update tests

tests/modeling_openai_test.py

@@ -88,13 +88,13 @@ class OpenAIGPTModelTest(unittest.TestCase):
                 total_voc = self.n_ctx + self.n_special + self.vocab_size
                 token_type_ids = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc)
 
-            multiple_choice_labels = None
+            mc_labels = None
             lm_labels = None
-            multiple_choice_token_mask = None
+            mc_token_mask = None
             if self.use_labels:
-                multiple_choice_labels = OpenAIGPTModelTest.ids_tensor([self.batch_size], self.type_sequence_label_size)
+                mc_labels = OpenAIGPTModelTest.ids_tensor([self.batch_size], self.type_sequence_label_size)
                 lm_labels = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels)
-                multiple_choice_token_mask = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], 2).float()
+                mc_token_mask = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], 2).float()
 
             config = OpenAIGPTConfig(
                 vocab_size_or_config_json_file=self.vocab_size,
@@ -110,10 +110,10 @@ class OpenAIGPTModelTest(unittest.TestCase):
                 initializer_range=self.initializer_range)
 
             return (config, input_ids, token_type_ids, position_ids,
-                    multiple_choice_labels, lm_labels, multiple_choice_token_mask)
+                    mc_labels, lm_labels, mc_token_mask)
 
         def create_openai_model(self, config, input_ids, token_type_ids, position_ids,
-                                multiple_choice_labels, lm_labels, multiple_choice_token_mask):
+                                mc_labels, lm_labels, mc_token_mask):
             model = OpenAIGPTModel(config)
             hidden_states = model(input_ids, position_ids, token_type_ids)
             outputs = {
@@ -128,7 +128,7 @@ class OpenAIGPTModelTest(unittest.TestCase):
 
 
         def create_openai_lm_head(self, config, input_ids, token_type_ids, position_ids,
-                                       multiple_choice_labels, lm_labels, multiple_choice_token_mask):
+                                       mc_labels, lm_labels, mc_token_mask):
             model = OpenAIGPTLMHeadModel(config)
             loss = model(input_ids, position_ids, token_type_ids, lm_labels)
             lm_logits = model(input_ids, position_ids, token_type_ids)
@@ -150,15 +150,16 @@ class OpenAIGPTModelTest(unittest.TestCase):
                 [])
 
         def create_openai_double_heads(self, config, input_ids, token_type_ids, position_ids,
-                                       multiple_choice_labels, lm_labels, multiple_choice_token_mask):
+                                       mc_labels, lm_labels, mc_token_mask):
             model = OpenAIGPTDoubleHeadsModel(config)
-            loss = model(input_ids, multiple_choice_token_mask, position_ids,
-                         token_type_ids, lm_labels, multiple_choice_labels)
-            lm_logits, multiple_choice_logits = model(input_ids, multiple_choice_token_mask, position_ids, token_type_ids)
+            loss = model(input_ids, mc_token_mask,
+                         lm_labels=lm_labels, mc_labels=mc_labels,
+                         token_type_ids=token_type_ids, position_ids=position_ids)
+            lm_logits, mc_logits = model(input_ids, mc_token_mask, position_ids=position_ids, token_type_ids=token_type_ids)
             outputs = {
                 "loss": loss,
                 "lm_logits": lm_logits,
-                "multiple_choice_logits": multiple_choice_logits,
+                "mc_logits": mc_logits,
             }
             return outputs
 
@@ -168,7 +169,7 @@ class OpenAIGPTModelTest(unittest.TestCase):
                 list(result["lm_logits"].size()),
                 [self.batch_size, self.n_choices, self.seq_length, total_voc])
             self.parent.assertListEqual(
-                list(result["multiple_choice_logits"].size()),
+                list(result["mc_logits"].size()),
                 [self.batch_size, self.n_choices])
 
         def check_openai_double_heads_loss_output(self, result):