Remove unnecessary test_input_fn.

PiperOrigin-RevId: 276394582

official/nlp/xlnet/run_classifier.py

@@ -184,7 +184,6 @@ def main(unused_argv):
       eval_fn=eval_fn,
       metric_fn=get_metric_fn,
       train_input_fn=train_input_fn,
-      test_input_fn=test_input_fn,
       init_checkpoint=FLAGS.init_checkpoint,
       init_from_transformerxl=FLAGS.init_from_transformerxl,
       total_training_steps=total_training_steps,

official/nlp/xlnet/run_pretrain.py

@@ -135,7 +135,6 @@ def main(unused_argv):
       eval_fn=None,
       metric_fn=None,
       train_input_fn=train_input_fn,
-      test_input_fn=None,
       init_checkpoint=FLAGS.init_checkpoint,
       init_from_transformerxl=FLAGS.init_from_transformerxl,
       total_training_steps=total_training_steps,

official/nlp/xlnet/run_squad.py

@@ -281,7 +281,6 @@ def main(unused_argv):
       eval_fn=eval_fn,
       metric_fn=None,
       train_input_fn=train_input_fn,
-      test_input_fn=test_input_fn,
       init_checkpoint=FLAGS.init_checkpoint,
       init_from_transformerxl=FLAGS.init_from_transformerxl,
       total_training_steps=total_training_steps,

official/nlp/xlnet/training_utils.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""XLNet classification finetuning runner in tf2.0."""
+"""XLNet training utils."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -61,7 +61,6 @@ def train(
     eval_fn: Optional[Callable[[tf.keras.Model, int, tf.summary.SummaryWriter],
                                Any]] = None,
     metric_fn: Optional[Callable[[], tf.keras.metrics.Metric]] = None,
-    test_input_fn: Optional[Callable] = None,
     init_checkpoint: Optional[Text] = None,
     init_from_transformerxl: Optional[bool] = False,
     model_dir: Optional[Text] = None,
@@ -86,8 +85,6 @@ def train(
       metric_fn: A metrics function returns a Keras Metric object to record
         evaluation result using evaluation dataset or with training dataset
         after every epoch.
-      test_input_fn:  Function returns a evaluation dataset. If none, evaluation
-        is skipped.
       init_checkpoint: Optional checkpoint to load to `sub_model` returned by
         `model_fn`.
       init_from_transformerxl: Whether to load to `transformerxl_model` of
@@ -124,7 +121,7 @@ def train(
     tf.io.gfile.mkdir(summary_dir)
   train_summary_writer = None
   eval_summary_writer = None
-  if test_input_fn:
+  if eval_fn:
     eval_summary_writer = tf.summary.create_file_writer(
         os.path.join(summary_dir, "eval"))
   if steps_per_loop >= _MIN_SUMMARY_STEPS:
@@ -288,7 +285,7 @@ def train(
         _save_checkpoint(checkpoint, model_dir,
                          checkpoint_name.format(step=current_step))
 
-      if test_input_fn and current_step % save_steps == 0:
+      if eval_fn and current_step % save_steps == 0:
 
         logging.info("Running evaluation after step: %s.", current_step)
 
@@ -296,7 +293,7 @@ def train(
     if model_dir:
       _save_checkpoint(checkpoint, model_dir,
                        checkpoint_name.format(step=current_step))
-    if test_input_fn:
+    if eval_fn:
       logging.info("Running final evaluation after training is complete.")
       eval_metric = eval_fn(model, current_step, eval_summary_writer)
 
@@ -306,7 +303,7 @@ def train(
     }
     if train_metric:
       training_summary["last_train_metrics"] = _float_metric_value(train_metric)
-    if test_input_fn:
+    if eval_fn:
       # eval_metric is supposed to be a float.
       training_summary["eval_metrics"] = eval_metric