finalize generation merge

src/transformers/configuration_bart.py

@@ -40,8 +40,9 @@ class BartConfig(PretrainedConfig):
         self,
         activation_dropout=0.0,
         vocab_size=50265,
+        bos_token_id=0,
         pad_token_id=1,
-        eos_token_id=2,
+        eos_token_ids=[2],
         d_model=1024,
         encoder_ffn_dim=4096,
         encoder_layers=12,
@@ -58,7 +59,6 @@ class BartConfig(PretrainedConfig):
         classifier_dropout=0.0,
         output_past=False,
         num_labels=3,
-        bos_token_id=0,
         is_encoder_decoder=True,
         **common_kwargs
     ):
@@ -73,12 +73,12 @@ class BartConfig(PretrainedConfig):
             output_past=output_past,
             pad_token_id=pad_token_id,
             bos_token_id=bos_token_id,
+            eos_token_ids=eos_token_ids,
             is_encoder_decoder=is_encoder_decoder,
             **common_kwargs,
         )
         self.vocab_size = vocab_size
         self.d_model = d_model  # encoder_embed_dim and decoder_embed_dim
-        self.eos_token_id = eos_token_id
         self.encoder_ffn_dim = encoder_ffn_dim
         self.encoder_layers = self.num_hidden_layers = encoder_layers
         self.encoder_attention_heads = encoder_attention_heads

src/transformers/modeling_bart.py

@@ -962,8 +962,8 @@ class BartForConditionalGeneration(PretrainedBartModel):
     def prepare_scores_for_generation(self, scores, cur_len, max_length):
         if cur_len == 1:
             self._force_token_ids_generation(scores, self.config.bos_token_id)
-        if cur_len == max_length - 1:
-            self._force_token_ids_generation(scores, self.config.eos_token_ids)
+        if cur_len == max_length - 1 and self.config.eos_token_ids[0] is not None:
+            self._force_token_ids_generation(scores, self.config.eos_token_ids[0])
         return scores
 
     @staticmethod
@@ -1056,7 +1056,7 @@ class BartForSequenceClassification(PretrainedBartModel):
             encoder_outputs=encoder_outputs,
         )
         x = outputs[0]  # last hidden state
-        eos_mask = input_ids.eq(self.config.eos_token_id)
+        eos_mask = input_ids.eq(self.config.eos_token_ids[0])
         if len(torch.unique(eos_mask.sum(1))) > 1:
             raise ValueError("All examples must have the same number of <eos> tokens.")
         sentence_representation = x[eos_mask, :].view(x.size(0), -1, x.size(-1))[:, -1, :]

src/transformers/modeling_utils.py

@@ -840,14 +840,11 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
 
         if self.config.is_encoder_decoder:
-            eos_token_id = eos_token_ids[0]
             assert bos_token_id is not None, "Encoder Decoder Models need to have a bos_token_id"
-            assert eos_token_id is not None, "Encoder Decoder Models need to have a eos_token_id"
             # encoder decoder need to start with empty input_ids and copy the input_ids to encoder_inputs
             encoder_inputs = input_ids
             input_ids = torch.full(
                 (effective_batch_size * num_beams, 1),
-                #                eos_token_id,  # TODO (PVP): to check if this is the only solution -> quite hacky to do this
                 bos_token_id,
                 dtype=torch.long,
                 device=next(self.parameters()).device,