lr_schedule is a more reasonable name. It is a callable, not a function.

PiperOrigin-RevId: 303840099

official/nlp/optimization.py

@@ -28,13 +28,12 @@ import tensorflow_addons.optimizers as tfa_optimizers
 class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
   """Applies a warmup schedule on a given learning rate decay schedule."""
 
-  def __init__(
-      self,
-      initial_learning_rate,
-      decay_schedule_fn,
-      warmup_steps,
-      power=1.0,
-      name=None):
+  def __init__(self,
+               initial_learning_rate,
+               decay_schedule_fn,
+               warmup_steps,
+               power=1.0,
+               name=None):
     super(WarmUp, self).__init__()
     self.initial_learning_rate = initial_learning_rate
     self.warmup_steps = warmup_steps
@@ -52,10 +51,11 @@ class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
       warmup_learning_rate = (
           self.initial_learning_rate *
           tf.math.pow(warmup_percent_done, self.power))
-      return tf.cond(global_step_float < warmup_steps_float,
-                     lambda: warmup_learning_rate,
-                     lambda: self.decay_schedule_fn(step),
-                     name=name)
+      return tf.cond(
+          global_step_float < warmup_steps_float,
+          lambda: warmup_learning_rate,
+          lambda: self.decay_schedule_fn(step),
+          name=name)
 
   def get_config(self):
     return {
@@ -67,23 +67,26 @@ 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(
+  lr_schedule = tf.keras.optimizers.schedules.PolynomialDecay(
       initial_learning_rate=init_lr,
       decay_steps=num_train_steps,
       end_learning_rate=0.0)
   if num_warmup_steps:
-    learning_rate_fn = WarmUp(initial_learning_rate=init_lr,
-                              decay_schedule_fn=learning_rate_fn,
-                              warmup_steps=num_warmup_steps)
+    lr_schedule = WarmUp(
+        initial_learning_rate=init_lr,
+        decay_schedule_fn=lr_schedule,
+        warmup_steps=num_warmup_steps)
 
   if optimizer_type == 'adamw':
     logging.info('using Adamw optimizer')
     optimizer = AdamWeightDecay(
-        learning_rate=learning_rate_fn,
+        learning_rate=lr_schedule,
         weight_decay_rate=0.01,
         beta_1=0.9,
         beta_2=0.999,
@@ -92,7 +95,7 @@ def create_optimizer(init_lr, num_train_steps, num_warmup_steps,
   elif optimizer_type == 'lamb':
     logging.info('using Lamb optimizer')
     optimizer = tfa_optimizers.LAMB(
-        learning_rate=learning_rate_fn,
+        learning_rate=lr_schedule,
         weight_decay_rate=0.01,
         beta_1=0.9,
         beta_2=0.999,
@@ -127,8 +130,8 @@ class AdamWeightDecay(tf.keras.optimizers.Adam):
                exclude_from_weight_decay=None,
                name='AdamWeightDecay',
                **kwargs):
-    super(AdamWeightDecay, self).__init__(
-        learning_rate, beta_1, beta_2, epsilon, amsgrad, name, **kwargs)
+    super(AdamWeightDecay, self).__init__(learning_rate, beta_1, beta_2,
+                                          epsilon, amsgrad, name, **kwargs)
     self.weight_decay_rate = weight_decay_rate
     self._include_in_weight_decay = include_in_weight_decay
     self._exclude_from_weight_decay = exclude_from_weight_decay
@@ -189,15 +192,15 @@ class AdamWeightDecay(tf.keras.optimizers.Adam):
     lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state)
     decay = self._decay_weights_op(var, lr_t, apply_state)
     with tf.control_dependencies([decay]):
-      return super(AdamWeightDecay, self)._resource_apply_dense(
-          grad, var, **kwargs)
+      return super(AdamWeightDecay,
+                   self)._resource_apply_dense(grad, var, **kwargs)
 
   def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
     lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state)
     decay = self._decay_weights_op(var, lr_t, apply_state)
     with tf.control_dependencies([decay]):
-      return super(AdamWeightDecay, self)._resource_apply_sparse(
-          grad, var, indices, **kwargs)
+      return super(AdamWeightDecay,
+                   self)._resource_apply_sparse(grad, var, indices, **kwargs)
 
   def get_config(self):
     config = super(AdamWeightDecay, self).get_config()