Add a run_classifier.py in albert folder.

PiperOrigin-RevId: 295202644

official/nlp/albert/run_classifier.py

+# Copyright 2019 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.
+# ==============================================================================
+"""ALBERT classification finetuning runner in tf2.x."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import json
+
+from absl import app
+from absl import flags
+import tensorflow as tf
+
+from official.nlp.albert import configs as albert_configs
+from official.nlp.bert import run_classifier as run_classifier_bert
+from official.utils.misc import distribution_utils
+
+FLAGS = flags.FLAGS
+
+
+def main(_):
+  # Users should always run this script under TF 2.x
+  assert tf.version.VERSION.startswith('2.')
+
+  with tf.io.gfile.GFile(FLAGS.input_meta_data_path, 'rb') as reader:
+    input_meta_data = json.loads(reader.read().decode('utf-8'))
+
+  if not FLAGS.model_dir:
+    FLAGS.model_dir = '/tmp/bert20/'
+
+  strategy = distribution_utils.get_distribution_strategy(
+      distribution_strategy=FLAGS.distribution_strategy,
+      num_gpus=FLAGS.num_gpus,
+      tpu_address=FLAGS.tpu)
+  max_seq_length = input_meta_data['max_seq_length']
+  train_input_fn = run_classifier_bert.get_dataset_fn(
+      FLAGS.train_data_path,
+      max_seq_length,
+      FLAGS.train_batch_size,
+      is_training=True)
+  eval_input_fn = run_classifier_bert.get_dataset_fn(
+      FLAGS.eval_data_path,
+      max_seq_length,
+      FLAGS.eval_batch_size,
+      is_training=False)
+
+  albert_config = albert_configs.AlbertConfig.from_json_file(
+      FLAGS.bert_config_file)
+  run_classifier_bert.run_bert(strategy, input_meta_data, albert_config,
+                               train_input_fn, eval_input_fn)
+
+
+if __name__ == '__main__':
+  flags.mark_flag_as_required('bert_config_file')
+  flags.mark_flag_as_required('input_meta_data_path')
+  flags.mark_flag_as_required('model_dir')
+  app.run(main)

official/nlp/bert/run_classifier.py

@@ -28,7 +28,6 @@ import tensorflow as tf
 
 from official.modeling import model_training_utils
 from official.nlp import optimization
-from official.nlp.albert import configs as albert_configs
 from official.nlp.bert import bert_models
 from official.nlp.bert import common_flags
 from official.nlp.bert import configs as bert_configs
@@ -285,22 +284,17 @@ def export_classifier(model_export_path, input_meta_data,
 
 def run_bert(strategy,
              input_meta_data,
+             model_config,
              train_input_fn=None,
              eval_input_fn=None):
   """Run BERT training."""
-  if FLAGS.model_type == 'bert':
-    bert_config = bert_configs.BertConfig.from_json_file(FLAGS.bert_config_file)
-  else:
-    assert FLAGS.model_type == 'albert'
-    bert_config = albert_configs.AlbertConfig.from_json_file(
-        FLAGS.bert_config_file)
   if FLAGS.mode == 'export_only':
     # As Keras ModelCheckpoint callback used with Keras compile/fit() API
     # internally uses model.save_weights() to save checkpoints, we must
     # use model.load_weights() when Keras compile/fit() is used.
     export_classifier(FLAGS.model_export_path, input_meta_data,
                       FLAGS.use_keras_compile_fit,
-                      bert_config, FLAGS.model_dir)
+                      model_config, FLAGS.model_dir)
     return
 
   if FLAGS.mode != 'train_and_eval':
@@ -320,7 +314,7 @@ def run_bert(strategy,
 
   trained_model = run_bert_classifier(
       strategy,
-      bert_config,
+      model_config,
       input_meta_data,
       FLAGS.model_dir,
       epochs,
@@ -372,7 +366,9 @@ def main(_):
       FLAGS.eval_batch_size,
       is_training=False)
 
-  run_bert(strategy, input_meta_data, train_input_fn, eval_input_fn)
+  bert_config = bert_configs.BertConfig.from_json_file(FLAGS.bert_config_file)
+  run_bert(strategy, input_meta_data, bert_config, train_input_fn,
+           eval_input_fn)
 
 
 if __name__ == '__main__':