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| import sys | |
| import os | |
| import argparse | |
| import collections | |
| import torch | |
| tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) | |
| sys.path.append(tencentpretrain_dir) | |
| from tencentpretrain.utils.config import load_hyperparam | |
| 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("--config_path", type=str, | |
| help=".") | |
| args = parser.parse_args() | |
| args = load_hyperparam(args) | |
| input_model = torch.load(args.input_model_path) | |
| if "word" in args.embedding: | |
| input_model["embedding.word.embedding.weight"] = input_model["embedding.word_embedding.weight"] | |
| input_model.pop("embedding.word_embedding.weight") | |
| if "pos" in args.embedding: | |
| input_model["embedding.pos.embedding.weight"] = input_model["embedding.position_embedding.weight"] | |
| input_model.pop("embedding.position_embedding.weight") | |
| if "seg" in args.embedding: | |
| input_model["embedding.seg.embedding.weight"] = input_model["embedding.segment_embedding.weight"] | |
| input_model.pop("embedding.segment_embedding.weight") | |
| if "sinusoidalpos" in args.embedding: | |
| input_model["embedding.sinusoidalpos.pe"] = input_model["embedding.pe"] | |
| input_model.pop("embedding.pe") | |
| if hasattr(args, "decoder") and args.decoder is not None: | |
| for n in list(input_model.keys()): # target.decoder -> decoder | |
| if n.split('.')[1] == "decoder": | |
| input_model[".".join(n.split('.')[1:])] = input_model[n] | |
| input_model.pop(n) | |
| if n.split('.')[1] == "embedding": | |
| input_model[".".join(["tgt_embedding"] + n.split('.')[2:])] = input_model[n] | |
| input_model.pop(n) | |
| if "word" in args.embedding: | |
| input_model["tgt_embedding.word.embedding.weight"] = input_model["tgt_embedding.word_embedding.weight"] | |
| input_model.pop("tgt_embedding.word_embedding.weight") | |
| if "pos" in args.embedding: | |
| input_model["tgt_embedding.pos.embedding.weight"] = input_model["tgt_embedding.position_embedding.weight"] | |
| input_model.pop("tgt_embedding.position_embedding.weight") | |
| if "seg" in args.embedding: | |
| input_model["tgt_embedding.seg.embedding.weight"] = input_model["tgt_embedding.segment_embedding.weight"] | |
| input_model.pop("tgt_embedding.segment_embedding.weight") | |
| if "sinusoidalpos" in args.embedding: | |
| input_model["tgt_embedding.sinusoidalpos.pe"] = input_model["tgt_embedding.pe"] | |
| input_model.pop("tgt_embedding.pe") | |
| if "mlm" in args.target: | |
| try: | |
| input_model["target.mlm.linear_1.weight"] = input_model["target.mlm_linear_1.weight"] | |
| input_model.pop("target.mlm_linear_1.weight") | |
| input_model["target.mlm.linear_1.bias"] = input_model["target.mlm_linear_1.bias"] | |
| input_model.pop("target.mlm_linear_1.bias") | |
| input_model["target.mlm.layer_norm.gamma"] = input_model["target.layer_norm.gamma"] | |
| input_model.pop("target.layer_norm.gamma") | |
| input_model["target.mlm.layer_norm.beta"] = input_model["target.layer_norm.beta"] | |
| input_model.pop("target.layer_norm.beta") | |
| input_model["target.mlm.linear_2.weight"] = input_model["target.mlm_linear_2.weight"] | |
| input_model.pop("target.mlm_linear_2.weight") | |
| input_model["target.mlm.linear_2.bias"] = input_model["target.mlm_linear_2.bias"] | |
| input_model.pop("target.mlm_linear_2.bias") | |
| except: | |
| pass | |
| if "sp" in args.target: | |
| try: | |
| input_model["target.sp.linear_1.weight"] = input_model["target.sp_linear_1.weight"] | |
| input_model.pop("target.sp_linear_1.weight") | |
| input_model["target.sp.linear_1.bias"] = input_model["target.sp_linear_1.bias"] | |
| input_model.pop("target.sp_linear_1.bias") | |
| input_model["target.sp.linear_2.weight"] = input_model["target.sp_linear_2.weight"] | |
| input_model.pop("target.sp_linear_2.weight") | |
| input_model["target.sp.linear_2.bias"] = input_model["target.sp_linear_2.bias"] | |
| input_model.pop("target.sp_linear_2.bias") | |
| except: | |
| pass | |
| try: | |
| input_model["target.sp.linear_1.weight"] = input_model["target.nsp_linear_1.weight"] | |
| input_model.pop("target.nsp_linear_1.weight") | |
| input_model["target.sp.linear_1.bias"] = input_model["target.nsp_linear_1.bias"] | |
| input_model.pop("target.nsp_linear_1.bias") | |
| input_model["target.sp.linear_2.weight"] = input_model["target.nsp_linear_2.weight"] | |
| input_model.pop("target.nsp_linear_2.weight") | |
| input_model["target.sp.linear_2.bias"] = input_model["target.nsp_linear_2.bias"] | |
| input_model.pop("target.nsp_linear_2.bias") | |
| except: | |
| pass | |
| try: | |
| input_model["target.sp.linear_1.weight"] = input_model["target.sop_linear_1.weight"] | |
| input_model.pop("target.sop_linear_1.weight") | |
| input_model["target.sp.linear_1.bias"] = input_model["target.sop_linear_1.bias"] | |
| input_model.pop("target.sop_linear_1.bias") | |
| input_model["target.sp.linear_2.weight"] = input_model["target.sop_linear_2.weight"] | |
| input_model.pop("target.sop_linear_2.weight") | |
| input_model["target.sp.linear_2.bias"] = input_model["target.sop_linear_2.bias"] | |
| input_model.pop("target.sop_linear_2.bias") | |
| except: | |
| pass | |
| if "lm" in args.target: | |
| try: | |
| input_model["target.lm.output_layer.weight"] = input_model["target.output_layer.weight"] | |
| input_model.pop("target.output_layer.weight") | |
| if args.has_lmtarget_bias: | |
| input_model["target.lm.output_layer.bias"] = input_model["target.output_layer.bias"] | |
| input_model.pop("target.output_layer.bias") | |
| except: | |
| pass | |
| torch.save(input_model, args.output_model_path) | |
| if __name__ == "__main__": | |
| main() | |