Support different --max-positions and --tokens-per-sample

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/924

Differential Revision: D16548165

Pulled By: myleott

fbshipit-source-id: 49569ece3e54fad7b4f0dfb201ac99123bfdd4f2

fairseq/models/roberta/hub_interface.py

@@ -43,6 +43,10 @@ class RobertaHubInterface(nn.Module):
     def extract_features(self, tokens: torch.LongTensor, return_all_hiddens=False) -> torch.Tensor:
         if tokens.dim() == 1:
             tokens = tokens.unsqueeze(0)
+        if tokens.size(-1) > self.model.max_positions():
+            raise ValueError('tokens exceeds maximum length: {} > {}'.format(
+                tokens.size(-1), self.model.max_positions()
+            ))
         features, extra = self.model(
             tokens.to(device=self.device),
             features_only=True,

fairseq/models/roberta/model.py

@@ -75,6 +75,8 @@ class RobertaModel(FairseqLanguageModel):
                             help='dropout probability after activation in FFN')
         parser.add_argument('--pooler-dropout', type=float, metavar='D',
                             help='dropout probability in the masked_lm pooler layers')
+        parser.add_argument('--max-positions', type=int,
+                            help='number of positional embeddings to learn')
 
     @classmethod
     def build_model(cls, args, task):

fairseq/tasks/masked_lm.py

@@ -178,8 +178,6 @@ class MaskedLMTask(FairseqTask):
         )
 
     def build_dataset_for_inference(self, src_tokens, src_lengths, sort=True):
-        if self.args.also_lowercase_words:
-            raise NotImplementedError
         src_dataset = PadDataset(
             TokenBlockDataset(
                 src_tokens,