re-add scoring filtering

src/transformers/modeling_utils.py

@@ -1084,10 +1084,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                 next_token_logits = next_token_logits / temperature
 
             scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)
-            #            if (
-            #                self.config.is_encoder_decoder and do_sample is False
-            #            ):  # TODO(PVP) to be refactored later - do we need this boolean flag here? Also Only add for beam_search or also for no_beam_search? The prepare scores fn is ugly here
-            #                scores = self.prepare_scores_for_generation(scores, cur_len, max_length)
+            if self.config.is_encoder_decoder and do_sample is False:
+                # TODO(PVP) to be refactored later - do we need this boolean flag here? Also Only add for beam_search or also for no_beam_search? The prepare scores fn is ugly here
+                scores = self.prepare_scores_for_generation(scores, cur_len, max_length)
 
             # set eos token prob to zero if min_length is not reached
             if eos_token_ids is not None and cur_len < min_length:
@@ -1279,10 +1278,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             assert (len(hypo) == max_length for hypo in best)
             decoded = torch.stack(best).type(torch.long).to(next(self.parameters()).device)
 
-        #        if self.config.is_encoder_decoder:
-        # do not return first <EOS> token
-        #            return decoded[:, 1:]
-        return decoded
+        if self.config.is_encoder_decoder:
+            # do not return first <EOS> token
+            return decoded[:, 1:]
+#        return decoded
 
     # force one of token_ids to be generated by setting prob of all other tokens to 0.
     def _force_token_ids_generation(self, scores, token_ids):

tests/test_modeling_bart.py

@@ -471,8 +471,7 @@ class BartModelIntegrationTest(unittest.TestCase):
             attention_mask=dct["attention_mask"].to(torch_device),
             num_beams=4,
             length_penalty=2.0,
-            #            max_length=max_length + 2,
-            max_length=max_length + 1,
+            max_length=max_length + 2,
             min_length=min_length + 1,
             no_repeat_ngram_size=3,
             do_sample=False,