初始化项目，由ModelHub XC社区提供模型
Model: DAMO-NLP-MT/polylm-multialpaca-13b Source: Original Platform
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ModelHub XC
170
polylm_web_demo_gradio.py
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170
polylm_web_demo_gradio.py
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import argparse
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import os
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import warnings
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import mdtex2html
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import gradio as gr
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import re
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pattern = re.compile("[\n]+")
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import torch
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from accelerate import init_empty_weights, load_checkpoint_and_dispatch
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from huggingface_hub import snapshot_download
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from transformers.generation.utils import logger
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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_name", default="DAMO-NLP-MT/polylm-multialpaca-13b",
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choices=["DAMO-NLP-MT/polylm-multialpaca-13b"], type=str)
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parser.add_argument("--gpu", default="0", type=str)
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args = parser.parse_args()
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os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
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num_gpus = len(args.gpu.split(","))
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if ('int8' in args.model_name or 'int4' in args.model_name) and num_gpus > 1:
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raise ValueError("Quantized models do not support model parallel. Please run on a single GPU (e.g., --gpu 0).")
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logger.setLevel("ERROR")
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warnings.filterwarnings("ignore")
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model_path = args.model_name
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if not os.path.exists(args.model_name):
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model_path = snapshot_download(args.model_name)
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config = AutoConfig.from_pretrained(model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
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if num_gpus > 1:
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print("Waiting for all devices to be ready, it may take a few minutes...")
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with init_empty_weights():
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raw_model = AutoModelForCausalLM.from_config(config)
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raw_model.tie_weights()
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model = load_checkpoint_and_dispatch(
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raw_model, model_path, device_map="auto", no_split_module_classes=["GPT2Block"]
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)
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else:
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print("Loading model files, it may take a few minutes...")
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model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).cuda()
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def postprocess(self, y):
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if y is None:
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return []
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for i, (message, response) in enumerate(y):
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y[i] = (
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None if message is None else mdtex2html.convert((message)),
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None if response is None else mdtex2html.convert(response),
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)
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return y
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gr.Chatbot.postprocess = postprocess
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def parse_text(text):
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"""copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
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lines = text.split("\n")
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lines = [line for line in lines if line != ""]
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count = 0
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for i, line in enumerate(lines):
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if "```" in line:
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count += 1
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items = line.split('`')
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if count % 2 == 1:
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lines[i] = f'<pre><code class="language-{items[-1]}">'
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else:
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lines[i] = f'<br></code></pre>'
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else:
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if i > 0:
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if count % 2 == 1:
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line = line.replace("`", "\`")
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line = line.replace("<", "<")
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line = line.replace(">", ">")
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line = line.replace(" ", " ")
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line = line.replace("*", "*")
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line = line.replace("_", "_")
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line = line.replace("-", "-")
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line = line.replace(".", ".")
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line = line.replace("!", "!")
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line = line.replace("(", "(")
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line = line.replace(")", ")")
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line = line.replace("$", "$")
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lines[i] = "<br>"+line
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text = "".join(lines)
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return text
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def predict(input, chatbot, max_length, top_p, temperature, history):
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query = input
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query = query.strip()
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query = re.sub(pattern, "\n", query)
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chatbot.append((query, ""))
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prompt = ""
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for i, (old_query, response) in enumerate(history):
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prompt += f"{old_query}\n\n" + f"{response}\n"
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prompt += f"{query}\n\n"
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inputs = tokenizer(prompt, return_tensors="pt")
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with torch.no_grad():
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outputs = model.generate(
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inputs.input_ids.cuda(),
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attention_mask=inputs.attention_mask.cuda(),
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max_length=max_length,
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do_sample=True,
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top_p=top_p,
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temperature=temperature,
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repetition_penalty=1.02,
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num_return_sequences=1,
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eos_token_id=2,
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early_stopping=True)
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response = tokenizer.decode(
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outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
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chatbot[-1] = (query, parse_text(response))
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history = history + [(query, response)]
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print("==========================================================================")
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print(f"chatbot is {chatbot}")
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print(f"history is {history}")
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print("==========================================================================")
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return chatbot, history
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def reset_user_input():
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return gr.update(value='')
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def reset_state():
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return [], []
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with gr.Blocks() as demo:
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gr.HTML("""<h1 align="center">欢迎使用 PolyLM 多语言人工智能助手！</h1>""")
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chatbot = gr.Chatbot()
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with gr.Row():
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with gr.Column(scale=4):
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with gr.Column(scale=12):
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user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(
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container=False)
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with gr.Column(min_width=32, scale=1):
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submitBtn = gr.Button("Submit", variant="primary")
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with gr.Column(scale=1):
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emptyBtn = gr.Button("Clear History")
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max_length = gr.Slider(
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0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
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top_p = gr.Slider(0, 1, value=0.8, step=0.01,
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label="Top P", interactive=True)
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temperature = gr.Slider(
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0, 1, value=0.7, step=0.01, label="Temperature", interactive=True)
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history = gr.State([]) # (message, bot_message)
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submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history],
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show_progress=True)
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submitBtn.click(reset_user_input, [], [user_input])
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emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
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demo.queue().launch(share=False, inbrowser=True)
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