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    使用UER-py库在下游数据集进一步预训练+微调

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    • 183****0229
      183****0229 last edited by 183****0229

      使用UER-py库在下游数据集进一步预训练+微调

      环境

      • RTX3090
      • pytorch 1.8.1
      • python 3.8
      • cuda 11.1

      流程

      # 切换路径
      cd /hy-tmp
      # 下载UER并安装其他依赖包
      git clone https://hub.fastgit.org/dbiir/UER-py.git
      cd UER-py
      pip install -U six
      pip install transformers
      

      转化huggingface的预训练权重为UER适配的权重

      convert.py如下

      # convert.py
      import torch
      import argparse
      import collections
      
      
      def convert_bert_transformer_encoder_from_huggingface_to_uer(input_model, output_model, layers_num):
          for i in range(layers_num):
              output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.0.weight"] = input_model["bert.encoder.layer." + str(i) + ".attention.self.query.weight"]
              output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.0.bias"] = input_model["bert.encoder.layer." + str(i) + ".attention.self.query.bias"]
              output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.1.weight"] = input_model["bert.encoder.layer." + str(i) + ".attention.self.key.weight"]
              output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.1.bias"] = input_model["bert.encoder.layer." + str(i) + ".attention.self.key.bias"]
              output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.2.weight"] = input_model["bert.encoder.layer." + str(i) + ".attention.self.value.weight"]
              output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.2.bias"] = input_model["bert.encoder.layer." + str(i) + ".attention.self.value.bias"]
              output_model["encoder.transformer." + str(i) + ".self_attn.final_linear.weight"] = input_model["bert.encoder.layer." + str(i) + ".attention.output.dense.weight"]
              output_model["encoder.transformer." + str(i) + ".self_attn.final_linear.bias"] = input_model["bert.encoder.layer." + str(i) + ".attention.output.dense.bias"]
              output_model["encoder.transformer." + str(i) + ".layer_norm_1.gamma"] = input_model["bert.encoder.layer." + str(i) + ".attention.output.LayerNorm.weight"]
              output_model["encoder.transformer." + str(i) + ".layer_norm_1.beta"] = input_model["bert.encoder.layer." + str(i) + ".attention.output.LayerNorm.bias"]
              output_model["encoder.transformer." + str(i) + ".feed_forward.linear_1.weight"] = input_model["bert.encoder.layer." + str(i) + ".intermediate.dense.weight"]
              output_model["encoder.transformer." + str(i) + ".feed_forward.linear_1.bias"] = input_model["bert.encoder.layer." + str(i) + ".intermediate.dense.bias"]
              output_model["encoder.transformer." + str(i) + ".feed_forward.linear_2.weight"] = input_model["bert.encoder.layer." + str(i) + ".output.dense.weight"]
              output_model["encoder.transformer." + str(i) + ".feed_forward.linear_2.bias"] = input_model["bert.encoder.layer." + str(i) + ".output.dense.bias"]
              output_model["encoder.transformer." + str(i) + ".layer_norm_2.gamma"] = input_model["bert.encoder.layer." + str(i) + ".output.LayerNorm.weight"]
              output_model["encoder.transformer." + str(i) + ".layer_norm_2.beta"] = input_model["bert.encoder.layer." + str(i) + ".output.LayerNorm.bias"]
      
      
      def main():
          parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
          parser.add_argument("--input_model_path", type=str, default="models/bert-base-chinese/pytorch_model.bin",
                              help=".")
          parser.add_argument("--output_model_path", type=str, default="models/google_zh_model.bin",
                              help=".")
          parser.add_argument("--layers_num", type=int, default=12, help=".")
          parser.add_argument("--target", choices=["bert", "mlm"], default="bert",
                              help="The training target of the pretraining model.")
      
          args = parser.parse_args()
          
          input_model = torch.load(args.input_model_path, map_location='cpu')
          
          output_model = collections.OrderedDict()
          
          output_model["embedding.word_embedding.weight"] = input_model["bert.embeddings.word_embeddings.weight"]
          output_model["embedding.position_embedding.weight"] = input_model["bert.embeddings.position_embeddings.weight"]
          output_model["embedding.segment_embedding.weight"] = torch.cat((torch.Tensor([[0]*input_model["bert.embeddings.token_type_embeddings.weight"].size()[1]]), input_model["bert.embeddings.token_type_embeddings.weight"]), dim=0)
          output_model["embedding.layer_norm.gamma"] = input_model["bert.embeddings.LayerNorm.weight"]
          output_model["embedding.layer_norm.beta"] = input_model["bert.embeddings.LayerNorm.bias"]
          
          convert_bert_transformer_encoder_from_huggingface_to_uer(input_model, output_model, args.layers_num)
          
          if args.target == "bert":
              output_model["target.nsp_linear_1.weight"] = input_model["bert.pooler.dense.weight"]
              output_model["target.nsp_linear_1.bias"] = input_model["bert.pooler.dense.bias"]
              output_model["target.nsp_linear_2.weight"] = input_model["cls.seq_relationship.weight"]
              output_model["target.nsp_linear_2.bias"] = input_model["cls.seq_relationship.bias"]
          output_model["target.mlm_linear_1.weight"] = input_model["cls.predictions.transform.dense.weight"]
          output_model["target.mlm_linear_1.bias"] = input_model["cls.predictions.transform.dense.bias"]
          output_model["target.layer_norm.gamma"] = input_model["cls.predictions.transform.LayerNorm.weight"]
          output_model["target.layer_norm.beta"] = input_model["cls.predictions.transform.LayerNorm.bias"]
          output_model["target.mlm_linear_2.weight"] = input_model["cls.predictions.decoder.weight"]
          output_model["target.mlm_linear_2.bias"] = input_model["cls.predictions.bias"]
          
          torch.save(output_model, args.output_model_path)
      
      if __name__ == "__main__":
          from transformers import BertForPreTraining
          model = BertForPreTraining.from_pretrained("bert-base-chinese")
          model.save_pretrained("models/bert-base-chinese")
          main()
      
      
      # 创建convert.py并将代码复制进去
      touch convert.py
      # 转换权重
      python convert.py
      

      # 数据预处理
      python preprocess.py    --corpus_path corpora/book_review_bert.txt \
                              --vocab_path models/google_zh_vocab.txt \
                              --dataset_path dataset.pt --processes_num 8 \
                              --target bert     
      

      # 进一步预训练
      python pretrain.py  --dataset_path dataset.pt --vocab_path models/google_zh_vocab.txt \
                          --pretrained_model_path models/google_zh_model.bin \
                          --output_model_path models/book_review_model.bin \
                          --gpu_ranks 0 \
                          --total_steps 5000 --save_checkpoint_steps 1000 --batch_size 16 \
                          --embedding word_pos_seg --encoder transformer --mask fully_visible --target bert
      # 修改权重名
      mv models/book_review_model.bin-5000 models/book_review_model.bin
      

      # 微调
      python finetune/run_classifier.py --pretrained_model_path models/book_review_model.bin \
                          --vocab_path models/google_zh_vocab.txt \
                          --train_path datasets/douban_book_review/train.tsv \
                          --dev_path datasets/douban_book_review/dev.tsv \
                          --test_path datasets/douban_book_review/test.tsv \
                          --epochs_num 3 --batch_size 64 \
                          --embedding word_pos_seg --encoder transformer --mask fully_visible
      

      REFERENCE

      https://github.com/dbiir/UER-py/wiki/Quickstart

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      • Alice_恒源云
        Alice_恒源云 last edited by

        UER-py(Universal Encoder Representations)是一个用于对通用语料进行预训练并对下游任务进行微调的工具包

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