pad on left for xlnet

examples/run_xlnet_classifier.py

@@ -198,14 +198,17 @@ def main():
             list(filter(None, args.xlnet_model.split('/'))).pop(),
                         str(args.max_seq_length),
                         str(task_name)))
-        try:
+        if os.path.exists(cached_train_features_file):
+            logger.info("Loading train features for cache file %s", cached_train_features_file)
             with open(cached_train_features_file, "rb") as reader:
                 train_features = pickle.load(reader)
-        except:
+        else:
+            logger.info("No cache file at %s, preparing train features", cached_train_features_file)
             train_features = convert_examples_to_features(
                 train_examples, label_list, args.max_seq_length, tokenizer, output_mode,
                 cls_token_at_end=True, cls_token=tokenizer.CLS_TOKEN,
-                sep_token=tokenizer.SEP_TOKEN, cls_token_segment_id=2)
+                sep_token=tokenizer.SEP_TOKEN, cls_token_segment_id=2,
+                pad_on_left=True, pad_token_segment_id=4)
             if args.local_rank == -1 or torch.distributed.get_rank() == 0:
                 logger.info("  Saving train features into cached file %s", cached_train_features_file)
                 with open(cached_train_features_file, "wb") as writer:
@@ -344,14 +347,17 @@ def main():
             list(filter(None, args.xlnet_model.split('/'))).pop(),
                         str(args.max_seq_length),
                         str(task_name)))
-        try:
+        if os.path.exists(cached_eval_features_file):
+            logger.info("Loading eval features for cache file %s", cached_eval_features_file)
             with open(cached_eval_features_file, "rb") as reader:
                 eval_features = pickle.load(reader)
-        except:
+        else:
+            logger.info("No cache file at %s, preparing eval features", cached_eval_features_file)
             eval_features = convert_examples_to_features(
                 eval_examples, label_list, args.max_seq_length, tokenizer, output_mode,
                 cls_token_at_end=True, cls_token=tokenizer.CLS_TOKEN,
-                sep_token=tokenizer.SEP_TOKEN, cls_token_segment_id=2)
+                sep_token=tokenizer.SEP_TOKEN, cls_token_segment_id=2,
+                pad_on_left=True, pad_token_segment_id=4)
             if args.local_rank == -1 or torch.distributed.get_rank() == 0:
                 logger.info("  Saving eval features into cached file %s", cached_eval_features_file)
                 with open(cached_eval_features_file, "wb") as writer:

examples/utils_glue.py

@@ -389,8 +389,11 @@ class WnliProcessor(DataProcessor):
 
 def convert_examples_to_features(examples, label_list, max_seq_length,
                                  tokenizer, output_mode,
-                                 cls_token_at_end=False, cls_token='[CLS]',
-                                 sep_token='[SEP]', cls_token_segment_id=0):
+                                 cls_token_at_end=False, pad_on_left=False,
+                                 cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
+                                 sequence_a_segment_id=0, sequence_b_segment_id=1,
+                                 cls_token_segment_id=1, pad_token_segment_id=0,
+                                 mask_padding_with_zero=True):
     """ Loads a data file into a list of `InputBatch`s
         `cls_token_at_end` define the location of the CLS token:
             - False (BERT pattern): [CLS] + A + [SEP] + B + [SEP]
@@ -438,11 +441,11 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
         # used as as the "sentence vector". Note that this only makes sense because
         # the entire model is fine-tuned.
         tokens = tokens_a + [sep_token]
-        segment_ids = [0] * len(tokens)
+        segment_ids = [sequence_a_segment_id] * len(tokens)
 
         if tokens_b:
             tokens += tokens_b + [sep_token]
-            segment_ids += [1] * (len(tokens_b) + 1)
+            segment_ids += [sequence_b_segment_id] * (len(tokens_b) + 1)
 
         if cls_token_at_end:
             tokens = tokens + [cls_token]
@@ -455,13 +458,18 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
 
         # The mask has 1 for real tokens and 0 for padding tokens. Only real
         # tokens are attended to.
-        input_mask = [1] * len(input_ids)
+        input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
 
         # Zero-pad up to the sequence length.
-        padding = [0] * (max_seq_length - len(input_ids))
-        input_ids += padding
-        input_mask += padding
-        segment_ids += padding
+        padding_length = max_seq_length - len(input_ids)
+        if pad_on_left:
+            input_ids = ([pad_token] * padding_length) + input_ids
+            input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
+            segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
+        else:
+            input_ids = input_ids + ([pad_token] * padding_length)
+            input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
+            segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)
 
         assert len(input_ids) == max_seq_length
         assert len(input_mask) == max_seq_length