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keras_nlp/models/distil_bert/distil_bert_masked_lm_preprocessor_test.py
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| # Copyright 2022 The KerasNLP Authors | ||
| # | ||
| # 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 | ||
| # | ||
| # https://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. | ||
|
|
||
| """Tests for DistilBERT masked language model preprocessor layer.""" | ||
|
|
||
| import tensorflow as tf | ||
|
|
||
| from keras_nlp.backend import keras | ||
| from keras_nlp.models.distil_bert.distil_bert_masked_lm_preprocessor import ( | ||
| DistilBertMaskedLMPreprocessor, | ||
| ) | ||
| from keras_nlp.models.distil_bert.distil_bert_tokenizer import ( | ||
| DistilBertTokenizer, | ||
| ) | ||
| from keras_nlp.tests.test_case import TestCase | ||
|
|
||
|
|
||
| class DistilBertMaskedLMPreprocessorTest(TestCase): | ||
| def setUp(self): | ||
| self.vocab = ["[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]"] | ||
| self.vocab += ["THE", "QUICK", "BROWN", "FOX"] | ||
| self.vocab += ["the", "quick", "brown", "fox"] | ||
|
|
||
| self.preprocessor = DistilBertMaskedLMPreprocessor( | ||
| tokenizer=DistilBertTokenizer( | ||
| vocabulary=self.vocab, | ||
| ), | ||
| # Simplify our testing by masking every available token. | ||
| mask_selection_rate=1.0, | ||
| mask_token_rate=1.0, | ||
| random_token_rate=0.0, | ||
| mask_selection_length=5, | ||
| sequence_length=8, | ||
| ) | ||
|
|
||
| def test_preprocess_strings(self): | ||
| input_data = " THE QUICK BROWN FOX." | ||
|
|
||
| x, y, sw = self.preprocessor(input_data) | ||
| self.assertAllEqual(x["token_ids"], [2, 4, 4, 4, 4, 4, 3, 0]) | ||
| self.assertAllEqual(x["padding_mask"], [1, 1, 1, 1, 1, 1, 1, 0]) | ||
| self.assertAllEqual(x["mask_positions"], [1, 2, 3, 4, 5]) | ||
| self.assertAllEqual(y, [5, 6, 7, 8, 1]) | ||
| self.assertAllEqual(sw, [1.0, 1.0, 1.0, 1.0, 1.0]) | ||
|
|
||
| def test_preprocess_list_of_strings(self): | ||
| input_data = [" THE QUICK BROWN FOX."] * 4 | ||
|
|
||
| x, y, sw = self.preprocessor(input_data) | ||
| self.assertAllEqual(x["token_ids"], [[2, 4, 4, 4, 4, 4, 3, 0]] * 4) | ||
| self.assertAllEqual(x["padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4) | ||
| self.assertAllEqual(x["mask_positions"], [[1, 2, 3, 4, 5]] * 4) | ||
| self.assertAllEqual(y, [[5, 6, 7, 8, 1]] * 4) | ||
| self.assertAllEqual(sw, [[1.0, 1.0, 1.0, 1.0, 1.0]] * 4) | ||
|
|
||
| def test_preprocess_dataset(self): | ||
| sentences = tf.constant([" THE QUICK BROWN FOX."] * 4) | ||
| ds = tf.data.Dataset.from_tensor_slices(sentences) | ||
| ds = ds.map(self.preprocessor) | ||
| x, y, sw = ds.batch(4).take(1).get_single_element() | ||
| self.assertAllEqual(x["token_ids"], [[2, 4, 4, 4, 4, 4, 3, 0]] * 4) | ||
| self.assertAllEqual(x["padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4) | ||
| self.assertAllEqual(x["mask_positions"], [[1, 2, 3, 4, 5]] * 4) | ||
| self.assertAllEqual(y, [[5, 6, 7, 8, 1]] * 4) | ||
| self.assertAllEqual(sw, [[1.0, 1.0, 1.0, 1.0, 1.0]] * 4) | ||
|
|
||
| def test_mask_multiple_sentences(self): | ||
| sentence_one = tf.constant(" THE QUICK") | ||
| sentence_two = tf.constant(" BROWN FOX.") | ||
|
|
||
| x, y, sw = self.preprocessor((sentence_one, sentence_two)) | ||
| self.assertAllEqual(x["token_ids"], [2, 4, 4, 3, 4, 4, 4, 3]) | ||
| self.assertAllEqual(x["padding_mask"], [1, 1, 1, 1, 1, 1, 1, 1]) | ||
| self.assertAllEqual(x["mask_positions"], [1, 2, 4, 5, 6]) | ||
| self.assertAllEqual(y, [5, 6, 7, 8, 1]) | ||
| self.assertAllEqual(sw, [1.0, 1.0, 1.0, 1.0, 1.0]) | ||
|
|
||
| def test_no_masking_zero_rate(self): | ||
| no_mask_preprocessor = DistilBertMaskedLMPreprocessor( | ||
| self.preprocessor.tokenizer, | ||
| mask_selection_rate=0.0, | ||
| mask_selection_length=5, | ||
| sequence_length=8, | ||
| ) | ||
| input_data = " THE QUICK BROWN FOX." | ||
|
|
||
| x, y, sw = no_mask_preprocessor(input_data) | ||
| self.assertAllEqual(x["token_ids"], [2, 5, 6, 7, 8, 1, 3, 0]) | ||
| self.assertAllEqual(x["padding_mask"], [1, 1, 1, 1, 1, 1, 1, 0]) | ||
| self.assertAllEqual(x["mask_positions"], [0, 0, 0, 0, 0]) | ||
| self.assertAllEqual(y, [0, 0, 0, 0, 0]) | ||
| self.assertAllEqual(sw, [0.0, 0.0, 0.0, 0.0, 0.0]) | ||
|
|
||
| def test_serialization(self): | ||
| config = keras.saving.serialize_keras_object(self.preprocessor) | ||
| new_preprocessor = keras.saving.deserialize_keras_object(config) | ||
| self.assertEqual( | ||
| new_preprocessor.get_config(), | ||
| self.preprocessor.get_config(), | ||
| ) | ||
| # Copyright 2022 The KerasNLP Authors | ||
| # | ||
| # 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 | ||
| # | ||
| # https://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. | ||
|
|
||
| """Tests for DistilBERT masked language model preprocessor layer.""" | ||
|
|
||
| import tensorflow as tf | ||
|
|
||
| from keras_nlp.backend import keras | ||
| from keras_nlp.models.distil_bert.distil_bert_masked_lm_preprocessor import ( | ||
| DistilBertMaskedLMPreprocessor, | ||
| ) | ||
| from keras_nlp.models.distil_bert.distil_bert_tokenizer import ( | ||
| DistilBertTokenizer, | ||
| ) | ||
| from keras_nlp.tests.test_case import TestCase | ||
|
|
||
|
|
||
| class DistilBertMaskedLMPreprocessorTest(TestCase): | ||
| def setUp(self): | ||
| self.vocab = ["[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]"] | ||
| self.vocab += ["THE", "QUICK", "BROWN", "FOX"] | ||
| self.vocab += ["the", "quick", "brown", "fox"] | ||
|
|
||
| self.preprocessor = DistilBertMaskedLMPreprocessor( | ||
| tokenizer=DistilBertTokenizer( | ||
| vocabulary=self.vocab, | ||
| ), | ||
| # Simplify our testing by masking every available token. | ||
| mask_selection_rate=1.0, | ||
| mask_token_rate=1.0, | ||
| random_token_rate=0.0, | ||
| mask_selection_length=5, | ||
| sequence_length=8, | ||
| ) | ||
|
|
||
| def test_preprocess_strings(self): | ||
| input_data = " THE QUICK BROWN FOX." | ||
|
|
||
| x, y, sw = self.preprocessor(input_data) | ||
| self.assertAllEqual(x["token_ids"], [2, 4, 4, 4, 4, 4, 3, 0]) | ||
| self.assertAllEqual(x["padding_mask"], [1, 1, 1, 1, 1, 1, 1, 0]) | ||
| self.assertAllEqual(x["mask_positions"], [1, 2, 3, 4, 5]) | ||
| self.assertAllEqual(y, [5, 6, 7, 8, 1]) | ||
| self.assertAllEqual(sw, [1.0, 1.0, 1.0, 1.0, 1.0]) | ||
|
|
||
| def test_preprocess_list_of_strings(self): | ||
| input_data = [" THE QUICK BROWN FOX."] * 4 | ||
|
|
||
| x, y, sw = self.preprocessor(input_data) | ||
| self.assertAllEqual(x["token_ids"], [[2, 4, 4, 4, 4, 4, 3, 0]] * 4) | ||
| self.assertAllEqual(x["padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4) | ||
| self.assertAllEqual(x["mask_positions"], [[1, 2, 3, 4, 5]] * 4) | ||
| self.assertAllEqual(y, [[5, 6, 7, 8, 1]] * 4) | ||
| self.assertAllEqual(sw, [[1.0, 1.0, 1.0, 1.0, 1.0]] * 4) | ||
|
|
||
| def test_preprocess_dataset(self): | ||
| sentences = tf.constant([" THE QUICK BROWN FOX."] * 4) | ||
| ds = tf.data.Dataset.from_tensor_slices(sentences) | ||
| ds = ds.map(self.preprocessor) | ||
| x, y, sw = ds.batch(4).take(1).get_single_element() | ||
| self.assertAllEqual(x["token_ids"], [[2, 4, 4, 4, 4, 4, 3, 0]] * 4) | ||
| self.assertAllEqual(x["padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4) | ||
| self.assertAllEqual(x["mask_positions"], [[1, 2, 3, 4, 5]] * 4) | ||
| self.assertAllEqual(y, [[5, 6, 7, 8, 1]] * 4) | ||
| self.assertAllEqual(sw, [[1.0, 1.0, 1.0, 1.0, 1.0]] * 4) | ||
|
|
||
| def test_mask_multiple_sentences(self): | ||
| sentence_one = tf.constant(" THE QUICK") | ||
| sentence_two = tf.constant(" BROWN FOX.") | ||
|
|
||
| x, y, sw = self.preprocessor((sentence_one, sentence_two)) | ||
| self.assertAllEqual(x["token_ids"], [2, 4, 4, 3, 4, 4, 4, 3]) | ||
| self.assertAllEqual(x["padding_mask"], [1, 1, 1, 1, 1, 1, 1, 1]) | ||
| self.assertAllEqual(x["mask_positions"], [1, 2, 4, 5, 6]) | ||
| self.assertAllEqual(y, [5, 6, 7, 8, 1]) | ||
| self.assertAllEqual(sw, [1.0, 1.0, 1.0, 1.0, 1.0]) | ||
|
|
||
| def test_no_masking_zero_rate(self): | ||
| no_mask_preprocessor = DistilBertMaskedLMPreprocessor( | ||
| self.preprocessor.tokenizer, | ||
| mask_selection_rate=0.0, | ||
| mask_selection_length=5, | ||
| sequence_length=8, | ||
| ) | ||
| input_data = " THE QUICK BROWN FOX." | ||
|
|
||
| x, y, sw = no_mask_preprocessor(input_data) | ||
| self.assertAllEqual(x["token_ids"], [2, 5, 6, 7, 8, 1, 3, 0]) | ||
| self.assertAllEqual(x["padding_mask"], [1, 1, 1, 1, 1, 1, 1, 0]) | ||
| self.assertAllEqual(x["mask_positions"], [0, 0, 0, 0, 0]) | ||
| self.assertAllEqual(y, [0, 0, 0, 0, 0]) | ||
| self.assertAllEqual(sw, [0.0, 0.0, 0.0, 0.0, 0.0]) | ||
|
|
||
| def test_serialization(self): | ||
| config = keras.saving.serialize_keras_object(self.preprocessor) | ||
| new_preprocessor = keras.saving.deserialize_keras_object(config) | ||
| self.assertEqual( | ||
| new_preprocessor.get_config(), | ||
| self.preprocessor.get_config(), | ||
| ) |
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