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convert_bert_from_uer_to_huggingface.py
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import argparse
import collections
import torch
def convert_bert_transformer_encoder_from_uer_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_uer_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.layer_norm.gamma"]
output_model["cls.predictions.transform.LayerNorm.bias"] = input_model["target.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()