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| import argparse | |
| import collections | |
| import torch | |
| 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) | |
| output_model = collections.OrderedDict() | |
| emb_size = \ | |
| input_model["embedding.word.embedding.weight"].shape[1] | |
| output_model["roberta.embeddings.word_embeddings.weight"] = \ | |
| input_model["embedding.word.embedding.weight"] | |
| output_model["roberta.embeddings.position_embeddings.weight"] = \ | |
| torch.cat((torch.zeros(2, emb_size), input_model["embedding.pos.embedding.weight"][:-2]),0) | |
| output_model["roberta.embeddings.token_type_embeddings.weight"] = \ | |
| input_model["embedding.seg.embedding.weight"][2:, :] | |
| output_model["roberta.embeddings.LayerNorm.weight"] = \ | |
| input_model["embedding.layer_norm.gamma"] | |
| output_model["roberta.embeddings.LayerNorm.bias"] = \ | |
| input_model["embedding.layer_norm.beta"] | |
| for i in range(args.layers_num): | |
| output_model["roberta.encoder.layer." + str(i) + ".attention.self.query.weight"] = \ | |
| input_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.0.weight"] | |
| output_model["roberta.encoder.layer." + str(i) + ".attention.self.query.bias"] = \ | |
| input_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.0.bias"] | |
| output_model["roberta.encoder.layer." + str(i) + ".attention.self.key.weight"] = \ | |
| input_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.1.weight"] | |
| output_model["roberta.encoder.layer." + str(i) + ".attention.self.key.bias"] = \ | |
| input_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.1.bias"] | |
| output_model["roberta.encoder.layer." + str(i) + ".attention.self.value.weight"] = \ | |
| input_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.2.weight"] | |
| output_model["roberta.encoder.layer." + str(i) + ".attention.self.value.bias"] = \ | |
| input_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.2.bias"] | |
| output_model["roberta.encoder.layer." + str(i) + ".attention.output.dense.weight"] = \ | |
| input_model["encoder.transformer." + str(i) + ".self_attn.final_linear.weight"] | |
| output_model["roberta.encoder.layer." + str(i) + ".attention.output.dense.bias"] = \ | |
| input_model["encoder.transformer." + str(i) + ".self_attn.final_linear.bias"] | |
| output_model["roberta.encoder.layer." + str(i) + ".attention.output.LayerNorm.weight"] = \ | |
| input_model["encoder.transformer." + str(i) + ".layer_norm_1.gamma"] | |
| output_model["roberta.encoder.layer." + str(i) + ".attention.output.LayerNorm.bias"] = \ | |
| input_model["encoder.transformer." + str(i) + ".layer_norm_1.beta"] | |
| output_model["roberta.encoder.layer." + str(i) + ".intermediate.dense.weight"] = \ | |
| input_model["encoder.transformer." + str(i) + ".feed_forward.linear_1.weight"] | |
| output_model["roberta.encoder.layer." + str(i) + ".intermediate.dense.bias"] = \ | |
| input_model["encoder.transformer." + str(i) + ".feed_forward.linear_1.bias"] | |
| output_model["roberta.encoder.layer." + str(i) + ".output.dense.weight"] = \ | |
| input_model["encoder.transformer." + str(i) + ".feed_forward.linear_2.weight"] | |
| output_model["roberta.encoder.layer." + str(i) + ".output.dense.bias"] = \ | |
| input_model["encoder.transformer." + str(i) + ".feed_forward.linear_2.bias"] | |
| output_model["roberta.encoder.layer." + str(i) + ".output.LayerNorm.weight"] = \ | |
| input_model["encoder.transformer." + str(i) + ".layer_norm_2.gamma"] | |
| output_model["roberta.encoder.layer." + str(i) + ".output.LayerNorm.bias"] = \ | |
| input_model["encoder.transformer." + str(i) + ".layer_norm_2.beta"] | |
| output_model["lm_head.dense.weight"] = \ | |
| input_model["target.mlm.linear_1.weight"] | |
| output_model["lm_head.dense.bias"] = \ | |
| input_model["target.mlm.linear_1.bias"] | |
| output_model["lm_head.layer_norm.weight"] = \ | |
| input_model["target.mlm.layer_norm.gamma"] | |
| output_model["lm_head.layer_norm.bias"] = \ | |
| input_model["target.mlm.layer_norm.beta"] | |
| output_model["lm_head.decoder.weight"] = \ | |
| input_model["target.mlm.linear_2.weight"] | |
| output_model["lm_head.decoder.bias"] = \ | |
| input_model["target.mlm.linear_2.bias"] | |
| output_model["lm_head.bias"] = \ | |
| input_model["target.mlm.linear_2.bias"] | |
| torch.save(output_model, args.output_model_path) | |