lr_schedule.py

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"""Learning rate schedule classes."""

import math
from typing import Mapping, Any, Union, Optional

import tensorflow as tf


def _make_offset_wrapper(new_class_name: str, base_lr_class):
  """Generates a offset wrapper of learning rate schedule.

  It will returns a subclass of the `base_lr_class`, the subclass takes an
  `offset` argument in the constructor. When the new class instance is called,
  the behavior is:
    new_class_object(step) = base_lr_class_object(step - offset)

  Example:
    CosineDecayWithOffset = _make_offset_wrapper(
                     'CosineDecayWithOffset', tf.keras.experimental.CosineDecay)
    # Use the lr:
    lr = CosineDecayWithOffset(offset=100, initial_learning_rate=0.1,
                               decay_steps=1000)
    lr(101) # equals to tf.keras.experimental.CosineDecay(...)(101-100)

  Args:
    new_class_name: the name of the new class.
    base_lr_class: the base learning rate schedule class. Should be subclass of
      tf.keras.optimizers.schedules.LearningRateSchedule

  Returns:
    A new class (subclass of the base_lr_class) that can take an offset.
  """
  assert issubclass(base_lr_class,
                    tf.keras.optimizers.schedules.LearningRateSchedule), (
                        "base_lr_class should be subclass of keras "
                        f"LearningRateSchedule, got {base_lr_class}")

  # pylint: disable=protected-access,pointless-statement
  def offset_learning_rate_init(self, offset=0, **kwargs):
    """Construct learning rate schedule object.

    When this object is called, its behavior is
       self.__call__(step) == base_lr_class.__call__(step - offset)
    Args:
      self: this object.
      offset: The offset when computing the learning rate schedule.
      **kwargs: Pass through to base learning rate class constructor.
    """
    base_lr_class.__init__(self, **kwargs)
    self._offset = offset

  def offset_learning_rate_call(self, step):
    step = tf.cast(step - self._offset, tf.float32)
    return base_lr_class.__call__(self, step)

  # pylint: enable=protected-access,pointless-statement

  return type(
      new_class_name, (base_lr_class,), {
          "base_lr_class": base_lr_class,
          "__init__": offset_learning_rate_init,
          "__call__": offset_learning_rate_call
      })


PiecewiseConstantDecayWithOffset = _make_offset_wrapper(
    "PiecewiseConstantDecayWithOffset",
    tf.keras.optimizers.schedules.PiecewiseConstantDecay)
PolynomialDecayWithOffset = _make_offset_wrapper(
    "PolynomialDecayWithOffset", tf.keras.optimizers.schedules.PolynomialDecay)
ExponentialDecayWithOffset = _make_offset_wrapper(
    "ExponentialDecayWithOffset",
    tf.keras.optimizers.schedules.ExponentialDecay)
CosineDecayWithOffset = _make_offset_wrapper("CosineDecayWithOffset",
                                             tf.keras.experimental.CosineDecay)


class LinearWarmup(tf.keras.optimizers.schedules.LearningRateSchedule):
  """Linear warmup schedule."""

  def __init__(self,
               after_warmup_lr_sched: Union[
                   tf.keras.optimizers.schedules.LearningRateSchedule, float],
               warmup_steps: int,
               warmup_learning_rate: float,
               name: Optional[str] = None):
    """Add linear warmup schedule to a learning rate schedule.

    warmup_lr is the initial learning rate, the final learning rate of the
    init_warmup period is the initial learning rate of lr_schedule in use.
    The learning rate at each step linearly increased according to the following
    formula:
      learning_rate = warmup_lr + step / warmup_steps
                    * (final_warmup_lr - warmup_lr).
    Using warmup overrides the learning rate schedule by the number of warmup
    steps.

    Args:
      after_warmup_lr_sched: tf.keras.optimizers.schedules .LearningRateSchedule
        or a constant.
      warmup_steps: Number of the warmup steps.
      warmup_learning_rate: Initial learning rate for the warmup.
      name: Optional, name of warmup schedule.
    """
    super().__init__()
    self._name = name
    self._after_warmup_lr_sched = after_warmup_lr_sched
    self._warmup_steps = warmup_steps
    self._init_warmup_lr = warmup_learning_rate
    if isinstance(after_warmup_lr_sched,
                  tf.keras.optimizers.schedules.LearningRateSchedule):
      self._final_warmup_lr = after_warmup_lr_sched(warmup_steps)
    else:
      self._final_warmup_lr = tf.cast(after_warmup_lr_sched, dtype=tf.float32)

  def __call__(self, step: int):

    global_step = tf.cast(step, dtype=tf.float32)

    linear_warmup_lr = (
        self._init_warmup_lr + global_step / self._warmup_steps *
        (self._final_warmup_lr - self._init_warmup_lr))

    if isinstance(self._after_warmup_lr_sched,
                  tf.keras.optimizers.schedules.LearningRateSchedule):
      after_warmup_lr = self._after_warmup_lr_sched(step)
    else:
      after_warmup_lr = tf.cast(self._after_warmup_lr_sched, dtype=tf.float32)

    lr = tf.cond(global_step < self._warmup_steps,
                 lambda: linear_warmup_lr,
                 lambda: after_warmup_lr)
    return lr

  def get_config(self) -> Mapping[str, Any]:
    if isinstance(self._after_warmup_lr_sched,
                  tf.keras.optimizers.schedules.LearningRateSchedule):
      config = {
          "after_warmup_lr_sched": self._after_warmup_lr_sched.get_config()}  # pytype: disable=attribute-error
    else:
      config = {"after_warmup_lr_sched": self._after_warmup_lr_sched}  # pytype: disable=attribute-error

    config.update({
        "warmup_steps": self._warmup_steps,
        "warmup_learning_rate": self._init_warmup_lr,
        "name": self._name
    })
    return config


class PolynomialWarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
  """Applies polynomial warmup schedule on a given learning rate decay schedule."""

  def __init__(self,
               after_warmup_lr_sched: Union[
                   tf.keras.optimizers.schedules.LearningRateSchedule, float],
               warmup_steps: int,
               power: float = 1.0,
               name: str = "PolynomialWarmup"):
    super().__init__()
    if isinstance(after_warmup_lr_sched,
                  tf.keras.optimizers.schedules.LearningRateSchedule):
      self._initial_learning_rate = after_warmup_lr_sched(warmup_steps)
    else:
      self._initial_learning_rate = tf.cast(
          after_warmup_lr_sched, dtype=tf.float32)

    self._warmup_steps = warmup_steps
    self._power = power
    self._after_warmup_lr_sched = after_warmup_lr_sched
    self._name = name

  def __call__(self, step):
    with tf.name_scope(self._name or "PolynomialWarmUp") 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)

      if self._warmup_steps <= 0:
        warmup_percent_done = 1.0
      else:
        # A zero `step` may cause Inf. So make `step` positive.
        step_non_zero = tf.math.maximum(global_step_float, 1.0)
        warmup_percent_done = step_non_zero / warmup_steps_float

      warmup_learning_rate = (
          self._initial_learning_rate *
          tf.math.pow(warmup_percent_done, self._power))

      if isinstance(self._after_warmup_lr_sched,
                    tf.keras.optimizers.schedules.LearningRateSchedule):
        after_warmup_lr = self._after_warmup_lr_sched(step)
      else:
        after_warmup_lr = tf.cast(self._after_warmup_lr_sched, dtype=tf.float32)

      return tf.cond(
          global_step_float < warmup_steps_float,
          lambda: warmup_learning_rate,
          lambda: after_warmup_lr,
          name=name)

  def get_config(self) -> Mapping[str, Any]:
    if isinstance(self._after_warmup_lr_sched,
                  tf.keras.optimizers.schedules.LearningRateSchedule):
      config = {
          "after_warmup_lr_sched": self._after_warmup_lr_sched.get_config()}  # pytype: disable=attribute-error
    else:
      config = {"after_warmup_lr_sched": self._after_warmup_lr_sched}  # pytype: disable=attribute-error

    config.update({
        "warmup_steps": self._warmup_steps,
        "power": self._power,
        "name": self._name
    })
    return config


class DirectPowerDecay(tf.keras.optimizers.schedules.LearningRateSchedule):
  """Learning rate schedule follows lr * (step)^power."""

  def __init__(self,
               initial_learning_rate: float,
               power: float = 1.0,
               name: str = "DirectPowerDecay"):
    """Initialize configuration of the learning rate schedule.

    Args:
      initial_learning_rate: The initial learning rate.
      power: The order of the polynomial.
      name: Optional, name of learning rate schedule.
    """
    super().__init__()
    self._initial_learning_rate = initial_learning_rate
    self._power = power
    self._name = name

  def __call__(self, step):
    with tf.name_scope(self._name or "DirectPowerDecay"):
      step = tf.cast(step, tf.float32)
      learning_rate = self._initial_learning_rate
      # A zero `step` may cause Inf. So make `step` positive.
      step_non_zero = tf.math.maximum(step, 1.0)
      learning_rate *= tf.math.pow(step_non_zero, self._power)
      return learning_rate

  def get_config(self):
    """Get the configuration of the learning rate schedule."""
    return {
        "initial_learning_rate": self._initial_learning_rate,
        "power": self._power,
        "name": self._name,
    }


class PowerAndLinearDecay(tf.keras.optimizers.schedules.LearningRateSchedule):
  """Learning rate schedule with multiplied by linear decay at the end.

  The schedule has the following behavoir.
  Let offset_step = step - offset.
  1) offset_step < 0, the actual learning rate equals initial_learning_rate.
  2) offset_step <= total_decay_steps * (1 - linear_decay_fraction), the
  actual learning rate equals lr * offset_step^power.
  3) total_decay_steps * (1 - linear_decay_fraction) <= offset_step <
  total_decay_steps, the actual learning rate equals lr * offset_step^power *
  (total_decay_steps - offset_step) / (total_decay_steps *
  linear_decay_fraction).
  4) offset_step >= total_decay_steps, the actual learning rate equals zero.
  """

  def __init__(self,
               initial_learning_rate: float,
               total_decay_steps: int,
               power: float = 1.0,
               linear_decay_fraction: float = 0.1,
               offset: int = 0,
               name: str = "PowerAndLinearDecay"):
    """Initialize configuration of the learning rate schedule.

    Args:
      initial_learning_rate: The initial learning rate.
      total_decay_steps: The total number of steps for power + linear decay.
      power: The order of the polynomial.
      linear_decay_fraction: In the last `linear_decay_fraction` steps, the
        learning rate will be multiplied by a linear decay.
      offset: The offset applied to steps.
      name: Optional, name of learning rate schedule.
    """
    super().__init__()
    self._initial_learning_rate = initial_learning_rate
    self._total_decay_steps = total_decay_steps
    self._power = power
    self._linear_decay_fraction = linear_decay_fraction
    self._offset = offset
    self._name = name

  def __call__(self, step):
    with tf.name_scope(self._name or "PowerAndLinearDecay"):
      step = tf.cast(step - self._offset, tf.float32)
      learning_rate = self._initial_learning_rate
      # A zero `step` may cause Inf. So make `step` positive.
      step_non_zero = tf.math.maximum(step, 1.0)
      learning_rate *= tf.math.pow(step_non_zero, self._power)
      if self._total_decay_steps * self._linear_decay_fraction > 0:
        learning_rate *= tf.minimum(
            1.0, (self._total_decay_steps - step) /
            (self._total_decay_steps * self._linear_decay_fraction))
        learning_rate = tf.maximum(0.0, learning_rate)
      return learning_rate

  def get_config(self):
    """Get the configuration of the learning rate schedule."""
    return {
        "initial_learning_rate": self._initial_learning_rate,
        "total_decay_steps": self._total_decay_steps,
        "power": self._power,
        "linear_decay_fraction": self._linear_decay_fraction,
        "offset": self._offset,
        "name": self._name,
    }


class PowerDecayWithOffset(tf.keras.optimizers.schedules.LearningRateSchedule):
  """Power learning rate decay with offset.

  Learning rate equals to `pre_offset_learning_rate` if `step` < `offset`.
  Otherwise, learning rate equals to lr * (step - offset)^power.
  """

  def __init__(self,
               initial_learning_rate: float,
               power: float = 1.0,
               offset: int = 0,
               pre_offset_learning_rate: float = 1.0e6,
               name: str = "PowerDecayWithOffset"):
    """Initialize configuration of the learning rate schedule.

    Args:
      initial_learning_rate: The initial learning rate.
      power: The order of the polynomial.
      offset: The offset when computing the power decay.
      pre_offset_learning_rate: The maximum learning rate we'll use.
      name: Optional, name of learning rate schedule.
    """
    super().__init__()
    self._initial_learning_rate = initial_learning_rate
    self._power = power
    self._offset = offset
    self._pre_offset_lr = pre_offset_learning_rate
    self._name = name

  def __call__(self, step):
    with tf.name_scope(self._name or "PowerDecayWithOffset"):
      step = tf.cast(step, tf.float32)
      lr_after_offset = tf.math.pow(
          tf.math.maximum(step - self._offset, 1.0), self._power) * (
              self._initial_learning_rate)

      sign = tf.cast(step > self._offset, tf.float32)
      lr_combined = (1.0 - sign) * self._pre_offset_lr + sign * lr_after_offset
      # Power may give infinitely large LR. So cap it with pre_offset_lr.
      return tf.math.minimum(lr_combined, self._pre_offset_lr)

  def get_config(self):
    """Get the configuration of the learning rate schedule."""
    return {
        "initial_learning_rate": self._initial_learning_rate,
        "power": self._power,
        "offset": self._offset,
        "pre_offset_learning_rate": self._pre_offset_lr,
        "name": self._name,
    }


class StepCosineDecayWithOffset(
    tf.keras.optimizers.schedules.LearningRateSchedule):
  """Stepwise cosine learning rate decay with offset.

  Learning rate is equivalent to one or more cosine decay(s) starting and
  ending at each interval.

  ExampleL

    ```python
    boundaries: [100000, 110000]
    values: [1.0, 0.5]
    lr_decayed_fn = (
    lr_schedule.StepCosineDecayWithOffset(
        boundaries,
        values))
    ```

    from 0 to 100000 step, it will cosine decay from 1.0 to 0.5
    from 100000 to 110000 step, it cosine decay from 0.5 to 0.0
  """

  def __init__(self,
               boundaries,
               values,
               offset: int = 0,
               name: str = "StepCosineDecayWithOffset"):
    """Initialize configuration of the learning rate schedule.

    Args:
      boundaries: A list of `Tensor`s or `int`s with strictly
        increasing entries, and with all elements having the same type as the
        optimizer step.
      values: A list of `Tensor`s or `float`s that specifies the
        values for the intervals defined by `boundaries`. It should have one
        more element than `boundaries`, and all elements should have the same
        type.
      offset: The offset when computing the power decay.
      name: Optional, name of learning rate schedule.
    """
    super().__init__()
    self.values = values
    self.boundaries = boundaries
    self.offset = offset
    self.name = name

    if len(self.values) < 1:
      raise ValueError(f"Expect non empty {self.values}")
    if len(self.boundaries) != len(self.values):
      raise ValueError(
          "Boundaries length is equal to learning rate levels length"
          f"{len(self.boundaries)} != {len(self.values)}")

    self.total_steps = (
        [boundaries[i + 1] - boundaries[i] for i in range(len(boundaries) - 1)
        ] + [0])

  def __call__(self, global_step):
    with tf.name_scope(self.name or "StepCosineDecayWithOffset"):
      global_step = tf.cast(global_step - self.offset, tf.float32)
      lr_levels = self.values
      lr_steps = self.boundaries
      level_total_steps = self.total_steps
      num_levels = len(lr_levels)

      init_lr = lr_levels[0]
      next_init_lr = lr_levels[1] if num_levels > 1 else 0.

      init_total_steps = level_total_steps[0]

      cosine_learning_rate = ((init_lr - next_init_lr) * (tf.cos(
          tf.constant(math.pi) * (global_step) /
          (init_total_steps)) + 1.0) / 2.0 + next_init_lr)
      learning_rate = cosine_learning_rate

      for i in range(1, num_levels):
        next_init_lr = lr_levels[i]
        next_start_step = lr_steps[i]
        next_total_steps = level_total_steps[i]
        next_next_init_lr = lr_levels[i + 1] if num_levels > i + 1 else 0.

        next_cosine_learning_rate = ((next_init_lr - next_next_init_lr) *
                                     (tf.cos(
                                         tf.constant(math.pi) *
                                         (global_step - next_start_step) /
                                         (next_total_steps)) + 1.0) / 2.0 +
                                     next_next_init_lr)
        learning_rate = tf.where(global_step >= next_start_step,
                                 next_cosine_learning_rate, learning_rate)

    return learning_rate

  def get_config(self):
    return {
        "boundaries": self.boundaries,
        "values": self.values,
        "offset": self.offset,
        "name": self.name
    }