optimization.py

# Copyright 2022 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
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"""Legacy functions and classes related to optimization."""

from absl import logging
import gin
import tensorflow as tf
import tensorflow_addons.optimizers as tfa_optimizers
from official.modeling.optimization import legacy_adamw

AdamWeightDecay = legacy_adamw.AdamWeightDecay


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):
    super(WarmUp, self).__init__()
    self.initial_learning_rate = initial_learning_rate
    self.warmup_steps = warmup_steps
    self.power = power
    self.decay_schedule_fn = decay_schedule_fn
    self.name = name

  def __call__(self, step):
    with tf.name_scope(self.name or 'WarmUp') as name:
      # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
      # learning rate will be `global_step/num_warmup_steps * init_lr`.
      global_step_float = tf.cast(step, tf.float32)
      warmup_steps_float = tf.cast(self.warmup_steps, tf.float32)
      warmup_percent_done = global_step_float / warmup_steps_float
      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)

  def get_config(self):
    return {
        'initial_learning_rate': self.initial_learning_rate,
        'decay_schedule_fn': self.decay_schedule_fn,
        'warmup_steps': self.warmup_steps,
        'power': self.power,
        'name': self.name
    }


@gin.configurable
def create_optimizer(init_lr,
                     num_train_steps,
                     num_warmup_steps,
                     end_lr=0.0,
                     optimizer_type='adamw',
                     beta_1=0.9,
                     poly_power=1.0):
  """Creates an optimizer with learning rate schedule."""
  # Implements linear decay of the learning rate.
  lr_schedule = tf.keras.optimizers.schedules.PolynomialDecay(
      initial_learning_rate=init_lr,
      decay_steps=num_train_steps,
      end_learning_rate=end_lr,
      power=poly_power)
  if 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=lr_schedule,
        weight_decay_rate=0.01,
        beta_1=beta_1,
        beta_2=0.999,
        epsilon=1e-6,
        exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'])
  elif optimizer_type == 'lamb':
    logging.info('using Lamb optimizer')
    optimizer = tfa_optimizers.LAMB(
        learning_rate=lr_schedule,
        weight_decay_rate=0.01,
        beta_1=beta_1,
        beta_2=0.999,
        epsilon=1e-6,
        exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'])
  else:
    raise ValueError('Unsupported optimizer type: ', optimizer_type)

  return optimizer