Internal change

PiperOrigin-RevId: 272777104

Author: saberkun

official/nlp/xlnet/data_utils.py

@@ -43,6 +43,10 @@
 SEP_ID = special_symbols["<sep>"]
 MASK_ID = special_symbols["<mask>"]
 EOD_ID = special_symbols["<eod>"]
+SEG_ID_P = 0
+SEG_ID_Q = 1
+SEG_ID_CLS = 2
+SEG_ID_PAD = 3
 
 
 def file_based_input_fn_builder(input_file, name_to_features, batch_size,

official/nlp/xlnet/run_pretrain.py

@@ -48,8 +48,11 @@
 
 
 def get_pretrainxlnet_model(model_config, run_config):
-  model = modeling.PretrainingXLNetModel(model_config, run_config, name="model")
-  return model
+  return modeling.PretrainingXLNetModel(
+      use_proj=True,
+      xlnet_config=model_config,
+      run_config=run_config,
+      name="model")
 
 
 def main(unused_argv):
@@ -69,8 +72,7 @@ def main(unused_argv):
   if strategy:
     logging.info("***** Number of cores used : %d",
                  strategy.num_replicas_in_sync)
-    logging.info("***** Number of hosts used : %d",
-                 num_hosts)
+    logging.info("***** Number of hosts used : %d", num_hosts)
   train_input_fn = functools.partial(
       data_utils.get_pretrain_input_data, FLAGS.train_batch_size, FLAGS.seq_len,
       strategy, FLAGS.train_tfrecord_path, FLAGS.reuse_len, FLAGS.perm_size,

official/nlp/xlnet/squad_utils.py

@@ -36,11 +36,6 @@
 
 SPIECE_UNDERLINE = u"▁"
 
-SEG_ID_P = 0
-SEG_ID_Q = 1
-SEG_ID_CLS = 2
-SEG_ID_PAD = 3
-
 
 class InputFeatures(object):
   """A single set of features of data."""
@@ -705,28 +700,28 @@ def _piece_to_id(x):
                                                split_token_index)
         token_is_max_context[len(tokens)] = is_max_context
         tokens.append(all_doc_tokens[split_token_index])
-        segment_ids.append(SEG_ID_P)
+        segment_ids.append(data_utils.SEG_ID_P)
         p_mask.append(0)
 
       paragraph_len = len(tokens)
 
       tokens.append(data_utils.SEP_ID)
-      segment_ids.append(SEG_ID_P)
+      segment_ids.append(data_utils.SEG_ID_P)
       p_mask.append(1)
 
       # note(zhiliny): we put P before Q
       # because during pretraining, B is always shorter than A
       for token in query_tokens:
         tokens.append(token)
-        segment_ids.append(SEG_ID_Q)
+        segment_ids.append(data_utils.SEG_ID_Q)
         p_mask.append(1)
       tokens.append(data_utils.SEP_ID)
-      segment_ids.append(SEG_ID_Q)
+      segment_ids.append(data_utils.SEG_ID_Q)
       p_mask.append(1)
 
       cls_index = len(segment_ids)
       tokens.append(data_utils.CLS_ID)
-      segment_ids.append(SEG_ID_CLS)
+      segment_ids.append(data_utils.SEG_ID_CLS)
       p_mask.append(0)
 
       input_ids = tokens
@@ -739,7 +734,7 @@ def _piece_to_id(x):
       while len(input_ids) < max_seq_length:
         input_ids.append(0)
         input_mask.append(1)
-        segment_ids.append(SEG_ID_PAD)
+        segment_ids.append(data_utils.SEG_ID_PAD)
         p_mask.append(1)
 
       assert len(input_ids) == max_seq_length

official/nlp/xlnet_config.py

@@ -30,7 +30,6 @@ def create_run_config(is_training, is_finetune, flags):
   kwargs = dict(
       is_training=is_training,
       use_tpu=flags.use_tpu,
-      use_bfloat16=flags.use_bfloat16,
       dropout=flags.dropout,
       dropout_att=flags.dropout_att,
       init_method=flags.init_method,
@@ -49,6 +48,7 @@ def create_run_config(is_training, is_finetune, flags):
   return RunConfig(**kwargs)
 
 
+# TODO(hongkuny): refactor XLNetConfig and RunConfig.
 class XLNetConfig(object):
   """Configs for XLNet model.
 
@@ -131,7 +131,6 @@ class RunConfig(object):
   def __init__(self,
                is_training,
                use_tpu,
-               use_bfloat16,
                dropout,
                dropout_att,
                init_method='normal',
@@ -141,13 +140,13 @@ def __init__(self,
                reuse_len=None,
                bi_data=False,
                clamp_len=-1,
-               same_length=False):
+               same_length=False,
+               use_cls_mask=True):
     """Initializes RunConfig.
 
     Args:
       is_training: bool, whether in training mode.
       use_tpu: bool, whether TPUs are used.
-      use_bfloat16: bool, use bfloat16 instead of float32.
       dropout: float, dropout rate.
       dropout_att: float, dropout rate on attention probabilities.
       init_method: str, the initialization scheme, either "normal" or "uniform".
@@ -164,6 +163,7 @@ def __init__(self,
         -1 means no clamping.
       same_length: bool, whether to use the same attention length
                    for each token.
+      use_cls_mask: bool, whether to introduce cls mask.
     """
 
     self.init_method = init_method
@@ -173,9 +173,9 @@ def __init__(self,
     self.dropout = dropout
     self.dropout_att = dropout_att
     self.use_tpu = use_tpu
-    self.use_bfloat16 = use_bfloat16
     self.mem_len = mem_len
     self.reuse_len = reuse_len
     self.bi_data = bi_data
     self.clamp_len = clamp_len
     self.same_length = same_length
+    self.use_cls_mask = use_cls_mask