code: # coding=utf-8
  # Copyright 2019 The Tensor2Tensor 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.
  
  """Multi-step optimizers simulating large batches.
  
  Optimizer variants which make it possible to use very large batch sizes with
  limited GPU memory. Optimizers in this module accumulate the gradients for n
  batches, and call the optimizer's update rule every n batches with the
  accumulated gradients.
  
  See [Saunders et al., 2018](https://arxiv.org/abs/1805.00456) for details.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow.compat.v1 as tf
  
  
  class MultistepAdamOptimizer(tf.compat.v1.train.AdamOptimizer):
    """Adam with SGD updates every n steps with accumulated gradients."""
  
    def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8,
                 use_locking=False, name="Adam", n=1):
      super(MultistepAdamOptimizer, self).__init__(
          learning_rate=learning_rate, beta1=beta1, beta2=beta2, epsilon=epsilon,
          use_locking=use_locking, name=name)
      self._n = n  # Call Adam optimizer every n batches with accumulated grads
      self._n_t = None  # n as tensor
  
    def _create_slots(self, var_list):
      """Create slot variables for Adam with accumulated gradients."""
      super(MultistepAdamOptimizer, self)._create_slots(var_list)
      first_var = min(var_list, key=lambda x: x.name)
      self._create_non_slot_variable(initial_value=0 if self._n == 1 else 1,
                                     name="iter",
                                     colocate_with=first_var)
      for v in var_list:
        self._zeros_slot(v, "grad_acc", self._name)
  
    def _get_iter_variable(self):
      graph = (
          None if tf.executing_eagerly() else tf.get_default_graph())
      return self._get_non_slot_variable("iter", graph=graph)
  
    def _prepare(self):
      super(MultistepAdamOptimizer, self)._prepare()
      self._n_t = tf.convert_to_tensor(self._n, name="n")
  
    def _apply_cond(self, apply_fn, grad, var, *args, **kwargs):
      """Apply conditionally if counter is zero."""
      grad_acc = self.get_slot(var, "grad_acc")
  
      def apply_adam(grad_acc, apply_fn, grad, var, *args, **kwargs):
        total_grad = (grad_acc + grad) / tf.cast(self._n_t, grad.dtype)
        adam_op = apply_fn(total_grad, var, *args, **kwargs)
        with tf.control_dependencies([adam_op]):
          grad_acc_to_zero_op = grad_acc.assign(tf.zeros_like(grad_acc),
                                                use_locking=self._use_locking)
        return tf.group(adam_op, grad_acc_to_zero_op)
  
      def accumulate_gradient(grad_acc, grad):
        assign_op = tf.assign_add(grad_acc, grad, use_locking=self._use_locking)
        return tf.group(assign_op)  # Strip return value
  
      return tf.cond(
          tf.equal(self._get_iter_variable(), 0),
          lambda: apply_adam(grad_acc, apply_fn, grad, var, *args, **kwargs),
          lambda: accumulate_gradient(grad_acc, grad))
  
    def _apply_dense(self, grad, var):
      return self._apply_cond(
          super(MultistepAdamOptimizer, self)._apply_dense, grad, var)
  
    def _resource_apply_dense(self, grad, var):
      return self._apply_cond(
          super(MultistepAdamOptimizer, self)._resource_apply_dense, grad, var)
  
    def _apply_sparse_shared(self, grad, var, indices, scatter_add):
      return self._apply_cond(
          super(MultistepAdamOptimizer, self)._apply_sparse_shared, grad, var,
          indices, scatter_add)
  
    def _apply_sparse(self, grad, var):
      # TODO(fstahlberg): Implement a sparse version
      tf.logging.warning("MultistepAdamOptimizer does not support sparse updates")
      dense_grad = tf.convert_to_tensor(grad)
      return self._apply_cond(
          super(MultistepAdamOptimizer, self)._apply_dense, dense_grad, var)
  
    def _resource_apply_sparse_duplicate_indices(self, grad, var, indices):
      tf.logging.warning("MultistepAdamOptimizer does not support sparse updates")
      # Note that conversion to a dense Tensor handles duplicate `indices`
      # correctly (summing them). A real sparse implementation will probably want
      # to override _resource_apply_sparse instead so it gets them de-duplicated
      # automatically.
      dense_grad = tf.convert_to_tensor(
          tf.IndexedSlices(values=grad, indices=indices,
                           dense_shape=tf.shape(var)))
      return self._apply_cond(
          super(MultistepAdamOptimizer, self)._resource_apply_dense,
          dense_grad, var)
  
    def _finish(self, update_ops, name_scope):
      """Updates beta_power variables every n batches and incrs counter."""
      iter_ = self._get_iter_variable()
      beta1_power, beta2_power = self._get_beta_accumulators()
      with tf.control_dependencies(update_ops):
        with tf.colocate_with(iter_):
  
          def update_beta_op():
            update_beta1 = beta1_power.assign(
                beta1_power * self._beta1_t,
                use_locking=self._use_locking)
            update_beta2 = beta2_power.assign(
                beta2_power * self._beta2_t,
                use_locking=self._use_locking)
            return tf.group(update_beta1, update_beta2)
          maybe_update_beta = tf.cond(
              tf.equal(iter_, 0), update_beta_op, tf.no_op)
          with tf.control_dependencies([maybe_update_beta]):
            update_iter = iter_.assign(tf.mod(iter_ + 1, self._n_t),
                                       use_locking=self._use_locking)
      return tf.group(
          *update_ops + [update_iter, maybe_update_beta], name=name_scope)
  
apis: tensorflow.compat.v1.group tensorflow.compat.v1.zeros_like tensorflow.compat.v1.equal tensorflow.compat.v1.executing_eagerly tensorflow.compat.v1.logging.warning tensorflow.compat.v1.control_dependencies tensorflow.compat.v1.mod tensorflow.compat.v1.assign_add tensorflow.compat.v1.cast tensorflow.compat.v1.shape tensorflow.compat.v1.get_default_graph tensorflow.compat.v1.convert_to_tensor tensorflow.compat.v1.colocate_with