Template updates (#6914)

templates/adding_a_new_model/README.md

@@ -79,7 +79,6 @@ You can then finish the addition step by adding imports for your classes in the
 - [ ] Add your configuration in `configuration_auto.py`.
 - [ ] Add your PyTorch and TF 2.0 model respectively in `modeling_auto.py` and `modeling_tf_auto.py`.
 - [ ] Add your tokenizer in `tokenization_auto.py`.
-- [ ] Add your models and tokenizer to `pipeline.py`.
 - [ ] Add a link to your conversion script in the main conversion utility (in `commands/convert.py`)
 - [ ] Edit the PyTorch to TF 2.0 conversion script to add your model in the `convert_pytorch_checkpoint_to_tf2.py`
   file.

templates/adding_a_new_model/tests/test_modeling_tf_xxx.py

@@ -17,10 +17,10 @@
 import unittest
 
 from transformers import XxxConfig, is_tf_available
+from transformers.testing_utils import CACHE_DIR, require_tf, slow
 
 from .test_configuration_common import ConfigTester
 from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
-from .utils import CACHE_DIR, require_tf, slow
 
 
 if is_tf_available():
@@ -137,7 +137,7 @@ def prepare_config_and_inputs(self):
 
             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 
-        def create_and_check_xxx_model(
+        def create_and_check_model(
             self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
         ):
             model = TFXxxModel(config=config)
@@ -154,15 +154,15 @@ def create_and_check_xxx_model(
             )
             self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
 
-        def create_and_check_xxx_for_masked_lm(
+        def create_and_check_for_masked_lm(
             self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
         ):
             model = TFXxxForMaskedLM(config=config)
             inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
             result = model(inputs)
             self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
 
-        def create_and_check_xxx_for_sequence_classification(
+        def create_and_check_for_sequence_classification(
             self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
         ):
             config.num_labels = self.num_labels
@@ -171,7 +171,7 @@ def create_and_check_xxx_for_sequence_classification(
             result = model(inputs)
             self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
 
-        def create_and_check_bert_for_multiple_choice(
+        def create_and_check_for_multiple_choice(
             self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
         ):
             config.num_choices = self.num_choices
@@ -187,7 +187,7 @@ def create_and_check_bert_for_multiple_choice(
             result = model(inputs)
             self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
 
-        def create_and_check_xxx_for_token_classification(
+        def create_and_check_for_token_classification(
             self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
         ):
             config.num_labels = self.num_labels
@@ -196,7 +196,7 @@ def create_and_check_xxx_for_token_classification(
             result = model(inputs)
             self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
 
-        def create_and_check_xxx_for_question_answering(
+        def create_and_check_for_question_answering(
             self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
         ):
             model = TFXxxForQuestionAnswering(config=config)
@@ -226,25 +226,29 @@ def setUp(self):
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    def test_xxx_model(self):
+    def test_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_xxx_model(*config_and_inputs)
+        self.model_tester.create_and_check_model(*config_and_inputs)
 
     def test_for_masked_lm(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_xxx_for_masked_lm(*config_and_inputs)
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
 
     def test_for_question_answering(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_xxx_for_question_answering(*config_and_inputs)
+        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
 
     def test_for_sequence_classification(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_xxx_for_sequence_classification(*config_and_inputs)
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
 
     def test_for_token_classification(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_xxx_for_token_classification(*config_and_inputs)
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
+
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
 
     @slow
     def test_model_from_pretrained(self):

templates/adding_a_new_model/tests/test_modeling_xxx.py

@@ -17,10 +17,10 @@
 import unittest
 
 from transformers import is_torch_available
+from transformers.testing_utils import require_torch, require_torch_and_cuda, slow, torch_device
 
 from .test_configuration_common import ConfigTester
 from .test_modeling_common import ModelTesterMixin, ids_tensor
-from .utils import require_torch, require_torch_and_cuda, slow, torch_device
 
 
 if is_torch_available():
@@ -29,6 +29,7 @@
         AutoTokenizer,
         XxxConfig,
         XxxForMaskedLM,
+        XxxForMultipleChoice,
         XxxForQuestionAnswering,
         XxxForSequenceClassification,
         XxxForTokenClassification,
@@ -126,7 +127,7 @@ def prepare_config_and_inputs(self):
 
         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 
-    def create_and_check_xxx_model(
+    def create_and_check_model(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         model = XxxModel(config=config)
@@ -138,18 +139,16 @@ def create_and_check_xxx_model(
         self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
         self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
 
-    def create_and_check_xxx_for_masked_lm(
+    def create_and_check_for_masked_lm(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         model = XxxForMaskedLM(config=config)
         model.to(torch_device)
         model.eval()
-        result = model(
-            input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels
-        )
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
 
-    def create_and_check_xxx_for_question_answering(
+    def create_and_check_for_question_answering(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         model = XxxForQuestionAnswering(config=config)
@@ -165,7 +164,7 @@ def create_and_check_xxx_for_question_answering(
         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))
 
-    def create_and_check_xxx_for_sequence_classification(
+    def create_and_check_for_sequence_classification(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         config.num_labels = self.num_labels
@@ -175,7 +174,7 @@ def create_and_check_xxx_for_sequence_classification(
         result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
 
-    def create_and_check_xxx_for_token_classification(
+    def create_and_check_for_token_classification(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
         config.num_labels = self.num_labels
@@ -185,6 +184,24 @@ def create_and_check_xxx_for_token_classification(
         result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
 
+    def create_and_check_for_multiple_choice(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_choices = self.num_choices
+        model = XxxForMultipleChoice(config=config)
+        model.to(torch_device)
+        model.eval()
+        multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        result = model(
+            multiple_choice_inputs_ids,
+            attention_mask=multiple_choice_input_mask,
+            token_type_ids=multiple_choice_token_type_ids,
+            labels=choice_labels,
+        )
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -216,25 +233,29 @@ def setUp(self):
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    def test_xxx_model(self):
+    def test_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_xxx_model(*config_and_inputs)
+        self.model_tester.create_and_check_model(*config_and_inputs)
 
     def test_for_masked_lm(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_xxx_for_masked_lm(*config_and_inputs)
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
 
     def test_for_question_answering(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_xxx_for_question_answering(*config_and_inputs)
+        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
 
     def test_for_sequence_classification(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_xxx_for_sequence_classification(*config_and_inputs)
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
 
     def test_for_token_classification(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_xxx_for_token_classification(*config_and_inputs)
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
+
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_electra_for_multiple_choice(*config_and_inputs)
 
     @slow
     def test_lm_outputs_same_as_reference_model(self):