text: # 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.
  
  """Functions and classes related to optimization (weight updates)."""
  
  import re
  
  from absl import logging
  import gin
  import tensorflow as tf
  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):
      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):
    """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)
    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
  
  
  class AdamWeightDecay(tf.keras.optimizers.Adam):
    """Adam enables L2 weight decay and clip_by_global_norm on gradients.
  
    Just adding the square of the weights to the loss function is *not* the
    correct way of using L2 regularization/weight decay with Adam, since that will
    interact with the m and v parameters in strange ways.
  
    Instead we want to decay the weights in a manner that doesn't interact with
    the m/v parameters. This is equivalent to adding the square of the weights to
    the loss with plain (non-momentum) SGD.
    """
  
    def __init__(self,
                 learning_rate=0.001,
                 beta_1=0.9,
                 beta_2=0.999,
                 epsilon=1e-7,
                 amsgrad=False,
                 weight_decay_rate=0.0,
                 include_in_weight_decay=None,
                 exclude_from_weight_decay=None,
                 gradient_clip_norm=1.0,
                 name='AdamWeightDecay',
                 **kwargs):
      super(AdamWeightDecay, self).__init__(learning_rate, beta_1, beta_2,
                                            epsilon, amsgrad, name, **kwargs)
      self.weight_decay_rate = weight_decay_rate
      self.gradient_clip_norm = gradient_clip_norm
      self._include_in_weight_decay = include_in_weight_decay
      self._exclude_from_weight_decay = exclude_from_weight_decay
      logging.info('gradient_clip_norm=%f', gradient_clip_norm)
  
    @classmethod
    def from_config(cls, config):
      """Creates an optimizer from its config with WarmUp custom object."""
      custom_objects = {'WarmUp': WarmUp}
      return super(AdamWeightDecay, cls).from_config(
          config, custom_objects=custom_objects)
  
    def _prepare_local(self, var_device, var_dtype, apply_state):
      super(AdamWeightDecay, self)._prepare_local(var_device, var_dtype,  # pytype: disable=attribute-error  # typed-keras
                                                  apply_state)
      apply_state[(var_device, var_dtype)]['weight_decay_rate'] = tf.constant(
          self.weight_decay_rate, name='adam_weight_decay_rate')
  
    def _decay_weights_op(self, var, learning_rate, apply_state):
      do_decay = self._do_use_weight_decay(var.name)
      if do_decay:
        return var.assign_sub(
            learning_rate * var *
            apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate'],
            use_locking=self._use_locking)
      return tf.no_op()
  
    def apply_gradients(self,
                        grads_and_vars,
                        name=None,
                        experimental_aggregate_gradients=True):
      grads, tvars = list(zip(*grads_and_vars))
      if experimental_aggregate_gradients and self.gradient_clip_norm > 0.0:
        # when experimental_aggregate_gradients = False, apply_gradients() no
        # longer implicitly allreduce gradients, users manually allreduce gradient
        # and passed the allreduced grads_and_vars. For now, the
        # clip_by_global_norm will be moved to before the explicit allreduce to
        # keep the math the same as TF 1 and pre TF 2.2 implementation.
        (grads, _) = tf.clip_by_global_norm(
            grads, clip_norm=self.gradient_clip_norm)
      return super(AdamWeightDecay, self).apply_gradients(
          zip(grads, tvars),
          name=name,
          experimental_aggregate_gradients=experimental_aggregate_gradients)
  
    def _get_lr(self, var_device, var_dtype, apply_state):
      """Retrieves the learning rate with the given state."""
      if apply_state is None:
        return self._decayed_lr_t[var_dtype], {}
  
      apply_state = apply_state or {}
      coefficients = apply_state.get((var_device, var_dtype))
      if coefficients is None:
        coefficients = self._fallback_apply_state(var_device, var_dtype)
        apply_state[(var_device, var_dtype)] = coefficients
  
      return coefficients['lr_t'], dict(apply_state=apply_state)
  
    def _resource_apply_dense(self, grad, var, 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_dense(grad, var, **kwargs)  # pytype: disable=attribute-error  # typed-keras
  
    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)  # pytype: disable=attribute-error  # typed-keras
  
    def get_config(self):
      config = super(AdamWeightDecay, self).get_config()
      config.update({
          'weight_decay_rate': self.weight_decay_rate,
      })
      return config
  
    def _do_use_weight_decay(self, param_name):
      """Whether to use L2 weight decay for `param_name`."""
      if self.weight_decay_rate == 0:
        return False
  
      if self._include_in_weight_decay:
        for r in self._include_in_weight_decay:
          if re.search(r, param_name) is not None:
            return True
  
      if self._exclude_from_weight_decay:
        for r in self._exclude_from_weight_decay:
          if re.search(r, param_name) is not None:
            return False
      return True
  