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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
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34
tests/test_modeling_tf_common.py
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@@ -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]