[XLNet] Changed post-processing of attention w.r.t to target_mapping

Whenever target_mapping is provided to the input, XLNet outputs two different attention streams.
Based on that the attention output would be on of the two:
- a list of tensors (usual case for most transformers)
- a list of 2-tuples of tensors, one tesor for each of attention streams
Docs and unit-tests have been updated

transformers/modeling_xlnet.py

@@ -581,8 +581,9 @@ class XLNetModel(XLNetPreTrainedModel):
             of shape ``(batch_size, sequence_length, hidden_size)``:
             Hidden-states of the model at the output of each layer plus the initial embedding outputs.
         **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
+            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
 
     Examples::
 
@@ -878,7 +879,11 @@ class XLNetModel(XLNetPreTrainedModel):
                 hidden_states = tuple(hs.permute(1, 0, 2).contiguous() for hs in hidden_states)
             outputs = outputs + (hidden_states,)
         if self.output_attentions:
-            attentions = tuple(tuple(att_stream.permute(2, 3, 0, 1).contiguous() for att_stream in t) for t in attentions)
+            if target_mapping is not None:
+                # when target_mapping is provided, there are 2-tuple of attentions
+                attentions = tuple(tuple(att_stream.permute(2, 3, 0, 1).contiguous() for att_stream in t) for t in attentions)
+            else:
+                attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions)
             outputs = outputs + (attentions,)
 
         return outputs  # outputs, (new_mems), (hidden_states), (attentions)
@@ -911,8 +916,9 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
             of shape ``(batch_size, sequence_length, hidden_size)``:
             Hidden-states of the model at the output of each layer plus the initial embedding outputs.
         **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
+            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
 
     Examples::
 
@@ -993,8 +999,9 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
             of shape ``(batch_size, sequence_length, hidden_size)``:
             Hidden-states of the model at the output of each layer plus the initial embedding outputs.
         **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
+            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
 
     Examples::
 
@@ -1093,8 +1100,9 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):
             of shape ``(batch_size, sequence_length, hidden_size)``:
             Hidden-states of the model at the output of each layer plus the initial embedding outputs.
         **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
+            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
 
     Examples::
 
@@ -1178,8 +1186,9 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
             of shape ``(batch_size, sequence_length, hidden_size)``:
             Hidden-states of the model at the output of each layer plus the initial embedding outputs.
         **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
+            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
 
     Examples::
 
@@ -1292,8 +1301,9 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
             of shape ``(batch_size, sequence_length, hidden_size)``:
             Hidden-states of the model at the output of each layer plus the initial embedding outputs.
         **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of 2-tuple of ``torch.FloatTensor`` (one for each layer, one for each attention stream) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
+            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
 
     Examples::
 

transformers/tests/modeling_xlnet_test.py

@@ -163,6 +163,18 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
                 list(list(mem.size()) for mem in result["mems_1"]),
                 [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
 
+        def create_and_check_xlnet_base_model_with_att_output(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
+                target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
+            model = XLNetModel(config)
+            model.eval()
+
+            _, _, attentions = model(input_ids_1, target_mapping=target_mapping)
+
+            self.parent.assertEqual(len(attentions), config.n_layer)
+            self.parent.assertIsInstance(attentions[0], tuple)
+            self.parent.assertEqual(len(attentions[0]), 2)
+            self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape)
+
         def create_and_check_xlnet_lm_head(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
                 target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
             model = XLNetLMHeadModel(config)
@@ -306,6 +318,12 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)
 
+    def test_xlnet_base_model_with_att_output(self):
+        self.model_tester.set_seed()
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        config_and_inputs[0].output_attentions = True
+        self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs)
+
     def test_xlnet_lm_head(self):
         self.model_tester.set_seed()
         config_and_inputs = self.model_tester.prepare_config_and_inputs()