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convert_bert_from_huggingface_to_uer.py
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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, args):
for i in range(args.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."+args.gamma]
output_model["encoder.transformer." + str(i) + ".layer_norm_1.beta"] = \
input_model["bert.encoder.layer." + str(i) + ".attention.output.LayerNorm."+args.beta]
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."+args.gamma]
output_model["encoder.transformer." + str(i) + ".layer_norm_2.beta"] = \
input_model["bert.encoder.layer." + str(i) + ".output.LayerNorm."+args.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("--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()
args.gamma, args.beta = "weight", "bias"
if "bert.embeddings.LayerNorm.gamma" in input_model:
args.gamma, args.beta = "gamma", "beta"
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."+args.gamma]
output_model["embedding.layer_norm.beta"] = input_model["bert.embeddings.LayerNorm."+args.beta]
convert_bert_transformer_encoder_from_huggingface_to_uer(input_model, output_model, args)
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."+args.gamma]
output_model["target.layer_norm.beta"] = input_model["cls.predictions.transform.LayerNorm."+args.beta]
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()