Internal change

PiperOrigin-RevId: 327919609

official/nlp/configs/encoders.py

@@ -28,7 +28,6 @@ from official.modeling import hyperparams
 from official.modeling import tf_utils
 from official.nlp.modeling import layers
 from official.nlp.modeling import networks
-from official.nlp.projects.mobilebert import modeling
 
 
 @dataclasses.dataclass
@@ -113,7 +112,7 @@ class EncoderConfig(hyperparams.OneOfConfig):
 
 ENCODER_CLS = {
     "bert": networks.TransformerEncoder,
-    "mobilebert": modeling.MobileBERTEncoder,
+    "mobilebert": networks.MobileBERTEncoder,
 }
 
 

official/nlp/modeling/networks/README.md

@@ -12,10 +12,14 @@ transformer layers and pooling layer.
 
 * [`AlbertTransformerEncoder`](albert_transformer_encoder.py) implements a
 Transformer-encoder described in the paper ["ALBERT: A Lite BERT for
-Self-supervised Learning of Language Representations]
+Self-supervised Learning of Language Representations"]
 (https://arxiv.org/abs/1909.11942). Compared with [BERT](https://arxiv.org/abs/1810.04805), ALBERT refactorizes embedding parameters
 into two smaller matrices and shares parameters across layers.
 
+* [`MobileBERTEncoder`](mobile_bert_encoder.py) implements the
+MobileBERT network described in the paper ["MobileBERT: a Compact Task-Agnostic
+BERT for Resource-Limited Devices"](https://arxiv.org/abs/2004.02984).
+
 * [`Classification`](classification.py) contains a single hidden layer, and is
 intended for use as a classification or regression (if number of classes is set
 to 1) head.

official/nlp/modeling/networks/__init__.py

@@ -16,5 +16,6 @@
 from official.nlp.modeling.networks.albert_transformer_encoder import AlbertTransformerEncoder
 from official.nlp.modeling.networks.classification import Classification
 from official.nlp.modeling.networks.encoder_scaffold import EncoderScaffold
+from official.nlp.modeling.networks.mobile_bert_encoder import MobileBERTEncoder
 from official.nlp.modeling.networks.span_labeling import SpanLabeling
 from official.nlp.modeling.networks.transformer_encoder import TransformerEncoder

official/nlp/modeling/networks/mobile_bert_encoder_test.py

+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+from absl.testing import parameterized
+
+import numpy as np
+import tensorflow as tf
+from official.nlp.modeling import models
+from official.nlp.modeling.networks import mobile_bert_encoder
+from official.nlp.projects.mobilebert import utils
+
+
+class ModelingTest(parameterized.TestCase, tf.test.TestCase):
+
+  def test_embedding_layer_with_token_type(self):
+    layer = mobile_bert_encoder.MobileBertEmbedding(10, 8, 2, 16)
+    input_seq = tf.Variable([[2, 3, 4, 5]])
+    token_type = tf.Variable([[0, 1, 1, 1]])
+    output = layer(input_seq, token_type)
+    output_shape = output.shape.as_list()
+    expected_shape = [1, 4, 16]
+    self.assertListEqual(output_shape, expected_shape, msg=None)
+
+  def test_embedding_layer_without_token_type(self):
+    layer = mobile_bert_encoder.MobileBertEmbedding(10, 8, 2, 16)
+    input_seq = tf.Variable([[2, 3, 4, 5]])
+    output = layer(input_seq)
+    output_shape = output.shape.as_list()
+    expected_shape = [1, 4, 16]
+    self.assertListEqual(output_shape, expected_shape, msg=None)
+
+  def test_no_norm(self):
+    layer = mobile_bert_encoder.NoNorm()
+    feature = tf.random.normal([2, 3, 4])
+    output = layer(feature)
+    output_shape = output.shape.as_list()
+    expected_shape = [2, 3, 4]
+    self.assertListEqual(output_shape, expected_shape, msg=None)
+
+  @parameterized.named_parameters(
+      ('with_kq_shared_bottleneck', False),
+      ('without_kq_shared_bottleneck', True))
+  def test_transfomer_kq_shared_bottleneck(self, is_kq_shared):
+    feature = tf.random.uniform([2, 3, 512])
+    layer = mobile_bert_encoder.TransformerLayer(
+        key_query_shared_bottleneck=is_kq_shared)
+    output = layer(feature)
+    output_shape = output.shape.as_list()
+    expected_shape = [2, 3, 512]
+    self.assertListEqual(output_shape, expected_shape, msg=None)
+
+  def test_transfomer_with_mask(self):
+    feature = tf.random.uniform([2, 3, 512])
+    input_mask = [[[0., 0., 1.],
+                   [0., 0., 1.],
+                   [0., 0., 1.]],
+                  [[0., 1., 1.],
+                   [0., 1., 1.],
+                   [0., 1., 1.]]]
+    input_mask = np.asarray(input_mask)
+    layer = mobile_bert_encoder.TransformerLayer()
+    output = layer(feature, input_mask)
+    output_shape = output.shape.as_list()
+    expected_shape = [2, 3, 512]
+    self.assertListEqual(output_shape, expected_shape, msg=None)
+
+  def test_transfomer_return_attention_score(self):
+    sequence_length = 5
+    num_attention_heads = 8
+    feature = tf.random.uniform([2, sequence_length, 512])
+    layer = mobile_bert_encoder.TransformerLayer(
+        num_attention_heads=num_attention_heads)
+    _, attention_score = layer(feature, return_attention_scores=True)
+    expected_shape = [2, num_attention_heads, sequence_length, sequence_length]
+    self.assertListEqual(attention_score.shape.as_list(), expected_shape,
+                         msg=None)
+
+  @parameterized.named_parameters(
+      ('default_setting', 'relu', True, 'no_norm', False),
+      ('gelu', 'gelu', True, 'no_norm', False),
+      ('kq_not_shared', 'relu', False, 'no_norm', False),
+      ('layer_norm', 'relu', True, 'layer_norm', False),
+      ('use_pooler', 'relu', True, 'no_norm', True),
+      ('with_pooler_layer', 'relu', True, 'layer_norm', False))
+  def test_mobilebert_encoder(self, act_fn, kq_shared_bottleneck,
+                              normalization_type, use_pooler):
+    hidden_size = 32
+    sequence_length = 16
+    num_blocks = 3
+    test_network = mobile_bert_encoder.MobileBERTEncoder(
+        word_vocab_size=100,
+        hidden_size=hidden_size,
+        num_blocks=num_blocks,
+        intermediate_act_fn=act_fn,
+        key_query_shared_bottleneck=kq_shared_bottleneck,
+        normalization_type=normalization_type,
+        classifier_activation=use_pooler)
+
+    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])
+
+    self.assertIsInstance(test_network.transformer_layers, list)
+    self.assertLen(test_network.transformer_layers, num_blocks)
+
+    layer_output_shape = [None, sequence_length, hidden_size]
+    self.assertAllEqual(layer_output.shape.as_list(), layer_output_shape)
+    pooler_output_shape = [None, hidden_size]
+    self.assertAllEqual(pooler_output.shape.as_list(), pooler_output_shape)
+    self.assertAllEqual(tf.float32, layer_output.dtype)
+
+  def test_mobilebert_encoder_return_all_layer_output(self):
+    hidden_size = 32
+    sequence_length = 16
+    num_blocks = 3
+    test_network = mobile_bert_encoder.MobileBERTEncoder(
+        word_vocab_size=100,
+        hidden_size=hidden_size,
+        num_blocks=num_blocks,
+        return_all_layers=True)
+
+    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])
+
+    self.assertIsInstance(all_layer_output, list)
+    self.assertLen(all_layer_output, num_blocks + 1)
+
+  def test_mobilebert_encoder_invocation(self):
+    vocab_size = 100
+    hidden_size = 32
+    sequence_length = 16
+    num_blocks = 3
+    test_network = mobile_bert_encoder.MobileBERTEncoder(
+        word_vocab_size=vocab_size,
+        hidden_size=hidden_size,
+        num_blocks=num_blocks,
+        return_all_layers=False)
+
+    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])
+
+    input_seq = utils.generate_fake_input(batch_size=1,
+                                          seq_len=sequence_length,
+                                          vocab_size=vocab_size)
+    input_mask = utils.generate_fake_input(batch_size=1,
+                                           seq_len=sequence_length,
+                                           vocab_size=2)
+    token_type = utils.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])
+
+    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)
+
+  def test_mobilebert_encoder_invocation_with_attention_score(self):
+    vocab_size = 100
+    hidden_size = 32
+    sequence_length = 16
+    num_blocks = 3
+    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)
+
+    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])
+
+    input_seq = utils.generate_fake_input(batch_size=1,
+                                          seq_len=sequence_length,
+                                          vocab_size=vocab_size)
+    input_mask = utils.generate_fake_input(batch_size=1,
+                                           seq_len=sequence_length,
+                                           vocab_size=2)
+    token_type = utils.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)
+
+  @parameterized.named_parameters(
+      ('sequence_classification', models.BertClassifier, [None, 5]),
+      ('token_classification', models.BertTokenClassifier, [None, 16, 5]))
+  def test_mobilebert_encoder_for_downstream_task(self, task, prediction_shape):
+    hidden_size = 32
+    sequence_length = 16
+    mobilebert_encoder = mobile_bert_encoder.MobileBERTEncoder(
+        word_vocab_size=100, hidden_size=hidden_size)
+    num_classes = 5
+    classifier = task(network=mobilebert_encoder,
+                      num_classes=num_classes)
+
+    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)
+    prediction = classifier([word_ids, mask, type_ids])
+    self.assertAllEqual(prediction.shape.as_list(), prediction_shape)
+
+if __name__ == '__main__':
+  tf.test.main()