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import argparse
import collections
import torch
def convert_sbert_transformer_encoder_from_huggingface_to_tencentpretrain(input_model, output_model, layers_num):
for i in range(layers_num):
for j in range(2):
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".self_attn.linear_layers.0.weight"] = \
input_model["encoder.layer." + str(i) + ".attention.self.query.weight"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".self_attn.linear_layers.0.bias"] = \
input_model["encoder.layer." + str(i) + ".attention.self.query.bias"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".self_attn.linear_layers.1.weight"] = \
input_model["encoder.layer." + str(i) + ".attention.self.key.weight"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".self_attn.linear_layers.1.bias"] = \
input_model["encoder.layer." + str(i) + ".attention.self.key.bias"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".self_attn.linear_layers.2.weight"] = \
input_model["encoder.layer." + str(i) + ".attention.self.value.weight"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".self_attn.linear_layers.2.bias"] = \
input_model["encoder.layer." + str(i) + ".attention.self.value.bias"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".self_attn.final_linear.weight"] = \
input_model["encoder.layer." + str(i) + ".attention.output.dense.weight"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".self_attn.final_linear.bias"] = \
input_model["encoder.layer." + str(i) + ".attention.output.dense.bias"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".layer_norm_1.gamma"] = \
input_model["encoder.layer." + str(i) + ".attention.output.LayerNorm.weight"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".layer_norm_1.beta"] = \
input_model["encoder.layer." + str(i) + ".attention.output.LayerNorm.bias"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".feed_forward.linear_1.weight"] = \
input_model["encoder.layer." + str(i) + ".intermediate.dense.weight"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".feed_forward.linear_1.bias"] = \
input_model["encoder.layer." + str(i) + ".intermediate.dense.bias"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".feed_forward.linear_2.weight"] = \
input_model["encoder.layer." + str(i) + ".output.dense.weight"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".feed_forward.linear_2.bias"] = \
input_model["encoder.layer." + str(i) + ".output.dense.bias"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".layer_norm_2.gamma"] = \
input_model["encoder.layer." + str(i) + ".output.LayerNorm.weight"]
output_model["encoder.encoder_" + str(j) + ".transformer." + str(i) + ".layer_norm_2.beta"] = \
input_model["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/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=".")
args = parser.parse_args()
input_model = torch.load(args.input_model_path, map_location='cpu')
output_model = collections.OrderedDict()
for i in range(2):
output_model["embedding.embedding_" + str(i) + ".word.embedding.weight"] = \
input_model["embeddings.word_embeddings.weight"]
output_model["embedding.embedding_" + str(i) + ".pos.embedding.weight"] = \
input_model["embeddings.position_embeddings.weight"]
output_model["embedding.embedding_" + str(i) + ".seg.embedding.weight"] = \
torch.cat((torch.Tensor([[0]*input_model["embeddings.token_type_embeddings.weight"].size()[1]]),
input_model["embeddings.token_type_embeddings.weight"]), dim=0)
output_model["embedding.embedding_" + str(i) + ".layer_norm.gamma"] = \
input_model["embeddings.LayerNorm.weight"]
output_model["embedding.embedding_" + str(i) + ".layer_norm.beta"] = \
input_model["embeddings.LayerNorm.bias"]
convert_sbert_transformer_encoder_from_huggingface_to_tencentpretrain(input_model, output_model, args.layers_num)
torch.save(output_model, args.output_model_path)
if __name__ == "__main__":
main()
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