fixing model to add torchscript, embedding resizing, head pruning and masking + tests

pytorch_transformers/modeling_bert.py

@@ -449,7 +449,7 @@ class BertEncoder(nn.Module):
             outputs = outputs + (all_hidden_states,)
         if self.output_attentions:
             outputs = outputs + (all_attentions,)
-        return outputs  # outputs, (hidden states), (attentions)
+        return outputs  # last-layer hidden state, (all hidden states), (all attentions)
 
 
 class BertPooler(nn.Module):

pytorch_transformers/tests/modeling_dilbert_test.py

@@ -31,10 +31,10 @@ class DilBertModelTest(CommonTestCases.CommonModelTester):
 
     all_model_classes = (DilBertModel, DilBertForMaskedLM, DilBertForQuestionAnswering,
                          DilBertForSequenceClassification)
-    test_pruning = False
-    test_torchscript = False
-    test_resize_embeddings = False
-    test_head_masking = False
+    test_pruning = True
+    test_torchscript = True
+    test_resize_embeddings = True
+    test_head_masking = True
 
     class DilBertModelTester(object):
 
@@ -122,22 +122,20 @@ class DilBertModelTest(CommonTestCases.CommonModelTester):
         def create_and_check_dilbert_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
             model = DilBertModel(config=config)
             model.eval()
-            sequence_output, pooled_output = model(input_ids, input_mask)
-            sequence_output, pooled_output = model(input_ids)
+            (sequence_output,) = model(input_ids, input_mask)
+            (sequence_output,) = model(input_ids)
 
             result = {
                 "sequence_output": sequence_output,
-                "pooled_output": pooled_output,
             }
             self.parent.assertListEqual(
                 list(result["sequence_output"].size()),
                 [self.batch_size, self.seq_length, self.hidden_size])
-            self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
 
         def create_and_check_dilbert_for_masked_lm(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
             model = DilBertForMaskedLM(config=config)
             model.eval()
-            loss, prediction_scores = model(input_ids, input_mask, token_labels)
+            loss, prediction_scores = model(input_ids, attention_mask=input_mask, masked_lm_labels=token_labels)
             result = {
                 "loss": loss,
                 "prediction_scores": prediction_scores,