Internal change

PiperOrigin-RevId: 330982547

official/nlp/modeling/networks/mobile_bert_encoder.py

@@ -406,8 +406,6 @@ class MobileBERTEncoder(tf.keras.Model):
                num_feedforward_networks=4,
                normalization_type='no_norm',
                classifier_activation=False,
-               return_all_layers=False,
-               return_attention_score=False,
                **kwargs):
     """Class initialization.
 
@@ -438,8 +436,6 @@ class MobileBERTEncoder(tf.keras.Model):
         MobileBERT paper. 'layer_norm' is used for the teacher model.
       classifier_activation: If using the tanh activation for the final
         representation of the [CLS] token in fine-tuning.
-      return_all_layers: If return all layer outputs.
-      return_attention_score: If return attention scores for each layer.
       **kwargs: Other keyworded and arguments.
     """
     self._self_setattr_tracking = False
@@ -513,12 +509,11 @@ class MobileBERTEncoder(tf.keras.Model):
     else:
       self._pooler_layer = None
 
-    if return_all_layers:
-      outputs = [all_layer_outputs, first_token]
-    else:
-      outputs = [prev_output, first_token]
-    if return_attention_score:
-      outputs.append(all_attention_scores)
+    outputs = dict(
+        sequence_output=prev_output,
+        pooled_output=first_token,
+        encoder_outputs=all_layer_outputs,
+        attention_scores=all_attention_scores)
 
     super(MobileBERTEncoder, self).__init__(
         inputs=self.inputs, outputs=outputs, **kwargs)

official/nlp/modeling/networks/mobile_bert_encoder_test.py

@@ -32,7 +32,7 @@ def generate_fake_input(batch_size=1, seq_len=5, vocab_size=10000, seed=0):
   return fake_input
 
 
-class ModelingTest(parameterized.TestCase, tf.test.TestCase):
+class MobileBertEncoderTest(parameterized.TestCase, tf.test.TestCase):
 
   def test_embedding_layer_with_token_type(self):
     layer = mobile_bert_encoder.MobileBertEmbedding(10, 8, 2, 16)
@@ -116,7 +116,9 @@ class ModelingTest(parameterized.TestCase, tf.test.TestCase):
     word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
     mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
     type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
-    layer_output, pooler_output = test_network([word_ids, mask, type_ids])
+    outputs = test_network([word_ids, mask, type_ids])
+    layer_output, pooler_output = outputs['sequence_output'], outputs[
+        'pooled_output']
 
     self.assertIsInstance(test_network.transformer_layers, list)
     self.assertLen(test_network.transformer_layers, num_blocks)
@@ -134,13 +136,13 @@ class ModelingTest(parameterized.TestCase, tf.test.TestCase):
     test_network = mobile_bert_encoder.MobileBERTEncoder(
         word_vocab_size=100,
         hidden_size=hidden_size,
-        num_blocks=num_blocks,
-        return_all_layers=True)
+        num_blocks=num_blocks)
 
     word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
     mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
     type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
-    all_layer_output, _ = test_network([word_ids, mask, type_ids])
+    outputs = test_network([word_ids, mask, type_ids])
+    all_layer_output = outputs['encoder_outputs']
 
     self.assertIsInstance(all_layer_output, list)
     self.assertLen(all_layer_output, num_blocks + 1)
@@ -153,16 +155,13 @@ class ModelingTest(parameterized.TestCase, tf.test.TestCase):
     test_network = mobile_bert_encoder.MobileBERTEncoder(
         word_vocab_size=vocab_size,
         hidden_size=hidden_size,
-        num_blocks=num_blocks,
-        return_all_layers=False)
+        num_blocks=num_blocks)
 
     word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
     mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
     type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
-    layer_out_tensor, pooler_out_tensor = test_network(
-        [word_ids, mask, type_ids])
-    model = tf.keras.Model([word_ids, mask, type_ids],
-                           [layer_out_tensor, pooler_out_tensor])
+    outputs = test_network([word_ids, mask, type_ids])
+    model = tf.keras.Model([word_ids, mask, type_ids], outputs)
 
     input_seq = generate_fake_input(
         batch_size=1, seq_len=sequence_length, vocab_size=vocab_size)
@@ -170,13 +169,12 @@ class ModelingTest(parameterized.TestCase, tf.test.TestCase):
         batch_size=1, seq_len=sequence_length, vocab_size=2)
     token_type = generate_fake_input(
         batch_size=1, seq_len=sequence_length, vocab_size=2)
-    layer_output, pooler_output = model.predict(
-        [input_seq, input_mask, token_type])
+    outputs = model.predict([input_seq, input_mask, token_type])
 
-    layer_output_shape = [1, sequence_length, hidden_size]
-    self.assertAllEqual(layer_output.shape, layer_output_shape)
-    pooler_output_shape = [1, hidden_size]
-    self.assertAllEqual(pooler_output.shape, pooler_output_shape)
+    sequence_output_shape = [1, sequence_length, hidden_size]
+    self.assertAllEqual(outputs['sequence_output'].shape, sequence_output_shape)
+    pooled_output_shape = [1, hidden_size]
+    self.assertAllEqual(outputs['pooled_output'].shape, pooled_output_shape)
 
   def test_mobilebert_encoder_invocation_with_attention_score(self):
     vocab_size = 100
@@ -186,18 +184,13 @@ class ModelingTest(parameterized.TestCase, tf.test.TestCase):
     test_network = mobile_bert_encoder.MobileBERTEncoder(
         word_vocab_size=vocab_size,
         hidden_size=hidden_size,
-        num_blocks=num_blocks,
-        return_all_layers=False,
-        return_attention_score=True)
+        num_blocks=num_blocks)
 
     word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
     mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
     type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
-    layer_out_tensor, pooler_out_tensor, attention_out_tensor = test_network(
-        [word_ids, mask, type_ids])
-    model = tf.keras.Model(
-        [word_ids, mask, type_ids],
-        [layer_out_tensor, pooler_out_tensor, attention_out_tensor])
+    outputs = test_network([word_ids, mask, type_ids])
+    model = tf.keras.Model([word_ids, mask, type_ids], outputs)
 
     input_seq = generate_fake_input(
         batch_size=1, seq_len=sequence_length, vocab_size=vocab_size)
@@ -205,9 +198,8 @@ class ModelingTest(parameterized.TestCase, tf.test.TestCase):
         batch_size=1, seq_len=sequence_length, vocab_size=2)
     token_type = generate_fake_input(
         batch_size=1, seq_len=sequence_length, vocab_size=2)
-    _, _, attention_score_output = model.predict(
-        [input_seq, input_mask, token_type])
-    self.assertLen(attention_score_output, num_blocks)
+    outputs = model.predict([input_seq, input_mask, token_type])
+    self.assertLen(outputs['attention_scores'], num_blocks)
 
   @parameterized.named_parameters(
       ('sequence_classification', models.BertClassifier, [None, 5]),