Update to match renamed attributes in fairseq master (#5972)

* Update to match renamed attributes in fairseq master

RobertaModel no longer have model.encoder and args.num_classes attributes as of 5/28/20.

* Quality
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

src/transformers/convert_roberta_original_pytorch_checkpoint_to_pytorch.py

@@ -47,7 +47,7 @@ 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
+    roberta_sent_encoder = roberta.model.encoder.sentence_encoder
     config = RobertaConfig(
         vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,
         hidden_size=roberta.args.encoder_embed_dim,
@@ -59,7 +59,7 @@ def convert_roberta_checkpoint_to_pytorch(
         layer_norm_eps=1e-5,  # PyTorch default used in fairseq
     )
     if classification_head:
-        config.num_labels = roberta.args.num_classes
+        config.num_labels = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
     print("Our BERT config:", config)
 
     model = RobertaForSequenceClassification(config) if classification_head else RobertaForMaskedLM(config)
@@ -126,12 +126,12 @@ def convert_roberta_checkpoint_to_pytorch(
         model.classifier.out_proj.bias = roberta.model.classification_heads["mnli"].out_proj.bias
     else:
         # LM Head
-        model.lm_head.dense.weight = roberta.model.decoder.lm_head.dense.weight
-        model.lm_head.dense.bias = roberta.model.decoder.lm_head.dense.bias
-        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.decoder.bias = roberta.model.decoder.lm_head.bias
+        model.lm_head.dense.weight = roberta.model.encoder.lm_head.dense.weight
+        model.lm_head.dense.bias = roberta.model.encoder.lm_head.dense.bias
+        model.lm_head.layer_norm.weight = roberta.model.encoder.lm_head.layer_norm.weight
+        model.lm_head.layer_norm.bias = roberta.model.encoder.lm_head.layer_norm.bias
+        model.lm_head.decoder.weight = roberta.model.encoder.lm_head.weight
+        model.lm_head.decoder.bias = roberta.model.encoder.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