New serving (#9419)

* 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

tests/test_modeling_tf_dpr.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import tempfile
 import unittest
 
 from transformers import is_tf_available
@@ -227,40 +226,6 @@ class TFDPRModelTest(TFModelTesterMixin, unittest.TestCase):
             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):