code: def __init__(self):
      super(GPT2_CPM_LM, self).__init__()
      # 实例化模型
      gpt2 = GPT2Model(
          vocab_size=30000,
          hidden_size=2560,
          num_hidden_layers=32,
          num_attention_heads=32,
          intermediate_size=10240,
          hidden_act="gelu",
          hidden_dropout_prob=0.1,
          attention_probs_dropout_prob=0.1,
          max_position_embeddings=1024,
          type_vocab_size=1,
          initializer_range=0.02,
          pad_token_id=0,
      )
      self.model = GPT2ForPretraining(gpt2)
  
      # 读取CPM-LM模型参数(FP16)
      state_dict = paddle.load(
          os.path.join(self.directory, "CPM-LM.pdparams")
      )
  
      # FP16 -> FP32
      for param in state_dict:
          state_dict[param] = state_dict[param].astype("float32")
  
      # 设置模型参数
      self.model.set_dict(state_dict)
  
      # 将模型设置为评估状态
      self.model.eval()
  
      # 加载编解码器
      self.tokenizer = GPT2ChineseTokenizer(
          vocab_file=os.path.join(self.directory, "vocab.json"),
          model_file=os.path.join(
              self.directory, "chinese_vocab.model"
          ),
      )
  
      # 初始化编码器
      _ = self.tokenizer.encode("_")
  
file_path: PaddleHub/modules/text/text_generation/GPT2_CPM_LM/module.py
imports: import os
  import paddle
  import numpy
  import paddle.nn
  from paddlehub.module.module import moduleinfo, serving
  from paddlenlp.transformers import GPT2ForPretraining, GPT2ChineseTokenizer, GPT2Model
label_desc: Use image node_id 1 to create a new GPT2_CPM_LM object from inherited base classes: nn with example: obj = GPT2_CPM_LM()