updating run_xlnet_classifier

.gitignore

@@ -124,3 +124,6 @@ tensorflow_code
 # Models
 models
 proc_data
+
+# examples
+examples/runs
\ No newline at end of file

examples/run_xlnet_classifier.py

@@ -54,91 +54,58 @@ def main():
     parser = argparse.ArgumentParser()
 
     ## Required parameters
-    parser.add_argument("--data_dir",
-                        default=None,
-                        type=str,
-                        required=True,
+    parser.add_argument("--data_dir", default=None, type=str, required=True,
                         help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
-    parser.add_argument("--xlnet_model", default="xlnet-large-cased", type=str,
-                        help="XLNet pre-trained model: currently only xlnet-large-cased.")
-    parser.add_argument("--task_name",
-                        default=None,
-                        type=str,
-                        required=True,
+    parser.add_argument("--task_name", default=None, type=str, required=True,
                         help="The name of the task to train.")
-    parser.add_argument("--output_dir",
-                        default=None,
-                        type=str,
-                        required=True,
+    parser.add_argument("--output_dir", default=None, type=str, required=True,
                         help="The output directory where the model predictions and checkpoints will be written.")
-
-    ## Other parameters
-    parser.add_argument("--cache_dir",
-                        default="",
-                        type=str,
-                        help="Where do you want to store the pre-trained models downloaded from s3")
-    parser.add_argument("--max_seq_length",
-                        default=128,
-                        type=int,
-                        help="The maximum total input sequence length after WordPiece tokenization. \n"
-                             "Sequences longer than this will be truncated, and sequences shorter \n"
-                             "than this will be padded.")
-    parser.add_argument("--do_train",
-                        action='store_true',
+    # training
+    parser.add_argument("--do_train", action='store_true',
                         help="Whether to run training.")
-    parser.add_argument("--do_eval",
-                        action='store_true',
-                        help="Whether to run eval on the dev set.")
-    parser.add_argument("--do_lower_case",
-                        action='store_true',
-                        help="Set this flag if you are using an uncased model.")
-    parser.add_argument("--train_batch_size",
-                        default=32,
-                        type=int,
-                        help="Total batch size for training.")
-    parser.add_argument("--eval_batch_size",
-                        default=8,
-                        type=int,
-                        help="Total batch size for eval.")
-    parser.add_argument("--learning_rate",
-                        default=5e-5,
-                        type=float,
+    parser.add_argument("--learning_rate", default=5e-5, type=float,
                         help="The initial learning rate for Adam.")
-    parser.add_argument("--num_train_epochs",
-                        default=3.0,
-                        type=float,
+    parser.add_argument("--num_train_epochs", default=3.0, type=float,
                         help="Total number of training epochs to perform.")
-    parser.add_argument("--warmup_proportion",
-                        default=0.1,
-                        type=float,
+    parser.add_argument("--warmup_proportion", default=0.1, type=float,
                         help="Proportion of training to perform linear learning rate warmup for. "
                              "E.g., 0.1 = 10%% of training.")
-    parser.add_argument("--no_cuda",
-                        action='store_true',
-                        help="Whether not to use CUDA when available")
-    parser.add_argument('--overwrite_output_dir',
-                        action='store_true',
-                        help="Overwrite the content of the output directory")
-    parser.add_argument("--local_rank",
-                        type=int,
-                        default=-1,
-                        help="local_rank for distributed training on gpus")
-    parser.add_argument('--seed',
-                        type=int,
-                        default=42,
-                        help="random seed for initialization")
-    parser.add_argument('--gradient_accumulation_steps',
-                        type=int,
-                        default=1,
+    parser.add_argument("--train_batch_size", default=32, type=int,
+                        help="Total batch size for training.")
+    parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
                         help="Number of updates steps to accumulate before performing a backward/update pass.")
-    parser.add_argument('--fp16',
-                        action='store_true',
+    parser.add_argument('--fp16', action='store_true',
                         help="Whether to use 16-bit float precision instead of 32-bit")
-    parser.add_argument('--loss_scale',
-                        type=float, default=0,
+    parser.add_argument('--loss_scale', type=float, default=0,
                         help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
                              "0 (default value): dynamic loss scaling.\n"
                              "Positive power of 2: static loss scaling value.\n")
+    # evaluation
+    parser.add_argument("--do_eval", action='store_true',
+                        help="Whether to run eval on the dev set.")
+    parser.add_argument("--eval_batch_size", default=8, type=int,
+                        help="Total batch size for eval.")
+    # Model
+    parser.add_argument("--xlnet_model", default="xlnet-large-cased", type=str,
+                        help="XLNet pre-trained model: currently only xlnet-large-cased.")
+    parser.add_argument("--do_lower_case", action='store_true',
+                        help="Set this flag if you are using an uncased model.")
+    parser.add_argument("--cache_dir", default="", type=str,
+                        help="Where do you want to store the pre-trained models downloaded from s3")
+    # task specific
+    parser.add_argument("--max_seq_length", default=128, type=int,
+                        help="The maximum total input sequence length after WordPiece tokenization. \n"
+                             "Sequences longer than this will be truncated, and sequences shorter \n"
+                             "than this will be padded.")
+    parser.add_argument('--overwrite_output_dir', action='store_true',
+                        help="Overwrite the content of the output directory")
+    # Misc
+    parser.add_argument("--no_cuda", action='store_true',
+                        help="Whether not to use CUDA when available")
+    parser.add_argument("--local_rank", type=int, default=-1,
+                        help="local_rank for distributed training on gpus")
+    parser.add_argument('--seed', type=int, default=42,
+                        help="random seed for initialization")
     parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
     parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
     args = parser.parse_args()
@@ -306,7 +273,7 @@ def main():
                 input_ids, input_mask, segment_ids, label_ids = batch
 
                 # define a new function to compute loss values for both output_modes
-                logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
+                logits, _ = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
 
                 if output_mode == "classification":
                     loss_fct = CrossEntropyLoss()
@@ -420,7 +387,7 @@ def main():
             label_ids = label_ids.to(device)
 
             with torch.no_grad():
-                logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
+                logits, _ = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
 
             # create eval loss and other metric required by the task
             if output_mode == "classification":
@@ -501,7 +468,7 @@ def main():
                 label_ids = label_ids.to(device)
 
                 with torch.no_grad():
-                    logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=None)
+                    logits, _ = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=None)
 
                 loss_fct = CrossEntropyLoss()
                 tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))

pytorch_pretrained_bert/modeling.py

@@ -606,7 +606,7 @@ class BertPreTrainedModel(nn.Module):
                 ))
         self.config = config
 
-    def init_bert_weights(self, module):
+    def init_weights(self, module):
         """ Initialize the weights.
         """
         if isinstance(module, (nn.Linear, nn.Embedding)):
@@ -823,7 +823,7 @@ class BertModel(BertPreTrainedModel):
         self.encoder = BertEncoder(config, output_attentions=output_attentions,
                                            keep_multihead_output=keep_multihead_output)
         self.pooler = BertPooler(config)
-        self.apply(self.init_bert_weights)
+        self.apply(self.init_weights)
 
     def prune_heads(self, heads_to_prune):
         """ Prunes heads of the model.
@@ -951,7 +951,7 @@ class BertForPreTraining(BertPreTrainedModel):
         self.bert = BertModel(config, output_attentions=output_attentions,
                                       keep_multihead_output=keep_multihead_output)
         self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight)
-        self.apply(self.init_bert_weights)
+        self.apply(self.init_weights)
 
     def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, next_sentence_label=None, head_mask=None):
         outputs = self.bert(input_ids, token_type_ids, attention_mask,
@@ -1030,7 +1030,7 @@ class BertForMaskedLM(BertPreTrainedModel):
         self.bert = BertModel(config, output_attentions=output_attentions,
                                       keep_multihead_output=keep_multihead_output)
         self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight)
-        self.apply(self.init_bert_weights)
+        self.apply(self.init_weights)
 
     def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, head_mask=None):
         outputs = self.bert(input_ids, token_type_ids, attention_mask,
@@ -1105,7 +1105,7 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
         self.bert = BertModel(config, output_attentions=output_attentions,
                                       keep_multihead_output=keep_multihead_output)
         self.cls = BertOnlyNSPHead(config)
-        self.apply(self.init_bert_weights)
+        self.apply(self.init_weights)
 
     def forward(self, input_ids, token_type_ids=None, attention_mask=None, next_sentence_label=None, head_mask=None):
         outputs = self.bert(input_ids, token_type_ids, attention_mask,
@@ -1184,7 +1184,7 @@ class BertForSequenceClassification(BertPreTrainedModel):
                                       keep_multihead_output=keep_multihead_output)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
         self.classifier = nn.Linear(config.hidden_size, num_labels)
-        self.apply(self.init_bert_weights)
+        self.apply(self.init_weights)
 
     def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
         outputs = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False, head_mask=head_mask)
@@ -1261,7 +1261,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
                                       keep_multihead_output=keep_multihead_output)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
         self.classifier = nn.Linear(config.hidden_size, 1)
-        self.apply(self.init_bert_weights)
+        self.apply(self.init_weights)
 
     def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
         flat_input_ids = input_ids.view(-1, input_ids.size(-1))
@@ -1343,7 +1343,7 @@ class BertForTokenClassification(BertPreTrainedModel):
                                       keep_multihead_output=keep_multihead_output)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
         self.classifier = nn.Linear(config.hidden_size, num_labels)
-        self.apply(self.init_bert_weights)
+        self.apply(self.init_weights)
 
     def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
         outputs = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False, head_mask=head_mask)
@@ -1428,7 +1428,7 @@ class BertForQuestionAnswering(BertPreTrainedModel):
         self.bert = BertModel(config, output_attentions=output_attentions,
                                       keep_multihead_output=keep_multihead_output)
         self.qa_outputs = nn.Linear(config.hidden_size, 2)
-        self.apply(self.init_bert_weights)
+        self.apply(self.init_weights)
 
     def forward(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None,
                 end_positions=None, head_mask=None):

pytorch_pretrained_bert/modeling_xlnet.py

@@ -633,7 +633,7 @@ class XLNetPreTrainedModel(nn.Module):
                 ))
         self.config = config
 
-    def init_xlnet_weights(self, module):
+    def init_weights(self, module):
         """ Initialize the weights.
         """
         if isinstance(module, (nn.Linear, nn.Embedding)):
@@ -904,14 +904,14 @@ class XLNetModel(XLNetPreTrainedModel):
         pos_emb = pos_emb.to(next(self.parameters()))
         return pos_emb
 
-    def forward(self, inp_k, seg_id=None, input_mask=None,
+    def forward(self, inp_k, token_type_ids=None, attention_mask=None,
                 mems=None, perm_mask=None, target_mapping=None, inp_q=None,
                 output_all_encoded_layers=True, head_mask=None):
         """
         Args:
             inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
-            seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
-            input_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
+            token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
+            attention_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
                 0 for real tokens and 1 for padding.
             mems: [optional] a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
                 from previous batches. The length of the list equals n_layer.
@@ -945,8 +945,8 @@ class XLNetModel(XLNetPreTrainedModel):
         # but we want a unified interface in the library with the batch size on the first dimension
         # so we move here the first dimension (batch) to the end
         inp_k = inp_k.transpose(0, 1).contiguous()
-        seg_id = seg_id.transpose(0, 1).contiguous() if seg_id is not None else None
-        input_mask = input_mask.transpose(0, 1).contiguous() if input_mask is not None else None
+        token_type_ids = token_type_ids.transpose(0, 1).contiguous() if token_type_ids is not None else None
+        attention_mask = attention_mask.transpose(0, 1).contiguous() if attention_mask is not None else None
         perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None
         target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None
         inp_q = inp_q.transpose(0, 1).contiguous() if inp_q is not None else None
@@ -969,11 +969,11 @@ class XLNetModel(XLNetPreTrainedModel):
             raise ValueError('Unsupported attention type: {}'.format(self.attn_type))
 
         # data mask: input mask & perm mask
-        if input_mask is not None and perm_mask is not None:
-            data_mask = input_mask[None] + perm_mask
-        elif input_mask is not None and perm_mask is None:
-            data_mask = input_mask[None]
-        elif input_mask is None and perm_mask is not None:
+        if attention_mask is not None and perm_mask is not None:
+            data_mask = attention_mask[None] + perm_mask
+        elif attention_mask is not None and perm_mask is None:
+            data_mask = attention_mask[None]
+        elif attention_mask is None and perm_mask is not None:
             data_mask = perm_mask
         else:
             data_mask = None
@@ -1011,13 +1011,13 @@ class XLNetModel(XLNetPreTrainedModel):
             output_g = None
 
         ##### Segment embedding
-        if seg_id is not None:
-            # Convert `seg_id` to one-hot `seg_mat`
+        if token_type_ids is not None:
+            # Convert `token_type_ids` to one-hot `seg_mat`
             mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device)
-            cat_ids = torch.cat([mem_pad, seg_id], dim=0)
+            cat_ids = torch.cat([mem_pad, token_type_ids], dim=0)
 
             # `1` indicates not in the same segment [qlen x klen x bsz]
-            seg_mat = (seg_id[:, None] != cat_ids[None, :]).long()
+            seg_mat = (token_type_ids[:, None] != cat_ids[None, :]).long()
             seg_mat = F.one_hot(seg_mat, num_classes=2).to(dtype_float)
         else:
             seg_mat = None
@@ -1076,8 +1076,8 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
 
     Inputs:
         inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
-        seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
-        input_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
+        token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
+        attention_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
             0 for real tokens and 1 for padding.
         mems: [optional] a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
             from previous batches. The length of the list equals n_layer.
@@ -1112,14 +1112,14 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
     ```python
     # Already been converted into WordPiece token ids
     input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
-    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    attention_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
     token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
 
     config = modeling.XLNetConfig(vocab_size_or_config_json_file=32000, d_model=768,
         n_layer=12, num_attention_heads=12, intermediate_size=3072)
 
     model = modeling.XLNetModel(config=config)
-    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
+    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, attention_mask)
     ```
     """
     def __init__(self, config, output_attentions=False, keep_multihead_output=False):
@@ -1134,7 +1134,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
 
         # Tie weights
 
-        self.apply(self.init_xlnet_weights)
+        self.apply(self.init_weights)
         self.tie_weights()
 
     def tie_weights(self):
@@ -1142,14 +1142,14 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
         """
         self.lm_loss.weight = self.transformer.word_embedding.weight
 
-    def forward(self, inp_k, seg_id=None, input_mask=None,
+    def forward(self, inp_k, token_type_ids=None, attention_mask=None,
                 mems=None, perm_mask=None, target_mapping=None, inp_q=None,
                 target=None, output_all_encoded_layers=True, head_mask=None):
         """
         Args:
             inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
-            seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
-            input_mask: float32 Tensor in shape [bsz, len], the input mask.
+            token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
+            attention_mask: float32 Tensor in shape [bsz, len], the input mask.
                 0 for real tokens and 1 for padding.
             mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
                 from previous batches. The length of the list equals n_layer.
@@ -1171,7 +1171,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
             summary_type: str, "last", "first", "mean", or "attn". The method
                 to pool the input to get a vector representation.
         """
-        output, hidden_states, new_mems = self.transformer(inp_k, seg_id, input_mask,
+        output, hidden_states, new_mems = self.transformer(inp_k, token_type_ids, attention_mask,
                                             mems, perm_mask, target_mapping, inp_q,
                                             output_all_encoded_layers, head_mask)
 
@@ -1200,7 +1200,7 @@ class XLNetSequenceSummary(nn.Module):
         super(XLNetSequenceSummary, self).__init__()
         self.summary_type = summary_type
         if use_proj:
-            self.summary = nn.Linear(config.hidden_size, num_labels)
+            self.summary = nn.Linear(config.d_model, config.d_model)
         else:
             self.summary = None
         if summary_type == 'attn':
@@ -1211,19 +1211,20 @@ class XLNetSequenceSummary(nn.Module):
         self.dropout = nn.Dropout(config.dropout)
         self.activation = nn.Tanh()
 
-    def forward(self, hidden_states, input_mask=None):
+    def forward(self, hidden_states):
+        """ hidden_states: float Tensor in shape [bsz, seq_len, d_model], the hidden-states of the last layer."""
         if self.summary_type == 'last':
-            output = hidden_states[-1]
+            output = hidden_states[:, -1]
         elif self.summary_type == 'first':
-            output = hidden_states[0]
+            output = hidden_states[:, 0]
         elif self.summary_type == 'mean':
-            output = hidden_states.mean(dim=0)
+            output = hidden_states.mean(dim=1)
         elif summary_type == 'attn':
             raise NotImplementedError
 
         output = self.summary(output)
-        output = self.dropout(output)
         output = self.activation(output)
+        output = self.dropout(output)
         return output
 
 
@@ -1240,8 +1241,8 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
 
     Inputs:
         inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
-        seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
-        input_mask: float32 Tensor in shape [bsz, len], the input mask.
+        token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
+        attention_mask: float32 Tensor in shape [bsz, len], the input mask.
             0 for real tokens and 1 for padding.
         mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
             from previous batches. The length of the list equals n_layer.
@@ -1277,14 +1278,14 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
     ```python
     # Already been converted into WordPiece token ids
     input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
-    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    attention_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
     token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
 
     config = modeling.XLNetConfig(vocab_size_or_config_json_file=32000, d_model=768,
         n_layer=12, num_attention_heads=12, intermediate_size=3072)
 
     model = modeling.XLNetModel(config=config)
-    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
+    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, attention_mask)
     ```
     """
     def __init__(self, config, summary_type="last", use_proj=True, num_labels=2,
@@ -1302,17 +1303,17 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
         self.sequence_summary = XLNetSequenceSummary(config, summary_type=summary_type,
                                                      use_proj=use_proj, output_attentions=output_attentions,
                                                      keep_multihead_output=keep_multihead_output)
-        self.loss_proj = nn.Linear(config.d_model, num_classes if not is_regression else 1)
-        self.apply(self.init_bert_weights)
+        self.loss_proj = nn.Linear(config.d_model, num_labels if not is_regression else 1)
+        self.apply(self.init_weights)
 
-    def forward(self, inp_k, seg_id=None, input_mask=None,
+    def forward(self, inp_k, token_type_ids=None, attention_mask=None,
                 mems=None, perm_mask=None, target_mapping=None, inp_q=None,
                 target=None, output_all_encoded_layers=True, head_mask=None):
         """
         Args:
             inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
-            seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
-            input_mask: float32 Tensor in shape [bsz, len], the input mask.
+            token_type_ids: int32 Tensor in shape [bsz, len], the input segment IDs.
+            attention_mask: float32 Tensor in shape [bsz, len], the input mask.
                 0 for real tokens and 1 for padding.
             mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
                 from previous batches. The length of the list equals n_layer.
@@ -1331,7 +1332,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
                 Only used during pretraining for two-stream attention.
                 Set to None during finetuning.
         """
-        output, _, new_mems = self.transformer(inp_k, seg_id, input_mask,
+        output, _, new_mems = self.transformer(inp_k, token_type_ids, attention_mask,
                                             mems, perm_mask, target_mapping, inp_q,
                                             output_all_encoded_layers, head_mask)
 
@@ -1356,3 +1357,96 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
         # if self.output_attentions:
         return logits, new_mems
         #     return all_attentions, encoded_layers, pooled_output
+
+class XLNetForQuestionAnswering(XLNetPreTrainedModel):
+    """XLNet model for Question Answering (span extraction).
+    This module is composed of the XLNet model with a linear layer on top of
+    the sequence output that computes start_logits and end_logits
+
+    Params:
+        `config`: a XLNetConfig class instance with the configuration to build a new model
+        `output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
+        `keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
+            This can be used to compute head importance metrics. Default: False
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see XLNet paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size].
+            Positions are clamped to the length of the sequence and position outside of the sequence are not taken
+            into account for computing the loss.
+        `end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size].
+            Positions are clamped to the length of the sequence and position outside of the sequence are not taken
+            into account for computing the loss.
+        `head_mask`: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
+            It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
+
+    Outputs:
+        if `start_positions` and `end_positions` are not `None`:
+            Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions.
+        if `start_positions` or `end_positions` is `None`:
+            Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end
+            position tokens of shape [batch_size, sequence_length].
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    attention_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+    config = XLNetConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+
+    model = XLNetForQuestionAnswering(config)
+    start_logits, end_logits = model(input_ids, token_type_ids, attention_mask)
+    ```
+    """
+    def __init__(self, config, output_attentions=False, keep_multihead_output=False):
+        super(XLNetForQuestionAnswering, self).__init__(config)
+        self.output_attentions = output_attentions
+        self.transformer = XLNetModel(config, output_attentions=output_attentions,
+                                      keep_multihead_output=keep_multihead_output)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+        self.apply(self.init_weights)
+
+    def forward(self, inp_k, token_type_ids=None, attention_mask=None,
+                mems=None, perm_mask=None, target_mapping=None, inp_q=None,
+                start_positions=None, end_positions=None,
+                output_all_encoded_layers=True, head_mask=None):
+        output, _, new_mems = self.transformer(inp_k, token_type_ids, attention_mask,
+                                            mems, perm_mask, target_mapping, inp_q,
+                                            output_all_encoded_layers, head_mask)
+
+        logits = self.qa_outputs(output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1)
+
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions.clamp_(0, ignored_index)
+            end_positions.clamp_(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+            return total_loss
+        elif self.output_attentions:
+            return all_attentions, start_logits, end_logits
+        return start_logits, end_logits