Export the pretrained transformer-xl model for finetuning stage.

Add to option to init checkpoint from transformer-xl model.

PiperOrigin-RevId: 274875006

official/nlp/xlnet/common_flags.py

@@ -38,6 +38,11 @@ flags.DEFINE_string(
     "init_checkpoint",
     default=None,
     help="Checkpoint path for initializing the model.")
+flags.DEFINE_bool(
+    "init_from_transformerxl",
+    default=False,
+    help="Init from a transformerxl model checkpoint. Otherwise, init from the "
+    "entire model checkpoint.")
 
 # Optimization config
 flags.DEFINE_float("learning_rate", default=1e-4, help="Maximum learning rate.")

official/nlp/xlnet/run_classifier.py

@@ -179,6 +179,7 @@ def main(unused_argv):
       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,
       steps_per_epoch=steps_per_epoch,
       steps_per_loop=steps_per_loop,

official/nlp/xlnet/run_pretrain.py

@@ -20,6 +20,7 @@ from __future__ import division
 from __future__ import print_function
 
 import functools
+import os
 
 from absl import app
 from absl import flags
@@ -103,7 +104,7 @@ def main(unused_argv):
   model_fn = functools.partial(get_pretrainxlnet_model, model_config,
                                run_config)
 
-  training_utils.train(
+  model = training_utils.train(
       strategy=strategy,
       model_fn=model_fn,
       input_meta_data=input_meta_data,
@@ -112,6 +113,7 @@ def main(unused_argv):
       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,
       steps_per_epoch=steps_per_epoch,
       steps_per_loop=steps_per_loop,
@@ -120,6 +122,13 @@ def main(unused_argv):
       model_dir=FLAGS.model_dir,
       save_steps=FLAGS.save_steps)
 
+  # Export transformer-xl model checkpoint to be used in finetuning.
+  checkpoint = tf.train.Checkpoint(transformer_xl=model.transformerxl_model)
+  saved_path = checkpoint.save(
+      os.path.join(FLAGS.model_dir, "pretrained/transformer_xl.ckpt"))
+  logging.info("Exporting the transformer-xl model as a new TF checkpoint: %s",
+               saved_path)
+
 
 if __name__ == "__main__":
   assert tf.version.VERSION.startswith('2.')

official/nlp/xlnet/run_squad.py

@@ -284,6 +284,7 @@ def main(unused_argv):
       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,
       steps_per_epoch=steps_per_epoch,
       steps_per_loop=steps_per_loop,

official/nlp/xlnet/training_utils.py

@@ -77,6 +77,7 @@ def train(
     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,
     save_steps: Optional[int] = None,
     run_eagerly: Optional[bool] = False):
@@ -106,6 +107,8 @@ def train(
         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
+        `model_fn`.
       model_dir: The directory of model (checkpoints, summaries).
       save_steps: The frequency to save checkpoints. Every save_steps, we save a
         model checkpoint.
@@ -151,6 +154,10 @@ def train(
 
     if init_checkpoint:
       logging.info("restore from %s", init_checkpoint)
+      if init_from_transformerxl:
+        checkpoint = tf.train.Checkpoint(
+            transformer_xl=model.transformerxl_model)
+      else:
         checkpoint = tf.train.Checkpoint(model=model)
       checkpoint.restore(init_checkpoint)