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Switch return_dict to True by default. (#8530)

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* 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
This commit is contained in:
Sylvain Gugger
2020-11-16 11:43:00 -05:00
committed by GitHub
parent 0d0a0785fd
commit 1073a2bde5
106 changed files with 138 additions and 234 deletions
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17
tests/test_modeling_deberta.py
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@@ -148,7 +148,7 @@ class DebertaModelTest(ModelTesterMixin, unittest.TestCase):
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def check_loss_output(self, result):
self.parent.assertListEqual(list(result["loss"].size()), [])
self.parent.assertListEqual(list(result.loss.size()), [])
def create_and_check_deberta_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -160,11 +160,8 @@ class DebertaModelTest(ModelTesterMixin, unittest.TestCase):
sequence_output = model(input_ids, token_type_ids=token_type_ids)[0]
sequence_output = model(input_ids)[0]
result = {
"sequence_output": sequence_output,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
list(sequence_output.size()), [self.batch_size, self.seq_length, self.hidden_size]
)
def create_and_check_deberta_for_sequence_classification(
@@ -174,14 +171,8 @@ class DebertaModelTest(ModelTesterMixin, unittest.TestCase):
model = DebertaForSequenceClassification(config)
model.to(torch_device)
model.eval()
loss, logits = model(
input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels
)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_labels])
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertListEqual(list(result.logits.size()), [self.batch_size, self.num_labels])
self.check_loss_output(result)
def prepare_config_and_inputs_for_common(self):