Support Lamb optimizer within BERT.

PiperOrigin-RevId: 303356961

official/nlp/bert/common_flags.py

@@ -76,6 +76,8 @@ def define_common_bert_flags():
       'If specified, init_checkpoint flag should not be used.')
   flags.DEFINE_bool('hub_module_trainable', True,
                     'True to make keras layers in the hub module trainable.')
+  flags.DEFINE_string('optimizer_type', 'adamw',
+                      'The type of optimizer to use for training (adamw|lamb)')
 
   flags_core.define_log_steps()
 

official/nlp/bert/run_classifier.py

@@ -125,7 +125,8 @@ def run_bert_classifier(strategy,
             hub_module_url=FLAGS.hub_module_url,
             hub_module_trainable=FLAGS.hub_module_trainable))
     optimizer = optimization.create_optimizer(
-        initial_lr, steps_per_epoch * epochs, warmup_steps)
+        initial_lr, steps_per_epoch * epochs, warmup_steps,
+        FLAGS.optimizer_type)
     classifier_model.optimizer = performance.configure_optimizer(
         optimizer,
         use_float16=common_flags.use_float16(),

official/nlp/bert/run_pretraining.py

@@ -105,7 +105,8 @@ def run_customized_training(strategy,
     pretrain_model, core_model = bert_models.pretrain_model(
         bert_config, max_seq_length, max_predictions_per_seq)
     optimizer = optimization.create_optimizer(
-        initial_lr, steps_per_epoch * epochs, warmup_steps)
+        initial_lr, steps_per_epoch * epochs, warmup_steps,
+        FLAGS.optimizer_type)
     pretrain_model.optimizer = performance.configure_optimizer(
         optimizer,
         use_float16=common_flags.use_float16(),

official/nlp/bert/run_squad_helper.py

@@ -244,7 +244,8 @@ def train_squad(strategy,
         hub_module_trainable=FLAGS.hub_module_trainable)
     optimizer = optimization.create_optimizer(FLAGS.learning_rate,
                                               steps_per_epoch * epochs,
-                                              warmup_steps)
+                                              warmup_steps,
+                                              FLAGS.optimizer_type)
 
     squad_model.optimizer = performance.configure_optimizer(
         optimizer,

official/nlp/optimization.py

@@ -20,7 +20,9 @@ from __future__ import print_function
 
 import re
 
+from absl import logging
 import tensorflow as tf
+import tensorflow_addons.optimizers as tfa_optimizers
 
 
 class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
@@ -65,7 +67,8 @@ class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
     }
 
 
-def create_optimizer(init_lr, num_train_steps, num_warmup_steps):
+def create_optimizer(init_lr, num_train_steps, num_warmup_steps,
+                     optimizer_type='adamw'):
   """Creates an optimizer with learning rate schedule."""
   # Implements linear decay of the learning rate.
   learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(
@@ -76,13 +79,28 @@ def create_optimizer(init_lr, num_train_steps, num_warmup_steps):
     learning_rate_fn = WarmUp(initial_learning_rate=init_lr,
                               decay_schedule_fn=learning_rate_fn,
                               warmup_steps=num_warmup_steps)
-  optimizer = AdamWeightDecay(
-      learning_rate=learning_rate_fn,
-      weight_decay_rate=0.01,
-      beta_1=0.9,
-      beta_2=0.999,
-      epsilon=1e-6,
-      exclude_from_weight_decay=['layer_norm', 'bias'])
+
+  if optimizer_type == 'adamw':
+    logging.info('using Adamw optimizer')
+    optimizer = AdamWeightDecay(
+        learning_rate=learning_rate_fn,
+        weight_decay_rate=0.01,
+        beta_1=0.9,
+        beta_2=0.999,
+        epsilon=1e-6,
+        exclude_from_weight_decay=['layer_norm', 'bias'])
+  elif optimizer_type == 'lamb':
+    logging.info('using Lamb optimizer')
+    optimizer = tfa_optimizers.LAMB(
+        learning_rate=learning_rate_fn,
+        weight_decay_rate=0.01,
+        beta_1=0.9,
+        beta_2=0.999,
+        epsilon=1e-6,
+        exclude_from_weight_decay=['layer_norm', 'bias'])
+  else:
+    raise ValueError('Unsupported optimizer type: ', optimizer_type)
+
   return optimizer