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Fix a bunch of slow tests (#8634)

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* CI should install `sentencepiece`

* Requiring TF

* Fixing some TFDPR bugs

* remove return_dict=False/True hack

Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
This commit is contained in:
Lysandre Debut
2020-11-19 10:41:41 -05:00
committed by GitHub
parent 5362bb8a6b
commit f2e07e7272
6 changed files with 94 additions and 48 deletions
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55
tests/test_modeling_tf_dpr.py
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@@ -12,8 +12,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
from transformers import is_tf_available
@@ -124,8 +123,6 @@ class TFDPRModelTester:
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
# MODIFY
return_dict=False,
)
config = DPRConfig(projection_dim=self.projection_dim, **config.to_dict())
@@ -137,7 +134,7 @@ class TFDPRModelTester:
model = TFDPRContextEncoder(config=config)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids, return_dict=True) # MODIFY
result = model(input_ids)
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
def create_and_check_dpr_question_encoder(
@@ -146,14 +143,14 @@ class TFDPRModelTester:
model = TFDPRQuestionEncoder(config=config)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids, return_dict=True) # MODIFY
result = model(input_ids)
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
def create_and_check_dpr_reader(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFDPRReader(config=config)
result = model(input_ids, attention_mask=input_mask, return_dict=True) # MODIFY
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
@@ -214,27 +211,61 @@ class TFDPRModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFDPRContextEncoder.from_pretrained(model_name, from_pt=True)
model = TFDPRContextEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFDPRContextEncoder.from_pretrained(model_name, from_pt=True)
model = TFDPRContextEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFDPRQuestionEncoder.from_pretrained(model_name, from_pt=True)
model = TFDPRQuestionEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFDPRReader.from_pretrained(model_name, from_pt=True)
model = TFDPRReader.from_pretrained(model_name)
self.assertIsNotNone(model)
@slow
def test_saved_model_with_attentions_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_attentions = True
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
for model_class in self.all_model_classes:
print(model_class)
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
num_out = len(model(class_inputs_dict))
model._saved_model_inputs_spec = None
model._set_save_spec(class_inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
tf.saved_model.save(model, tmpdirname)
model = tf.keras.models.load_model(tmpdirname)
outputs = model(class_inputs_dict)
if self.is_encoder_decoder:
output = outputs["encoder_attentions"] if isinstance(outputs, dict) else outputs[-1]
else:
output = outputs["attentions"] if isinstance(outputs, dict) else outputs[-1]
attentions = [t.numpy() for t in output]
self.assertEqual(len(outputs), num_out)
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
@require_tf
class TFDPRModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
model = TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base", return_dict=False)
model = TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
input_ids = tf.constant(
[[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]