Make BertPretrainer to accept embedding_table explicitly

official/nlp/bert/bert_models.py

@@ -212,6 +212,7 @@ def pretrain_model(bert_config,
         stddev=bert_config.initializer_range)
   pretrainer_model = models.BertPretrainer(
       network=transformer_encoder,
+      embedding_table=transformer_encoder.get_embedding_table(),
       num_classes=2,  # The next sentence prediction label has two classes.
       num_token_predictions=max_predictions_per_seq,
       initializer=initializer,

official/nlp/modeling/models/bert_pretrainer.py

@@ -39,14 +39,15 @@ class BertPretrainer(tf.keras.Model):
 
   Arguments:
     network: A transformer network. This network should output a sequence output
-      and a classification output. Furthermore, it should expose its embedding
-      table via a "get_embedding_table" method.
+      and a classification output.
     num_classes: Number of classes to predict from the classification network.
     num_token_predictions: Number of tokens to predict from the masked LM.
     activation: The activation (if any) to use in the masked LM and
       classification networks. If None, no activation will be used.
     initializer: The initializer (if any) to use in the masked LM and
       classification networks. Defaults to a Glorot uniform initializer.
+    embedding_table: Embedding table of a network. If None, the
+      "network.get_embedding_table()" is used.
     output: The output style for this network. Can be either 'logits' or
       'predictions'.
   """
@@ -58,6 +59,7 @@ class BertPretrainer(tf.keras.Model):
                activation=None,
                initializer='glorot_uniform',
                output='logits',
+               embedding_table=None,
                **kwargs):
     self._self_setattr_tracking = False
     self._config = {
@@ -100,6 +102,7 @@ class BertPretrainer(tf.keras.Model):
         num_predictions=num_token_predictions,
         input_width=sequence_output.shape[-1],
         source_network=network,
+        embedding_table=embedding_table,
         activation=activation,
         initializer=initializer,
         output=output,

official/nlp/modeling/networks/masked_lm.py

@@ -37,6 +37,8 @@ class MaskedLM(network.Network):
     num_predictions: The number of predictions to make per sequence.
     source_network: The network with the embedding layer to use for the
       embedding layer.
+    embedding_table: The embedding table of a source network, If None, the
+      `source_network.get_embedding_table()` method is used.
     activation: The activation, if any, for the dense layer in this network.
     initializer: The intializer for the dense layer in this network. Defaults to
       a Glorot uniform initializer.
@@ -48,12 +50,16 @@ class MaskedLM(network.Network):
                input_width,
                num_predictions,
                source_network,
+               embedding_table=None,
                activation=None,
                initializer='glorot_uniform',
                output='logits',
                **kwargs):
 
-    embedding_table = source_network.get_embedding_table()
+
+    if embedding_table is None:
+      embedding_table = source_network.get_embedding_table()
+
     vocab_size, hidden_size = embedding_table.shape
 
     sequence_data = tf.keras.layers.Input(