Adds deprecation warning for old bert code and deprecated pattern: pack_inputs/unpack_inputs

PiperOrigin-RevId: 290111892

official/modeling/tf_utils.py

@@ -21,9 +21,16 @@ from __future__ import print_function
 import six
 import tensorflow as tf
 
+from tensorflow.python.util import deprecation
 from official.modeling import activations
 
 
+@deprecation.deprecated(
+    None,
+    "tf.keras.layers.Layer supports multiple positional args and kwargs as "
+    "input tensors. pack/unpack inputs to override __call__ is no longer "
+    "needed."
+)
 def pack_inputs(inputs):
   """Pack a list of `inputs` tensors to a tuple.
 
@@ -44,6 +51,12 @@ def pack_inputs(inputs):
   return tuple(outputs)
 
 
+@deprecation.deprecated(
+    None,
+    "tf.keras.layers.Layer supports multiple positional args and kwargs as "
+    "input tensors. pack/unpack inputs to override __call__ is no longer "
+    "needed."
+)
 def unpack_inputs(inputs):
   """unpack a tuple of `inputs` tensors to a tuple.
 

official/nlp/bert_modeling.py

@@ -24,6 +24,7 @@ import math
 import six
 import tensorflow as tf
 
+from tensorflow.python.util import deprecation
 from official.modeling import tf_utils
 
 
@@ -145,6 +146,7 @@ class AlbertConfig(BertConfig):
     return config
 
 
+@deprecation.deprecated(None, "The function should not be used any more.")
 def get_bert_model(input_word_ids,
                    input_mask,
                    input_type_ids,
@@ -183,6 +185,8 @@ class BertModel(tf.keras.layers.Layer):
   ```
   """
 
+  @deprecation.deprecated(
+      None, "Please use `nlp.modeling.networks.TransformerEncoder` instead.")
   def __init__(self, config, float_type=tf.float32, **kwargs):
     super(BertModel, self).__init__(**kwargs)
     self.config = (
@@ -240,6 +244,7 @@ class BertModel(tf.keras.layers.Layer):
     Args:
       inputs: packed input tensors.
       mode: string, `bert` or `encoder`.
+
     Returns:
       Output tensor of the last layer for BERT training (mode=`bert`) which
       is a float Tensor of shape [batch_size, seq_length, hidden_size] or
@@ -358,8 +363,8 @@ class EmbeddingPostprocessor(tf.keras.layers.Layer):
 
     self.output_layer_norm = tf.keras.layers.LayerNormalization(
         name="layer_norm", axis=-1, epsilon=1e-12, dtype=tf.float32)
-    self.output_dropout = tf.keras.layers.Dropout(rate=self.dropout_prob,
-                                                  dtype=tf.float32)
+    self.output_dropout = tf.keras.layers.Dropout(
+        rate=self.dropout_prob, dtype=tf.float32)
     super(EmbeddingPostprocessor, self).build(input_shapes)
 
   def __call__(self, word_embeddings, token_type_ids=None, **kwargs):
@@ -546,8 +551,8 @@ class Dense3D(tf.keras.layers.Layer):
     use_bias: A bool, whether the layer uses a bias.
     output_projection: A bool, whether the Dense3D layer is used for output
       linear projection.
-    backward_compatible: A bool, whether the variables shape are compatible
-      with checkpoints converted from TF 1.x.
+    backward_compatible: A bool, whether the variables shape are compatible with
+      checkpoints converted from TF 1.x.
   """
 
   def __init__(self,
@@ -647,7 +652,8 @@ class Dense3D(tf.keras.layers.Layer):
     """
     if self.backward_compatible:
       kernel = tf.keras.backend.reshape(self.kernel, self.kernel_shape)
-      bias = (tf.keras.backend.reshape(self.bias, self.bias_shape)
+      bias = (
+          tf.keras.backend.reshape(self.bias, self.bias_shape)
           if self.use_bias else None)
     else:
       kernel = self.kernel
@@ -784,7 +790,9 @@ class TransformerBlock(tf.keras.layers.Layer):
         rate=self.hidden_dropout_prob)
     self.attention_layer_norm = (
         tf.keras.layers.LayerNormalization(
-            name="self_attention_layer_norm", axis=-1, epsilon=1e-12,
+            name="self_attention_layer_norm",
+            axis=-1,
+            epsilon=1e-12,
             # We do layer norm in float32 for numeric stability.
             dtype=tf.float32))
     self.intermediate_dense = Dense2DProjection(
@@ -909,6 +917,7 @@ class Transformer(tf.keras.layers.Layer):
       inputs: packed inputs.
       return_all_layers: bool, whether to return outputs of all layers inside
         encoders.
+
     Returns:
       Output tensor of the last layer or a list of output tensors.
     """