add draft version of propsoed changes for ROGUE score

src/transformers/modeling_utils.py

@@ -846,7 +846,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             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
+                #                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,
             )
@@ -1079,10 +1080,10 @@ 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:
@@ -1271,9 +1272,9 @@ 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:
+        #        if self.config.is_encoder_decoder:
         # do not return first <EOS> token
-            return decoded[:, 1:]
+        #            return decoded[:, 1:]
         return decoded
 
     # force one of token_ids to be generated by setting prob of all other tokens to 0.

tests/test_modeling_bart.py

@@ -214,6 +214,9 @@ class BartHeadTests(unittest.TestCase):
             decoder_ffn_dim=32,
             max_position_embeddings=48,
             output_past=output_past,
+            eos_token_id=2,
+            pad_token_id=1,
+            bos_token_id=0,
         )
         return config, input_ids, batch_size
 
@@ -468,7 +471,8 @@ 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 + 2,
+            max_length=max_length + 1,
             min_length=min_length + 1,
             no_repeat_ngram_size=3,
             do_sample=False,