Remove pack_inputs/unpack_inputs in bert

PiperOrigin-RevId: 291082425

official/nlp/bert_models.py

@@ -72,20 +72,6 @@ class BertPretrainLossAndMetricLayer(tf.keras.layers.Layer):
         'vocab_size': vocab_size,
     }
 
-  def __call__(self,
-               lm_output,
-               sentence_output=None,
-               lm_label_ids=None,
-               lm_label_weights=None,
-               sentence_labels=None,
-               **kwargs):
-    inputs = tf_utils.pack_inputs([
-        lm_output, sentence_output, lm_label_ids, lm_label_weights,
-        sentence_labels
-    ])
-    return super(BertPretrainLossAndMetricLayer,
-                 self).__call__(inputs, **kwargs)
-
   def _add_metrics(self, lm_output, lm_labels, lm_label_weights,
                    lm_example_loss, sentence_output, sentence_labels,
                    next_sentence_loss):
@@ -110,14 +96,10 @@ class BertPretrainLossAndMetricLayer(tf.keras.layers.Layer):
     self.add_metric(
         next_sentence_loss, name='next_sentence_loss', aggregation='mean')
 
-  def call(self, inputs):
+  def call(self, lm_output, sentence_output, lm_label_ids, lm_label_weights,
+           sentence_labels):
     """Implements call() for the layer."""
-    unpacked_inputs = tf_utils.unpack_inputs(inputs)
-    lm_output = unpacked_inputs[0]
-    sentence_output = unpacked_inputs[1]
-    lm_label_ids = unpacked_inputs[2]
-    lm_label_weights = tf.keras.backend.cast(unpacked_inputs[3], tf.float32)
-    sentence_labels = unpacked_inputs[4]
+    lm_label_weights = tf.keras.backend.cast(lm_label_weights, tf.float32)
 
     mask_label_loss = losses.weighted_sparse_categorical_crossentropy_loss(
         labels=lm_label_ids, predictions=lm_output, weights=lm_label_weights)