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# -*- coding: utf-8 -*-
import warnings
from .base_scheduler import BaseScheduler
__all__ = ["CubicSL"]
def _clamp(x, lo, hi):
return max(lo, min(hi, x))
class CubicSL(BaseScheduler):
r"""Sets the sparsity level of each parameter group to the final sl
plus a given exponential function.
.. math::
s_i = s_f + (s_0 - s_f) \cdot \left( 1 - \frac{t - t_0}{n\Delta t} \right)^3
where :math:`s_i` is the sparsity at epoch :math:`t`, :math;`s_f` is the final
sparsity level, :math:`f(i)` is the function to be applied to the current epoch
:math:`t`, initial epoch :math:`t_0`, and final epoch :math:`t_f`.
:math:`\Delta t` is used to control how often the update of the sparsity level
happens. By default,
Args:
sparsifier (BaseSparsifier): Wrapped sparsifier.
init_sl (int, list): Initial level of sparsity
init_t (int, list): Initial step, when pruning starts
delta_t (int, list): Pruning frequency
total_t (int, list): Total number of pruning steps
initially_zero (bool, list): If True, sets the level of sparsity to 0
before init_t (:math:`t_0`). Otherwise, the sparsity level before
init_t (:math:`t_0`) is set to init_sl(:math:`s_0`)
last_epoch (int): The index of last epoch. Default: -1.
verbose (bool): If ``True``, prints a message to stdout for
each update. Default: ``False``.
"""
def __init__(self,
sparsifier,
init_sl=0.0,
init_t=0,
delta_t=10,
total_t=100,
initially_zero=False,
last_epoch=-1,
verbose=False
):
self.sparsifier = sparsifier
self.init_sl = self._make_sure_a_list(init_sl)
self.init_t = self._make_sure_a_list(init_t)
self.delta_t = self._make_sure_a_list(delta_t)
self.total_t = self._make_sure_a_list(total_t)
self.initially_zero = self._make_sure_a_list(initially_zero)
super().__init__(sparsifier, last_epoch, verbose)
@staticmethod
def sparsity_compute_fn(s_0, s_f, t, t_0, dt, n, initially_zero=False):
r""""Computes the current level of sparsity.
Based on https://arxiv.org/pdf/1710.01878.pdf
Args:
s_0: Initial level of sparsity, :math:`s_i`
s_f: Target level of sparsity, :math:`s_f`
t: Current step, :math:`t`
t_0: Initial step, :math:`t_0`
dt: Pruning frequency, :math:`\Delta T`
n: Pruning steps, :math:`n`
initially_zero: Sets the level of sparsity to 0 before t_0.
If False, sets to s_0
Returns:
The sparsity level :math:`s_t` at the current step :math:`t`
"""
if initially_zero and t < t_0:
return 0
s_t = s_f + (s_0 - s_f) * (1.0 - (t - t_0) / (dt * n)) ** 3
s_t = _clamp(s_t, s_0, s_f)
return s_t
def get_sl(self):
if not self._get_sl_called_within_step:
warnings.warn(
"To get the last sparsity level computed by the scheduler, "
"please use `get_last_sl()`.")
return [
self.sparsity_compute_fn(
s_0=initial_sparsity,
s_f=final_sparsity,
t=self.last_epoch,
t_0=initial_epoch,
dt=delta_epoch,
n=interval_epochs,
initially_zero=initially_zero
) for initial_sparsity, final_sparsity, initial_epoch, delta_epoch, interval_epochs, initially_zero in
zip(
self.init_sl,
self.base_sl,
self.init_t,
self.delta_t,
self.total_t,
self.initially_zero
)
]
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