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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'<pre><code class="language-{items[-1]}">'
else:
lines[i] = f'<br></code></pre>'
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] = "<br>"+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("""<h1 align="center">欢迎使用 PolyLM 多语言人工智能助手！</h1>""")
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)
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