official/nlp/modeling/layers/mobile_bert_layers_test.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from official.nlp.modeling.layers import mobile_bert_layers
from official.nlp.modeling.networks import mobile_bert_encoder
def generate_fake_input(batch_size=1, seq_len=5, vocab_size=10000, seed=0):
"""Generate consistent fake integer input sequences."""
np.random.seed(seed)
fake_input = []
for _ in range(batch_size):
fake_input.append([])
for _ in range(seq_len):
fake_input[-1].append(np.random.randint(0, vocab_size))
fake_input = np.asarray(fake_input)
return fake_input
class MobileBertEncoderTest(parameterized.TestCase, tf.test.TestCase):
def test_embedding_layer_with_token_type(self):
layer = mobile_bert_layers.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_layers.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_embedding_layer_get_config(self):
layer = mobile_bert_layers.MobileBertEmbedding(
word_vocab_size=16,
word_embed_size=32,
type_vocab_size=4,
output_embed_size=32,
max_sequence_length=32,
normalization_type='layer_norm',
initializer=tf_keras.initializers.TruncatedNormal(stddev=0.01),
dropout_rate=0.5)
layer_config = layer.get_config()
new_layer = mobile_bert_layers.MobileBertEmbedding.from_config(layer_config)
self.assertEqual(layer_config, new_layer.get_config())
def test_no_norm(self):
layer = mobile_bert_layers.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_layers.MobileBertTransformer(
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_layers.MobileBertTransformer()
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_layers.MobileBertTransformer(
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)
def test_transformer_get_config(self):
layer = mobile_bert_layers.MobileBertTransformer(
hidden_size=32,
num_attention_heads=2,
intermediate_size=48,
intermediate_act_fn='gelu',
hidden_dropout_prob=0.5,
attention_probs_dropout_prob=0.4,
intra_bottleneck_size=64,
use_bottleneck_attention=True,
key_query_shared_bottleneck=False,
num_feedforward_networks=2,
normalization_type='layer_norm',
initializer=tf_keras.initializers.TruncatedNormal(stddev=0.01),
name='block')
layer_config = layer.get_config()
new_layer = mobile_bert_layers.MobileBertTransformer.from_config(
layer_config)
self.assertEqual(layer_config, new_layer.get_config())
class MobileBertMaskedLMTest(tf.test.TestCase):
def create_layer(self,
vocab_size,
hidden_size,
embedding_width,
output='predictions',
xformer_stack=None):
# First, create a transformer stack that we can use to get the LM's
# vocabulary weight.
if xformer_stack is None:
xformer_stack = mobile_bert_encoder.MobileBERTEncoder(
word_vocab_size=vocab_size,
num_blocks=1,
hidden_size=hidden_size,
num_attention_heads=4,
word_embed_size=embedding_width)
# Create a maskedLM from the transformer stack.
test_layer = mobile_bert_layers.MobileBertMaskedLM(
embedding_table=xformer_stack.get_embedding_table(), output=output)
return test_layer
def test_layer_creation(self):
vocab_size = 100
sequence_length = 32
hidden_size = 64
embedding_width = 32
num_predictions = 21
test_layer = self.create_layer(
vocab_size=vocab_size,
hidden_size=hidden_size,
embedding_width=embedding_width)
# Make sure that the output tensor of the masked LM is the right shape.
lm_input_tensor = tf_keras.Input(shape=(sequence_length, hidden_size))
masked_positions = tf_keras.Input(shape=(num_predictions,), dtype=tf.int32)
output = test_layer(lm_input_tensor, masked_positions=masked_positions)
expected_output_shape = [None, num_predictions, vocab_size]
self.assertEqual(expected_output_shape, output.shape.as_list())
def test_layer_invocation_with_external_logits(self):
vocab_size = 100
sequence_length = 32
hidden_size = 64
embedding_width = 32
num_predictions = 21
xformer_stack = mobile_bert_encoder.MobileBERTEncoder(
word_vocab_size=vocab_size,
num_blocks=1,
hidden_size=hidden_size,
num_attention_heads=4,
word_embed_size=embedding_width)
test_layer = self.create_layer(
vocab_size=vocab_size,
hidden_size=hidden_size,
embedding_width=embedding_width,
xformer_stack=xformer_stack,
output='predictions')
logit_layer = self.create_layer(
vocab_size=vocab_size,
hidden_size=hidden_size,
embedding_width=embedding_width,
xformer_stack=xformer_stack,
output='logits')
# Create a model from the masked LM layer.
lm_input_tensor = tf_keras.Input(shape=(sequence_length, hidden_size))
masked_positions = tf_keras.Input(shape=(num_predictions,), dtype=tf.int32)
output = test_layer(lm_input_tensor, masked_positions)
logit_output = logit_layer(lm_input_tensor, masked_positions)
logit_output = tf_keras.layers.Activation(tf.nn.log_softmax)(logit_output)
logit_layer.set_weights(test_layer.get_weights())
model = tf_keras.Model([lm_input_tensor, masked_positions], output)
logits_model = tf_keras.Model(([lm_input_tensor, masked_positions]),
logit_output)
# Invoke the masked LM on some fake data to make sure there are no runtime
# errors in the code.
batch_size = 3
lm_input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, hidden_size))
masked_position_data = np.random.randint(
sequence_length, size=(batch_size, num_predictions))
# ref_outputs = model.predict([lm_input_data, masked_position_data])
# outputs = logits_model.predict([lm_input_data, masked_position_data])
ref_outputs = model([lm_input_data, masked_position_data])
outputs = logits_model([lm_input_data, masked_position_data])
# Ensure that the tensor shapes are correct.
expected_output_shape = (batch_size, num_predictions, vocab_size)
self.assertEqual(expected_output_shape, ref_outputs.shape)
self.assertEqual(expected_output_shape, outputs.shape)
self.assertAllClose(ref_outputs, outputs)
def test_layer_invocation(self):
vocab_size = 100
sequence_length = 32
hidden_size = 64
embedding_width = 32
num_predictions = 21
test_layer = self.create_layer(
vocab_size=vocab_size,
hidden_size=hidden_size,
embedding_width=embedding_width)
# Create a model from the masked LM layer.
lm_input_tensor = tf_keras.Input(shape=(sequence_length, hidden_size))
masked_positions = tf_keras.Input(shape=(num_predictions,), dtype=tf.int32)
output = test_layer(lm_input_tensor, masked_positions)
model = tf_keras.Model([lm_input_tensor, masked_positions], output)
# Invoke the masked LM on some fake data to make sure there are no runtime
# errors in the code.
batch_size = 3
lm_input_data = 10 * np.random.random_sample(
(batch_size, sequence_length, hidden_size))
masked_position_data = np.random.randint(
2, size=(batch_size, num_predictions))
_ = model.predict([lm_input_data, masked_position_data])
def test_unknown_output_type_fails(self):
with self.assertRaisesRegex(ValueError, 'Unknown `output` value "bad".*'):
_ = self.create_layer(
vocab_size=8, hidden_size=8, embedding_width=4, output='bad')
def test_hidden_size_smaller_than_embedding_width(self):
hidden_size = 8
sequence_length = 32
num_predictions = 20
with self.assertRaisesRegex(
ValueError, 'hidden size 8 cannot be smaller than embedding width 16.'):
test_layer = self.create_layer(
vocab_size=8, hidden_size=8, embedding_width=16)
lm_input_tensor = tf_keras.Input(shape=(sequence_length, hidden_size))
masked_positions = tf_keras.Input(
shape=(num_predictions,), dtype=tf.int32)
_ = test_layer(lm_input_tensor, masked_positions)
if __name__ == '__main__':
tf.test.main()