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convert_bert_from_huggingface_to_uer.py
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import argparse
import collections
import torch
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"] # change last word from weight to gamma (bert)
output_model["encoder.transformer." + str(i) + ".layer_norm_1.beta"] = input_model["bert.encoder.layer." + str(i) + ".attention.output.LayerNorm.bias"] # changed last word from bias to beta (bert)
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"] # change last word from weight to gamma (bert)
output_model["encoder.transformer." + str(i) + ".layer_norm_2.beta"] = input_model["bert.encoder.layer." + str(i) + ".output.LayerNorm.bias"] # changed last word from bias to beta (bert)
def main():
# python3 convert_bert_from_huggingface_to_uer.py --input_model_path ../pre_trained_model_bert/pytorch_model.bin --output_model_path ../pre_trained_model_bert/output_model.bin
# python3 convert_bert_from_huggingface_to_uer.py --input_model_path ../pre_trained_model_biobert/biobert-base-cased-v1.2/pytorch_model.bin --output_model_path ../pre_trained_model_biobert/output_model.bin
# python3 convert_bert_from_huggingface_to_uer.py --input_model_path ../pre_trained_model_scibert/scibert_scivocab_uncased/pytorch_model.bin --output_model_path ../pre_trained_model_scibert/output_model.bin
# python3 convert_bert_from_huggingface_to_uer.py --input_model_path ../pre_trained_model_pubmedbert/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext/pytorch_model.bin --output_model_path ../pre_trained_model_pubmedbert/output_model.bin
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--input_model_path", type=str, default="input_model.bin",
help=".")
parser.add_argument("--output_model_path", type=str, default="output_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"] # change last word from weight to gamma (bert)
output_model["embedding.layer_norm.beta"] = input_model["bert.embeddings.LayerNorm.bias"] # changed last word from bias to beta (bert)
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"] # added .weight to final (bert, biobert, scibert, pubmedbert)
output_model["target.mlm_linear_1.bias"] = input_model["cls.predictions.transform.dense.bias"] # added .bias to final (bert, biobert, scibert, pubmedbert)
output_model["target.layer_norm.gamma"] = input_model["cls.predictions.transform.LayerNorm.weight"] # change last word from weight to gamma (bert)
output_model["target.layer_norm.beta"] = input_model["cls.predictions.transform.LayerNorm.bias"] # changed last word from bias to beta (bert)
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__":
main()