code: # Copyright 2019 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.
  # ==============================================================================
  """Contains the loss scaling optimizer class."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import smart_cond
  from keras import backend
  from keras import optimizers
  from keras.mixed_precision import loss_scale as keras_loss_scale_module
  from keras.optimizer_v2 import optimizer_v2
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.training.experimental import mixed_precision
  from tensorflow.python.training.tracking import base as trackable
  from tensorflow.python.util.tf_export import keras_export
  
  
  class _UnwrapPreventer(object):
    """Wrapper that DistributionStrategy will not unwrap.
  
    Typically, DistributionStrategy will unwrap values when going from a cross-
    replica context to a replica context via `call_for_each_replica`. This class
    is a wrapper that DistributionStrategy will not unwrap, so it can be used to
    prevent it from unwrapping a value.
  
    TODO(reedwm): Find/implement a better way of preventing values from being
    unwrapped by DistributionStrategy
    """
  
    __slots__ = ['value']
  
    def __init__(self, value):
      self.value = value
  
  
  class _DelegatingTrackableMixin(object):
    """A mixin that delegates all Trackable methods to another trackable object.
  
    This class must be used with multiple inheritance. A class that subclasses
    Trackable can also subclass this class, which causes all Trackable methods to
    be delegated to the trackable object passed in the constructor.
  
    A subclass can use this mixin to appear as if it were the trackable passed to
    the constructor, from a Checkpoint's perspective. LossScaleOptimizer uses this
    mixin, so that the checkpoint format for a LossScaleOptimizer is identical to
    the checkpoint format for a normal optimizer. This allows a model to be saved
    with a normal Optimizer and restored with a LossScaleOptimizer, or vice versa.
    The only difference in checkpoint format is that the loss scale is also saved
    with a LossScaleOptimizer.
    """
  
    def __init__(self, trackable_obj):
      self._trackable = trackable_obj
  
    # pylint: disable=protected-access
    @property
    def _setattr_tracking(self):
      return self._trackable._setattr_tracking
  
    @_setattr_tracking.setter
    def _setattr_tracking(self, value):
      self._trackable._setattr_tracking = value
  
    @property
    def _update_uid(self):
      return self._trackable._update_uid
  
    @_update_uid.setter
    def _update_uid(self, value):
      self._trackable._update_uid = value
  
    @property
    def _unconditional_checkpoint_dependencies(self):
      return self._trackable._unconditional_checkpoint_dependencies
  
    @property
    def _unconditional_dependency_names(self):
      return self._trackable._unconditional_dependency_names
  
    @property
    def _name_based_restores(self):
      return self._trackable._name_based_restores
  
    def _maybe_initialize_trackable(self):
      return self._trackable._maybe_initialize_trackable()
  
    @property
    def _object_identifier(self):
      return self._trackable._object_identifier
  
    @property
    def _tracking_metadata(self):
      return self._trackable._tracking_metadata
  
    def _no_dependency(self, value):
      return self._trackable._no_dependency(value)
  
    def _name_based_attribute_restore(self, checkpoint):
      return self._trackable._name_based_attribute_restore(checkpoint)
  
    @property
    def _checkpoint_dependencies(self):
      return self._trackable._checkpoint_dependencies
  
    @property
    def _deferred_dependencies(self):
      return self._trackable._deferred_dependencies
  
    def _lookup_dependency(self, name):
      self._trackable._lookup_dependency(name)
  
    def _add_variable_with_custom_getter(self,
                                         name,
                                         shape=None,
                                         dtype=tf.float32,
                                         initializer=None,
                                         getter=None,
                                         overwrite=False,
                                         **kwargs_for_getter):
      return self._trackable._add_variable_with_custom_getter(
          name, shape, dtype, initializer, getter, overwrite, **kwargs_for_getter)
  
    def _preload_simple_restoration(self, name):
      return self._trackable._preload_simple_restoration(name)
  
    def _track_trackable(self, trackable, name, overwrite=False):  # pylint: disable=redefined-outer-name
      return self._trackable._track_trackable(trackable, name, overwrite)
  
    def _handle_deferred_dependencies(self, name, trackable):  # pylint: disable=redefined-outer-name
      return self._trackable._handle_deferred_dependencies(name, trackable)
  
    def _restore_from_checkpoint_position(self, checkpoint_position):
      return self._trackable._restore_from_checkpoint_position(
          checkpoint_position)
  
    def _single_restoration_from_checkpoint_position(self, checkpoint_position,
                                                     visit_queue):
      return self._trackable._single_restoration_from_checkpoint_position(
          checkpoint_position, visit_queue)
  
    def _gather_saveables_for_checkpoint(self):
      return self._trackable._gather_saveables_for_checkpoint()
  
    def _list_extra_dependencies_for_serialization(self, serialization_cache):
      return self._trackable._list_extra_dependencies_for_serialization(
          serialization_cache)
  
    def _list_functions_for_serialization(self, serialization_cache):
      return self._trackable._list_functions_for_serialization(
          serialization_cache)
    # pylint: enable=protected-access
  
  
  def _is_all_finite(grads):
    """Returns a scalar boolean tensor indicating if all gradients are finite."""
    is_finite_per_grad = [
        tf.reduce_all(tf.math.is_finite(g)) for g in grads if g is not None
    ]
    return tf.reduce_all(is_finite_per_grad)
  
  
  def _op_in_graph_mode(tensor):
    """Returns the tensor's op in graph mode, or the tensor in eager mode.
  
    This is useful because sometimes an op is needed in graph mode instead of a
    tensor. In eager mode, there are no ops.
  
    Args:
      tensor: A tensor.
  
    Returns:
      The tensor's op in graph mode. The tensor in eager mode.
    """
    if tf.executing_eagerly():
      return tensor
    return tensor.op
  
  
  def _assign_if_finite(var, value):
    """Assigns a value to a variable if the value is finite."""
    return tf.compat.v1.cond(
        tf.math.is_finite(value), lambda: _op_in_graph_mode(var.assign(value)),
        tf.no_op)
  
  
  class _DynamicLossScaleState(tf.__internal__.tracking.Trackable):
    """The state of a dynamic loss scale."""
  
    def __init__(self,
                 initial_loss_scale,
                 growth_steps,
                 multiplier):
      """Creates the dynamic loss scale."""
      super(_DynamicLossScaleState, self).__init__()
      self._initial_loss_scale = float(initial_loss_scale)
      self._growth_steps = int(growth_steps)
      self._multiplier = float(multiplier)
  
      self._weights = {}
      self._current_loss_scale = self._add_weight(
          name='current_loss_scale',
          dtype=tf.float32,
          initial_value=self._initial_loss_scale)
      # The number of consecutive steps with finite gradients since the last
      # nonfinite gradient or change in loss scale. The name is 'good_steps' for
      # backwards compatibility with older checkpoints.
      self._counter = self._add_weight(
          name='good_steps', dtype=tf.int64, initial_value=0)
  
    def _add_weight(self, name, initial_value, dtype=None):
      """Adds a weight to this loss scale.
  
      Args:
        name: Variable name.
        initial_value: The variable's initial value.
        dtype: The type of the variable.
  
      Returns:
        A variable.
  
      Raises:
        RuntimeError: If a weight with `name` has already been added.
      """
      variable = tf.compat.v1.Variable(
          initial_value=initial_value,
          name=name,
          dtype=dtype,
          trainable=False,
          use_resource=True,
          synchronization=tf.VariableSynchronization.AUTO,
          # Set aggregation to NONE, as loss scaling variables should never be
          # aggregated.
          aggregation=tf.compat.v1.VariableAggregation.NONE)
      if tf.executing_eagerly():
        graph_key = None
      else:
        graph = tf.compat.v1.get_default_graph()
        graph_key = graph._graph_key  # pylint: disable=protected-access
  
      key = (name, graph_key)
      self._weights[key] = variable
      self._handle_deferred_dependencies(name=name, trackable=variable)
      backend.track_variable(variable)
      return variable
  
    @property
    def _checkpoint_dependencies(self):
      """From Trackable. Gather graph-specific weights to save."""
      if tf.executing_eagerly():
        graph_key = None
      else:
        graph = tf.compat.v1.get_default_graph()
        graph_key = graph._graph_key  # pylint: disable=protected-access
      weights = []
      for (name, g), v in sorted(self._weights.items(), key=lambda i: i[0][0]):
        if g == graph_key:
          weights.append(trackable.TrackableReference(name=name, ref=v))
      return (super(_DynamicLossScaleState, self)._checkpoint_dependencies +
              weights)
  
    def _lookup_dependency(self, name):
      """From Trackable. Find a weight in the current graph."""
      unconditional = super(_DynamicLossScaleState, self)._lookup_dependency(name)
      if unconditional is not None:
        return unconditional
      if tf.executing_eagerly():
        graph_key = None
      else:
        graph = tf.compat.v1.get_default_graph()
        graph_key = graph._graph_key  # pylint: disable=protected-access
      return self._weights.get((name, graph_key), None)
  
    @property
    def initial_loss_scale(self):
      return self._initial_loss_scale
  
    @property
    def growth_steps(self):
      return self._growth_steps
  
    @property
    def multiplier(self):
      return self._multiplier
  
    @property
    def current_loss_scale(self):
      """Returns the current loss scale as a float32 `tf.Variable`."""
      return self._current_loss_scale
  
    @property
    def counter(self):
      """Returns the counter as a float32 `tf.Variable`."""
      return self._counter
  
    def __call__(self):
      """Returns the current loss scale as a scalar `float32` tensor."""
      return ops.convert_to_tensor(self._current_loss_scale)
  
    def update(self, grads):
      """Updates the value of the loss scale.
  
      Args:
        grads: A nested structure of unscaled gradients, each which is the
          gradient of the loss with respect to a weight.
  
      Returns:
        update_op: In eager mode, None. In graph mode, an op to update the loss
          scale.
        should_apply_gradients: Either a bool or a scalar boolean tensor. If
          False, the caller should skip applying `grads` to the variables this
          step.
      """
      grads = tf.nest.flatten(grads)
      if tf.distribute.has_strategy():
        distribution = tf.distribute.get_strategy()
  
        def get_is_finite(grads):
          is_finite = _is_all_finite(grads)
          # We cast to float, because we cannot reduce booleans with
          # DistributionStrategy.
          return tf.cast(is_finite, tf.float32)
  
        is_finite_float = distribution.extended.call_for_each_replica(
            get_is_finite, args=(grads,))
        reduced_is_finite_float = distribution.reduce(tf.distribute.ReduceOp.SUM,
                                                      is_finite_float, axis=None)
        is_finite = tf.equal(reduced_is_finite_float,
                                   distribution.num_replicas_in_sync)
      else:
        is_finite = _is_all_finite(grads)
  
      def update_if_finite_grads():
        """Update assuming the gradients are finite."""
  
        def incr_loss_scale():
          new_loss_scale = self.current_loss_scale * self.multiplier
          return tf.group(
              _assign_if_finite(self.current_loss_scale, new_loss_scale),
              self.counter.assign(0))
  
        return tf.compat.v1.cond(
            self.counter + 1 >= self.growth_steps,
            incr_loss_scale,
            lambda: _op_in_graph_mode(self.counter.assign_add(1)))
  
      def update_if_not_finite_grads():
        """Update assuming the gradients are nonfinite."""
  
        new_loss_scale = tf.maximum(
            self.current_loss_scale / self.multiplier, 1)
        return tf.group(
            self.counter.assign(0),
            self.current_loss_scale.assign(new_loss_scale))
  
      update_op = tf.compat.v1.cond(is_finite, update_if_finite_grads,
                                        update_if_not_finite_grads)
      should_apply_gradients = is_finite
      return update_op, should_apply_gradients
  
  
  # See LossScaleOptimizer docstring for why this is so big
  _DEFAULT_INITIAL_SCALE = 2 ** 15
  _DEFAULT_GROWTH_STEPS = 2000
  
  
  # pylint: disable=g-classes-have-attributes
  @keras_export('keras.mixed_precision.LossScaleOptimizer')
  class LossScaleOptimizer(_DelegatingTrackableMixin, optimizer_v2.OptimizerV2):
    """An optimizer that applies loss scaling to prevent numeric underflow.
  
    Loss scaling is a technique to prevent numeric underflow in intermediate
    gradients when float16 is used. To prevent underflow, the loss is multiplied
    (or "scaled") by a certain factor called the "loss scale", which causes
    intermediate gradients to be scaled by the loss scale as well. The final
    gradients are divided (or "unscaled") by the loss scale to bring them back to
    their original value.
  
    `LossScaleOptimizer` wraps another optimizer and applies loss scaling to it.
    By default, the loss scale is dynamically updated over time so you do not have
    to choose the loss scale. The `minimize` method automatically scales the loss,
    unscales the gradients, and updates the loss scale so all you have to do is
    wrap your optimizer with a `LossScaleOptimizer` if you use `minimize`. For
    example:
  
    >>> opt = tf.keras.optimizers.SGD(0.25)
    >>> opt = tf.keras.mixed_precision.LossScaleOptimizer(opt)
    >>> var = tf.Variable(1.)
    >>> loss_fn = lambda: var ** 2
    >>> # 'minimize' applies loss scaling and updates the loss sale.
    >>> opt.minimize(loss_fn, var_list=var)
    >>> var.numpy()
    0.5
  
    If a `tf.GradientTape` is used to compute gradients instead of `minimize`, you
    must scale the loss and gradients manually. This can be done with the
    `LossScaleOptimizer.get_scaled_loss` and
    `LossScaleOptimizer.get_unscaled_gradients` methods. For example:
  
    >>> with tf.GradientTape() as tape:
    ...   loss = loss_fn()
    ...   scaled_loss = opt.get_scaled_loss(loss)
    >>> scaled_grad = tape.gradient(scaled_loss, var)
    >>> (grad,) = opt.get_unscaled_gradients([scaled_grad])
    >>> opt.apply_gradients([(grad, var)])  # Loss scale is updated here
    >>> var.numpy()
    0.25
  
    Warning: If you forget to call `get_scaled_loss` or `get_unscaled_gradients`
    (or both) when using a `tf.GradientTape`, the model will likely converge to a
    worse quality. Please make sure you call each function exactly once.
  
    When mixed precision with float16 is used, there is typically no risk of
    underflow affecting model quality if loss scaling is properly used. See
    [the mixed precision guide](
    https://www.tensorflow.org/guide/keras/mixed_precision) for more information
    on how to use mixed precision.
  
    Args:
      inner_optimizer: The `tf.keras.optimizers.Optimizer` instance to wrap.
      dynamic: Bool indicating whether dynamic loss scaling is used. Defaults to
        True. If True, the loss scale will be dynamically updated over time using
        an algorithm that keeps the loss scale at approximately its optimal value.
        If False, a single fixed loss scale is used and `initial_scale` must be
        specified, which is used as the loss scale. Recommended to keep as True,
        as choosing a fixed loss scale can be tricky. Currently, there is a small
        performance overhead to dynamic loss scaling compared to fixed loss
        scaling.
      initial_scale: The initial loss scale. If `dynamic` is True, this defaults
        to `2 ** 15`. If `dynamic` is False, this must be specified and acts as
        the sole loss scale, as the loss scale does not change over time. When
        dynamic loss scaling is used, is better for this to be a very high number,
        because a loss scale that is too high gets lowered far more quickly than a
        loss scale that is too low gets raised.
      dynamic_growth_steps: With dynamic loss scaling, every
        `dynamic_growth_steps` steps with finite gradients, the loss scale is
        doubled. Defaults to 2000. If a nonfinite gradient is encountered, the
        count is reset back to zero, gradients are skipped that step, and the loss
        scale is halved. The count can be queried with
        `LossScaleOptimizer.dynamic_counter`. This argument can only be specified
        if `dynamic` is True.
  
    `LossScaleOptimizer` will occasionally skip applying gradients to the
    variables, in which case the trainable variables will not change that step.
    This is done because the dynamic loss scale will sometimes be raised too
    high, causing overflow in the gradients. Typically, the first 2 to 15 steps of
    the model are skipped as the initial loss scale is very high, but afterwards
    steps will only be skipped on average 0.05% of the time (the fraction of steps
    skipped is `1 / dynamic_growth_steps`).
  
    `LossScaleOptimizer` delegates all public `Optimizer` methods to the inner
    optimizer. Additionally, in methods `minimize` and `get_gradients, it scales
    the loss and unscales the gradients. In methods `minimize` and
    `apply_gradients`, it additionally updates the loss scale and skips applying
    gradients if any gradient has a nonfinite value.
  
    ### Hyperparameters
  
    Hyperparameters can be accessed and set on the LossScaleOptimizer, which will
    be delegated to the wrapped optimizer.
  
    >>> opt = tf.keras.optimizers.Adam(beta_1=0.8, epsilon=1e-5)
    >>> opt = tf.keras.mixed_precision.LossScaleOptimizer(opt)
    >>> opt.beta_1  # Equivalent to `opt.inner_optimizer.beta_1`
    0.8
    >>> opt.beta_1 = 0.7  # Equivalent to `opt.inner_optimizer.beta_1 = 0.7`
    >>> opt.beta_1
    0.7
    >>> opt.inner_optimizer.beta_1
    0.7
  
    However, accessing or setting non-hyperparameters is not delegated to the
    LossScaleOptimizer. In an Adam optimizer, `beta_1` is a hyperparameter but
    `epsilon` is not, as the Adam optimizer only calls `Optimizer._set_hyper` on
    `beta_1`.
  
    >>> opt.inner_optimizer.epsilon
    1e-5
    >>> opt.epsilon
    Traceback (most recent call last):
    ...
    AttributeError: 'LossScaleOptimizer' object has no attribute 'epsilon'
    >>> opt.epsilon = 1e-4  # This does NOT set epsilon on `opt.inner_optimizer`
    >>> opt.inner_optimizer.epsilon
    >>> 1e-5
  
    In the above example, despite epsilon being set on the LossScaleOptimizer, the
    old epsilon value will still be used when training as epsilon was not set on
    the inner optimizer.
    """
  
    _HAS_AGGREGATE_GRAD = True
  
    def __init__(self, inner_optimizer, dynamic=True, initial_scale=None,
                 dynamic_growth_steps=None):
      if not isinstance(inner_optimizer, optimizer_v2.OptimizerV2):
        raise TypeError('"inner_optimizer" must be an instance of OptimizerV2, '
                        'but got: %s' % inner_optimizer)
      if not isinstance(dynamic, bool):
        # Catch errors if a user incorrectly passes a string or float to the
        # second argument argument, as this is commonly done for
        # LossScaleOptimizerV1.
        raise TypeError('"dynamic" argument to LossScaleOptimizer.__init__ must '
                        'be a bool, but got: %r' % (dynamic,))
      self._raise_if_strategy_unsupported()
      self._optimizer = inner_optimizer
  
      # We don't call super().__init__, since we do not want to call OptimizerV2's
      # constructor.
      _DelegatingTrackableMixin.__init__(self, self._optimizer)
  
      if dynamic:
        if initial_scale is None:
          initial_scale = _DEFAULT_INITIAL_SCALE
        if dynamic_growth_steps is None:
          dynamic_growth_steps = _DEFAULT_GROWTH_STEPS
        self._loss_scale = _DynamicLossScaleState(
            initial_scale, dynamic_growth_steps, multiplier=2)
        self._track_trackable(self._loss_scale, 'loss_scale')
      else:
        if initial_scale is None:
          raise ValueError('"initial_scale" must be specified if "dynamic" is '
                           'False')
        self._loss_scale = float(initial_scale)
        if dynamic_growth_steps is not None:
          raise ValueError('"dynamic_growth_steps" must be None if "dynamic" '
                           'is False, but got: %s' % (dynamic_growth_steps,))
  
      # To support restoring TensorFlow 2.2 checkpoints.
      self._track_trackable(FakeOptimizerForRestoration(self._optimizer),
                            'base_optimizer')
  
    @property
    def dynamic(self):
      """Bool indicating whether dynamic loss scaling is used."""
      return isinstance(self._loss_scale, _DynamicLossScaleState)
  
    @property
    def loss_scale(self):
      """The current loss scale as a float32 scalar tensor."""
      if isinstance(self._loss_scale, _DynamicLossScaleState):
        return ops.convert_to_tensor(self._loss_scale.current_loss_scale)
      else:
        return ops.convert_to_tensor(self._loss_scale)
  
    @property
    def dynamic_counter(self):
      """The number of steps since the loss scale was last increased or decreased.
  
      This is None if `LossScaleOptimizer.dynamic` is False.
  
      The counter is incremented every step. Once it reaches
      `LossScaleOptimizer.dynamic_growth_steps`, the loss scale will be doubled
      and the counter will be reset back to zero. If nonfinite gradients are
      encountered, the loss scale will be halved and the counter will be reset
      back to zero.
      """
      if isinstance(self._loss_scale, _DynamicLossScaleState):
        return self._loss_scale.counter
      else:
        return None
  
    @property
    def initial_scale(self):
      """The initial loss scale.
  
      If `LossScaleOptimizer.dynamic` is False, this is the same number as
      `LossScaleOptimizer.loss_scale`, as the loss scale never changes.
      """
      if isinstance(self._loss_scale, _DynamicLossScaleState):
        return self._loss_scale.initial_loss_scale
      else:
        return self._loss_scale
  
    @property
    def dynamic_growth_steps(self):
      """The number of steps it takes to increase the loss scale.
  
      This is None if `LossScaleOptimizer.dynamic` is False.
  
      Every `dynamic_growth_steps` consecutive steps with finite gradients, the
      loss scale is increased.
      """
      if isinstance(self._loss_scale, _DynamicLossScaleState):
        return self._loss_scale.growth_steps
      else:
        return None
  
    @property
    def inner_optimizer(self):
      """The optimizer that this LossScaleOptimizer is wrapping."""
      return self._optimizer
  
    def get_scaled_loss(self, loss):
      """Scales the loss by the loss scale.
  
      This method is only needed if you compute gradients manually, e.g. with
      `tf.GradientTape`. In that case, call this method to scale the loss before
      passing the loss to `tf.GradientTape`. If you use
      `LossScaleOptimizer.minimize` or `LossScaleOptimizer.get_gradients`, loss
      scaling is automatically applied and this method is unneeded.
  
      If this method is called, `get_unscaled_gradients` should also be called.
      See the `tf.keras.mixed_precision.LossScaleOptimizer` doc for
      an example.
  
      Args:
        loss: The loss, which will be multiplied by the loss scale. Can either be
          a tensor or a callable returning a tensor.
  
      Returns:
        `loss` multiplied by `LossScaleOptimizer.loss_scale`.
      """
      if callable(loss):
        def new_loss():
          loss_val = loss()
          return loss_val * tf.cast(self.loss_scale, loss_val.dtype)
        return new_loss
      else:
        return loss * tf.cast(self.loss_scale, loss.dtype)
  
    def get_unscaled_gradients(self, grads):
      """Unscales the gradients by the loss scale.
  
      This method is only needed if you compute gradients manually, e.g. with
      `tf.GradientTape`. In that case, call this method to unscale the gradients
      after computing them with `tf.GradientTape`. If you use
      `LossScaleOptimizer.minimize` or `LossScaleOptimizer.get_gradients`, loss
      scaling is automatically applied and this method is unneeded.
  
      If this method is called, `get_scaled_loss` should also be called. See
      the `tf.keras.mixed_precision.LossScaleOptimizer` doc for an
      example.
  
      Args:
        grads: A list of tensors, each which will be divided by the loss scale.
          Can have None values, which are ignored.
  
      Returns:
        A new list the same size as `grads`, where every non-None value in `grads`
        is divided by `LossScaleOptimizer.loss_scale`.
      """
      loss_scale_reciprocal = 1. / self.loss_scale
      return [
          _multiply_gradient(g, loss_scale_reciprocal) if g is not None else None
          for g in grads
      ]
  
    def _compute_gradients(self, loss, var_list, grad_loss=None, tape=None):
      tape = tf.GradientTape() if tape is None else tape
      with tape:
        loss = self.get_scaled_loss(loss)
      grads_and_vars = self._optimizer._compute_gradients(  # pylint: disable=protected-access
          loss,
          var_list,
          grad_loss,
          tape=tape)
      grads = [g for g, _ in grads_and_vars]
      weights = [v for _, v in grads_and_vars]
      unscaled_grads = self.get_unscaled_gradients(grads)
      return list(zip(unscaled_grads, weights))
  
    def get_gradients(self, loss, params):
      loss = self.get_scaled_loss(loss)
      grads = self._optimizer.get_gradients(loss, params)
      return self.get_unscaled_gradients(grads)
  
    def _create_all_weights(self, var_list):
      self._optimizer._create_all_weights(var_list)    # pylint: disable=protected-access
  
    def apply_gradients(self,
                        grads_and_vars,
                        name=None,
                        experimental_aggregate_gradients=True):
      if tf.distribute.in_cross_replica_context():
        raise ValueError('apply_gradients() must be called in a replica context.')
      # We check for the strategy here despite already checking in the constructor
      # as frequently the optimizer is created outside the strategy's scope.
      self._raise_if_strategy_unsupported()
  
      grads_and_vars = tuple(grads_and_vars)
      return tf.distribute.get_replica_context().merge_call(
          self._apply_gradients_cross_replica,
          args=(grads_and_vars, name, experimental_aggregate_gradients))
  
    def _apply_gradients_cross_replica(self, distribution, grads_and_vars, name,
                                       experimental_aggregate_gradients):
      grads = [g for g, _ in grads_and_vars]
      if isinstance(self._loss_scale, _DynamicLossScaleState):
        loss_scale_update_op, should_apply_grads = self._loss_scale.update(grads)
      else:
        loss_scale_update_op = tf.no_op()
        should_apply_grads = True
  
      def apply_fn():
        # We do not want DistributionStrategy to unwrap any MirroredVariables in
        # grads_and_vars, because even in a replica context, the wrapped optimizer
        # expects mirrored variables. So we wrap the variables with an
        # _UnwrapPreventer, preventing DistributionStrategy from unwrapping the
        # MirroredVariables.
        wrapped_vars = _UnwrapPreventer([v for _, v in grads_and_vars])
        return distribution.extended.call_for_each_replica(
            self._apply_gradients,
            args=(grads, wrapped_vars, name, experimental_aggregate_gradients))
  
      def do_not_apply_fn():
        # Normally self._optimizer.iterations is incremented in
        # self._optimizer.apply_gradients(). Since that is not called in this
        # branch, we increment it here instead.
        return self._optimizer.iterations.assign_add(1, read_value=False)
  
      # Note: We must call this cond() in a cross-replica context.
      # DistributionStrategy does not support having a cond in a replica context
      # with a branch that calls `merge_call`, and self._optimizer.apply_gradients
      # calls `merge_call`.
      maybe_apply_op = smart_cond.smart_cond(should_apply_grads, apply_fn,
                                             do_not_apply_fn)
      return tf.group(maybe_apply_op, loss_scale_update_op)
  
    def _apply_gradients(self, grads, wrapped_vars, name,
                         experimental_aggregate_gradients):
      # TODO(reedwm): This will raise a fairly cryptic error message if
      # self._optimizer.apply_gradients does not take
      # experimental_aggregate_gradients.
      return self._optimizer.apply_gradients(
          list(zip(grads, wrapped_vars.value)), name,
          experimental_aggregate_gradients=experimental_aggregate_gradients)
  
    def get_config(self):
      serialized_optimizer = optimizers.serialize(self._optimizer)
      return {
          'inner_optimizer': serialized_optimizer,
          'dynamic': self.dynamic,
          'initial_scale': self.initial_scale,
          'dynamic_growth_steps': self.dynamic_growth_steps,
      }
  
    @classmethod
    def from_config(cls, config, custom_objects=None):
      config = config.copy()  # Make a copy, since we mutate config
      if 'loss_scale' in config:
        # If loss_scale is in config, we assume we are deserializing a
        # LossScaleOptimizer from TF 2.3 or below. We convert the config so it
        # can be deserialized in the current LossScaleOptimizer.
        loss_scale = keras_loss_scale_module.deserialize(
            config.pop('loss_scale'))
        if isinstance(loss_scale, tf.mixed_precision.experimental.FixedLossScale):
          config['dynamic'] = False
          config['initial_scale'] = loss_scale._loss_scale_value  # pylint: disable=protected-access
        elif isinstance(loss_scale, tf.mixed_precision.experimental.DynamicLossScale):
          config['dynamic'] = True
          config['initial_scale'] = loss_scale.initial_loss_scale
          config['dynamic_growth_steps'] = loss_scale.increment_period
          if loss_scale.multiplier != 2:
            raise ValueError('Cannot deserialize LossScaleOptimizer with a '
                             'DynamicLossScale whose multiplier is not 2. Got '
                             'DynamicLossScale: %s' % (loss_scale,))
        else:
          raise ValueError(
              'Serialized LossScaleOptimizers with a LossScale that is neither a '
              'FixedLossScale nor a DynamicLossScale can no longer be '
              'deserialized')
        config['inner_optimizer'] = config.pop('optimizer')
      config['inner_optimizer'] = optimizers.deserialize(
          config['inner_optimizer'], custom_objects=custom_objects)
      return cls(**config)
  
    def _raise_if_strategy_unsupported(self):
      if not strategy_supports_loss_scaling():
        strategy = tf.distribute.get_strategy()
        if isinstance(strategy,
                      (tf.distribute.experimental.TPUStrategy, tf.compat.v1.distribute.experimental.TPUStrategy,
                       tf.distribute.TPUStrategy)):
          raise ValueError(
              'Loss scaling is not supported with TPUStrategy. Loss scaling is '
              'unnecessary with TPUs, since they support bfloat16 instead of '
              'float16 and bfloat16 does not require loss scaling. You should '
              'remove the use of the LossScaleOptimizer when TPUs are used.')
        else:
          raise ValueError('Loss scaling is not supported with the '
                           'tf.distribute.Strategy: %s. Try using a different '
                           'Strategy, e.g. a MirroredStrategy' %
                           strategy.__class__.__name__)
  
    # Delegations: We delegate most OptimizerV2 methods to the wrapped optimizer
    # below.
  
    @property
    def iterations(self):
      return self._optimizer.iterations
  
    @iterations.setter
    def iterations(self, variable):
      self._optimizer.iterations = variable
  
    def get_slot_names(self):
      return self._optimizer.get_slot_names()
  
    def variables(self):
      return self._optimizer.variables()
  
    @property
    def weights(self):
      return self._optimizer.weights
  
    def get_weights(self):
      return self._optimizer.get_weights()
  
    def set_weights(self, weights):
      return self._optimizer.set_weights(weights)
  
    @property
    def clipnorm(self):
      return self._optimizer.clipnorm
  
    @clipnorm.setter
    def clipnorm(self, val):
      self._optimizer.clipnorm = val
  
    @property
    def global_clipnorm(self):
      return self._optimizer.global_clipnorm
  
    @global_clipnorm.setter
    def global_clipnorm(self, val):
      self._optimizer.global_clipnorm = val
  
    @property
    def clipvalue(self):
      return self._optimizer.clipvalue
  
    @clipvalue.setter
    def clipvalue(self, val):
      self._optimizer.clipvalue = val
  
    def _aggregate_gradients(self, grads_and_vars):
      return self._optimizer._aggregate_gradients(grads_and_vars)  # pylint: disable=protected-access
  
    def _restore_slot_variable(self, slot_name, variable, slot_variable):
      return self._optimizer._restore_slot_variable(slot_name, variable,  # pylint: disable=protected-access
                                                    slot_variable)
  
    def _create_or_restore_slot_variable(self, slot_variable_position, slot_name,
                                         variable):
      return self._optimizer._create_or_restore_slot_variable(  # pylint: disable=protected-access
          slot_variable_position, slot_name, variable)
  
    def get_slot(self, var, slot_name):
      return self._optimizer.get_slot(var, slot_name)
  
    def add_slot(self, var, slot_name, initializer='zeros'):
      return self._optimizer.add_slot(var, slot_name, initializer)
  
    def __getattribute__(self, name):
      try:
        return object.__getattribute__(self, name)
      except AttributeError as e:
        if name == '_optimizer' or name == '_hyper':
          # Avoid infinite recursion
          raise e
  
        # Delegate hyperparameter accesses to inner optimizer.
        if name == 'lr':
          name = 'learning_rate'
        if name in self._optimizer._hyper:
          return self._optimizer._get_hyper(name)
        raise e
  
    def __dir__(self):
      result = set(super(LossScaleOptimizer, self).__dir__())
      if '_optimizer' in result:
        result |= self._optimizer._hyper.keys()
        if 'learning_rate' in self._optimizer._hyper.keys():
          result.add('lr')
      return list(result)
  
    def __setattr__(self, name, value):
      if name == 'lr':
        name = 'learning_rate'
      # Delegate setting hyperparameter to inner optimizer if the attribute does
      # not exist on the LossScaleOptimizer
      try:
        # We cannot check for the 'iterations' attribute as it cannot be set after
        # it is accessed.
        if name != 'iterations':
          object.__getattribute__(self, name)
        has_attribute = True
      except AttributeError:
        has_attribute = False
      if (name != '_optimizer' and name in self._optimizer._hyper
          and not has_attribute):
        self._optimizer._set_hyper(name, value)
      else:
        super(LossScaleOptimizer, self).__setattr__(name, value)
  
    # We do not override some OptimizerV2 methods. For each, we describe why we do
    # not delegate them to self._optimizer:
    # * get_updates: get_updates() calls get_gradients(). Since we override
    #   get_gradients(), we cannot delegate get_updates() to self._optimizer,
    #   otherwise the overridden get_gradients() method would not be called.
    #   Luckily, get_updates() does not access any OptimizerV2 fields, so
    #   inheriting the OptimizerV2 version works fine.
    # * minimize: We don't delegate for a similar as get_updates(): it calls
    #   both self._compute_gradients() and self.apply_gradients(), and both need
    #   to have the LossScaleOptimizer version called.
  
    # TODO(reedwm): Maybe throw an error if mixed precision is used without this
    # optimizer being used.
  
  
  @keras_export('keras.mixed_precision.experimental.LossScaleOptimizer')
  class LossScaleOptimizerV1(LossScaleOptimizer):
    """An deprecated optimizer that applies loss scaling.
  
    Warning: This class is deprecated and will be removed in TensorFlow 2.5.
    Please use the non-experimental class
    `tf.keras.mixed_precision.LossScaleOptimizer` instead.
  
    This class is identical to the non-experimental
    `keras.mixed_precision.LossScaleOptimizer` except its constructor takes
    different arguments. For this class (the experimental version), the
    constructor takes a `loss_scale` argument.  For the non-experimental class,
    the constructor encodes the loss scaling information in multiple arguments.
    Note that unlike this class, the non-experimental class does not accept a
    `tf.compat.v1.mixed_precision.LossScale`, which is deprecated.
  
    If you currently use this class, you should switch to the non-experimental
    `tf.keras.mixed_precision.LossScaleOptimizer` instead. We show several
    examples of converting the use of the experimental class to the equivalent
    non-experimental class.
  
    >>> # In all of the the examples below, `opt1` and `opt2` are identical
    >>> opt1 = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
    ...     tf.keras.optimizers.SGD(), loss_scale='dynamic')
    >>> opt2 = tf.keras.mixed_precision.LossScaleOptimizer(
    ...     tf.keras.optimizers.SGD())
    >>> assert opt1.get_config() == opt2.get_config()
  
    >>> opt1 = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
    ...     tf.keras.optimizers.SGD(), loss_scale=123)
    >>> # dynamic=False indicates to use fixed loss scaling. initial_scale=123
    >>> # refers to the initial loss scale, which is the single fixed loss scale
    >>> # when dynamic=False.
    >>> opt2 = tf.keras.mixed_precision.LossScaleOptimizer(
    ...     tf.keras.optimizers.SGD(), dynamic=False, initial_scale=123)
    >>> assert opt1.get_config() == opt2.get_config()
  
    >>> loss_scale = tf.compat.v1.mixed_precision.experimental.DynamicLossScale(
    ...     initial_loss_scale=2048, increment_period=500)
    >>> opt1 = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
    ...     tf.keras.optimizers.SGD(), loss_scale=loss_scale)
    >>> opt2 = tf.keras.mixed_precision.LossScaleOptimizer(
    ...     tf.keras.optimizers.SGD(), initial_scale=2048,
    ...     dynamic_growth_steps=500)
    >>> assert opt1.get_config() == opt2.get_config()
  
    Make sure to also switch from this class to the non-experimental class in
    isinstance checks, if you have any. If you do not do this, your model may run
    into hard-to-debug issues, as the experimental `LossScaleOptimizer` subclasses
    the non-experimental `LossScaleOptimizer`, but not vice versa. It is safe to
    switch isinstance checks to the non-experimental `LossScaleOptimizer` even
    before using the non-experimental `LossScaleOptimizer`.
  
    >>> opt1 = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
    ...     tf.keras.optimizers.SGD(), loss_scale='dynamic')
    >>> # The experimental class subclasses the non-experimental class
    >>> isinstance(opt1, tf.keras.mixed_precision.LossScaleOptimizer)
    True
    >>> opt2 = tf.keras.mixed_precision.LossScaleOptimizer(
    ...     tf.keras.optimizers.SGD())
    >>> # The non-experimental class does NOT subclass the experimental class.
    >>> isinstance(opt2, tf.keras.mixed_precision.experimental.LossScaleOptimizer)
    False
  
    Args:
      optimizer: The Optimizer instance to wrap.
      loss_scale: The loss scale to scale the loss and gradients. This can
        either be an int/float to use a fixed loss scale, the string "dynamic"
        to use dynamic loss scaling, or an instance of a LossScale. The string
        "dynamic" equivalent to passing `DynamicLossScale()`, and passing an
        int/float is equivalent to passing a FixedLossScale with the given loss
        scale. If a DynamicLossScale is passed, DynamicLossScale.multiplier must
        be 2 (the default).
    """
  
    def __init__(self, optimizer, loss_scale):
      warn_msg_prefix = (
          'tf.keras.mixed_precision.experimental.LossScaleOptimizer is '
          'deprecated. Please use tf.keras.mixed_precision.LossScaleOptimizer '
          'instead. ')
  
      if isinstance(loss_scale, dict):
        loss_scale = keras_loss_scale_module.deserialize(loss_scale)
  
      if isinstance(loss_scale, (int, float)):
        tf_logging.warn(
            warn_msg_prefix + 'For example\n'
            '  opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer('
            'opt, dynamic=False, initial_scale={})'.format(loss_scale))
        super(LossScaleOptimizerV1, self).__init__(optimizer, dynamic=False,
                                                   initial_scale=loss_scale)
      elif isinstance(loss_scale, tf.mixed_precision.experimental.FixedLossScale):
        ls_val = loss_scale._loss_scale_value  # pylint: disable=protected-access
        tf_logging.warn(
            warn_msg_prefix + 'For example\n'
            '  opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer('
            'opt, dynamic=False, initial_scale={})'.format(ls_val))
        super(LossScaleOptimizerV1, self).__init__(optimizer, dynamic=False,
                                                   initial_scale=ls_val)
      elif loss_scale == 'dynamic':
        tf_logging.warn(
            warn_msg_prefix + 'For example\n'
            '  opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer('
            'opt)')
        super(LossScaleOptimizerV1, self).__init__(optimizer)
      elif isinstance(loss_scale, tf.mixed_precision.experimental.DynamicLossScale):
        kwargs = {}
        extra_arguments = ''
        if loss_scale.initial_loss_scale != _DEFAULT_INITIAL_SCALE:
          kwargs['initial_scale'] = loss_scale.initial_loss_scale
          extra_arguments += (', initial_scale=%s' %
                              loss_scale.initial_loss_scale)
        if loss_scale.increment_period != _DEFAULT_GROWTH_STEPS:
          kwargs['dynamic_growth_steps'] = loss_scale.increment_period
          extra_arguments += (', dynamic_growth_steps=%s' %
                              loss_scale.increment_period)
        if loss_scale.multiplier != 2:
          raise ValueError('When passing a DynamicLossScale to "loss_scale", '
                           'DynamicLossScale.multiplier must be 2. Got: %s'
                           % (loss_scale,))
        tf_logging.warn(
            warn_msg_prefix +
            'Note that the non-experimental LossScaleOptimizer does not take a '
            'DynamicLossScale but instead takes the dynamic configuration '
            'directly in the constructor. For example:\n'
            '  opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer('
            'opt{})\n'.format(extra_arguments))
        super(LossScaleOptimizerV1, self).__init__(optimizer, **kwargs)
      elif isinstance(loss_scale, tf.mixed_precision.experimental.LossScale):
        raise TypeError('Passing a LossScale that is not a FixedLossScale or a '
                        'DynamicLossScale is no longer supported. Got: {}'
                        .format(loss_scale))
      else:
        raise ValueError('Invalid value passed to loss_scale. loss_scale '
                         'must be the string "dynamic" (recommended), an int, '
                         'a float, a FixedLossScale, or a DynamicLossScale. Got '
                         'value: {}'.format(loss_scale))
  
    @classmethod
    def from_config(cls, config, custom_objects=None):
      config = config.copy()  # Make a copy, since we mutate config
  
      # If loss_scale is in config, we assume we are deserializing a
      # LossScaleOptimizer from TF 2.3 or below. Otherwise, we assume we are
      # deserializing a LossScaleOptimizer from TF 2.4 or above.
      if 'loss_scale' in config:
        config['loss_scale'] = keras_loss_scale_module.deserialize(
            config['loss_scale'])
        if (isinstance(config['loss_scale'], tf.mixed_precision.experimental.DynamicLossScale)
            and config['loss_scale'].multiplier != 2):
          raise ValueError('Cannot deserialize LossScaleOptimizer with a '
                           'DynamicLossScale whose multiplier is not 2. Got '
                           'DynamicLossScale: %s' % (config['loss_scale'],))
        config['optimizer'] = optimizers.deserialize(
            config['optimizer'], custom_objects=custom_objects)
        return cls(**config)
  
      # We convert the config, as generated by LossScaleOptimizer.get_config, to a
      # version that can be passed to LossScaleOptimizerV1.__init__
      if config['dynamic']:
        config['loss_scale'] = tf.mixed_precision.experimental.DynamicLossScale(
            config['initial_scale'], config['dynamic_growth_steps'], multiplier=2)
      else:
        config['loss_scale'] = tf.mixed_precision.experimental.FixedLossScale(
            config['initial_scale'])
  
      del config['dynamic']
      del config['initial_scale']
      del config['dynamic_growth_steps']
      config['optimizer'] = optimizers.deserialize(
          config.pop('inner_optimizer'), custom_objects=custom_objects)
      return cls(**config)
  
  
  class FakeOptimizerForRestoration(tf.__internal__.tracking.Trackable):
    """A fake optimizer used to support restoring TensorFlow 2.2 checkpoints.
  
    The checkpoint format for LossScaleOptimizers changed after TF 2.2. This class
    exists to support restoring TF 2.2 checkpoints in newer version of TensorFlow.
  
    In TF 2.2, LossScaleOptimizer would track the wrapped optimizer by calling the
    following in LossScaleOptimizer.__init__
  
    ```
    self._track_trackable(self._optimizer, 'base_optimizer')
    ```
  
    This means a dependency from the LossScaleOptimizer to the wrapped optimizer
    would be stored in the checkpoint. However now, the checkpoint format with a
    LossScaleOptimizer is the same as the format without a LossScaleOptimizer,
    except the loss scale is also stored. This means there is no dependency from
    the LossScaleOptimizer to the wrapped optimizer. Instead, the
    LossScaleOptimizer acts as if it is the wrapped optimizer, from a checkpoint's
    perspective, by overriding all Trackable methods and delegating them to the
    wrapped optimizer.
  
    To allow restoring TF 2.2. checkpoints, LossScaleOptimizer adds a dependency
    on this class instead of the inner optimizer. When restored, this class will
    instead restore the slot variables of the inner optimizer. Since this class
    has no variables, it does not affect the checkpoint when saved.
    """
  
    def __init__(self, optimizer):
      self._optimizer = optimizer
  
    def get_slot_names(self):
      return self._optimizer.get_slot_names()
  
    def _create_or_restore_slot_variable(self, slot_variable_position, slot_name,
                                         variable):
      return self._optimizer._create_or_restore_slot_variable(  # pylint: disable=protected-access
          slot_variable_position, slot_name, variable)
  
  
  # pylint: disable=protected-access
  mixed_precision._register_wrapper_optimizer_cls(optimizer_v2.OptimizerV2,
                                                  LossScaleOptimizerV1)
  
  
  def _multiply_gradient(gradient, scale):
    """Multiply a (possibly sparse) gradient by the given scale factor."""
    scale = tf.cast(scale, gradient.dtype)
    if isinstance(gradient, tf.IndexedSlices):
      return tf.IndexedSlices(
          gradient.values * scale,
          gradient.indices,
          dense_shape=gradient.dense_shape)
    else:
      return gradient * scale
  
  
  def strategy_supports_loss_scaling():
    """Returns True if the current Strategy supports loss scaling."""
    if not tf.distribute.has_strategy():
      return True
    strategy = tf.distribute.get_strategy()
    # Strategies are supported if either there is only one replica or if variables
    # are replicated per device. Otherwise, the current model.fit() implementation
    # and most custom training loops incorrectly unscale the gradients. Currently,
    # gradients are unscaled once per compute replica, but they should be unscaled
    # once per variable replica. When there is one variable replica for each
    # compute replica, this works fine, but otherwise issues will occur.
    # TODO(reedwm): Support all strategies.
    return isinstance(strategy, (
        tf.distribute.MultiWorkerMirroredStrategy,
        tf.compat.v1.distribute.experimental.MultiWorkerMirroredStrategy,
        tf.distribute.OneDeviceStrategy,
        tf.compat.v1.distribute.OneDeviceStrategy,
        tf.distribute.MirroredStrategy,
        tf.compat.v1.distribute.MirroredStrategy,
    ))
  