[XLNET] Fix tf.data bad usages.

PiperOrigin-RevId: 314177321

official/nlp/xlnet/data_utils.py

@@ -93,26 +93,18 @@ def file_based_input_fn_builder(input_file, name_to_features, batch_size,
       # file level shuffle
       d = d.shuffle(len(input_file)).repeat()
 
-      d = d.apply(
-          tf.data.experimental.parallel_interleave(
-              tf.data.TFRecordDataset,
-              sloppy=is_training,
-              cycle_length=cycle_length))
+      d = d.interleave(
+          tf.data.TFRecordDataset,
+          sloppy=is_training,
+          cycle_length=cycle_length)
 
       if is_training:
         # sample level shuffle
         d = d.shuffle(buffer_size=2048)
-
-    # TODO(b/138223458): Hard-code drop_remainder=True to get around the bug
-    # that under TPU strategy, setting drop_remainder=False in
-    # tf.data.Dataset.batch() while data_size can be divided by global
-    # batch_size will trigger dynamic_dimension related TPU compilation error.
-    d = d.apply(
-        tf.data.experimental.map_and_batch(
-            lambda record: _decode_record(record, name_to_features),
-            batch_size=batch_size,
-            num_parallel_batches=num_threads,
-            drop_remainder=True))
+    d = d.map(
+        lambda record: _decode_record(record, name_to_features),
+        num_parallel_calls=tf.data.experimental.AUTOTUNE)
+    d = d.batch(batch_size, drop_remainder=is_training)
 
     # When `input_file` is a path to a single file or a list
     # containing a single path, disable auto sharding so that
@@ -737,11 +729,7 @@ def parse_files_to_dataset(parser,
     logging.info("Perform sample-level shuffle with size %d", buffer_size)
     dataset = dataset.shuffle(buffer_size=buffer_size)
 
-  # (zihang): since we are doing online preprocessing, the parsed result of
-  # the same input at each time will be different. Thus, cache processed data
-  # is not helpful. It will use a lot of memory and lead to contrainer OOM.
-  # So, change to cache non-parsed raw data instead.
-  dataset = dataset.cache().map(parser).repeat()
+  dataset = dataset.cache().repeat().map(parser)
   dataset = dataset.batch(bsz_per_core, drop_remainder=True)
   dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)