re-add eos token to get good bart results

src/transformers/modeling_utils.py

@@ -628,6 +628,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
         no_repeat_ngram_size=None,
         num_return_sequences=None,
         attention_mask=None,
+        decoder_start_token_id=None,
     ):
         r""" Generates sequences for models with a LM head. The method currently supports greedy or penalized greedy decoding, sampling with top-k or nucleus sampling
         and beam-search.
@@ -739,6 +740,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
         num_return_sequences = (
             num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
         )
+        # TODO: think about how to make this cleaner
+        decoder_start_token_id = (
+            decoder_start_token_id if decoder_start_token_id is not None else self.config.bos_token_id
+        )
 
         if input_ids is not None:
             batch_size = input_ids.shape[0]  # overriden by the input batch_size
@@ -765,6 +770,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
         assert (eos_token_ids is None) or (
             isinstance(eos_token_ids, (list, tuple)) and ((isinstance(e, int) and e >= 0) for e in eos_token_ids)
         ), "`eos_token_ids` should be a positive integer or a list/tuple of positive integers."
+        assert (
+            decoder_start_token_id is not None or self.config.is_encoder_decoder is False
+        ), "`decoder_start_token_id` has to be defined if model is encoder-decoder model"
         assert length_penalty > 0, "`length_penalty` should be strictly positive."
         assert (
             isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0
@@ -845,7 +853,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             encoder_inputs = input_ids
             input_ids = torch.full(
                 (effective_batch_size * num_beams, 1),
-                bos_token_id,  # TODO: wait for results of Bart CNN summarization
+                decoder_start_token_id,  # TODO: see whether this is the best result
                 dtype=torch.long,
                 device=next(self.parameters()).device,
             )