fix convert (#397)

Co-authored-by: janinezhao <janinezhao@tencent.com>

scripts/convert_pegasus_from_huggingface_to_uer.py

@@ -7,25 +7,23 @@
 uer_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 sys.path.insert(0, uer_dir)
 
-from scripts.convert_bart_from_huggingface_to_uer import\
+from scripts.convert_bart_from_huggingface_to_uer import \
     convert_encoder_decoder_transformer_from_huggingface_to_uer
 
 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=6, help=".")
-parser.add_argument("--decoder_layers_num", type=int, default=6, help=".")
+parser.add_argument("--layers_num", type=int, default=12, help=".")
+parser.add_argument("--decoder_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()
 
-output_model["embedding.sinusoidalpos.pe"] = input_model["model.encoder.embed_positions.weight"].unsqueeze(1)
-output_model["tgt_embedding.sinusoidalpos.pe"] = input_model["model.decoder.embed_positions.weight"].unsqueeze(1)
 output_model["embedding.word.embedding.weight"] = input_model["model.encoder.embed_tokens.weight"]
 output_model["tgt_embedding.word.embedding.weight"] = input_model["model.decoder.embed_tokens.weight"]
 output_model["target.lm.output_layer.weight"] = input_model["lm_head.weight"]

scripts/convert_pegasus_from_uer_to_huggingface.py

@@ -3,11 +3,12 @@
 import argparse
 import collections
 import torch
+import math
 
 uer_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 sys.path.insert(0, uer_dir)
 
-from scripts.convert_bart_from_uer_to_huggingface import\
+from scripts.convert_bart_from_uer_to_huggingface import \
     convert_encoder_decoder_transformer_from_uer_to_huggingface
 
 parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@@ -17,6 +18,7 @@
                     help=".")
 parser.add_argument("--layers_num", type=int, default=12, help=".")
 parser.add_argument("--decoder_layers_num", type=int, default=12, help=".")
+parser.add_argument("--max_seq_length", type=int, default=1024, help=".")
 
 args = parser.parse_args()
 
@@ -25,8 +27,21 @@
 output_model = collections.OrderedDict()
 
 output_model["model.shared.weight"] = input_model["embedding.word.embedding.weight"]
-output_model["model.encoder.embed_positions.weight"] = input_model["embedding.sinusoidalpos.pe"].squeeze(1)
-output_model["model.decoder.embed_positions.weight"] = input_model["tgt_embedding.sinusoidalpos.pe"].squeeze(1)
+
+emb_size = input_model["embedding.word.embedding.weight"].shape[1]
+pe = torch.zeros(args.max_seq_length, emb_size)
+position = torch.arange(0, args.max_seq_length).unsqueeze(1)
+div_term = torch.exp(
+    (
+        torch.arange(0, emb_size, 2, dtype=torch.float)
+        *- (math.log(10000.0) / emb_size)
+    )
+)
+pe[:, 0::2] = torch.sin(position.float() * div_term)
+pe[:, 1::2] = torch.cos(position.float() * div_term)
+
+output_model["model.encoder.embed_positions.weight"] = pe
+output_model["model.decoder.embed_positions.weight"] = pe
 output_model["model.encoder.embed_tokens.weight"] = input_model["embedding.word.embedding.weight"]
 output_model["model.decoder.embed_tokens.weight"] = input_model["tgt_embedding.word.embedding.weight"]
 output_model["lm_head.weight"] = input_model["target.lm.output_layer.weight"]