android-12.0.0_r34/s

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# See the License for the specific language governing permissions and
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# ==============================================================================
"""Maintain moving averages of parameters."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.distribute import distribute_lib
from tensorflow.python.distribute import distribution_strategy_context
from tensorflow.python.distribute import reduce_util as ds_reduce_util
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.training import slot_creator
from tensorflow.python.util.tf_export import tf_export


# TODO(touts): switch to variables.Variable.
def assign_moving_average(variable, value, decay, zero_debias=True, name=None):
  """Compute the moving average of a variable.

  The moving average of 'variable' updated with 'value' is:
    variable * decay + value * (1 - decay)

  The returned Operation sets 'variable' to the newly computed moving average,
  by performing this subtraction:
     variable -= (1 - decay) * (variable - value)

  Since variables that are initialized to a `0` value will be `0` biased,
  `zero_debias` optionally enables scaling by the mathematically correct
  debiasing factor of
    1 - decay ** num_updates
  See Section 3 of (Kingma et al., 2015) for more details.

  The names of the debias shadow variables, by default, include both the scope
  they were created in and the scope of the variables they debias. They are also
  given a uniquifying-suffix.

  E.g.:

  ```
    with tf.compat.v1.variable_scope('scope1'):
      with tf.compat.v1.variable_scope('scope2'):
        var = tf.compat.v1.get_variable('foo')
        update_1 = tf.assign_moving_average(var, 0.0, 1.0)
        update_2 = tf.assign_moving_average(var, 0.0, 0.9)

    # var.name: 'scope1/scope2/foo'
    # shadow var names: 'scope1/scope2/scope1/scope2/foo/biased'
    #                   'scope1/scope2/scope1/scope2/foo/biased_1'
  ```

  Args:
    variable: A Variable.
    value: A tensor with the same shape as 'variable'.
    decay: A float Tensor or float value.  The moving average decay.
    zero_debias: A python bool. If true, assume the variable is 0-initialized
      and unbias it, as in (Kingma et al., 2015). See docstring in
        `_zero_debias` for more details.
    name: Optional name of the returned operation.

  Returns:
    A tensor which if evaluated will compute and return the new moving average.

  References:
    Adam - A Method for Stochastic Optimization:
      [Kingma et al., 2015](https://arxiv.org/abs/1412.6980)
      ([pdf](https://arxiv.org/pdf/1412.6980.pdf))
  """
  with ops.name_scope(name, "AssignMovingAvg",
                      [variable, value, decay]) as scope:
    decay = ops.convert_to_tensor(1.0 - decay, name="decay")
    if decay.dtype != variable.dtype.base_dtype:
      decay = math_ops.cast(decay, variable.dtype.base_dtype)

    def update_fn(v, value):
      return state_ops.assign_sub(v, (v - value) * decay, name=scope)

    def update(strategy, v, value):
      if zero_debias:
        return _zero_debias(strategy, v, value, decay)
      else:
        return _update(strategy, v, update_fn, args=(value,))

    replica_context = distribution_strategy_context.get_replica_context()
    if replica_context:
      # In a replica context, we update variable using the mean of value across
      # replicas.
      def merge_fn(strategy, v, value):
        value = strategy.extended.reduce_to(ds_reduce_util.ReduceOp.MEAN, value,
                                            v)
        return update(strategy, v, value)

      return replica_context.merge_call(merge_fn, args=(variable, value))
    else:
      strategy = distribution_strategy_context.get_cross_replica_context()
      return update(strategy, variable, value)


def weighted_moving_average(value,
                            decay,
                            weight,
                            truediv=True,
                            collections=None,
                            name=None):
  """Compute the weighted moving average of `value`.

  Conceptually, the weighted moving average is:
    `moving_average(value * weight) / moving_average(weight)`,
  where a moving average updates by the rule
    `new_value = decay * old_value + (1 - decay) * update`
  Internally, this Op keeps moving average variables of both `value * weight`
  and `weight`.

  Args:
    value: A numeric `Tensor`.
    decay: A float `Tensor` or float value.  The moving average decay.
    weight:  `Tensor` that keeps the current value of a weight. Shape should be
      able to multiply `value`.
    truediv:  Boolean, if `True`, dividing by `moving_average(weight)` is
      floating point division.  If `False`, use division implied by dtypes.
    collections:  List of graph collections keys to add the internal variables
      `value * weight` and `weight` to. Defaults to
      `[GraphKeys.GLOBAL_VARIABLES]`.
    name: Optional name of the returned operation. Defaults to
      "WeightedMovingAvg".

  Returns:
    An Operation that updates and returns the weighted moving average.
  """
  # Unlike assign_moving_average, the weighted moving average doesn't modify
  # user-visible variables. It is the ratio of two internal variables, which are
  # moving averages of the updates.  Thus, the signature of this function is
  # quite different than assign_moving_average.
  if collections is None:
    collections = [ops.GraphKeys.GLOBAL_VARIABLES]
  with variable_scope.variable_scope(name, "WeightedMovingAvg",
                                     [value, weight, decay]) as scope:
    value_x_weight_var = variable_scope.get_variable(
        "value_x_weight",
        shape=value.get_shape(),
        dtype=value.dtype,
        initializer=init_ops.zeros_initializer(),
        trainable=False,
        collections=collections)
    weight_var = variable_scope.get_variable(
        "weight",
        shape=weight.get_shape(),
        dtype=weight.dtype,
        initializer=init_ops.zeros_initializer(),
        trainable=False,
        collections=collections)
    numerator = assign_moving_average(
        value_x_weight_var, value * weight, decay, zero_debias=False)
    denominator = assign_moving_average(
        weight_var, weight, decay, zero_debias=False)

    if truediv:
      return math_ops.truediv(numerator, denominator, name=scope.name)
    else:
      return math_ops.divide(numerator, denominator, name=scope.name)


def _update(strategy, var, update_fn, args):
  """Applies updates depending on the context."""
  assert distribution_strategy_context.in_cross_replica_context(), (
      "_update can only be called in cross-replica context")
  if distribute_lib.get_update_replica_id() is not None:
    # Call update_fn on var to delegate the implementation. We expect `var` will
    # do the right thing in update context, e.g, if `var` is a MirroredVariable,
    # it should pick its component variable based on `update_replica_id` and
    # only update that.
    return update_fn(var, *args)
  else:
    return strategy.extended.update(var, update_fn, args)


def _zero_debias(strategy, unbiased_var, value, decay):
  """Compute the delta required for a debiased Variable.

  All exponential moving averages initialized with Tensors are initialized to 0,
  and therefore are biased to 0. Variables initialized to 0 and used as EMAs are
  similarly biased. This function creates the debias updated amount according to
  a scale factor, as in (Kingma et al., 2015).

  To demonstrate the bias the results from 0-initialization, take an EMA that
  was initialized to `0` with decay `b`. After `t` timesteps of seeing the
  constant `c`, the variable have the following value:

  ```
    EMA = 0*b^(t) + c*(1 - b)*b^(t-1) + c*(1 - b)*b^(t-2) + ...
        = c*(1 - b^t)
  ```

  To have the true value `c`, we would divide by the scale factor `1 - b^t`.

  In order to perform debiasing, we use two shadow variables. One keeps track of
  the biased estimate, and the other keeps track of the number of updates that
  have occurred.

  Args:
    strategy: `Strategy` used to create and update variables.
    unbiased_var: A Variable representing the current value of the unbiased EMA.
    value: A Tensor representing the most recent value.
    decay: A Tensor representing `1-decay` for the EMA.

  Returns:
    The amount that the unbiased variable should be updated. Computing this
    tensor will also update the shadow variables appropriately.

  References:
    Adam - A Method for Stochastic Optimization:
      [Kingma et al., 2015](https://arxiv.org/abs/1412.6980)
      ([pdf](https://arxiv.org/pdf/1412.6980.pdf))

  """
  with variable_scope.variable_scope(
      unbiased_var.name[:-len(":0")], values=[unbiased_var, value, decay]):
    with ops.init_scope():
      biased_initializer = init_ops.zeros_initializer()
      local_step_initializer = init_ops.zeros_initializer()

    def _maybe_get_unique(name):
      """Get name for a unique variable, if not `reuse=True`."""
      if variable_scope.get_variable_scope().reuse:
        return name
      vs_vars = [
          x.op.name
          for x in variable_scope.get_variable_scope().global_variables()
      ]
      full_name = variable_scope.get_variable_scope().name + "/" + name
      if full_name not in vs_vars:
        return name
      idx = 1
      while full_name + ("_%d" % idx) in vs_vars:
        idx += 1
      return name + ("_%d" % idx)

    with strategy.extended.colocate_vars_with(unbiased_var):
      biased_var = variable_scope.get_variable(
          _maybe_get_unique("biased"),
          initializer=biased_initializer,
          shape=unbiased_var.get_shape(),
          dtype=unbiased_var.dtype,
          trainable=False)
      local_step = variable_scope.get_variable(
          _maybe_get_unique("local_step"),
          shape=[],
          dtype=unbiased_var.dtype,
          initializer=local_step_initializer,
          trainable=False)

  def update_fn(v, value, biased_var, local_step):
    update_biased = state_ops.assign_sub(biased_var,
                                         (biased_var - value) * decay)
    update_local_step = local_step.assign_add(1)

    # This function gets `1 - decay`, so use `1.0 - decay` in the exponent.
    bias_factor = 1 - math_ops.pow(1.0 - decay, update_local_step)
    return state_ops.assign(
        v, update_biased / bias_factor, name=ops.get_name_scope() + "/")

  return _update(
      strategy, unbiased_var, update_fn, args=(value, biased_var, local_step))


@tf_export("train.ExponentialMovingAverage")
class ExponentialMovingAverage(object):
  """Maintains moving averages of variables by employing an exponential decay.

  When training a model, it is often beneficial to maintain moving averages of
  the trained parameters.  Evaluations that use averaged parameters sometimes
  produce significantly better results than the final trained values.

  The `apply()` method adds shadow copies of trained variables and add ops that
  maintain a moving average of the trained variables in their shadow copies.
  It is used when building the training model.  The ops that maintain moving
  averages are typically run after each training step.
  The `average()` and `average_name()` methods give access to the shadow
  variables and their names.  They are useful when building an evaluation
  model, or when restoring a model from a checkpoint file.  They help use the
  moving averages in place of the last trained values for evaluations.

  The moving averages are computed using exponential decay.  You specify the
  decay value when creating the `ExponentialMovingAverage` object.  The shadow
  variables are initialized with the same initial values as the trained
  variables.  When you run the ops to maintain the moving averages, each
  shadow variable is updated with the formula:

    `shadow_variable -= (1 - decay) * (shadow_variable - variable)`

  This is mathematically equivalent to the classic formula below, but the use
  of an `assign_sub` op (the `"-="` in the formula) allows concurrent lockless
  updates to the variables:

    `shadow_variable = decay * shadow_variable + (1 - decay) * variable`

  Reasonable values for `decay` are close to 1.0, typically in the
  multiple-nines range: 0.999, 0.9999, etc.

  Example usage when creating a training model:

  ```python
  # Create variables.
  var0 = tf.Variable(...)
  var1 = tf.Variable(...)
  # ... use the variables to build a training model...
  ...
  # Create an op that applies the optimizer.  This is what we usually
  # would use as a training op.
  opt_op = opt.minimize(my_loss, [var0, var1])

  # Create an ExponentialMovingAverage object
  ema = tf.train.ExponentialMovingAverage(decay=0.9999)

  with tf.control_dependencies([opt_op]):
      # Create the shadow variables, and add ops to maintain moving averages
      # of var0 and var1. This also creates an op that will update the moving
      # averages after each training step.  This is what we will use in place
      # of the usual training op.
      training_op = ema.apply([var0, var1])

  ...train the model by running training_op...
  ```

  There are two ways to use the moving averages for evaluations:

  *  Build a model that uses the shadow variables instead of the variables.
     For this, use the `average()` method which returns the shadow variable
     for a given variable.
  *  Build a model normally but load the checkpoint files to evaluate by using
     the shadow variable names.  For this use the `average_name()` method.  See
     the `tf.compat.v1.train.Saver` for more
     information on restoring saved variables.

  Example of restoring the shadow variable values:

  ```python
  # Create a Saver that loads variables from their saved shadow values.
  shadow_var0_name = ema.average_name(var0)
  shadow_var1_name = ema.average_name(var1)
  saver = tf.compat.v1.train.Saver({shadow_var0_name: var0, shadow_var1_name:
  var1})
  saver.restore(...checkpoint filename...)
  # var0 and var1 now hold the moving average values
  ```
  """

  def __init__(self,
               decay,
               num_updates=None,
               zero_debias=False,
               name="ExponentialMovingAverage"):
    """Creates a new ExponentialMovingAverage object.

    The `apply()` method has to be called to create shadow variables and add
    ops to maintain moving averages.

    The optional `num_updates` parameter allows one to tweak the decay rate
    dynamically. It is typical to pass the count of training steps, usually
    kept in a variable that is incremented at each step, in which case the
    decay rate is lower at the start of training.  This makes moving averages
    move faster.  If passed, the actual decay rate used is:

      `min(decay, (1 + num_updates) / (10 + num_updates))`

    Args:
      decay: Float.  The decay to use.
      num_updates: Optional count of number of updates applied to variables.
      zero_debias: If `True`, zero debias moving-averages that are initialized
        with tensors.
      name: String. Optional prefix name to use for the name of ops added in
        `apply()`.
    """
    self._decay = decay
    self._num_updates = num_updates
    self._zero_debias = zero_debias
    self._name = name
    self._averages = {}

  @property
  def name(self):
    """The name of this ExponentialMovingAverage object."""
    return self._name

  def apply(self, var_list=None):
    """Maintains moving averages of variables.

    `var_list` must be a list of `Variable` or `Tensor` objects.  This method
    creates shadow variables for all elements of `var_list`.  Shadow variables
    for `Variable` objects are initialized to the variable's initial value.
    They will be added to the `GraphKeys.MOVING_AVERAGE_VARIABLES` collection.
    For `Tensor` objects, the shadow variables are initialized to 0 and zero
    debiased (see docstring in `assign_moving_average` for more details).

    shadow variables are created with `trainable=False` and added to the
    `GraphKeys.ALL_VARIABLES` collection.  They will be returned by calls to
    `tf.compat.v1.global_variables()`.

    Returns an op that updates all shadow variables from the current value of
    their associated variables.

    Note that `apply()` can be called multiple times. When eager execution is
    enabled each call to apply will update the variables once, so this needs to
    be called in a loop.

    Args:
      var_list: A list of Variable or Tensor objects. The variables and Tensors
        must be of types bfloat16, float16, float32, or float64.

    Returns:
      An Operation that updates the moving averages.

    Raises:
      TypeError: If the arguments are not an allowed type.
    """
    # TODO(touts): op_scope
    if var_list is None:
      var_list = variables.trainable_variables()
    for v in var_list:
      if isinstance(v, ops.EagerTensor):
        raise TypeError(
            "tf.train.ExponentialMovingAverage does not support non-Variable"
            " tensors when eager execution is enabled.")
    zero_debias_true = set()  # set of vars to set `zero_debias=True`
    for var in var_list:
      if var.dtype.base_dtype not in [
          dtypes.bfloat16, dtypes.float16, dtypes.float32, dtypes.float64
      ]:
        raise TypeError("The variables must be half, float, or double: %s" %
                        var.name)

      if var.ref() not in self._averages:
        # For variables: to lower communication bandwidth across devices we keep
        # the moving averages on the same device as the variables. For other
        # tensors, we rely on the existing device allocation mechanism.
        with ops.init_scope():
          if isinstance(var, variables.Variable):
            with ops.device(var.device):
              initialized_value = var.initialized_value()
            avg = slot_creator.create_slot(
                var,
                initialized_value,
                self.name,
                colocate_with_primary=True,
                copy_xla_sharding=True)
            # NOTE(mrry): We only add `tf.Variable` objects to the
            # `MOVING_AVERAGE_VARIABLES` collection.
            ops.add_to_collection(ops.GraphKeys.MOVING_AVERAGE_VARIABLES, var)
          else:
            avg = slot_creator.create_zeros_slot(
                var,
                self.name,
                colocate_with_primary=(var.op.type in [
                    "Variable", "VariableV2", "VarHandleOp"
                ]),
                copy_xla_sharding=True)
            if self._zero_debias:
              zero_debias_true.add(avg.ref())
        self._averages[var.ref()] = avg

    with ops.name_scope(self.name) as scope:
      decay = ops.convert_to_tensor(
          self._decay, dtype=dtypes.float32, name="decay")
      if self._num_updates is not None:
        num_updates = math_ops.cast(
            self._num_updates, dtypes.float32, name="num_updates")
        decay = math_ops.minimum(decay,
                                 (1.0 + num_updates) / (10.0 + num_updates))
      updates = []
      for var in var_list:
        avg = self._averages[var.ref()]
        zero_debias = avg.ref() in zero_debias_true
        updates.append(assign_moving_average(avg, var, decay, zero_debias))
      return control_flow_ops.group(*updates, name=scope)

  def average(self, var):
    """Returns the `Variable` holding the average of `var`.

    Args:
      var: A `Variable` object.

    Returns:
      A `Variable` object or `None` if the moving average of `var`
      is not maintained.
    """
    return self._averages.get(var.ref(), None)

  def average_name(self, var):
    """Returns the name of the `Variable` holding the average for `var`.

    The typical scenario for `ExponentialMovingAverage` is to compute moving
    averages of variables during training, and restore the variables from the
    computed moving averages during evaluations.

    To restore variables, you have to know the name of the shadow variables.
    That name and the original variable can then be passed to a `Saver()` object
    to restore the variable from the moving average value with:
      `saver = tf.compat.v1.train.Saver({ema.average_name(var): var})`

    `average_name()` can be called whether or not `apply()` has been called.

    Args:
      var: A `Variable` object.

    Returns:
      A string: The name of the variable that will be used or was used
      by the `ExponentialMovingAverage class` to hold the moving average of
      `var`.
    """
    if var.ref() in self._averages:
      return self._averages[var.ref()].op.name
    return ops.get_default_graph().unique_name(
        var.op.name + "/" + self.name, mark_as_used=False)

  def variables_to_restore(self, moving_avg_variables=None):
    """Returns a map of names to `Variables` to restore.

    If a variable has a moving average, use the moving average variable name as
    the restore name; otherwise, use the variable name.

    For example,

    ```python
      variables_to_restore = ema.variables_to_restore()
      saver = tf.compat.v1.train.Saver(variables_to_restore)
    ```

    Below is an example of such mapping:

    ```
      conv/batchnorm/gamma/ExponentialMovingAverage: conv/batchnorm/gamma,
      conv_4/conv2d_params/ExponentialMovingAverage: conv_4/conv2d_params,
      global_step: global_step
    ```

    Args:
      moving_avg_variables: a list of variables that require to use of the
        moving average variable name to be restored. If None, it will default to
        variables.moving_average_variables() + variables.trainable_variables()

    Returns:
      A map from restore_names to variables. The restore_name is either the
      original or the moving average version of the variable name, depending
      on whether the variable name is in the `moving_avg_variables`.
    """
    name_map = {}
    if moving_avg_variables is None:
      # Include trainable variables and variables which have been explicitly
      # added to the moving_average_variables collection.
      moving_avg_variables = variables.trainable_variables()
      moving_avg_variables += variables.moving_average_variables()
    # Remove duplicates
    moving_avg_variables = set(moving_avg_variables)
    # Collect all the variables with moving average,
    for v in moving_avg_variables:
      name_map[self.average_name(v)] = v
    # Make sure we restore variables without moving averages as well.
    moving_avg_variable_names = set(v.name for v in moving_avg_variables)
    for v in list(set(variables.global_variables())):
      if v.name not in moving_avg_variable_names and v.op.name not in name_map:
        name_map[v.op.name] = v
    return name_map