small clean-up

src/transformers/modeling_utils.py

@@ -845,7 +845,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             encoder_inputs = input_ids
             input_ids = torch.full(
                 (effective_batch_size * num_beams, 1),
-                bos_token_id,
+                bos_token_id,  # TODO: wait for results of Bart CNN summarization
                 dtype=torch.long,
                 device=next(self.parameters()).device,
             )
@@ -1082,7 +1082,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
 
             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
+                # TODO: maybe give better naming
                 scores = self.prepare_scores_for_generation(scores, cur_len, max_length)
 
             # set eos token prob to zero if min_length is not reached
@@ -1276,7 +1276,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             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