text: # coding=utf-8
  # Copyright 2022 The Google Research Authors.
  #
  # 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)."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import re
  import tensorflow.compat.v1 as tf
  
  from mobilebert import lamb_optimizer
  
  
  def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, use_tpu,
                       optimizer="adamw", weight_decay_rate=0.01,
                       end_lr_rate=0.0001, use_layer_wise_warmup=False,
                       total_warmup_phases=0):
    """Creates an optimizer training op."""
    global_step = tf.train.get_or_create_global_step()
  
    learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32)
  
    # Implements linear decay of the learning rate.
    learning_rate = tf.train.polynomial_decay(
        learning_rate,
        global_step,
        num_train_steps,
        end_learning_rate=learning_rate * end_lr_rate,
        power=1.0,
        cycle=False)
  
    # Implements linear warmup. I.e., if global_step < num_warmup_steps, the
    # learning rate will be `global_step/num_warmup_steps * init_lr`.
    if num_warmup_steps:
      global_steps_int = tf.cast(global_step, tf.int32)
      warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)
  
      global_steps_float = tf.cast(global_steps_int, tf.float32)
      warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)
  
      warmup_percent_done = global_steps_float / warmup_steps_float
      warmup_learning_rate = init_lr * warmup_percent_done
  
      is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)
      learning_rate = (
          (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate)
  
    # It is recommended that you use this optimizer for fine tuning, since this
    # is how the model was trained (note that the Adam m/v variables are NOT
    # loaded from init_checkpoint.)
    if optimizer == "adamw":
      optimizer = AdamWeightDecayOptimizer(
          learning_rate=learning_rate,
          weight_decay_rate=weight_decay_rate,
          beta_1=0.9,
          beta_2=0.999,
          epsilon=1e-6,
          exclude_from_weight_decay=[
              "teacher", "LayerNorm", "layer_norm", "bias", "FakeLayerNorm"],
          use_layer_wise_warmup=use_layer_wise_warmup,
          total_warmup_phases=total_warmup_phases,
          num_train_steps=num_train_steps)
    elif optimizer == "lamb":
      optimizer = lamb_optimizer.LAMBOptimizer(
          learning_rate=learning_rate,
          weight_decay_rate=weight_decay_rate,
          beta_1=0.9,
          beta_2=0.999,
          epsilon=1e-6,
          exclude_from_weight_decay=[
              "teacher", "LayerNorm", "layer_norm", "bias", "FakeLayerNorm"],
          use_layer_wise_warmup=use_layer_wise_warmup,
          total_warmup_phases=total_warmup_phases,
          num_train_steps=num_train_steps)
    else:
      raise ValueError("Not supported optimizer: ", optimizer)
  
    if use_tpu:
      optimizer = tf.tpu.CrossShardOptimizer(optimizer)
  
    tvars = tf.trainable_variables()
    grads = tf.gradients(loss, tvars)
  
    tvars = [var for var in tf.trainable_variables()
             if not var.name.startswith("teacher")]
    grads = tf.gradients(loss, tvars, colocate_gradients_with_ops=True)
  
    # This is how the model was pre-trained.
    (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)
  
    ntvars = [var for var in tf.trainable_variables()
              if var.name.startswith("teacher")]
    ngrads = [None for var in ntvars]
  
    train_op = optimizer.apply_gradients(
        zip(grads + ngrads, tvars + ntvars), global_step=global_step)
  
    # Normally the global step update is done inside of `apply_gradients`.
    # However, `AdamWeightDecayOptimizer` doesn't do this. But if you use
    # a different optimizer, you should probably take this line out.
    new_global_step = global_step + 1
    train_op = tf.group(train_op, [global_step.assign(new_global_step)])
    return train_op
  
  
  class AdamWeightDecayOptimizer(tf.train.Optimizer):
    """A basic Adam optimizer that includes "correct" L2 weight decay."""
  
    def __init__(self,
                 learning_rate,
                 weight_decay_rate=0.0,
                 beta_1=0.9,
                 beta_2=0.999,
                 epsilon=1e-6,
                 exclude_from_weight_decay=None,
                 name="AdamWeightDecayOptimizer",
                 use_layer_wise_warmup=False,
                 total_warmup_phases=0,
                 num_train_steps=0):
      """Constructs a AdamWeightDecayOptimizer."""
      super(AdamWeightDecayOptimizer, self).__init__(False, name)
  
      del use_layer_wise_warmup
      del total_warmup_phases
      del num_train_steps
  
      self.learning_rate = learning_rate
      self.weight_decay_rate = weight_decay_rate
      self.beta_1 = beta_1
      self.beta_2 = beta_2
      self.epsilon = epsilon
      self.exclude_from_weight_decay = exclude_from_weight_decay
  
    def apply_gradients(self, grads_and_vars, global_step=None, name=None):
      """See base class."""
      assignments = []
      for (grad, param) in grads_and_vars:
        if grad is None or param is None:
          continue
  
        param_name = self._get_variable_name(param.name)
  
        m = tf.get_variable(
            name=param_name + "/adam_m",
            shape=param.shape.as_list(),
            dtype=tf.float32,
            trainable=False,
            initializer=tf.zeros_initializer())
        v = tf.get_variable(
            name=param_name + "/adam_v",
            shape=param.shape.as_list(),
            dtype=tf.float32,
            trainable=False,
            initializer=tf.zeros_initializer())
  
        # Standard Adam update.
        next_m = (
            tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad))
        next_v = (
            tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2,
                                                      tf.square(grad)))
  
        update = next_m / (tf.sqrt(next_v) + self.epsilon)
  
        # 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 ot 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.
        if self._do_use_weight_decay(param_name):
          update += self.weight_decay_rate * param
  
        update_with_lr = self.learning_rate * update
  
        next_param = param - update_with_lr
  
        assignments.extend(
            [param.assign(next_param),
             m.assign(next_m),
             v.assign(next_v)])
      return tf.group(*assignments, name=name)
  
    def _do_use_weight_decay(self, param_name):
      """Whether to use L2 weight decay for `param_name`."""
      if not self.weight_decay_rate:
        return False
      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
  
    def _get_variable_name(self, param_name):
      """Get the variable name from the tensor name."""
      m = re.match("^(.*):\\d+$", param_name)
      if m is not None:
        param_name = m.group(1)
      return param_name
  