import argparse import os import warnings import mdtex2html import gradio as gr import re pattern = re.compile("[\n]+") import torch from accelerate import init_empty_weights, load_checkpoint_and_dispatch from huggingface_hub import snapshot_download from transformers.generation.utils import logger from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer parser = argparse.ArgumentParser() parser.add_argument("--model_name", default=f"DAMO-NLP-MT/polylm-chat-13b", choices=["DAMO-NLP-MT/polylm-chat-13b"], type=str) parser.add_argument("--gpu", default="0", type=str) args = parser.parse_args() os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu num_gpus = len(args.gpu.split(",")) if ('int8' in args.model_name or 'int4' in args.model_name) and num_gpus > 1: raise ValueError("Quantized models do not support model parallel. Please run on a single GPU (e.g., --gpu 0).") logger.setLevel("ERROR") warnings.filterwarnings("ignore") model_path = args.model_name if not os.path.exists(args.model_name): model_path = snapshot_download(args.model_name) config = AutoConfig.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) if num_gpus > 1: print("Waiting for all devices to be ready, it may take a few minutes...") with init_empty_weights(): raw_model = AutoModelForCausalLM.from_config(config) raw_model.tie_weights() model = load_checkpoint_and_dispatch( raw_model, model_path, device_map="auto", no_split_module_classes=["GPT2Block"] ) else: print("Loading model files, it may take a few minutes...") model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).cuda() print(model.dtype) 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: 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("$", "$") if i > 0: lines[i] = "
"+line text = "".join(lines) return text def predict(input, chatbot, max_length, top_p, temperature, history): query = input query = query.strip() chatbot.append((query, "")) prompt = "" for i, (old_query, response) in enumerate(history): response = response.strip() prompt += "<|user|>\n" + f"{old_query}\n" + "<|assistant|>\n" + f"{response}\n" prompt += "<|user|>\n" + f"{query}\n" + "<|assistant|>\n" inputs = tokenizer(prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( inputs.input_ids.cuda(), attention_mask=inputs.attention_mask.cuda(), max_length=max_length, do_sample=True, top_p=top_p, temperature=temperature, repetition_penalty=1.02, num_return_sequences=1, eos_token_id=2, early_stopping=True) response = tokenizer.decode( outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) chatbot[-1] = (parse_text(query), parse_text(response.replace("\n ", "\n"))) history = history + [(query, response)] print("==========================================================================") print(f"chatbot is {chatbot}") print(f"history is {history}") print("==========================================================================") 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 PolyLM 多语言人工智能助手!

""") 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, 4096, value=2048, step=1.0, label="Maximum length", interactive=True) top_p = gr.Slider(0, 1, value=0.8, step=0.01, label="Top P", interactive=True) temperature = gr.Slider( 0, 1, value=0.7, 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(share=False, inbrowser=True)