Switch from return_tuple to return_dict (#6138)

* Switch from return_tuple to return_dict

* Fix test

* [WIP] Test TF Flaubert + Add {XLM, Flaubert}{TokenClassification, MultipleC… (#5614)

* Test TF Flaubert + Add {XLM, Flaubert}{TokenClassification, MultipleChoice} models and tests

* AutoModels


Tiny tweaks

* Style

* Final changes before merge

* Re-order for simpler review

* Final fixes

* Addressing @sgugger's comments

* Test MultipleChoice

* Rework TF trainer (#6038)

* Fully rework training/prediction loops

* fix method name

* Fix variable name

* Fix property name

* Fix scope

* Fix method name

* Fix tuple index

* Fix tuple index

* Fix indentation

* Fix variable name

* fix eval before log

* Add drop remainder for test dataset

* Fix step number + fix logging datetime

* fix eval loss value

* use global step instead of step + fix logging at step 0

* Fix logging datetime

* Fix global_step usage

* Fix breaking loop + logging datetime

* Fix step in prediction loop

* Fix step breaking

* Fix train/test loops

* Force TF at least 2.2 for the trainer

* Use assert_cardinality to facilitate the dataset size computation

* Log steps per epoch

* Make tfds compliant with TPU

* Make tfds compliant with TPU

* Use TF dataset enumerate instead of the Python one

* revert previous commit

* Fix data_dir

* Apply style

* rebase on master

* Address Sylvain's comments

* Address Sylvain's and Lysandre comments

* Trigger CI

* Remove unused import

* Switch from return_tuple to return_dict

* Fix test

* Add recent model

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Julien Plu <plu.julien@gmail.com>

tests/test_modeling_common.py

@@ -74,6 +74,7 @@ class ModelTesterMixin:
 
     def test_save_load(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
 
         for model_class in self.all_model_classes:
             model = model_class(config)
@@ -803,8 +804,6 @@ class ModelTesterMixin:
 
             # Wrap model in nn.DataParallel
             model = torch.nn.DataParallel(model)
-            # Our model outputs do not work with DataParallel, so forcing return tuple.
-            inputs_dict["return_tuple"] = True
             with torch.no_grad():
                 _ = model(**self._prepare_for_class(inputs_dict, model_class))
 

tests/test_modeling_t5.py

@@ -329,7 +329,6 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
         import tempfile
 
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        config_and_inputs[0].return_tuple = True
         model = T5Model(config_and_inputs[0]).to(torch_device)
         with tempfile.TemporaryDirectory() as tmpdirname:
             torch.onnx.export(