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/lxmert/demo.ipynb

@@ -210,7 +210,6 @@
     "        visual_feats=features,\n",
     "        visual_pos=normalized_boxes,\n",
     "        token_type_ids=inputs.token_type_ids,\n",
-    "        return_dict=True,\n",
     "        output_attentions=False,\n",
     "    )\n",
     "    output_vqa = lxmert_vqa(\n",
@@ -219,7 +218,6 @@
     "        visual_feats=features,\n",
     "        visual_pos=normalized_boxes,\n",
     "        token_type_ids=inputs.token_type_ids,\n",
-    "        return_dict=True,\n",
     "        output_attentions=False,\n",
     "    )\n",
     "    # get prediction\n",
@@ -266,4 +264,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

examples/question-answering/run_squad.py

@@ -321,7 +321,7 @@ def evaluate(args, model, tokenizer, prefix=""):
             eval_feature = features[feature_index.item()]
             unique_id = int(eval_feature.unique_id)
 
-            output = [to_list(output[i]) for output in outputs]
+            output = [to_list(output[i]) for output in outputs.to_tuple()]
 
             # Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
             # models only use two.

examples/rag/eval_rag.py

@@ -95,7 +95,7 @@ def evaluate_batch_retrieval(args, rag_model, questions):
         truncation=True,
     )["input_ids"].to(args.device)
 
-    question_enc_outputs = rag_model.rag.question_encoder(retriever_input_ids, return_dict=True)
+    question_enc_outputs = rag_model.rag.question_encoder(retriever_input_ids)
     question_enc_pool_output = question_enc_outputs.pooler_output
 
     result = rag_model.retriever(

examples/rag/finetune.py

@@ -204,7 +204,6 @@ class GenerativeQAModule(BaseTransformer):
             decoder_input_ids=decoder_input_ids,
             use_cache=False,
             labels=lm_labels,
-            return_dict=True,
             **rag_kwargs,
         )
 

examples/rag/use_own_knowledge_dataset.py

@@ -47,7 +47,7 @@ def embed(documents: dict, ctx_encoder: DPRContextEncoder, ctx_tokenizer: DPRCon
     input_ids = ctx_tokenizer(
         documents["title"], documents["text"], truncation=True, padding="longest", return_tensors="pt"
     )["input_ids"]
-    embeddings = ctx_encoder(input_ids.to(device=device), return_dict=True).pooler_output
+    embeddings = ctx_encoder(input_ids.to(device=device)).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}
 
 

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?