[roberta.conversion] Do not hardcode vocab size

and support for fairseq 0.9+

transformers/convert_roberta_original_pytorch_checkpoint_to_pytorch.py

@@ -22,6 +22,12 @@ import numpy as np
 import torch
 import pathlib
 
+import fairseq
+from packaging import version
+
+if version.parse(fairseq.__version__) < version.parse("0.9.0"):
+    raise Exception("requires fairseq >= 0.9.0")
+
 from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
 from fairseq.modules import TransformerSentenceEncoderLayer
 from transformers.modeling_bert import (BertConfig, BertEncoder,
@@ -46,8 +52,9 @@ def convert_roberta_checkpoint_to_pytorch(roberta_checkpoint_path, pytorch_dump_
     """
     roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)
     roberta.eval()  # disable dropout
+    roberta_sent_encoder = roberta.model.decoder.sentence_encoder
     config = BertConfig(
-        vocab_size=50265,
+        vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,
         hidden_size=roberta.args.encoder_embed_dim,
         num_hidden_layers=roberta.args.encoder_layers,
         num_attention_heads=roberta.args.encoder_attention_heads,
@@ -65,7 +72,6 @@ def convert_roberta_checkpoint_to_pytorch(roberta_checkpoint_path, pytorch_dump_
 
     # Now let's copy all the weights.
     # Embeddings
-    roberta_sent_encoder = roberta.model.decoder.sentence_encoder
     model.roberta.embeddings.word_embeddings.weight = roberta_sent_encoder.embed_tokens.weight
     model.roberta.embeddings.position_embeddings.weight = roberta_sent_encoder.embed_positions.weight
     model.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like(model.roberta.embeddings.token_type_embeddings.weight)  # just zero them out b/c RoBERTa doesn't use them.