95 lines
3.3 KiB
Python
95 lines
3.3 KiB
Python
import argparse
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import os
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import platform
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import warnings
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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("--multi_round", action="store_true",
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help="Turn multiple rounds interaction on.")
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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 args.model_name in ["DAMO-NLP-MT/polylm-multialpaca-13b-int8", "DAMO-NLP-MT/polylm-multialpaca-13b-int4"] 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, device_map="auto").cuda()
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def clear():
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os.system('cls' if platform.system() == 'Windows' else 'clear')
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def main():
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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。")
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prompt = ""
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while True:
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query = input()
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if query.strip() == "stop":
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break
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if query.strip() == "clear":
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if args.multi_round:
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prompt = ""
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clear()
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continue
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text = query.strip()
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text = re.sub(pattern, "\n", text)
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if args.multi_round:
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prompt += f"{text}\n\n"
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else:
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prompt = f"{text}\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=1024,
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do_sample=True,
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top_p=0.8,
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temperature=0.7,
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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(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
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if args.multi_round:
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prompt += f"{response}\n"
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print(f">>> {response}")
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if __name__ == "__main__":
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main()
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