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

tests/test_modeling_electra.py

@@ -101,7 +101,6 @@ class ElectraModelTester:
             type_vocab_size=self.type_vocab_size,
             is_decoder=False,
             initializer_range=self.initializer_range,
-            return_dict=True,
         )
 
         return (

tests/test_modeling_encoder_decoder.py

@@ -85,7 +85,6 @@ class EncoderDecoderMixin:
             decoder_input_ids=decoder_input_ids,
             attention_mask=attention_mask,
             decoder_attention_mask=decoder_attention_mask,
-            return_dict=True,
         )
 
         self.assertEqual(
@@ -117,7 +116,6 @@ class EncoderDecoderMixin:
             decoder_input_ids=decoder_input_ids,
             attention_mask=attention_mask,
             decoder_attention_mask=decoder_attention_mask,
-            return_dict=True,
         )
         self.assertEqual(
             outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
@@ -132,7 +130,6 @@ class EncoderDecoderMixin:
             decoder_input_ids=decoder_input_ids,
             attention_mask=attention_mask,
             decoder_attention_mask=decoder_attention_mask,
-            return_dict=True,
         )
 
         self.assertEqual(
@@ -278,7 +275,6 @@ class EncoderDecoderMixin:
             attention_mask=attention_mask,
             decoder_attention_mask=decoder_attention_mask,
             labels=labels,
-            return_dict=True,
         )
 
         loss = outputs_encoder_decoder["loss"]
@@ -313,7 +309,6 @@ class EncoderDecoderMixin:
             attention_mask=attention_mask,
             decoder_attention_mask=decoder_attention_mask,
             output_attentions=True,
-            return_dict=True,
         )
 
         encoder_attentions = outputs_encoder_decoder["encoder_attentions"]

tests/test_modeling_flaubert.py

@@ -113,7 +113,6 @@ class FlaubertModelTester(object):
             initializer_range=self.initializer_range,
             summary_type=self.summary_type,
             use_proj=self.use_proj,
-            return_dict=True,
         )
 
         return (

tests/test_modeling_flax_bert.py

@@ -29,7 +29,7 @@ class FlaxBertModelTest(unittest.TestCase):
                 # Check for simple input
                 pt_inputs = tokenizer.encode_plus("This is a simple input", return_tensors=TensorType.PYTORCH)
                 fx_inputs = tokenizer.encode_plus("This is a simple input", return_tensors=TensorType.JAX)
-                pt_outputs = pt_model(**pt_inputs)
+                pt_outputs = pt_model(**pt_inputs).to_tuple()
                 fx_outputs = fx_model(**fx_inputs)
 
                 self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")

tests/test_modeling_flax_roberta.py

@@ -34,7 +34,7 @@ class FlaxRobertaModelTest(unittest.TestCase):
 
                 self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
 
-                for fx_output, pt_output in zip(fx_outputs, pt_outputs):
+                for fx_output, pt_output in zip(fx_outputs, pt_outputs.to_tuple()):
                     self.assert_almost_equals(fx_output, pt_output.numpy(), 5e-4)
 
     def assert_almost_equals(self, a: ndarray, b: ndarray, tol: float):

tests/test_modeling_fsmt.py

@@ -259,7 +259,6 @@ class FSMTHeadTests(unittest.TestCase):
             eos_token_id=2,
             pad_token_id=1,
             bos_token_id=0,
-            return_dict=True,
         )
 
     def _get_config_and_data(self):

tests/test_modeling_funnel.py

@@ -140,7 +140,6 @@ class FunnelModelTester:
             activation_dropout=self.activation_dropout,
             max_position_embeddings=self.max_position_embeddings,
             type_vocab_size=self.type_vocab_size,
-            return_dict=True,
         )
 
         return (

tests/test_modeling_gpt2.py

@@ -131,7 +131,6 @@ class GPT2ModelTester:
             bos_token_id=self.bos_token_id,
             eos_token_id=self.eos_token_id,
             pad_token_id=self.pad_token_id,
-            return_dict=True,
             gradient_checkpointing=gradient_checkpointing,
         )
 

tests/test_modeling_layoutlm.py

@@ -125,7 +125,6 @@ class LayoutLMModelTester:
             max_position_embeddings=self.max_position_embeddings,
             type_vocab_size=self.type_vocab_size,
             initializer_range=self.initializer_range,
-            return_dict=True,
         )
 
         return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

tests/test_modeling_longformer.py

@@ -113,7 +113,6 @@ class LongformerModelTester:
             type_vocab_size=self.type_vocab_size,
             initializer_range=self.initializer_range,
             attention_window=self.attention_window,
-            return_dict=True,
         )
 
         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

tests/test_modeling_lxmert.py

@@ -282,7 +282,6 @@ class LxmertModelTester:
             attention_mask=input_mask,
             labels=ans,
             output_attentions=output_attentions,
-            return_dict=True,
         )
         result = model(input_ids, visual_feats, bounding_boxes, labels=ans)
         result = model(
@@ -302,7 +301,6 @@ class LxmertModelTester:
             attention_mask=input_mask,
             labels=ans,
             output_attentions=not output_attentions,
-            return_dict=True,
         )
 
         self.parent.assertEqual(result.question_answering_score.shape, (self.batch_size, self.num_qa_labels))
@@ -335,7 +333,6 @@ class LxmertModelTester:
             matched_label=matched_label,
             ans=ans,
             output_attentions=output_attentions,
-            return_dict=True,
         )
         result = model(
             input_ids,
@@ -390,7 +387,6 @@ class LxmertModelTester:
             matched_label=matched_label,
             ans=ans,
             output_attentions=not output_attentions,
-            return_dict=True,
         )
 
         self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
@@ -427,7 +423,6 @@ class LxmertModelTester:
             token_type_ids=token_type_ids,
             attention_mask=input_mask,
             ans=ans,
-            return_dict=True,
         )
 
         result_qa = model_qa(
@@ -437,7 +432,6 @@ class LxmertModelTester:
             labels=ans,
             token_type_ids=token_type_ids,
             attention_mask=input_mask,
-            return_dict=True,
         )
 
         model_pretrain.resize_num_qa_labels(num_small_labels)
@@ -450,7 +444,6 @@ class LxmertModelTester:
             token_type_ids=token_type_ids,
             attention_mask=input_mask,
             ans=less_labels_ans,
-            return_dict=True,
         )
 
         result_qa_less = model_qa(
@@ -460,7 +453,6 @@ class LxmertModelTester:
             labels=less_labels_ans,
             token_type_ids=token_type_ids,
             attention_mask=input_mask,
-            return_dict=True,
         )
 
         model_pretrain.resize_num_qa_labels(num_large_labels)
@@ -473,7 +465,6 @@ class LxmertModelTester:
             token_type_ids=token_type_ids,
             attention_mask=input_mask,
             ans=more_labels_ans,
-            return_dict=True,
         )
 
         result_qa_more = model_qa(
@@ -483,7 +474,6 @@ class LxmertModelTester:
             labels=more_labels_ans,
             token_type_ids=token_type_ids,
             attention_mask=input_mask,
-            return_dict=True,
         )
 
         model_qa_labels = model_qa.num_qa_labels

tests/test_modeling_marian.py

@@ -50,7 +50,6 @@ class ModelTester:
             decoder_ffn_dim=32,
             max_position_embeddings=48,
             add_final_layer_norm=True,
-            return_dict=True,
         )
 
     def prepare_config_and_inputs_for_common(self):

tests/test_modeling_mbart.py

@@ -37,7 +37,6 @@ class ModelTester:
             decoder_ffn_dim=32,
             max_position_embeddings=48,
             add_final_layer_norm=True,
-            return_dict=True,
         )
 
     def prepare_config_and_inputs_for_common(self):
@@ -132,7 +131,6 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
             decoder_ffn_dim=32,
             max_position_embeddings=48,
             add_final_layer_norm=True,
-            return_dict=True,
         )
         lm_model = MBartForConditionalGeneration(config).to(torch_device)
         context = torch.Tensor([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]).long().to(torch_device)

tests/test_modeling_mobilebert.py

@@ -124,7 +124,6 @@ class MobileBertModelTester:
             type_vocab_size=self.type_vocab_size,
             is_decoder=False,
             initializer_range=self.initializer_range,
-            return_dict=True,
         )
 
         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

tests/test_modeling_openai.py

@@ -94,7 +94,6 @@ class OpenAIGPTModelTester:
             # type_vocab_size=self.type_vocab_size,
             # initializer_range=self.initializer_range
             pad_token_id=self.pad_token_id,
-            return_dict=True,
         )
 
         head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)

tests/test_modeling_pegasus.py

@@ -33,7 +33,6 @@ class ModelTester:
             decoder_ffn_dim=32,
             max_position_embeddings=48,
             add_final_layer_norm=True,
-            return_dict=True,
         )
 
     def prepare_config_and_inputs_for_common(self):

tests/test_modeling_prophetnet.py

@@ -142,7 +142,6 @@ class ProphetNetModelTester:
             disable_ngram_loss=self.disable_ngram_loss,
             max_position_embeddings=self.max_position_embeddings,
             is_encoder_decoder=self.is_encoder_decoder,
-            return_dict=True,
         )
 
         return (
@@ -344,7 +343,6 @@ class ProphetNetModelTester:
                 decoder_input_ids=decoder_input_ids,
                 attention_mask=attention_mask,
                 decoder_attention_mask=decoder_attention_mask,
-                return_dict=True,
             )
 
             tied_model_result = tied_model(
@@ -352,7 +350,6 @@ class ProphetNetModelTester:
                 decoder_input_ids=decoder_input_ids,
                 attention_mask=attention_mask,
                 decoder_attention_mask=decoder_attention_mask,
-                return_dict=True,
             )
 
             # check that models has less parameters
@@ -419,7 +416,6 @@ class ProphetNetModelTester:
                 attention_mask=attention_mask,
                 decoder_attention_mask=decoder_attention_mask,
                 labels=lm_labels,
-                return_dict=True,
             )
         self.parent.assertTrue(torch.allclose(result.loss, torch.tensor(128.2925, device=torch_device), atol=1e-3))
 
@@ -433,9 +429,7 @@ class ProphetNetModelTester:
         model.to(torch_device)
         model.eval()
 
-        outputs_no_mask = model(
-            input_ids=input_ids[:, :5], decoder_input_ids=decoder_input_ids[:, :5], return_dict=True
-        )
+        outputs_no_mask = model(input_ids=input_ids[:, :5], decoder_input_ids=decoder_input_ids[:, :5])
         attention_mask = torch.ones_like(input_ids)
         decoder_attention_mask = torch.ones_like(decoder_input_ids)
 
@@ -446,7 +440,6 @@ class ProphetNetModelTester:
             attention_mask=attention_mask,
             decoder_input_ids=decoder_input_ids,
             decoder_attention_mask=decoder_attention_mask,
-            return_dict=True,
         )
 
         # check encoder
@@ -524,7 +517,6 @@ class ProphetNetStandaloneDecoderModelTester:
         bos_token_id=1,
         eos_token_id=2,
         ngram=2,
-        return_dict=True,
         num_buckets=32,
         relative_max_distance=128,
         disable_ngram_loss=False,
@@ -562,7 +554,6 @@ class ProphetNetStandaloneDecoderModelTester:
         self.max_position_embeddings = max_position_embeddings
         self.add_cross_attention = add_cross_attention
         self.is_encoder_decoder = is_encoder_decoder
-        self.return_dict = return_dict
 
         self.scope = None
         self.decoder_key_length = decoder_seq_length
@@ -602,7 +593,6 @@ class ProphetNetStandaloneDecoderModelTester:
             max_position_embeddings=self.max_position_embeddings,
             add_cross_attention=self.add_cross_attention,
             is_encoder_decoder=self.is_encoder_decoder,
-            return_dict=self.return_dict,
         )
 
         return (
@@ -757,7 +747,6 @@ class ProphetNetStandaloneEncoderModelTester:
         pad_token_id=0,
         bos_token_id=1,
         eos_token_id=2,
-        return_dict=True,
         num_buckets=32,
         relative_max_distance=128,
         disable_ngram_loss=False,
@@ -794,7 +783,6 @@ class ProphetNetStandaloneEncoderModelTester:
         self.max_position_embeddings = max_position_embeddings
         self.add_cross_attention = add_cross_attention
         self.is_encoder_decoder = is_encoder_decoder
-        self.return_dict = return_dict
 
         self.scope = None
         self.decoder_key_length = decoder_seq_length
@@ -829,7 +817,6 @@ class ProphetNetStandaloneEncoderModelTester:
             max_position_embeddings=self.max_position_embeddings,
             add_cross_attention=self.add_cross_attention,
             is_encoder_decoder=self.is_encoder_decoder,
-            return_dict=self.return_dict,
         )
 
         return (
@@ -919,7 +906,6 @@ class ProphetNetModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.Test
     # methods overwrite method in `test_modeling_common.py`
     def test_attention_outputs(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-        config.return_dict = True
 
         seq_len = getattr(self.model_tester, "seq_length", None)
         decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
@@ -933,7 +919,6 @@ class ProphetNetModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.Test
         for model_class in self.all_model_classes:
             inputs_dict["output_attentions"] = True
             inputs_dict["output_hidden_states"] = False
-            config.return_dict = True
             model = model_class(config)
             model.to(torch_device)
             model.eval()
@@ -1121,7 +1106,6 @@ class ProphetNetModelIntegrationTest(unittest.TestCase):
             attention_mask=None,
             encoder_outputs=None,
             decoder_input_ids=decoder_prev_ids,
-            return_dict=True,
         )
         output_predited_logits = output[0]
         expected_shape = torch.Size((1, 12, 30522))
@@ -1143,9 +1127,7 @@ class ProphetNetModelIntegrationTest(unittest.TestCase):
         assert torch.allclose(encoder_outputs[:, :3, :3], expected_encoder_outputs_slice, atol=1e-4)
 
         # decoder outputs
-        decoder_outputs = model.prophetnet.decoder(
-            decoder_prev_ids, encoder_hidden_states=encoder_outputs, return_dict=True
-        )
+        decoder_outputs = model.prophetnet.decoder(decoder_prev_ids, encoder_hidden_states=encoder_outputs)
         predicting_streams = decoder_outputs[1].view(1, model.config.ngram, 12, -1)
         predicting_streams_logits = model.lm_head(predicting_streams)
         next_first_stream_logits = predicting_streams_logits[:, 0]

tests/test_modeling_reformer.py

@@ -174,7 +174,6 @@ class ReformerModelTester:
             attn_layers=self.attn_layers,
             pad_token_id=self.pad_token_id,
             hash_seed=self.hash_seed,
-            return_dict=True,
         )
 
         return (

tests/test_modeling_roberta.py

@@ -103,7 +103,6 @@ class RobertaModelTester:
             max_position_embeddings=self.max_position_embeddings,
             type_vocab_size=self.type_vocab_size,
             initializer_range=self.initializer_range,
-            return_dict=True,
         )
 
         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

tests/test_modeling_squeezebert.py

@@ -131,7 +131,6 @@ if is_torch_available():
                 post_attention_groups=self.post_attention_groups,
                 intermediate_groups=self.intermediate_groups,
                 output_groups=self.output_groups,
-                return_dict=True,
             )
 
             return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels