trim vocab(s) before saving checkpoint (OpenNMT#1453)

onmt/models/model_saver.py

@@ -108,10 +108,25 @@ def _save(self, step, model):
         model_state_dict = {k: v for k, v in model_state_dict.items()
                             if 'generator' not in k}
         generator_state_dict = real_generator.state_dict()
+
+        # NOTE: We need to trim the vocab to remove any unk tokens that
+        # were not originally here.
+
+        vocab = deepcopy(self.fields)
+        for side in ["src", "tgt"]:
+            keys_to_pop = []
+            if hasattr(vocab[side], "fields"):
+                unk_token = vocab[side].fields[0][1].vocab.itos[0]
+                for key, value in vocab[side].fields[0][1].vocab.stoi.items():
+                    if value == 0 and key != unk_token:
+                        keys_to_pop.append(key)
+                for key in keys_to_pop:
+                    vocab[side].fields[0][1].vocab.stoi.pop(key, None)
+
         checkpoint = {
             'model': model_state_dict,
             'generator': generator_state_dict,
-            'vocab': self.fields,
+            'vocab': vocab,
             'opt': self.model_opt,
             'optim': self.optim.state_dict(),
         }