Add a unit test to demonstrate how to load an encoder through yaml and checkpoint.

PiperOrigin-RevId: 363593694

official/nlp/configs/encoders_test.py

+# 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 official.nlp.configs.encoders."""
+import os
+
+import tensorflow as tf
+
+from official.modeling import hyperparams
+from official.nlp.configs import encoders
+
+
+class EncodersTest(tf.test.TestCase):
+
+  def test_encoder_from_yaml(self):
+    config = encoders.EncoderConfig(
+        type="bert", bert=encoders.BertEncoderConfig(num_layers=1))
+    encoder = encoders.build_encoder(config)
+    ckpt = tf.train.Checkpoint(encoder=encoder)
+    ckpt_path = ckpt.save(self.get_temp_dir() + "/ckpt")
+    params_save_path = os.path.join(self.get_temp_dir(), "params.yaml")
+    hyperparams.save_params_dict_to_yaml(config, params_save_path)
+
+    retored_cfg = encoders.EncoderConfig.from_yaml(params_save_path)
+    retored_encoder = encoders.build_encoder(retored_cfg)
+    status = tf.train.Checkpoint(encoder=retored_encoder).restore(ckpt_path)
+    status.assert_consumed()
+
+
+if __name__ == "__main__":
+  tf.test.main()