Internal change

PiperOrigin-RevId: 381152422

Author: tensorflower-gardener

official/nlp/data/classifier_data_lib.py

@@ -1287,20 +1287,17 @@ class SuperGLUEDataProcessor(DataProcessor):
 
   def get_train_examples(self, data_dir):
     """See base class."""
-    return self._create_examples(
-        self._read_jsonl(os.path.join(data_dir, "train.jsonl")), "train")
+    return self._create_examples_tfds("train")
 
   def get_dev_examples(self, data_dir):
     """See base class."""
-    return self._create_examples(
-        self._read_jsonl(os.path.join(data_dir, "val.jsonl")), "dev")
+    return self._create_examples_tfds("validation")
 
   def get_test_examples(self, data_dir):
     """See base class."""
-    return self._create_examples(
-        self._read_jsonl(os.path.join(data_dir, "test.jsonl")), "test")
+    return self._create_examples_tfds("test")
 
-  def _create_examples(self, lines, set_type):
+  def _create_examples_tfds(self, set_type):
     """Creates examples for the training/dev/test sets."""
     raise NotImplementedError()
 
@@ -1317,17 +1314,18 @@ def get_processor_name():
     """See base class."""
     return "BoolQ"
 
-  def _create_examples(self, lines, set_type):
+  def _create_examples_tfds(self, set_type):
     """Creates examples for the training/dev/test sets."""
+    dataset = tfds.load(
+        "super_glue/boolq", split=set_type, try_gcs=True).as_numpy_iterator()
     examples = []
-    for line in lines:
-      guid = "%s-%s" % (set_type, self.process_text_fn(str(line["idx"])))
-      text_a = self.process_text_fn(line["question"])
-      text_b = self.process_text_fn(line["passage"])
-      if set_type == "test":
-        label = "False"
-      else:
-        label = str(line["label"])
+    for example in dataset:
+      guid = "%s-%s" % (set_type, self.process_text_fn(str(example["idx"])))
+      text_a = self.process_text_fn(example["question"])
+      text_b = self.process_text_fn(example["passage"])
+      label = "False"
+      if set_type != "test":
+        label = self.get_labels()[example["label"]]
       examples.append(
           InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
     return examples
@@ -1345,17 +1343,18 @@ def get_processor_name():
     """See base class."""
     return "CB"
 
-  def _create_examples(self, lines, set_type):
+  def _create_examples_tfds(self, set_type):
     """Creates examples for the training/dev/test sets."""
+    dataset = tfds.load(
+        "super_glue/cb", split=set_type, try_gcs=True).as_numpy_iterator()
     examples = []
-    for line in lines:
-      guid = "%s-%s" % (set_type, self.process_text_fn(str(line["idx"])))
-      text_a = self.process_text_fn(line["premise"])
-      text_b = self.process_text_fn(line["hypothesis"])
-      if set_type == "test":
-        label = "entailment"
-      else:
-        label = self.process_text_fn(line["label"])
+    for example in dataset:
+      guid = "%s-%s" % (set_type, self.process_text_fn(str(example["idx"])))
+      text_a = self.process_text_fn(example["premise"])
+      text_b = self.process_text_fn(example["hypothesis"])
+      label = "entailment"
+      if set_type != "test":
+        label = self.get_labels()[example["label"]]
       examples.append(
           InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
     return examples
@@ -1375,17 +1374,18 @@ def get_processor_name():
     """See base class."""
     return "RTESuperGLUE"
 
-  def _create_examples(self, lines, set_type):
+  def _create_examples_tfds(self, set_type):
     """Creates examples for the training/dev/test sets."""
     examples = []
-    for i, line in enumerate(lines):
-      guid = "%s-%s" % (set_type, i)
-      text_a = self.process_text_fn(line["premise"])
-      text_b = self.process_text_fn(line["hypothesis"])
-      if set_type == "test":
-        label = "entailment"
-      else:
-        label = self.process_text_fn(line["label"])
+    dataset = tfds.load(
+        "super_glue/rte", split=set_type, try_gcs=True).as_numpy_iterator()
+    for example in dataset:
+      guid = "%s-%s" % (set_type, self.process_text_fn(str(example["idx"])))
+      text_a = self.process_text_fn(example["premise"])
+      text_b = self.process_text_fn(example["hypothesis"])
+      label = "entailment"
+      if set_type != "test":
+        label = self.get_labels()[example["label"]]
       examples.append(
           InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
     return examples

official/nlp/data/classifier_data_lib_test.py

@@ -0,0 +1,93 @@
+# Copyright 2021 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.
+
+"""Tests for third_party.tensorflow_models.official.nlp.data.classifier_data_lib."""
+
+import os
+import tempfile
+
+from absl.testing import parameterized
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+from official.nlp.bert import tokenization
+from official.nlp.data import classifier_data_lib
+
+
+def decode_record(record, name_to_features):
+  """Decodes a record to a TensorFlow example."""
+  return tf.io.parse_single_example(record, name_to_features)
+
+
+class BertClassifierLibTest(tf.test.TestCase, parameterized.TestCase):
+
+  def setUp(self):
+    super(BertClassifierLibTest, self).setUp()
+    self.model_dir = self.get_temp_dir()
+    self.processors = {
+        "CB": classifier_data_lib.CBProcessor,
+        "SUPERGLUE-RTE": classifier_data_lib.SuperGLUERTEProcessor,
+        "BOOLQ": classifier_data_lib.BoolQProcessor,
+    }
+
+    vocab_tokens = [
+        "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
+        "##ing", ","
+    ]
+    with tempfile.NamedTemporaryFile(delete=False) as vocab_writer:
+      vocab_writer.write("".join([x + "\n" for x in vocab_tokens
+                                 ]).encode("utf-8"))
+    vocab_file = vocab_writer.name
+    self.tokenizer = tokenization.FullTokenizer(vocab_file)
+
+  @parameterized.parameters(
+      {"task_type": "CB"},
+      {"task_type": "BOOLQ"},
+      {"task_type": "SUPERGLUE-RTE"},
+  )
+  def test_generate_dataset_from_tfds_processor(self, task_type):
+    with tfds.testing.mock_data(num_examples=5):
+      output_path = os.path.join(self.model_dir, task_type)
+
+      processor = self.processors[task_type]()
+
+      classifier_data_lib.generate_tf_record_from_data_file(
+          processor,
+          None,
+          self.tokenizer,
+          train_data_output_path=output_path,
+          eval_data_output_path=output_path,
+          test_data_output_path=output_path)
+      files = tf.io.gfile.glob(output_path)
+      self.assertNotEmpty(files)
+
+      train_dataset = tf.data.TFRecordDataset(output_path)
+      seq_length = 128
+      label_type = tf.int64
+      name_to_features = {
+          "input_ids": tf.io.FixedLenFeature([seq_length], tf.int64),
+          "input_mask": tf.io.FixedLenFeature([seq_length], tf.int64),
+          "segment_ids": tf.io.FixedLenFeature([seq_length], tf.int64),
+          "label_ids": tf.io.FixedLenFeature([], label_type),
+      }
+      train_dataset = train_dataset.map(
+          lambda record: decode_record(record, name_to_features))
+
+      # If data is retrieved without error, then all requirements
+      # including data type/shapes are met.
+      _ = next(iter(train_dataset))
+
+
+if __name__ == "__main__":
+  tf.test.main()

official/nlp/data/create_finetuning_data.py

@@ -175,7 +175,7 @@ def generate_classifier_dataset():
   """Generates classifier dataset and returns input meta data."""
   if FLAGS.classification_task_name in [
       "COLA", "WNLI", "SST-2", "MRPC", "QQP", "STS-B", "MNLI", "QNLI", "RTE",
-      "AX"
+      "AX", "SUPERGLUE-RTE", "CB", "BoolQ"
   ]:
     assert not FLAGS.input_data_dir or FLAGS.tfds_params
   else: