import argparse import collections import torch def convert_bert_transformer_encoder_from_tencentpretrain_to_huggingface(input_model, output_model, layers_num): for i in range(layers_num): output_model["bert.encoder.layer." + str(i) + ".attention.self.query.weight"] = \ input_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.0.weight"] output_model["bert.encoder.layer." + str(i) + ".attention.self.query.bias"] = \ input_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.0.bias"] output_model["bert.encoder.layer." + str(i) + ".attention.self.key.weight"] = \ input_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.1.weight"] output_model["bert.encoder.layer." + str(i) + ".attention.self.key.bias"] = \ input_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.1.bias"] output_model["bert.encoder.layer." + str(i) + ".attention.self.value.weight"] = \ input_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.2.weight"] output_model["bert.encoder.layer." + str(i) + ".attention.self.value.bias"] = \ input_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.2.bias"] output_model["bert.encoder.layer." + str(i) + ".attention.output.dense.weight"] = \ input_model["encoder.transformer." + str(i) + ".self_attn.final_linear.weight"] output_model["bert.encoder.layer." + str(i) + ".attention.output.dense.bias"] = \ input_model["encoder.transformer." + str(i) + ".self_attn.final_linear.bias"] output_model["bert.encoder.layer." + str(i) + ".attention.output.LayerNorm.weight"] = \ input_model["encoder.transformer." + str(i) + ".layer_norm_1.gamma"] output_model["bert.encoder.layer." + str(i) + ".attention.output.LayerNorm.bias"] = \ input_model["encoder.transformer." + str(i) + ".layer_norm_1.beta"] output_model["bert.encoder.layer." + str(i) + ".intermediate.dense.weight"] = \ input_model["encoder.transformer." + str(i) + ".feed_forward.linear_1.weight"] output_model["bert.encoder.layer." + str(i) + ".intermediate.dense.bias"] = \ input_model["encoder.transformer." + str(i) + ".feed_forward.linear_1.bias"] output_model["bert.encoder.layer." + str(i) + ".output.dense.weight"] = \ input_model["encoder.transformer." + str(i) + ".feed_forward.linear_2.weight"] output_model["bert.encoder.layer." + str(i) + ".output.dense.bias"] = \ input_model["encoder.transformer." + str(i) + ".feed_forward.linear_2.bias"] output_model["bert.encoder.layer." + str(i) + ".output.LayerNorm.weight"] = \ input_model["encoder.transformer." + str(i) + ".layer_norm_2.gamma"] output_model["bert.encoder.layer." + str(i) + ".output.LayerNorm.bias"] = \ input_model["encoder.transformer." + str(i) + ".layer_norm_2.beta"] def main(): parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("--input_model_path", type=str, default="models/input_model.bin", help=".") parser.add_argument("--output_model_path", type=str, default="models/output_model.bin", help=".") parser.add_argument("--layers_num", type=int, default=12, help=".") parser.add_argument("--type", 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) output_model = collections.OrderedDict() output_model["bert.embeddings.word_embeddings.weight"] = input_model["embedding.word.embedding.weight"] output_model["bert.embeddings.position_embeddings.weight"] = input_model["embedding.pos.embedding.weight"] output_model["bert.embeddings.token_type_embeddings.weight"] = input_model["embedding.seg.embedding.weight"][1:, :] output_model["bert.embeddings.LayerNorm.weight"] = input_model["embedding.layer_norm.gamma"] output_model["bert.embeddings.LayerNorm.bias"] = input_model["embedding.layer_norm.beta"] convert_bert_transformer_encoder_from_tencentpretrain_to_huggingface(input_model, output_model, args.layers_num) if args.type == "bert": output_model["bert.pooler.dense.weight"] = input_model["target.sp.linear_1.weight"] output_model["bert.pooler.dense.bias"] = input_model["target.sp.linear_1.bias"] output_model["cls.seq_relationship.weight"] = input_model["target.sp.linear_2.weight"] output_model["cls.seq_relationship.bias"] = input_model["target.sp.linear_2.bias"] output_model["cls.predictions.transform.dense.weight"] = input_model["target.mlm.linear_1.weight"] output_model["cls.predictions.transform.dense.bias"] = input_model["target.mlm.linear_1.bias"] output_model["cls.predictions.transform.LayerNorm.weight"] = input_model["target.mlm.layer_norm.gamma"] output_model["cls.predictions.transform.LayerNorm.bias"] = input_model["target.mlm.layer_norm.beta"] output_model["cls.predictions.decoder.weight"] = input_model["target.mlm.linear_2.weight"] output_model["cls.predictions.bias"] = input_model["target.mlm.linear_2.bias"] torch.save(output_model, args.output_model_path) if __name__ == "__main__": main()