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Ci test tf super slow (#8007)

Browse Source 
* Test TF GPU CI

* Change cache

* Fix missing torch requirement

* Fix some model tests


Style

* LXMERT

* MobileBERT

* Longformer skip test

* XLNet

* The rest of the tests

* RAG goes OOM in multi gpu setup

* YAML test files

* Last fixes

* Skip doctests

* Fill mask tests

* Yaml files

* Last test fix

* Style

* Update cache

* Change ONNX tests to slow + use tiny model
This commit is contained in:
Lysandre Debut
2020-10-30 14:25:48 +00:00
committed by GitHub
parent 7e36deec7a
commit 10f8c63620
25 changed files with 562 additions and 126 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
tests/test_doc_samples.py
View File

@@ -27,6 +27,7 @@ from transformers.testing_utils import require_tf, require_torch, slow
logger = logging.getLogger()
@unittest.skip("Temporarily disable the doc tests.")
@require_torch
@require_tf
@slow

4
tests/test_modeling_common.py
View File

@@ -1087,6 +1087,10 @@ class ModelUtilsTest(unittest.TestCase):
self.assertEqual(len(value), 0)
config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
# Not sure this is the intended behavior. TODO fix Lysandre & Thom
config.name_or_path = model_name
model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(model.config, config)

1
tests/test_modeling_marian.py
View File

@@ -69,6 +69,7 @@ class SelectiveCommonTest(unittest.TestCase):
class ModelManagementTests(unittest.TestCase):
@slow
@require_torch
def test_model_names(self):
model_list = HfApi().model_list()
model_ids = [x.modelId for x in model_list if x.modelId.startswith(ORG_NAME)]

1
tests/test_modeling_prophetnet.py
View File

@@ -959,6 +959,7 @@ class ProphetNetStandaloneEncoderModelTest(ModelTesterMixin, unittest.TestCase):
self.config_tester.run_common_tests()
@require_torch
class ProphetNetModelIntegrationTest(unittest.TestCase):
@slow
def test_pretrained_checkpoint_hidden_states(self):

10
tests/test_modeling_rag.py
View File

@@ -25,7 +25,14 @@ import numpy as np
from transformers import BartTokenizer, T5Tokenizer
from transformers.file_utils import cached_property, is_datasets_available, is_faiss_available, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
require_torch_non_multigpu,
slow,
torch_device,
)
from transformers.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.tokenization_dpr import DPRQuestionEncoderTokenizer
from transformers.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
@@ -574,6 +581,7 @@ class RagDPRT5Test(RagTestMixin, unittest.TestCase):
@require_retrieval
@require_sentencepiece
@require_tokenizers
@require_torch_non_multigpu
class RagModelIntegrationTests(unittest.TestCase):
@cached_property
def sequence_model(self):

1
tests/test_modeling_roberta.py
View File

@@ -396,6 +396,7 @@ class RobertaModelTest(ModelTesterMixin, unittest.TestCase):
@require_sentencepiece
@require_tokenizers
@require_torch
class RobertaModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):

1
tests/test_modeling_squeezebert.py
View File

@@ -273,6 +273,7 @@ class SqueezeBertModelTest(ModelTesterMixin, unittest.TestCase):
@require_sentencepiece
@require_tokenizers
@require_torch
class SqueezeBertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_classification_head(self):

2
tests/test_modeling_tf_camembert.py
View File

@@ -39,7 +39,7 @@ class TFCamembertModelIntegrationTest(unittest.TestCase):
dtype=tf.int32,
) # J'aime le camembert !"
output = model(input_ids)["last_hidden_state"]
output = model(input_ids, return_dict=True)["last_hidden_state"]
expected_shape = tf.TensorShape((1, 10, 768))
self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice.

34
tests/test_modeling_tf_common.py
View File

@@ -76,7 +76,7 @@ class TFModelTesterMixin:
test_resize_embeddings = True
is_encoder_decoder = False
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
inputs_dict = copy.deepcopy(inputs_dict)
if model_class in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
@@ -165,16 +165,16 @@ class TFModelTesterMixin:
config.output_hidden_states = True
for model_class in self.all_model_classes:
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
num_out = len(model(inputs_dict))
num_out = len(model(class_inputs_dict))
model._saved_model_inputs_spec = None
model._set_save_spec(inputs_dict)
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(inputs_dict)
outputs = model(class_inputs_dict)
if self.is_encoder_decoder:
output = outputs["encoder_hidden_states"] if isinstance(outputs, dict) else outputs[-1]
@@ -183,7 +183,10 @@ class TFModelTesterMixin:
hidden_states = [t.numpy() for t in output]
self.assertEqual(len(outputs), num_out)
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size],
@@ -193,26 +196,21 @@ class TFModelTesterMixin:
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 = (
self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "encoder_seq_length")
else self.model_tester.seq_length
)
encoder_key_length = (
self.model_tester.key_length if hasattr(self.model_tester, "key_length") else encoder_seq_length
)
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:
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
num_out = len(model(inputs_dict))
num_out = len(model(class_inputs_dict))
model._saved_model_inputs_spec = None
model._set_save_spec(inputs_dict)
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(inputs_dict)
outputs = model(class_inputs_dict)
if self.is_encoder_decoder:
output = outputs["encoder_attentions"] if isinstance(outputs, dict) else outputs[-1]

8
tests/test_modeling_tf_flaubert.py
View File

@@ -330,6 +330,14 @@ class TFFlaubertModelTest(TFModelTesterMixin, unittest.TestCase):
model = TFFlaubertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_saved_model_with_hidden_states_output(self):
# Should be uncommented during patrick TF refactor
pass
def test_saved_model_with_attentions_output(self):
# Should be uncommented during patrick TF refactor
pass
@require_tf
@require_sentencepiece

4
tests/test_modeling_tf_longformer.py
View File

@@ -302,6 +302,10 @@ class TFLongformerModelTest(TFModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_question_answering()
self.model_tester.create_and_check_longformer_for_question_answering(*config_and_inputs)
@slow
def test_saved_model_with_attentions_output(self):
pass
@require_tf
@require_sentencepiece

76
tests/test_modeling_tf_lxmert.py
View File

@@ -678,3 +678,79 @@ class TFLxmertModelTest(TFModelTesterMixin, unittest.TestCase):
# Compile extended model
extended_model = tf.keras.Model(inputs=[input_ids, visual_feats, visual_pos], outputs=[outputs])
extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
@slow
def test_saved_model_with_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
for model_class in self.all_model_classes:
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
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)
language_hidden_states, vision_hidden_states = outputs[-2], outputs[-1]
self.assertEqual(len(language_hidden_states), self.model_tester.num_hidden_layers["language"] + 1)
self.assertEqual(len(vision_hidden_states), self.model_tester.num_hidden_layers["vision"] + 1)
seq_length = self.model_tester.seq_length
num_visual_features = self.model_tester.num_visual_features
self.assertListEqual(
list(language_hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
self.assertListEqual(
list(vision_hidden_states[0].shape[-2:]),
[num_visual_features, self.model_tester.hidden_size],
)
@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:
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
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)
language_attentions, vision_attentions, cross_encoder_attentions = (
outputs[-3],
outputs[-2],
outputs[-1],
)
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
attention_shapes = [
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
[
self.model_tester.num_attention_heads,
self.model_tester.num_visual_features,
self.model_tester.num_visual_features,
],
[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
]
for attention, attention_shape in zip(attentions, attention_shapes):
self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)

2
tests/test_modeling_tf_mobilebert.py
View File

@@ -287,6 +287,6 @@ class TFMobileBertModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["mobilebert-uncased"]:
for model_name in ["google/mobilebert-uncased"]:
model = TFMobileBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)

10
tests/test_modeling_tf_t5.py
View File

@@ -12,8 +12,6 @@
# 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 unittest
from transformers import T5Config, is_tf_available
@@ -282,6 +280,14 @@ class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase):
model = TFT5Model.from_pretrained("t5-small")
self.assertIsNotNone(model)
@slow
def test_saved_model_with_attentions_output(self):
pass
@slow
def test_saved_model_with_hidden_states_output(self):
pass
@require_tf
@require_sentencepiece

2
tests/test_modeling_tf_xlm_roberta.py
View File

@@ -39,7 +39,7 @@ class TFFlaubertModelIntegrationTest(unittest.TestCase):
"attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]], dtype=tf.int32),
}
output = model(features)["last_hidden_state"]
output = model(features, return_dict=True)["last_hidden_state"]
expected_shape = tf.TensorShape((1, 6, 768))
self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice.

1
tests/test_modeling_transfo_xl.py
View File

@@ -279,6 +279,7 @@ class TransfoXLModelTest(ModelTesterMixin, unittest.TestCase):
self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0])
@require_torch
class TransfoXLModelLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_transfo_xl_wt103(self):

3
tests/test_modeling_xlm_prophetnet.py
View File

@@ -17,7 +17,7 @@
import unittest
from transformers import is_torch_available
from transformers.testing_utils import slow, torch_device
from transformers.testing_utils import require_torch, slow, torch_device
if is_torch_available():
@@ -26,6 +26,7 @@ if is_torch_available():
from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer
@require_torch
class XLMProphetNetModelIntegrationTest(unittest.TestCase):
@slow
def test_pretrained_checkpoint_hidden_states(self):

3
tests/test_modeling_xlm_roberta.py
View File

@@ -17,7 +17,7 @@
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, slow
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
@@ -28,6 +28,7 @@ if is_torch_available():
@require_sentencepiece
@require_tokenizers
@require_torch
class XLMRobertaModelIntegrationTest(unittest.TestCase):
@slow
def test_xlm_roberta_base(self):

10
tests/test_onnx.py
View File

@@ -95,26 +95,28 @@ class OnnxExportTestCase(unittest.TestCase):
@require_torch
@require_tokenizers
@slow
def test_infer_dynamic_axis_pytorch(self):
"""
Validate the dynamic axis generated for each parameters are correct
"""
from transformers import BertModel
model = BertModel(BertConfig.from_pretrained("bert-base-cased"))
tokenizer = BertTokenizerFast.from_pretrained("bert-base-cased")
model = BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"))
tokenizer = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random")
self._test_infer_dynamic_axis(model, tokenizer, "pt")
@require_tf
@require_tokenizers
@slow
def test_infer_dynamic_axis_tf(self):
"""
Validate the dynamic axis generated for each parameters are correct
"""
from transformers import TFBertModel
model = TFBertModel(BertConfig.from_pretrained("bert-base-cased"))
tokenizer = BertTokenizerFast.from_pretrained("bert-base-cased")
model = TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"))
tokenizer = BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random")
self._test_infer_dynamic_axis(model, tokenizer, "tf")
def _test_infer_dynamic_axis(self, model, tokenizer, framework):

124
tests/test_pipelines_fill_mask.py
View File

@@ -96,21 +96,53 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
framework="pt",
topk=2,
)
self._test_mono_column_pipeline(
nlp,
valid_inputs,
mandatory_keys,
expected_multi_result=EXPECTED_FILL_MASK_RESULT,
expected_check_keys=["sequence"],
)
self._test_mono_column_pipeline(
nlp,
valid_inputs[:1],
mandatory_keys,
expected_multi_result=EXPECTED_FILL_MASK_TARGET_RESULT,
expected_check_keys=["sequence"],
targets=valid_targets,
)
mono_result = nlp(valid_inputs[0], targets=valid_targets)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], dict)
for mandatory_key in mandatory_keys:
self.assertIn(mandatory_key, mono_result[0])
multi_result = [nlp(valid_input) for valid_input in valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
for result, expected in zip(multi_result, EXPECTED_FILL_MASK_RESULT):
self.assertEqual(set([o["sequence"] for o in result]), set([o["sequence"] for o in result]))
if isinstance(multi_result[0], list):
multi_result = multi_result[0]
for result in multi_result:
for key in mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, [None])
valid_inputs = valid_inputs[:1]
mono_result = nlp(valid_inputs[0], targets=valid_targets)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], dict)
for mandatory_key in mandatory_keys:
self.assertIn(mandatory_key, mono_result[0])
multi_result = [nlp(valid_input) for valid_input in valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
for result, expected in zip(multi_result, EXPECTED_FILL_MASK_TARGET_RESULT):
self.assertEqual(set([o["sequence"] for o in result]), set([o["sequence"] for o in result]))
if isinstance(multi_result[0], list):
multi_result = multi_result[0]
for result in multi_result:
for key in mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, [None])
@require_tf
@slow
@@ -123,18 +155,50 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
valid_targets = [" Patrick", " Clara"]
for model_name in self.large_models:
nlp = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf", topk=2)
self._test_mono_column_pipeline(
nlp,
valid_inputs,
mandatory_keys,
expected_multi_result=EXPECTED_FILL_MASK_RESULT,
expected_check_keys=["sequence"],
)
self._test_mono_column_pipeline(
nlp,
valid_inputs[:1],
mandatory_keys,
expected_multi_result=EXPECTED_FILL_MASK_TARGET_RESULT,
expected_check_keys=["sequence"],
targets=valid_targets,
)
mono_result = nlp(valid_inputs[0], targets=valid_targets)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], dict)
for mandatory_key in mandatory_keys:
self.assertIn(mandatory_key, mono_result[0])
multi_result = [nlp(valid_input) for valid_input in valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
for result, expected in zip(multi_result, EXPECTED_FILL_MASK_RESULT):
self.assertEqual(set([o["sequence"] for o in result]), set([o["sequence"] for o in result]))
if isinstance(multi_result[0], list):
multi_result = multi_result[0]
for result in multi_result:
for key in mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, [None])
valid_inputs = valid_inputs[:1]
mono_result = nlp(valid_inputs[0], targets=valid_targets)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], dict)
for mandatory_key in mandatory_keys:
self.assertIn(mandatory_key, mono_result[0])
multi_result = [nlp(valid_input) for valid_input in valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
for result, expected in zip(multi_result, EXPECTED_FILL_MASK_TARGET_RESULT):
self.assertEqual(set([o["sequence"] for o in result]), set([o["sequence"] for o in result]))
if isinstance(multi_result[0], list):
multi_result = multi_result[0]
for result in multi_result:
for key in mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, [None])