No public description

PiperOrigin-RevId: 566435327

mediapipe/model_maker/python/text/text_classifier/BUILD

@@ -30,10 +30,12 @@ py_library(
     srcs = ["__init__.py"],
     visibility = ["//visibility:public"],
     deps = [
+        ":bert_tokenizer",
         ":dataset",
         ":hyperparameters",
         ":model_options",
         ":model_spec",
+        ":preprocessor",
         ":text_classifier",
         ":text_classifier_options",
     ],
@@ -48,7 +50,10 @@ py_library(
 py_library(
     name = "hyperparameters",
     srcs = ["hyperparameters.py"],
-    deps = ["//mediapipe/model_maker/python/core:hyperparameters"],
+    deps = [
+        ":bert_tokenizer",
+        "//mediapipe/model_maker/python/core:hyperparameters",
+    ],
 )
 
 py_library(
@@ -88,10 +93,26 @@ py_test(
     deps = [":dataset"],
 )
 
+py_library(
+    name = "bert_tokenizer",
+    srcs = ["bert_tokenizer.py"],
+)
+
+py_test(
+    name = "bert_tokenizer_test",
+    srcs = ["bert_tokenizer_test.py"],
+    tags = ["requires-net:external"],
+    deps = [
+        ":bert_tokenizer",
+        ":model_spec",
+    ],
+)
+
 py_library(
     name = "preprocessor",
     srcs = ["preprocessor.py"],
     deps = [
+        ":bert_tokenizer",
         ":dataset",
         "//mediapipe/model_maker/python/core/data:cache_files",
     ],
@@ -102,6 +123,7 @@ py_test(
     srcs = ["preprocessor_test.py"],
     tags = ["requires-net:external"],
     deps = [
+        ":bert_tokenizer",
         ":dataset",
         ":model_spec",
         ":preprocessor",

mediapipe/model_maker/python/text/text_classifier/__init__.py

@@ -13,10 +13,12 @@
 # limitations under the License.
 """MediaPipe Public Python API for Text Classifier."""
 
+from mediapipe.model_maker.python.text.text_classifier import bert_tokenizer
 from mediapipe.model_maker.python.text.text_classifier import dataset
 from mediapipe.model_maker.python.text.text_classifier import hyperparameters
 from mediapipe.model_maker.python.text.text_classifier import model_options
 from mediapipe.model_maker.python.text.text_classifier import model_spec
+from mediapipe.model_maker.python.text.text_classifier import preprocessor
 from mediapipe.model_maker.python.text.text_classifier import text_classifier
 from mediapipe.model_maker.python.text.text_classifier import text_classifier_options
 
@@ -33,12 +35,14 @@ Dataset = dataset.Dataset
 SupportedModels = model_spec.SupportedModels
 TextClassifier = text_classifier.TextClassifier
 TextClassifierOptions = text_classifier_options.TextClassifierOptions
+SupportedBertTokenizers = bert_tokenizer.SupportedBertTokenizers
 
 # Remove duplicated and non-public API
+del bert_tokenizer
 del hyperparameters
 del dataset
 del model_options
 del model_spec
-del preprocessor  # pylint: disable=undefined-variable
+del preprocessor
 del text_classifier
 del text_classifier_options

mediapipe/model_maker/python/text/text_classifier/bert_tokenizer.py

@@ -0,0 +1,118 @@
+# Copyright 2023 The MediaPipe 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
+#
+#     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.
+"""Text classifier BERT tokenizer library."""
+import abc
+import enum
+from typing import Mapping, Sequence
+
+import tensorflow as tf
+import tensorflow_text as tf_text
+
+from official.nlp.tools import tokenization
+
+
+@enum.unique
+class SupportedBertTokenizers(enum.Enum):
+  """Supported preprocessors."""
+
+  FULL_TOKENIZER = "fulltokenizer"
+  FAST_BERT_TOKENIZER = "fastberttokenizer"
+
+
+class BertTokenizer(abc.ABC):
+  """Abstract BertTokenizer class."""
+
+  name: str
+
+  @abc.abstractmethod
+  def __init__(self, vocab_file: str, do_lower_case: bool, seq_len: int):
+    pass
+
+  @abc.abstractmethod
+  def process(self, input_tensor: tf.Tensor) -> Mapping[str, Sequence[int]]:
+    pass
+
+
+class BertFullTokenizer(BertTokenizer):
+  """Tokenizer using the FullTokenizer from tensorflow_models."""
+
+  name = "fulltokenizer"
+
+  def __init__(self, vocab_file: str, do_lower_case: bool, seq_len: int):
+    self._tokenizer = tokenization.FullTokenizer(
+        vocab_file=vocab_file, do_lower_case=do_lower_case
+    )
+    self._seq_len = seq_len
+
+  def process(self, input_tensor: tf.Tensor) -> Mapping[str, Sequence[int]]:
+    tokens = self._tokenizer.tokenize(input_tensor.numpy()[0].decode("utf-8"))
+    tokens = tokens[0 : (self._seq_len - 2)]  # account for [CLS] and [SEP]
+    tokens.insert(0, "[CLS]")
+    tokens.append("[SEP]")
+    input_ids = self._tokenizer.convert_tokens_to_ids(tokens)
+    input_mask = [1] * len(input_ids)
+    while len(input_ids) < self._seq_len:
+      input_ids.append(0)
+      input_mask.append(0)
+    segment_ids = [0] * self._seq_len
+    return {
+        "input_word_ids": input_ids,
+        "input_type_ids": segment_ids,
+        "input_mask": input_mask,
+    }
+
+
+class BertFastTokenizer(BertTokenizer):
+  """Tokenizer using the FastBertTokenizer from tensorflow_text.
+
+  For more information, see:
+  https://www.tensorflow.org/text/api_docs/python/text/FastBertTokenizer
+  """
+
+  name = "fastberttokenizer"
+
+  def __init__(self, vocab_file: str, do_lower_case: bool, seq_len: int):
+    with tf.io.gfile.GFile(vocab_file, "r") as f:
+      vocab = f.read().splitlines()
+    self._tokenizer = tf_text.FastBertTokenizer(
+        vocab=vocab,
+        token_out_type=tf.int32,
+        support_detokenization=False,
+        lower_case_nfd_strip_accents=do_lower_case,
+    )
+    self._seq_len = seq_len
+    self._cls_id = vocab.index("[CLS]")
+    self._sep_id = vocab.index("[SEP]")
+    self._pad_id = vocab.index("[PAD]")
+
+  def process(self, input_tensor: tf.Tensor) -> Mapping[str, Sequence[int]]:
+    input_ids = self._tokenizer.tokenize(input_tensor).flat_values
+    input_ids = input_ids[: (self._seq_len - 2)]
+    input_ids = tf.concat(
+        [
+            tf.constant([self._cls_id]),
+            input_ids,
+            tf.constant([self._sep_id]),
+            tf.fill((self._seq_len,), self._pad_id),
+        ],
+        axis=0,
+    )
+    input_ids = input_ids[: self._seq_len]
+    input_type_ids = tf.zeros(self._seq_len, dtype=tf.int32)
+    input_mask = tf.cast(input_ids != self._pad_id, dtype=tf.int32)
+    return {
+        "input_word_ids": input_ids.numpy().tolist(),
+        "input_type_ids": input_type_ids.numpy().tolist(),
+        "input_mask": input_mask.numpy().tolist(),
+    }

mediapipe/model_maker/python/text/text_classifier/bert_tokenizer_test.py

@@ -0,0 +1,91 @@
+# Copyright 2022 The MediaPipe 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
+#
+#     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.
+
+import os
+import tempfile
+from unittest import mock as unittest_mock
+
+from absl.testing import parameterized
+import tensorflow as tf
+import tensorflow_hub
+
+from mediapipe.model_maker.python.text.text_classifier import bert_tokenizer
+from mediapipe.model_maker.python.text.text_classifier import model_spec
+
+
+class BertTokenizerTest(tf.test.TestCase, parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    # Mock tempfile.gettempdir() to be unique for each test to avoid race
+    # condition when downloading model since these tests may run in parallel.
+    mock_gettempdir = unittest_mock.patch.object(
+        tempfile,
+        'gettempdir',
+        return_value=self.create_tempdir(),
+        autospec=True,
+    )
+    self.mock_gettempdir = mock_gettempdir.start()
+    self.addCleanup(mock_gettempdir.stop)
+    ms = model_spec.SupportedModels.MOBILEBERT_CLASSIFIER.value()
+    self._vocab_file = os.path.join(
+        tensorflow_hub.resolve(ms.get_path()), 'assets', 'vocab.txt'
+    )
+
+  @parameterized.named_parameters(
+      dict(
+          testcase_name='fulltokenizer',
+          tokenizer_class=bert_tokenizer.BertFullTokenizer,
+      ),
+      dict(
+          testcase_name='fasttokenizer',
+          tokenizer_class=bert_tokenizer.BertFastTokenizer,
+      ),
+  )
+  def test_bert_full_tokenizer(self, tokenizer_class):
+    tokenizer = tokenizer_class(self._vocab_file, True, 16)
+    text_input = tf.constant(['this is an éxamplé input ¿foo'.encode('utf-8')])
+    result = tokenizer.process(text_input)
+    self.assertAllEqual(
+        result['input_word_ids'],
+        [
+            101,
+            2023,
+            2003,
+            2019,
+            2742,
+            7953,
+            1094,
+            29379,
+            102,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+        ],
+    )
+    self.assertAllEqual(
+        result['input_mask'], [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
+    )
+    self.assertAllEqual(
+        result['input_type_ids'],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+    )
+
+
+if __name__ == '__main__':
+  tf.test.main()

mediapipe/model_maker/python/text/text_classifier/hyperparameters.py

@@ -18,6 +18,7 @@ import enum
 from typing import Sequence, Union
 
 from mediapipe.model_maker.python.core import hyperparameters as hp
+from mediapipe.model_maker.python.text.text_classifier import bert_tokenizer
 
 
 @dataclasses.dataclass
@@ -53,6 +54,8 @@ class BertHParams(hp.BaseHParams):
       on recall. Only supported for binary classification.
     gamma: Gamma parameter for focal loss. To use cross entropy loss, set this
       value to 0. Defaults to 2.0.
+    tokenizer: Tokenizer to use for preprocessing. Must be one of the enum
+      options of SupportedBertTokenizers. Defaults to FULL_TOKENIZER.
   """
 
   learning_rate: float = 3e-5
@@ -68,5 +71,9 @@ class BertHParams(hp.BaseHParams):
 
   gamma: float = 2.0
 
+  tokenizer: bert_tokenizer.SupportedBertTokenizers = (
+      bert_tokenizer.SupportedBertTokenizers.FULL_TOKENIZER
+  )
+
 
 HParams = Union[BertHParams, AverageWordEmbeddingHParams]

mediapipe/model_maker/python/text/text_classifier/preprocessor.py

@@ -24,8 +24,8 @@ import tensorflow as tf
 import tensorflow_hub
 
 from mediapipe.model_maker.python.core.data import cache_files as cache_files_lib
+from mediapipe.model_maker.python.text.text_classifier import bert_tokenizer
 from mediapipe.model_maker.python.text.text_classifier import dataset as text_classifier_ds
-from official.nlp.tools import tokenization
 
 
 def _validate_text_and_label(text: tf.Tensor, label: tf.Tensor) -> None:
@@ -68,9 +68,9 @@ def _decode_record(
     example[name] = tf.cast(example[name], tf.int32)
 
   bert_features = {
-      "input_word_ids": example["input_ids"],
+      "input_word_ids": example["input_word_ids"],
       "input_mask": example["input_mask"],
-      "input_type_ids": example["segment_ids"]
+      "input_type_ids": example["input_type_ids"],
   }
   return bert_features, example["label_ids"]
 
@@ -224,10 +224,17 @@ class BertClassifierPreprocessor:
     tokenizer: BERT tokenizer.
     model_name: Name of the model provided by the model_spec. Used to associate
       cached files with specific Bert model vocab.
+    preprocessor: Which preprocessor to use. Must be one of the enum values of
+      SupportedBertPreprocessors.
   """
 
   def __init__(
-      self, seq_len: int, do_lower_case: bool, uri: str, model_name: str
+      self,
+      seq_len: int,
+      do_lower_case: bool,
+      uri: str,
+      model_name: str,
+      tokenizer: bert_tokenizer.SupportedBertTokenizers,
   ):
     self._seq_len = seq_len
     # Vocab filepath is tied to the BERT module's URI.
@@ -235,17 +242,27 @@ class BertClassifierPreprocessor:
         tensorflow_hub.resolve(uri), "assets", "vocab.txt"
     )
     self._do_lower_case = do_lower_case
-    self._tokenizer = tokenization.FullTokenizer(
+    self._tokenizer: bert_tokenizer.BertTokenizer = None
-        self._vocab_file, self._do_lower_case
+    if tokenizer == bert_tokenizer.SupportedBertTokenizers.FULL_TOKENIZER:
-    )
+      self._tokenizer = bert_tokenizer.BertFullTokenizer(
+          self._vocab_file, self._do_lower_case, self._seq_len
+      )
+    elif (
+        tokenizer == bert_tokenizer.SupportedBertTokenizers.FAST_BERT_TOKENIZER
+    ):
+      self._tokenizer = bert_tokenizer.BertFastTokenizer(
+          self._vocab_file, self._do_lower_case, self._seq_len
+      )
+    else:
+      raise ValueError(f"Unsupported tokenizer: {tokenizer}")
     self._model_name = model_name
 
   def _get_name_to_features(self):
     """Gets the dictionary mapping record keys to feature types."""
     return {
-        "input_ids": tf.io.FixedLenFeature([self._seq_len], tf.int64),
+        "input_word_ids": tf.io.FixedLenFeature([self._seq_len], tf.int64),
         "input_mask": tf.io.FixedLenFeature([self._seq_len], tf.int64),
-        "segment_ids": tf.io.FixedLenFeature([self._seq_len], tf.int64),
+        "input_type_ids": tf.io.FixedLenFeature([self._seq_len], tf.int64),
         "label_ids": tf.io.FixedLenFeature([], tf.int64),
     }
 
@@ -269,6 +286,7 @@ class BertClassifierPreprocessor:
       2. model_name
       3. seq_len
       4. do_lower_case
+      5. tokenizer name
 
     Args:
       ds_cache_files: TFRecordCacheFiles from the original raw dataset object
@@ -282,6 +300,7 @@ class BertClassifierPreprocessor:
     hasher.update(self._model_name.encode("utf-8"))
     hasher.update(str(self._seq_len).encode("utf-8"))
     hasher.update(str(self._do_lower_case).encode("utf-8"))
+    hasher.update(self._tokenizer.name.encode("utf-8"))
     cache_prefix_filename = hasher.hexdigest()
     return cache_files_lib.TFRecordCacheFiles(
         cache_prefix_filename,
@@ -289,23 +308,6 @@ class BertClassifierPreprocessor:
         ds_cache_files.num_shards,
     )
 
-  def _process_bert_features(self, text: str) -> Mapping[str, Sequence[int]]:
-    tokens = self._tokenizer.tokenize(text)
-    tokens = tokens[0 : (self._seq_len - 2)]  # account for [CLS] and [SEP]
-    tokens.insert(0, "[CLS]")
-    tokens.append("[SEP]")
-    input_ids = self._tokenizer.convert_tokens_to_ids(tokens)
-    input_mask = [1] * len(input_ids)
-    while len(input_ids) < self._seq_len:
-      input_ids.append(0)
-      input_mask.append(0)
-    segment_ids = [0] * self._seq_len
-    return {
-        "input_ids": input_ids,
-        "input_mask": input_mask,
-        "segment_ids": segment_ids,
-    }
-
   def preprocess(
       self, dataset: text_classifier_ds.Dataset
   ) -> text_classifier_ds.Dataset:
@@ -326,18 +328,20 @@ class BertClassifierPreprocessor:
       size = 0
       for index, (text, label) in enumerate(dataset.gen_tf_dataset()):
         _validate_text_and_label(text, label)
-        feature = self._process_bert_features(text.numpy()[0].decode("utf-8"))
+        feature = self._tokenizer.process(text)
         def create_int_feature(values):
-          f = tf.train.Feature(
+          f = tf.train.Feature(int64_list=tf.train.Int64List(value=values))
-              int64_list=tf.train.Int64List(value=list(values))
-          )
           return f
 
         features = collections.OrderedDict()
-        features["input_ids"] = create_int_feature(feature["input_ids"])
+        features["input_word_ids"] = create_int_feature(
+            feature["input_word_ids"]
+        )
         features["input_mask"] = create_int_feature(feature["input_mask"])
-        features["segment_ids"] = create_int_feature(feature["segment_ids"])
+        features["input_type_ids"] = create_int_feature(
-        features["label_ids"] = create_int_feature([label.numpy()[0]])
+            feature["input_type_ids"]
+        )
+        features["label_ids"] = create_int_feature(label.numpy().tolist())
         tf_example = tf.train.Example(
             features=tf.train.Features(feature=features)
         )

mediapipe/model_maker/python/text/text_classifier/preprocessor_test.py

@@ -18,18 +18,20 @@ import os
 import tempfile
 from unittest import mock as unittest_mock
 
+from absl.testing import parameterized
 import mock
 import numpy as np
 import numpy.testing as npt
 import tensorflow as tf
 
 from mediapipe.model_maker.python.core.data import cache_files
+from mediapipe.model_maker.python.text.text_classifier import bert_tokenizer
 from mediapipe.model_maker.python.text.text_classifier import dataset as text_classifier_ds
 from mediapipe.model_maker.python.text.text_classifier import model_spec
 from mediapipe.model_maker.python.text.text_classifier import preprocessor
 
 
-class PreprocessorTest(tf.test.TestCase):
+class PreprocessorTest(tf.test.TestCase, parameterized.TestCase):
   CSV_PARAMS_ = text_classifier_ds.CSVParameters(
       text_column='text', label_column='label')
 
@@ -83,7 +85,19 @@ class PreprocessorTest(tf.test.TestCase):
     npt.assert_array_equal(
         np.stack(features_list), np.array([[1, 3, 3, 3, 3], [1, 5, 6, 0, 0]]))
 
-  def test_bert_preprocessor(self):
+  @parameterized.named_parameters(
+      dict(
+          testcase_name='fulltokenizer',
+          tokenizer=bert_tokenizer.SupportedBertTokenizers.FULL_TOKENIZER,
+      ),
+      dict(
+          testcase_name='fastberttokenizer',
+          tokenizer=bert_tokenizer.SupportedBertTokenizers.FAST_BERT_TOKENIZER,
+      ),
+  )
+  def test_bert_preprocessor(
+      self, tokenizer: bert_tokenizer.SupportedBertTokenizers
+  ):
     csv_file = self._get_csv_file()
     dataset = text_classifier_ds.Dataset.from_csv(
         filename=csv_file, csv_params=self.CSV_PARAMS_)
@@ -93,6 +107,7 @@ class PreprocessorTest(tf.test.TestCase):
         do_lower_case=bert_spec.do_lower_case,
         uri=bert_spec.get_path(),
         model_name=bert_spec.name,
+        tokenizer=tokenizer,
     )
     preprocessed_dataset = bert_preprocessor.preprocess(dataset)
     labels = []
@@ -122,11 +137,13 @@ class PreprocessorTest(tf.test.TestCase):
         cache_dir=self.get_temp_dir(),
     )
     bert_spec = model_spec.SupportedModels.MOBILEBERT_CLASSIFIER.value()
+    tokenizer = bert_tokenizer.SupportedBertTokenizers.FULL_TOKENIZER
     bert_preprocessor = preprocessor.BertClassifierPreprocessor(
         seq_len=5,
         do_lower_case=bert_spec.do_lower_case,
         uri=bert_spec.get_path(),
         model_name=bert_spec.name,
+        tokenizer=tokenizer,
     )
     ds_cache_files = dataset.tfrecord_cache_files
     preprocessed_cache_files = bert_preprocessor._get_tfrecord_cache_files(
@@ -149,12 +166,13 @@ class PreprocessorTest(tf.test.TestCase):
         f' {preprocessed_cache_files.cache_prefix}\n',
     )
 
-  def _get_new_prefix(self, cf, bert_spec, seq_len, do_lower_case):
+  def _get_new_prefix(self, cf, bert_spec, seq_len, do_lower_case, tokenizer):
     bert_preprocessor = preprocessor.BertClassifierPreprocessor(
         seq_len=seq_len,
         do_lower_case=do_lower_case,
         uri=bert_spec.get_path(),
         model_name=bert_spec.name,
+        tokenizer=tokenizer,
     )
     new_cf = bert_preprocessor._get_tfrecord_cache_files(cf)
     return new_cf.cache_prefix_filename
@@ -167,19 +185,31 @@ class PreprocessorTest(tf.test.TestCase):
         cache_dir=self.get_temp_dir(),
         num_shards=1,
     )
+    tokenizer = bert_tokenizer.SupportedBertTokenizers.FULL_TOKENIZER
     mobilebert_spec = model_spec.SupportedModels.MOBILEBERT_CLASSIFIER.value()
-    all_cf_prefixes.add(self._get_new_prefix(cf, mobilebert_spec, 5, True))
+    all_cf_prefixes.add(
-    all_cf_prefixes.add(self._get_new_prefix(cf, mobilebert_spec, 10, True))
+        self._get_new_prefix(cf, mobilebert_spec, 5, True, tokenizer)
-    all_cf_prefixes.add(self._get_new_prefix(cf, mobilebert_spec, 5, False))
+    )
+    all_cf_prefixes.add(
+        self._get_new_prefix(cf, mobilebert_spec, 10, True, tokenizer)
+    )
+    all_cf_prefixes.add(
+        self._get_new_prefix(cf, mobilebert_spec, 5, False, tokenizer)
+    )
     new_cf = cache_files.TFRecordCacheFiles(
         cache_prefix_filename='new_cache_prefix',
         cache_dir=self.get_temp_dir(),
         num_shards=1,
     )
-    all_cf_prefixes.add(self._get_new_prefix(new_cf, mobilebert_spec, 5, True))
+    all_cf_prefixes.add(
-
+        self._get_new_prefix(new_cf, mobilebert_spec, 5, True, tokenizer)
+    )
+    new_tokenizer = bert_tokenizer.SupportedBertTokenizers.FAST_BERT_TOKENIZER
+    all_cf_prefixes.add(
+        self._get_new_prefix(cf, mobilebert_spec, 5, True, new_tokenizer)
+    )
     # Each item of all_cf_prefixes should be unique.
-    self.assertLen(all_cf_prefixes, 4)
+    self.assertLen(all_cf_prefixes, 5)
 
 
 if __name__ == '__main__':

mediapipe/model_maker/python/text/text_classifier/text_classifier.py

@@ -437,6 +437,7 @@ class _BertClassifier(TextClassifier):
         do_lower_case=self._model_spec.do_lower_case,
         uri=self._model_spec.get_path(),
         model_name=self._model_spec.name,
+        tokenizer=self._hparams.tokenizer,
     )
     return (
         self._text_preprocessor.preprocess(train_data),

mediapipe/model_maker/requirements.txt

@@ -6,4 +6,5 @@ tensorflow>=2.10
 tensorflow-addons
 tensorflow-datasets
 tensorflow-hub
+tensorflow-text
 tf-models-official>=2.13.1