Fix roberta checkpoint conversion script (#3642)

src/transformers/convert_roberta_original_pytorch_checkpoint_to_pytorch.py

@@ -25,15 +25,8 @@ from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
 from fairseq.modules import TransformerSentenceEncoderLayer
 from packaging import version
 
-from transformers.modeling_bert import (
-    BertConfig,
-    BertIntermediate,
-    BertLayer,
-    BertOutput,
-    BertSelfAttention,
-    BertSelfOutput,
-)
-from transformers.modeling_roberta import RobertaForMaskedLM, RobertaForSequenceClassification
+from transformers.modeling_bert import BertIntermediate, BertLayer, BertOutput, BertSelfAttention, BertSelfOutput
+from transformers.modeling_roberta import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
 
 
 if version.parse(fairseq.__version__) < version.parse("0.9.0"):
@@ -55,7 +48,7 @@ def convert_roberta_checkpoint_to_pytorch(
     roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)
     roberta.eval()  # disable dropout
     roberta_sent_encoder = roberta.model.decoder.sentence_encoder
-    config = BertConfig(
+    config = RobertaConfig(
         vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,
         hidden_size=roberta.args.encoder_embed_dim,
         num_hidden_layers=roberta.args.encoder_layers,
@@ -138,7 +131,7 @@ def convert_roberta_checkpoint_to_pytorch(
         model.lm_head.layer_norm.weight = roberta.model.decoder.lm_head.layer_norm.weight
         model.lm_head.layer_norm.bias = roberta.model.decoder.lm_head.layer_norm.bias
         model.lm_head.decoder.weight = roberta.model.decoder.lm_head.weight
-        model.lm_head.bias = roberta.model.decoder.lm_head.bias
+        model.lm_head.decoder.bias = roberta.model.decoder.lm_head.bias
 
     # Let's check that we get the same results.
     input_ids: torch.Tensor = roberta.encode(SAMPLE_TEXT).unsqueeze(0)  # batch of size 1