input():
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if len(shared.history['internal']) > 0:
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return shared.history['internal'][-1][1]
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else:
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return ''
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def replace_last_reply(text, name1, name2):
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if len(shared.history['visible']) > 0:
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if shared.args.cai_chat:
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shared.history['visible'][-1][1] = text
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else:
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shared.history['visible'][-1] = (shared.history['visible'][-1][0], text)
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shared.history['internal'][-1][1] = apply_extensions(text, "input")
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if shared.args.cai_chat:
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return generate_chat_html(shared.history['visible'], name1, name2, shared.character)
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else:
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return shared.history['visible']
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def clear_html():
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return generate_chat_html([], "", "", shared.character)
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def clear_chat_log(name1, name2):
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if shared.character != 'None':
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for i in range(len(shared.history['internal'])):
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if '<|BEGIN-VISIBLE-CHAT|>' in shared.history['internal'][i][0]:
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shared.history['visible'] = [['', apply_extensions(shared.history['internal'][i][1], "output")]]
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shared.history['internal'] = shared.history['internal'][:i+1]
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break
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else:
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shared.history['internal'] = []
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shared.history['visible'] = []
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if shared.args.cai_chat:
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return generate_chat_html(shared.history['visible'], name1, name2, shared.character)
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else:
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return shared.history['visible']
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def redraw_html(name1, name2):
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return generate_chat_html(shared.history['visible'], name1, name2, shared.character)
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def tokenize_dialogue(dialogue, name1, name2):
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_history = []
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dialogue = re.sub('<START>', '', dialogue)
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dialogue = re.sub('<start>', '', dialogue)
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dialogue = re.sub('(\n|^)[Aa]non:', '\\1You:', dialogue)
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dialogue = re.sub('(\n|^)\[CHARACTER\]:', f'\\g<1>{name2}:', dialogue)
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idx = [m.start() for m in re.finditer(f"(^|\n)({re.escape(name1)}|{re.escape(name2)}):", dialogue)]
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if len(idx) == 0:
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return _history
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messages = []
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for i in range(len(idx)-1):
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messages.append(dialogue[idx[i]:idx[i+1]].strip())
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messages.append(dialogue[idx[-1]:].strip())
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entry = ['', '']
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for i in messages:
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if i.startswith(f'{name1}:'):
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entry[0] = i[len(f'{name1}:'):].strip()
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elif i.startswith(f'{name2}:'):
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entry[1] = i[len(f'{name2}:'):].strip()
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if not (len(entry[0]) == 0 and len(entry[1]) == 0):
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_history.append(entry)
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entry = ['', '']
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print(f"\033[1;32;1m\nDialogue tokenized to:\033[0;37;0m\n", end='')
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for row in _history:
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for column in row:
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print("\n")
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for line in column.strip().split('\n'):
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print("| "+line+"\n")
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print("|\n")
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print("------------------------------")
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return _history
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def save_history(timestamp=True):
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if timestamp:
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fname = f"{shared.character or ''}{'_' if shared.character else ''}{datetime.now().strftime('%Y%m%d-%H%M%S')}.json"
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else:
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fname = f"{shared.character or ''}{'_' if shared.character else ''}persistent.json"
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if not Path('logs').exists():
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Path('logs').mkdir()
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with open(Path(f'logs/{fname}'), 'w') as f:
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f.write(json.dumps({'data': shared.history['internal'], 'data_visible': shared.history['visible']}, indent=2))
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return Path(f'logs/{fname}')
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def load_history(file, name1, name2):
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file = file.decode('utf-8')
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try:
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j = json.loads(file)
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if 'data' in j:
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shared.history['internal'] = j['data']
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if 'data_visible' in j:
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shared.history['visible'] = j['data_visible']
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else:
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shared.history['visible'] = copy.deepcopy(shared.history['internal'])
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# Compatibility with Pygmalion AI's official web UI
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elif 'chat' in j:
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shared.history['internal'] = [':'.join(x.split(':')[1:]).strip() for x in j['chat']]
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if len(j['chat']) > 0 and j['chat'][0].startswith(f'{name2}:'):
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shared.history['internal'] = [['<|BEGIN-VISIBLE-CHAT|>', shared.history['internal'][0]]] + [[shared.history['internal'][i], shared.history['internal'][i+1]] for i in range(1, len(shared.history['internal'])-1, 2)]
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shared.history['visible'] = copy.deepcopy(shared.history['internal'])
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shared.history['visible'][0][0] = ''
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else:
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shared.history['internal'] = [[shared.history['internal'][i], shared.history['internal'][i+1]] for i in range(0, len(shared.history['internal'])-1, 2)]
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shared.history['visible'] = copy.deepcopy(shared.history['internal'])
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except:
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shared.history['internal'] = tokenize_dialogue(file, name1, name2)
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shared.history['visible'] = copy.deepcopy(shared.history['internal'])
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def load_character(_character, name1, name2):
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context = ""
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shared.history['internal'] = []
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shared.history['visible'] = []
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if _character != 'None':
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shared.character = _character
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data = json.loads(open(Path(f'characters/{_character}.json'), 'r').read())
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name2 = data['char_name']
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if 'char_persona' in data and data['char_persona'] != '':
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context += f"{data['char_name']}'s Persona: {data['char_persona']}\n"
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if 'world_scenario' in data and data['world_scenario'] != '':
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context += f"Scenario: {data['world_scenario']}\n"
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context = f"{context.strip()}\n<START>\n"
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if 'example_dialogue' in data and data['example_dialogue'] != '':
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shared.history['internal'] = tokenize_dialogue(data['example_dialogue'], name1, name2)
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if 'char_greeting' in data and len(data['char_greeting'].strip()) > 0:
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shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', data['char_greeting']]]
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shared.history['visible'] += [['', apply_extensions(data['char_greeting'], "output")]]
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else:
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shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', "Hello there!"]]
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shared.history['visible'] += [['', "Hello there!"]]
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||||
else:
|
||||
shared.character = None
|
||||
context = shared.settings['context_pygmalion']
|
||||
name2 = shared.settings['name2_pygmalion']
|
||||
|
||||
if Path(f'logs/{shared.character}_persistent.json').exists():
|
||||
load_history(open(Path(f'logs/{shared.character}_persistent.json'), 'rb').read(), name1, name2)
|
||||
|
||||
if shared.args.cai_chat:
|
||||
return name2, context, generate_chat_html(shared.history['visible'], name1, name2, shared.character)
|
||||
else:
|
||||
return name2, context, shared.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"')
|
||||
64
modules/extensions.py
Normal file
64
modules/extensions.py
Normal file
|
|
@ -0,0 +1,64 @@
|
|||
import extensions
|
||||
import modules.shared as shared
|
||||
import gradio as gr
|
||||
|
||||
extension_state = {}
|
||||
available_extensions = []
|
||||
|
||||
def load_extensions():
|
||||
global extension_state
|
||||
for i,ext in enumerate(shared.args.extensions.split(',')):
|
||||
if ext in available_extensions:
|
||||
print(f'Loading the extension "{ext}"... ', end='')
|
||||
ext_string = f"extensions.{ext}.script"
|
||||
exec(f"import {ext_string}")
|
||||
extension_state[ext] = [True, i]
|
||||
print(f'Ok.')
|
||||
|
||||
def apply_extensions(text, typ):
|
||||
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
|
||||
|
||||
def get_params(name):
|
||||
return eval(f"extensions.{name}.script.params")
|
||||
|
||||
def create_extensions_block():
|
||||
extensions_ui_elements = []
|
||||
default_values = []
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
gr.Markdown('## Extensions parameters')
|
||||
for ext in sorted(extension_state, key=lambda x : extension_state[x][1]):
|
||||
if extension_state[ext][0] == True:
|
||||
params = get_params(ext)
|
||||
for param in params:
|
||||
_id = f"{ext}-{param}"
|
||||
default_value = shared.settings[_id] if _id in shared.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], [])
|
||||
|
|
@ -5,7 +5,6 @@ This is a library for formatting GPT-4chan and chat outputs as nice HTML.
|
|||
'''
|
||||
|
||||
import base64
|
||||
import copy
|
||||
import os
|
||||
import re
|
||||
from io import BytesIO
|
||||
|
|
|
|||
150
modules/models.py
Normal file
150
modules/models.py
Normal file
|
|
@ -0,0 +1,150 @@
|
|||
import json
|
||||
import os
|
||||
import time
|
||||
import zipfile
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import transformers
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
import modules.shared as shared
|
||||
|
||||
transformers.logging.set_verbosity_error()
|
||||
|
||||
local_rank = None
|
||||
|
||||
if shared.args.flexgen:
|
||||
from flexgen.flex_opt import (CompressionConfig, Env, OptLM, Policy,
|
||||
TorchDevice, TorchDisk, TorchMixedDevice,
|
||||
get_opt_config)
|
||||
|
||||
if shared.args.deepspeed:
|
||||
import deepspeed
|
||||
from transformers.deepspeed import (HfDeepSpeedConfig,
|
||||
is_deepspeed_zero3_enabled)
|
||||
|
||||
from modules.deepspeed_parameters import generate_ds_config
|
||||
|
||||
# Distributed setup
|
||||
local_rank = shared.args.local_rank if shared.args.local_rank is not None else int(os.getenv("LOCAL_RANK", "0"))
|
||||
world_size = int(os.getenv("WORLD_SIZE", "1"))
|
||||
torch.cuda.set_device(local_rank)
|
||||
deepspeed.init_distributed()
|
||||
ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir)
|
||||
dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
|
||||
|
||||
def load_model(model_name):
|
||||
print(f"Loading {model_name}...")
|
||||
t0 = time.time()
|
||||
|
||||
# Default settings
|
||||
if not (shared.args.cpu or shared.args.load_in_8bit or shared.args.auto_devices or shared.args.disk or shared.args.gpu_memory is not None or shared.args.cpu_memory is not None or shared.args.deepspeed or shared.args.flexgen):
|
||||
if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
|
||||
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
|
||||
else:
|
||||
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16).cuda()
|
||||
|
||||
# FlexGen
|
||||
elif shared.args.flexgen:
|
||||
gpu = TorchDevice("cuda:0")
|
||||
cpu = TorchDevice("cpu")
|
||||
disk = TorchDisk(shared.args.disk_cache_dir)
|
||||
env = Env(gpu=gpu, cpu=cpu, disk=disk, mixed=TorchMixedDevice([gpu, cpu, disk]))
|
||||
|
||||
# Offloading policy
|
||||
policy = Policy(1, 1,
|
||||
shared.args.percent[0], shared.args.percent[1],
|
||||
shared.args.percent[2], shared.args.percent[3],
|
||||
shared.args.percent[4], shared.args.percent[5],
|
||||
overlap=True, sep_layer=True, pin_weight=True,
|
||||
cpu_cache_compute=False, attn_sparsity=1.0,
|
||||
compress_weight=shared.args.compress_weight,
|
||||
comp_weight_config=CompressionConfig(
|
||||
num_bits=4, group_size=64,
|
||||
group_dim=0, symmetric=False),
|
||||
compress_cache=False,
|
||||
comp_cache_config=CompressionConfig(
|
||||
num_bits=4, group_size=64,
|
||||
group_dim=2, symmetric=False))
|
||||
|
||||
opt_config = get_opt_config(f"facebook/{shared.model_name}")
|
||||
model = OptLM(opt_config, env, "models", policy)
|
||||
model.init_all_weights()
|
||||
|
||||
# DeepSpeed ZeRO-3
|
||||
elif shared.args.deepspeed:
|
||||
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
|
||||
model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
|
||||
model.module.eval() # Inference
|
||||
print(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
|
||||
|
||||
# Custom
|
||||
else:
|
||||
command = "AutoModelForCausalLM.from_pretrained"
|
||||
params = ["low_cpu_mem_usage=True"]
|
||||
if not shared.args.cpu and not torch.cuda.is_available():
|
||||
print("Warning: no GPU has been detected.\nFalling back to CPU mode.\n")
|
||||
shared.args.cpu = True
|
||||
|
||||
if shared.args.cpu:
|
||||
params.append("low_cpu_mem_usage=True")
|
||||
params.append("torch_dtype=torch.float32")
|
||||
else:
|
||||
params.append("device_map='auto'")
|
||||
params.append("load_in_8bit=True" if shared.args.load_in_8bit else "torch_dtype=torch.bfloat16" if shared.args.bf16 else "torch_dtype=torch.float16")
|
||||
|
||||
if shared.args.gpu_memory:
|
||||
params.append(f"max_memory={{0: '{shared.args.gpu_memory or '99'}GiB', 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}")
|
||||
elif not shared.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")
|
||||
params.append(f"max_memory={{0: '{suggestion}GiB', 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}")
|
||||
if shared.args.disk:
|
||||
params.append(f"offload_folder='{shared.args.disk_cache_dir}'")
|
||||
|
||||
command = f"{command}(Path(f'models/{shared.model_name}'), {', '.join(set(params))})"
|
||||
model = eval(command)
|
||||
|
||||
# Loading the tokenizer
|
||||
if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path(f"models/gpt-j-6B/").exists():
|
||||
tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
|
||||
else:
|
||||
tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{shared.model_name}/"))
|
||||
tokenizer.truncation_side = 'left'
|
||||
|
||||
print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
|
||||
return model, tokenizer
|
||||
|
||||
def load_soft_prompt(name):
|
||||
if name == 'None':
|
||||
shared.soft_prompt = False
|
||||
shared.soft_prompt_tensor = None
|
||||
else:
|
||||
with zipfile.ZipFile(Path(f'softprompts/{name}.zip')) as zf:
|
||||
zf.extract('tensor.npy')
|
||||
zf.extract('meta.json')
|
||||
j = json.loads(open('meta.json', 'r').read())
|
||||
print(f"\nLoading the softprompt \"{name}\".")
|
||||
for field in j:
|
||||
if field != 'name':
|
||||
if type(j[field]) is list:
|
||||
print(f"{field}: {', '.join(j[field])}")
|
||||
else:
|
||||
print(f"{field}: {j[field]}")
|
||||
print()
|
||||
tensor = np.load('tensor.npy')
|
||||
Path('tensor.npy').unlink()
|
||||
Path('meta.json').unlink()
|
||||
tensor = torch.Tensor(tensor).to(device=shared.model.device, dtype=shared.model.dtype)
|
||||
tensor = torch.reshape(tensor, (1, tensor.shape[0], tensor.shape[1]))
|
||||
|
||||
shared.soft_prompt = True
|
||||
shared.soft_prompt_tensor = tensor
|
||||
|
||||
return name
|
||||
62
modules/shared.py
Normal file
62
modules/shared.py
Normal file
|
|
@ -0,0 +1,62 @@
|
|||
import argparse
|
||||
|
||||
model = None
|
||||
tokenizer = None
|
||||
model_name = ""
|
||||
soft_prompt_tensor = None
|
||||
soft_prompt = False
|
||||
|
||||
# Chat variables
|
||||
history = {'internal': [], 'visible': []}
|
||||
character = 'None'
|
||||
stop_everything = False
|
||||
|
||||
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,
|
||||
'chat_prompt_size': 2048,
|
||||
'chat_prompt_size_min': 0,
|
||||
'chat_prompt_size_max': 2048,
|
||||
'preset_pygmalion': 'Pygmalion',
|
||||
'name1_pygmalion': 'You',
|
||||
'name2_pygmalion': 'Kawaii',
|
||||
'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>",
|
||||
'stop_at_newline_pygmalion': False,
|
||||
}
|
||||
|
||||
parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54))
|
||||
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 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.')
|
||||
parser.add_argument('--picture', action='store_true', help='Adds an ability to send pictures in chat UI modes. Captions are generated by BLIP.')
|
||||
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.')
|
||||
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, default="cache", help='Directory to save the disk cache to. Defaults to "cache".')
|
||||
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.')
|
||||
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.')
|
||||
parser.add_argument('--flexgen', action='store_true', help='Enable the use of FlexGen offloading.')
|
||||
parser.add_argument('--percent', nargs="+", type=int, default=[0, 100, 100, 0, 100, 0], help='FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0).')
|
||||
parser.add_argument("--compress-weight", action="store_true", help="FlexGen: activate weight compression.")
|
||||
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.')
|
||||
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.')
|
||||
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".')
|
||||
parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')
|
||||
parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
|
||||
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.')
|
||||
parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
|
||||
args = parser.parse_args()
|
||||
|
|
@ -8,6 +8,7 @@ https://github.com/PygmalionAI/gradio-ui/
|
|||
import torch
|
||||
import transformers
|
||||
|
||||
|
||||
class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
|
||||
|
||||
def __init__(self, sentinel_token_ids: torch.LongTensor,
|
||||
|
|
|
|||
178
modules/text_generation.py
Normal file
178
modules/text_generation.py
Normal file
|
|
@ -0,0 +1,178 @@
|
|||
import re
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import transformers
|
||||
from tqdm import tqdm
|
||||
|
||||
import modules.shared as shared
|
||||
from modules.extensions import apply_extensions
|
||||
from modules.html_generator import generate_4chan_html, generate_basic_html
|
||||
from modules.models import local_rank
|
||||
from modules.stopping_criteria import _SentinelTokenStoppingCriteria
|
||||
|
||||
|
||||
def get_max_prompt_length(tokens):
|
||||
max_length = 2048-tokens
|
||||
if shared.soft_prompt:
|
||||
max_length -= shared.soft_prompt_tensor.shape[1]
|
||||
return max_length
|
||||
|
||||
def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
|
||||
input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens)
|
||||
if shared.args.cpu or shared.args.flexgen:
|
||||
return input_ids
|
||||
elif shared.args.deepspeed:
|
||||
return input_ids.to(device=local_rank)
|
||||
else:
|
||||
return input_ids.cuda()
|
||||
|
||||
def decode(output_ids):
|
||||
reply = shared.tokenizer.decode(output_ids, skip_special_tokens=True)
|
||||
reply = reply.replace(r'<|endoftext|>', '')
|
||||
return reply
|
||||
|
||||
def generate_softprompt_input_tensors(input_ids):
|
||||
inputs_embeds = shared.model.transformer.wte(input_ids)
|
||||
inputs_embeds = torch.cat((shared.soft_prompt_tensor, inputs_embeds), dim=1)
|
||||
filler_input_ids = torch.zeros((1, inputs_embeds.shape[1]), dtype=input_ids.dtype).to(shared.model.device)
|
||||
filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens
|
||||
return inputs_embeds, filler_input_ids
|
||||
|
||||
# Removes empty replies from gpt4chan outputs
|
||||
def fix_gpt4chan(s):
|
||||
for i in range(10):
|
||||
s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
|
||||
s = re.sub("--- [0-9]*\n *\n---", "---", s)
|
||||
s = re.sub("--- [0-9]*\n\n\n---", "---", s)
|
||||
return s
|
||||
|
||||
# Fix the LaTeX equations in galactica
|
||||
def fix_galactica(s):
|
||||
s = s.replace(r'\[', r'$')
|
||||
s = s.replace(r'\]', r'$')
|
||||
s = s.replace(r'\(', r'$')
|
||||
s = s.replace(r'\)', r'$')
|
||||
s = s.replace(r'$$', r'$')
|
||||
s = re.sub(r'\n', r'\n\n', s)
|
||||
s = re.sub(r"\n{3,}", "\n\n", s)
|
||||
return s
|
||||
|
||||
def formatted_outputs(reply, model_name):
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
if shared.model_name.lower().startswith('galactica'):
|
||||
reply = fix_galactica(reply)
|
||||
return reply, reply, generate_basic_html(reply)
|
||||
elif shared.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)
|
||||
else:
|
||||
return reply
|
||||
|
||||
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, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None):
|
||||
original_question = question
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
question = apply_extensions(question, "input")
|
||||
if shared.args.verbose:
|
||||
print(f"\n\n{question}\n--------------------\n")
|
||||
|
||||
input_ids = encode(question, tokens)
|
||||
cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()"
|
||||
if not shared.args.flexgen:
|
||||
n = shared.tokenizer.eos_token_id if eos_token is None else shared.tokenizer.encode(eos_token, return_tensors='pt')[0][-1]
|
||||
else:
|
||||
n = shared.tokenizer(eos_token).input_ids[0] if eos_token else None
|
||||
|
||||
if stopping_string is not None:
|
||||
# The stopping_criteria code below was copied from
|
||||
# https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
|
||||
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])
|
||||
)
|
||||
])
|
||||
else:
|
||||
stopping_criteria_list = None
|
||||
|
||||
if not shared.args.flexgen:
|
||||
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 shared.args.no_stream else 0}",
|
||||
f"no_repeat_ngram_size={no_repeat_ngram_size}",
|
||||
f"num_beams={num_beams}",
|
||||
f"penalty_alpha={penalty_alpha}",
|
||||
f"length_penalty={length_penalty}",
|
||||
f"early_stopping={early_stopping}",
|
||||
]
|
||||
else:
|
||||
generate_params = [
|
||||
f"do_sample={do_sample}",
|
||||
f"temperature={temperature}",
|
||||
f"stop={n}",
|
||||
]
|
||||
|
||||
if shared.args.deepspeed:
|
||||
generate_params.append("synced_gpus=True")
|
||||
if shared.args.no_stream:
|
||||
generate_params.append(f"max_new_tokens=tokens")
|
||||
else:
|
||||
generate_params.append(f"max_new_tokens=8")
|
||||
|
||||
if shared.soft_prompt:
|
||||
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
|
||||
generate_params.insert(0, "inputs_embeds=inputs_embeds")
|
||||
generate_params.insert(0, "filler_input_ids")
|
||||
else:
|
||||
generate_params.insert(0, "input_ids")
|
||||
|
||||
# Generate the entire reply at once
|
||||
if shared.args.no_stream:
|
||||
t0 = time.time()
|
||||
with torch.no_grad():
|
||||
output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
|
||||
if shared.soft_prompt:
|
||||
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
|
||||
|
||||
reply = decode(output)
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
reply = original_question + apply_extensions(reply[len(question):], "output")
|
||||
yield formatted_outputs(reply, shared.model_name)
|
||||
|
||||
t1 = time.time()
|
||||
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0)/8:.2f} it/s, {len(output)-len(input_ids[0])} tokens)")
|
||||
|
||||
# Generate the reply 8 tokens at a time
|
||||
else:
|
||||
yield formatted_outputs(original_question, shared.model_name)
|
||||
for i in tqdm(range(tokens//8+1)):
|
||||
with torch.no_grad():
|
||||
output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
|
||||
if shared.soft_prompt:
|
||||
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
|
||||
|
||||
reply = decode(output)
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
reply = original_question + apply_extensions(reply[len(question):], "output")
|
||||
yield formatted_outputs(reply, shared.model_name)
|
||||
|
||||
if not shared.args.flexgen:
|
||||
input_ids = torch.reshape(output, (1, output.shape[0]))
|
||||
else:
|
||||
input_ids = np.reshape(output, (1, output.shape[0]))
|
||||
if shared.soft_prompt:
|
||||
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
|
||||
|
||||
if output[-1] == n:
|
||||
break
|
||||
Loading…
Reference in a new issue