Fix FP16 version comparison

fairseq/models/fconv.py

@@ -497,7 +497,7 @@ class FConvDecoder(FairseqIncrementalDecoder):
         return self.embed_positions.max_positions() if self.embed_positions is not None else float('inf')
 
     def upgrade_state_dict(self, state_dict):
-        if state_dict.get('decoder.version', torch.Tensor([1]))[0] < 2:
+        if utils.item(state_dict.get('decoder.version', torch.Tensor([1]))[0]) < 2:
             # old models use incorrect weight norm dimension
             for i, conv in enumerate(self.convolutions):
                 # reconfigure weight norm

fairseq/models/transformer.py

@@ -277,7 +277,7 @@ class TransformerEncoder(FairseqEncoder):
             if 'encoder.embed_positions.weights' in state_dict:
                 del state_dict['encoder.embed_positions.weights']
             state_dict['encoder.embed_positions._float_tensor'] = torch.FloatTensor(1)
-        if state_dict.get('encoder.version', torch.Tensor([1]))[0] < 2:
+        if utils.item(state_dict.get('encoder.version', torch.Tensor([1]))[0]) < 2:
             # earlier checkpoints did not normalize after the stack of layers
             self.layer_norm = None
             self.normalize = False
@@ -415,7 +415,7 @@ class TransformerDecoder(FairseqIncrementalDecoder):
                     if k in state_dict:
                         state_dict['decoder.layers.{}.{}.{}'.format(i, new, m)] = state_dict[k]
                         del state_dict[k]
-        if state_dict.get('decoder.version', torch.Tensor([1]))[0] < 2:
+        if utils.item(state_dict.get('decoder.version', torch.Tensor([1]))[0]) < 2:
             # earlier checkpoints did not normalize after the stack of layers
             self.layer_norm = None
             self.normalize = False