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[Bart] enable test_torchscript, update test_tie_weights (#5457) 

* Passing all but one torchscript test

* Style

* move comment

* remove unneeded assert
This commit is contained in:
Sam Shleifer 2020-07-07 10:06:48 -04:00 committed by GitHub
parent e49393c361
commit d4886173b2
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2 changed files with 1 additions and 7 deletions
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3
tests/test_modeling_bart.py
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@ -120,7 +120,7 @@ class BARTModelTest(ModelTesterMixin, unittest.TestCase):
is_encoder_decoder = True
# TODO(SS): fix the below in a separate PR
test_pruning = False
test_torchscript = False
test_torchscript = True
test_head_masking = False
test_resize_embeddings = True # This requires inputs_dict['input_ids']
test_missing_keys = False # because BartForConditionalGeneration and BartModel now have identical state_dict
@ -133,7 +133,6 @@ class BARTModelTest(ModelTesterMixin, unittest.TestCase):
self.config_tester.run_common_tests()
def test_initialization_more(self):
# (config, input_ids, token_type_ids, input_mask, *unused) = \
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = BartModel(config)
model.to(torch_device)

5
tests/test_modeling_common.py
View File

@ -612,15 +612,11 @@ class ModelTesterMixin:
if model_not_tied.get_output_embeddings() is None:
continue
params_not_tied = list(model_not_tied.parameters())
config_tied = copy.deepcopy(config)
config_tied.torchscript = False
model_tied = model_class(config_tied)
params_tied = list(model_tied.parameters())
# Check that the embedding layer and decoding layer are the same in size and in value
self.assertGreater(len(params_not_tied), len(params_tied))
# self.assertTrue(check_same_values(embeddings, decoding))
# # Check that after modification, they remain the same.
@ -638,7 +634,6 @@ class ModelTesterMixin:
# Check that after resize they remain tied.
model_tied.resize_token_embeddings(config.vocab_size + 10)
params_tied_2 = list(model_tied.parameters())
self.assertGreater(len(params_not_tied), len(params_tied))
self.assertEqual(len(params_tied_2), len(params_tied))
# decoding.weight.data.mul_(20)