Internal cleanup.

PiperOrigin-RevId: 314822016

official/nlp/bert/run_classifier.py

@@ -54,6 +54,12 @@ flags.DEFINE_string(
     'to be used for training and evaluation.')
 flags.DEFINE_string('predict_checkpoint_path', None,
                     'Path to the checkpoint for predictions.')
+flags.DEFINE_integer(
+    'num_eval_per_epoch', 1,
+    'Number of evaluations per epoch. The purpose of this flag is to provide '
+    'more granular evaluation scores and checkpoints. For example, if original '
+    'data has N samples and num_eval_per_epoch is n, then each epoch will be '
+    'evaluated every N/n samples.')
 flags.DEFINE_integer('train_batch_size', 32, 'Batch size for training.')
 flags.DEFINE_integer('eval_batch_size', 32, 'Batch size for evaluation.')
 
@@ -92,8 +98,11 @@ def get_regression_loss_fn():
   return regression_loss_fn
 
 
-def get_dataset_fn(input_file_pattern, max_seq_length, global_batch_size,
-                   is_training, label_type=tf.int64):
+def get_dataset_fn(input_file_pattern,
+                   max_seq_length,
+                   global_batch_size,
+                   is_training,
+                   label_type=tf.int64):
   """Gets a closure to create a dataset."""
 
   def _dataset_fn(ctx=None):
@@ -151,17 +160,21 @@ def run_bert_classifier(strategy,
         use_graph_rewrite=common_flags.use_graph_rewrite())
     return classifier_model, core_model
 
-  loss_fn = (get_regression_loss_fn() if is_regression
-             else get_loss_fn(num_classes))
+  loss_fn = (
+      get_regression_loss_fn() if is_regression else get_loss_fn(num_classes))
 
   # Defines evaluation metrics function, which will create metrics in the
   # correct device and strategy scope.
   if is_regression:
-    metric_fn = functools.partial(tf.keras.metrics.MeanSquaredError,
-                                  'mean_squared_error', dtype=tf.float32)
+    metric_fn = functools.partial(
+        tf.keras.metrics.MeanSquaredError,
+        'mean_squared_error',
+        dtype=tf.float32)
   else:
-    metric_fn = functools.partial(tf.keras.metrics.SparseCategoricalAccuracy,
-                                  'accuracy', dtype=tf.float32)
+    metric_fn = functools.partial(
+        tf.keras.metrics.SparseCategoricalAccuracy,
+        'accuracy',
+        dtype=tf.float32)
 
   # Start training using Keras compile/fit API.
   logging.info('Training using TF 2.x Keras compile/fit API with '
@@ -349,8 +362,9 @@ def run_bert(strategy,
   keras_utils.set_session_config(FLAGS.enable_xla)
   performance.set_mixed_precision_policy(common_flags.dtype())
 
-  epochs = FLAGS.num_train_epochs
-  train_data_size = input_meta_data['train_data_size']
+  epochs = FLAGS.num_train_epochs * FLAGS.num_eval_per_epoch
+  train_data_size = (
+      input_meta_data['train_data_size'] // FLAGS.num_eval_per_epoch)
   steps_per_epoch = int(train_data_size / FLAGS.train_batch_size)
   warmup_steps = int(epochs * train_data_size * 0.1 / FLAGS.train_batch_size)
   eval_steps = int(

official/nlp/data/classifier_data_lib.py

@@ -127,15 +127,14 @@ class XnliProcessor(DataProcessor):
     """See base class."""
     lines = []
     for language in self.languages:
+      # Skips the header.
       lines.extend(
           self._read_tsv(
               os.path.join(data_dir, "multinli",
-                           "multinli.train.%s.tsv" % language)))
+                           "multinli.train.%s.tsv" % language))[1:])
 
     examples = []
     for (i, line) in enumerate(lines):
-      if i == 0:
-        continue
       guid = "train-%d" % i
       text_a = self.process_text_fn(line[0])
       text_b = self.process_text_fn(line[1])
@@ -825,7 +824,8 @@ def generate_tf_record_from_data_file(processor,
       eval_data_output_path: Output to which processed tf record for evaluation
         will be saved.
       test_data_output_path: Output to which processed tf record for testing
-        will be saved. Must be a pattern template with {} if processor is XNLI.
+        will be saved. Must be a pattern template with {} if processor has
+        language specific test data.
       max_seq_length: Maximum sequence length of the to be generated
         training/eval data.
 

official/nlp/data/create_finetuning_data.py

@@ -99,7 +99,8 @@ flags.DEFINE_string(
 flags.DEFINE_string(
     "test_data_output_path", None,
     "The path in which generated test input data will be written as tf"
-    " records. If None, do not generate test data.")
+    " records. If None, do not generate test data. Must be a pattern template"
+    " as test_{}.tfrecords if processor has language specific test data.")
 
 flags.DEFINE_string("meta_data_file_path", None,
                     "The path in which input meta data will be written.")