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Use tiny models for get_pretrained_model in TFEncoderDecoderModelTest (#15989)

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* Use tiny model for TFRembertEncoderDecoderModelTest.get_pretrained_model()

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar 2022-03-09 17:16:25 +01:00 committed by GitHub
parent 8feede229c
commit b7fa1e3dee
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1 changed files with 17 additions and 6 deletions
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23
tests/encoder_decoder/test_modeling_tf_encoder_decoder.py
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@ -509,8 +509,7 @@ class TFEncoderDecoderMixin:
model = TFEncoderDecoderModel(encoder_decoder_config)
model(**inputs_dict)
@slow
def test_real_model_save_load_from_pretrained(self):
def test_model_save_load_from_pretrained(self):
model_2 = self.get_pretrained_model()
input_ids = ids_tensor([13, 5], model_2.config.encoder.vocab_size)
decoder_input_ids = ids_tensor([13, 1], model_2.config.decoder.vocab_size)
@ -542,7 +541,10 @@ class TFEncoderDecoderMixin:
@require_tf
class TFBertEncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model(self):
return TFEncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
return TFEncoderDecoderModel.from_encoder_decoder_pretrained(
"hf-internal-testing/tiny-random-bert",
"hf-internal-testing/tiny-random-bert",
)
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = TFBertModel(config, name="encoder")
@ -637,7 +639,10 @@ class TFBertEncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase):
@require_tf
class TFGPT2EncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model(self):
return TFEncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-cased", "../gpt2")
return TFEncoderDecoderModel.from_encoder_decoder_pretrained(
"hf-internal-testing/tiny-random-bert",
"hf-internal-testing/tiny-random-gpt2",
)
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = TFBertModel(config, name="encoder")
@ -726,7 +731,10 @@ class TFGPT2EncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase):
@require_tf
class TFRoBertaEncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model(self):
return TFEncoderDecoderModel.from_encoder_decoder_pretrained("roberta-base", "roberta-base")
return TFEncoderDecoderModel.from_encoder_decoder_pretrained(
"hf-internal-testing/tiny-random-roberta",
"hf-internal-testing/tiny-random-roberta",
)
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = TFRobertaModel(config, name="encoder")
@ -782,7 +790,10 @@ class TFRoBertaEncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase)
@require_tf
class TFRembertEncoderDecoderModelTest(TFEncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model(self):
return TFEncoderDecoderModel.from_encoder_decoder_pretrained("google/rembert", "google/rembert")
return TFEncoderDecoderModel.from_encoder_decoder_pretrained(
"hf-internal-testing/tiny-random-rembert",
"hf-internal-testing/tiny-random-rembert",
)
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = TFRemBertModel(config, name="encoder")