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import argparse
import os
import platform
import warnings
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="DAMO-NLP-MT/polylm-multialpaca-13b",
choices=["DAMO-NLP-MT/polylm-multialpaca-13b"], type=str)
parser.add_argument("--multi_round", action="store_true",
help="Turn multiple rounds interaction on.")
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 args.model_name in ["DAMO-NLP-MT/polylm-multialpaca-13b-int8", "DAMO-NLP-MT/polylm-multialpaca-13b-int4"] 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, device_map="auto").cuda()
def clear():
os.system('cls' if platform.system() == 'Windows' else 'clear')
def main():
print("欢迎使用 PolyLM 多语言人工智能助手！输入内容即可进行对话。输入 clear 以清空对话历史，输入 stop 以终止对话。")
prompt = ""
while True:
query = input()
if query.strip() == "stop":
break
if query.strip() == "clear":
if args.multi_round:
prompt = ""
clear()
continue
text = query.strip()
text = re.sub(pattern, "\n", text)
if args.multi_round:
prompt += f"{text}\n\n"
else:
prompt = f"{text}\n\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=1024,
do_sample=True,
top_p=0.8,
temperature=0.7,
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)
if args.multi_round:
prompt += f"{response}\n"
print(f">>> {response}")
if __name__ == "__main__":
main()
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