#!/usr/bin/env python3 import argparse import torch import transformers from distutils.util import strtobool from tokenizers import pre_tokenizers from transformers.generation.utils import logger import mdtex2html import gradio as gr import warnings import os logger.setLevel("ERROR") warnings.filterwarnings("ignore") import os os.system("export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:20") os.system("export batch_size=1") warnings.filterwarnings("ignore") def _strtobool(x): return bool(strtobool(x)) QA_SPECIAL_TOKENS = { "Question": "<|prompter|>", "Answer": "<|assistant|>", "System": "<|system|>", "StartPrefix": "<|prefix_begin|>", "EndPrefix": "<|prefix_end|>", "InnerThought": "<|inner_thoughts|>", "EndOfThought": "" } def format_pairs(pairs, eos_token, add_initial_reply_token=False): conversations = [ "{}{}{}".format( QA_SPECIAL_TOKENS["Question" if i % 2 == 0 else "Answer"], pairs[i], eos_token) for i in range(len(pairs)) ] if add_initial_reply_token: conversations.append(QA_SPECIAL_TOKENS["Answer"]) return conversations def format_system_prefix(prefix, eos_token): return "{}{}{}".format( QA_SPECIAL_TOKENS["System"], prefix, eos_token, ) def get_specific_model( model_name, seq2seqmodel=False, without_head=False, cache_dir=".cache", quantization=False, **kwargs ): # encoder-decoder support for Flan-T5 like models # for now, we can use an argument but in the future, # we can automate this model = transformers.LlamaForCausalLM.from_pretrained(model_name,torch_dtype=torch.float16, ).half() return model parser = argparse.ArgumentParser() parser.add_argument("--model_path", type=str, required=True) parser.add_argument("--max_new_tokens", type=int, default=200) parser.add_argument("--top_k", type=int, default=40) parser.add_argument("--do_sample", type=_strtobool, default=True) # parser.add_argument("--system_prefix", type=str, default=None) parser.add_argument("--per-digit-tokens", action="store_true") args = parser.parse_args() # # 开放问答 # system_prefix = \ # "<|system|>"'''你是一个人工智能助手，名字叫EduChat。 # - EduChat是一个由华东师范大学开发的对话式语言模型。 # EduChat的工具 # - Web search: Disable. # - Calculators: Disable. # EduChat的能力 # - Inner Thought: Disable. # 对话主题 # - General: Enable. # - Psychology: Disable. # - Socrates: Disable.'''"" # # 启发式教学 # system_prefix = \ # "<|system|>"'''你是一个人工智能助手，名字叫EduChat。 # - EduChat是一个由华东师范大学开发的对话式语言模型。 # EduChat的工具 # - Web search: Disable. # - Calculators: Disable. # EduChat的能力 # - Inner Thought: Disable. # 对话主题 # - General: Disable. # - Psychology: Disable. # - Socrates: Enable.'''"" # 情感支持 system_prefix = \ "<|system|>"'''你是一个人工智能助手，名字叫EduChat。 - EduChat是一个由华东师范大学开发的对话式语言模型。 EduChat的工具 - Web search: Disable. - Calculators: Disable. EduChat的能力 - Inner Thought: Disable. 对话主题 - General: Disable. - Psychology: Enable. - Socrates: Disable.'''"" # # 情感支持(with InnerThought) # system_prefix = \ # "<|system|>"'''你是一个人工智能助手，名字叫EduChat。 # - EduChat是一个由华东师范大学开发的对话式语言模型。 # EduChat的工具 # - Web search: Disable. # - Calculators: Disable. # EduChat的能力 # - Inner Thought: Enable. # 对话主题 # - General: Disable. # - Psychology: Enable. # - Socrates: Disable.'''"" print('Loading model......') model = get_specific_model(args.model_path) model.gradient_checkpointing_enable() # reduce number of stored activations print('Loading tokenizer...') tokenizer = transformers.LlamaTokenizer.from_pretrained(args.model_path) tokenizer.add_special_tokens( { "pad_token": "", "eos_token": "", "sep_token": "", } ) additional_special_tokens = ( [] if "additional_special_tokens" not in tokenizer.special_tokens_map else tokenizer.special_tokens_map["additional_special_tokens"] ) additional_special_tokens = list( set(additional_special_tokens + list(QA_SPECIAL_TOKENS.values()))) print("additional_special_tokens:", additional_special_tokens) tokenizer.add_special_tokens( {"additional_special_tokens": additional_special_tokens}) if args.per_digit_tokens: tokenizer._tokenizer.pre_processor = pre_tokenizers.Digits(True) human_token_id = tokenizer.additional_special_tokens_ids[ tokenizer.additional_special_tokens.index(QA_SPECIAL_TOKENS["Question"]) ] print('Type "quit" to exit') print("Press Control + C to restart conversation (spam to exit)") conversation_history = [] """Override Chatbot.postprocess""" def postprocess(self, y): if y is None: return [] for i, (message, response) in enumerate(y): y[i] = ( None if message is None else mdtex2html.convert((message)), None if response is None else mdtex2html.convert(response), ) return y gr.Chatbot.postprocess = postprocess def parse_text(text): """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/""" lines = text.split("\n") lines = [line for line in lines if line != ""] count = 0 for i, line in enumerate(lines): if "```" in line: count += 1 items = line.split('`') if count % 2 == 1: lines[i] = f'
'
            else:
                lines[i] = f'
' else: if i > 0: if count % 2 == 1: line = line.replace("`", "\`") line = line.replace("<", "<") line = line.replace(">", ">") line = line.replace(" ", " ") line = line.replace("*", "*") line = line.replace("_", "_") line = line.replace("-", "-") line = line.replace(".", ".") line = line.replace("!", "!") line = line.replace("(", "(") line = line.replace(")", ")") line = line.replace("$", "$") lines[i] = "
"+line text = "".join(lines) return text def predict(input, chatbot, max_length, top_p, temperature, history): query = parse_text(input) chatbot.append((query, "")) conversation_history = [] for i, (old_query, response) in enumerate(history): conversation_history.append(old_query) conversation_history.append(response) conversation_history.append(query) query_str = "".join(format_pairs(conversation_history, tokenizer.eos_token, add_initial_reply_token=True)) if system_prefix: query_str = system_prefix + query_str print("query:", query_str) batch = tokenizer.encode( query_str, return_tensors="pt", ) with torch.cuda.amp.autocast(): out = model.generate( input_ids=batch.to(model.device), # The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt. max_new_tokens=args.max_new_tokens, do_sample=args.do_sample, max_length=max_length, top_k=args.top_k, top_p=top_p, temperature=temperature, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, ) if out[0][-1] == tokenizer.eos_token_id: response = out[0][:-1] else: response = out[0] response = tokenizer.decode(out[0]).split(QA_SPECIAL_TOKENS["Answer"])[-1] conversation_history.append(response) with open("./educhat_query_record.txt", 'a+') as f: f.write(str(conversation_history) + '\n') chatbot[-1] = (query, parse_text(response)) history = history + [(query, response)] print(f"chatbot is {chatbot}") print(f"history is {history}") return chatbot, history def reset_user_input(): return gr.update(value='') def reset_state(): return [], [] with gr.Blocks() as demo: gr.HTML("""

欢迎使用 EduChat 人工智能助手！

""") chatbot = gr.Chatbot() with gr.Row(): with gr.Column(scale=4): with gr.Column(scale=12): user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style( container=False) with gr.Column(min_width=32, scale=1): submitBtn = gr.Button("Submit", variant="primary") with gr.Column(scale=1): emptyBtn = gr.Button("Clear History") max_length = gr.Slider( 0, 2048, value=2048, step=1.0, label="Maximum length", interactive=True) top_p = gr.Slider(0, 1, value=0.2, step=0.01, label="Top P", interactive=True) temperature = gr.Slider( 0, 1, value=1, step=0.01, label="Temperature", interactive=True) history = gr.State([]) # (message, bot_message) submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history], show_progress=True) submitBtn.click(reset_user_input, [], [user_input]) emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True) demo.queue().launch(inbrowser=True, share=True)