[examples/s2s] clean up finetune_trainer (#7509)

examples/seq2seq/seq2seq_trainer.py

@@ -20,11 +20,13 @@ logger = logging.getLogger(__name__)
 
 
 class Seq2SeqTrainer(Trainer):
-    def __init__(self, data_args, *args, **kwargs):
+    def __init__(self, config, data_args, *args, **kwargs):
         super().__init__(*args, **kwargs)
+        self.config = config
         self.data_args = data_args
         self.max_gen_length = data_args.val_max_target_length
-        self.pad_token_id = self.model.config.pad_token_id
+        self.pad_token_id = self.config.pad_token_id
+        self.vocab_size = self.config.vocab_size
 
     def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
         if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
@@ -53,7 +55,7 @@ class Seq2SeqTrainer(Trainer):
         if self.args.label_smoothing == 0:
             # Same behavior as modeling_bart.py
             loss_fct = torch.nn.CrossEntropyLoss(ignore_index=ignore_index)
-            assert logits.shape[-1] == self.model.config.vocab_size
+            assert logits.shape[-1] == self.vocab_size
             loss = loss_fct(logits.view(-1, logits.shape[-1]), labels.view(-1))
         else:
             lprobs = torch.nn.functional.log_softmax(logits, dim=-1)