Switch return_dict to True by default. (#8530)

* Use the CI to identify failing tests

* Remove from all examples and tests

* More default switch

* Fixes

* More test fixes

* More fixes

* Last fixes hopefully

* Use the CI to identify failing tests

* Remove from all examples and tests

* More default switch

* Fixes

* More test fixes

* More fixes

* Last fixes hopefully

* Run on the real suite

* Fix slow tests

examples/seq2seq/distillation.py

@@ -153,7 +153,6 @@ class SummarizationDistiller(SummarizationModule):
             output_hidden_states=self.do_calc_hidden_loss,
             output_attentions=False,
             use_cache=False,
-            return_dict=True,
         )
         lm_logits = student_outputs.logits
 
@@ -179,7 +178,6 @@ class SummarizationDistiller(SummarizationModule):
                 input_ids,
                 attention_mask=src_mask,
                 output_hidden_states=self.do_calc_hidden_loss,
-                return_dict=True,
             )
             if self.different_base_models:
                 teacher_enc_outputs = all_teacher_encoder_outputs.last_hidden_state
@@ -199,7 +197,6 @@ class SummarizationDistiller(SummarizationModule):
             decoder_input_ids=decoder_input_ids,
             output_hidden_states=self.do_calc_hidden_loss,
             use_cache=False,  # since we are not passing labels, never let this default to True
-            return_dict=True,
         )
         dec_mask = decoder_input_ids.ne(pad_token_id)
         loss_ce = self.calc_ce_loss(dec_mask, lm_logits, teacher_outputs.logits)

examples/seq2seq/test_seq2seq_examples.py

@@ -185,7 +185,7 @@ class TestSummarizationDistiller(TestCasePlus):
 
     @require_torch_non_multi_gpu_but_fix_me
     def test_loss_fn(self):
-        model = AutoModelForSeq2SeqLM.from_pretrained(BART_TINY, return_dict=True)
+        model = AutoModelForSeq2SeqLM.from_pretrained(BART_TINY)
         input_ids, mask = model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
         target_ids = torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]], dtype=torch.long, device=model.device)
         decoder_input_ids = target_ids[:, :-1].contiguous()  # Why this line?