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New serving (#9419)

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* Add a serving method

* Add albert

* Add serving for BERT and BART

* Add more models

* Finish the serving addition

* Temp fix

* Restore DPR

* Fix funnel attribute

* Fix attributes GPT2

* Fix OpenAIGPT attribute

* Fix T5 attributes

* Fix Bart attributes

* Fix TransfoXL attributes

* Add versioning

* better test

* Update template

* Fix Flaubert

* Fix T5

* Apply style

* Remove unused imports

* Deactivate extra parameters

* Remove too long test + saved_model default to False

* Ignore the saved model test for some models

* Fix some inputs

* Fix mpnet serving

* Trigger CI

* Address all comments
This commit is contained in:
Julien Plu
2021-01-07 11:48:49 +01:00
committed by GitHub
parent 390cf16bc8
commit 812045adcc
36 changed files with 1773 additions and 68 deletions
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58
tests/test_modeling_tf_common.py
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@@ -122,7 +122,7 @@ class TFModelTesterMixin:
outputs = model(self._prepare_for_class(inputs_dict, model_class))
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model.save_pretrained(tmpdirname, saved_model=False)
model = model_class.from_pretrained(tmpdirname)
after_outputs = model(self._prepare_for_class(inputs_dict, model_class))
@@ -164,6 +164,46 @@ class TFModelTesterMixin:
expected_arg_names = ["input_ids"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_saved_model_creation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = False
config.output_attentions = False
if hasattr(config, "use_cache"):
config.use_cache = False
model_class = self.all_model_classes[0]
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
model(class_inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, saved_model=True)
saved_model_dir = os.path.join(tmpdirname, "saved_model")
self.assertTrue(os.path.exists(saved_model_dir))
@slow
def test_saved_model_creation_extended(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
if hasattr(config, "use_cache"):
config.use_cache = 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(class_inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, saved_model=True)
saved_model_dir = os.path.join(tmpdirname, "saved_model")
self.assertTrue(os.path.exists(saved_model_dir))
@slow
def test_saved_model_with_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -178,12 +218,11 @@ class TFModelTesterMixin:
config.use_cache = class_inputs_dict.pop("use_cache")
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)
model.save_pretrained(tmpdirname)
saved_model_dir = os.path.join(tmpdirname, "saved_model")
model = tf.keras.models.load_model(saved_model_dir)
outputs = model(class_inputs_dict)
if self.is_encoder_decoder:
@@ -219,12 +258,11 @@ class TFModelTesterMixin:
config.use_cache = class_inputs_dict.pop("use_cache")
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)
saved_model_dir = os.path.join(tmpdirname, "saved_model")
model.save_pretrained(saved_model_dir)
model = tf.keras.models.load_model(saved_model_dir)
outputs = model(class_inputs_dict)
if self.is_encoder_decoder:
@@ -489,7 +527,7 @@ class TFModelTesterMixin:
model(self._prepare_for_class(inputs_dict, model_class)) # Model must be called before saving.
# Let's load it from the disk to be sure we can use pretrained weights
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model.save_pretrained(tmpdirname, saved_model=False)
model = model_class.from_pretrained(tmpdirname)
outputs_dict = model(input_ids)