from typing import Literal
import torch
from evox.core import Algorithm, Mutable, Parameter
from .adam_step import adam_single_tensor
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class ESMC(Algorithm):
"""The implementation of the DES algorithm.
Reference:
Learn2Hop: Learned Optimization on Rough Landscapes
(https://proceedings.mlr.press/v139/merchant21a.html)
This code has been inspired by or utilizes the algorithmic implementation from evosax.
More information about evosax can be found at the following URL:
GitHub Link: https://github.com/RobertTLange/evosax
"""
def __init__(
self,
pop_size: int,
center_init: torch.Tensor,
optimizer: Literal["adam"] | None = None,
sigma_decay: float = 1.0,
sigma_limit: float = 0.01,
lr: float = 0.05,
sigma: float = 0.03,
device: torch.device | None = None,
):
"""Initialize the ESMC algorithm with the given parameters.
:param pop_size: The size of the population.
:param center_init: The initial center of the population. Must be a 1D tensor.
:param elite_ratio: The ratio of elite population. Defaults to 0.1.
:param lr: The learning rate for the optimizer. Defaults to 0.05.
:param sigma_decay: The decay factor for the standard deviation. Defaults to 1.0.
:param sigma_limit: The minimum value for the standard deviation. Defaults to 0.01.
:param optimizer: The optimizer to use. Defaults to None. Currently, only "adam" or None is supported.
:param device: The device to use for the tensors. Defaults to None.
"""
super().__init__()
assert pop_size > 1
dim = center_init.shape[0]
# set hyperparameters
self.lr = Parameter(lr, device=device)
self.sigma_decay = Parameter(sigma_decay, device=device)
self.sigma_limit = Parameter(sigma_limit, device=device)
# set value
self.dim = dim
self.pop_size = pop_size
self.optimizer = optimizer
# setup
center_init = center_init.to(device=device)
self.center = Mutable(center_init)
self.sigma = Mutable(torch.ones(self.dim, device=device) * sigma)
if optimizer == "adam":
self.exp_avg = Mutable(torch.zeros_like(self.center))
self.exp_avg_sq = Mutable(torch.zeros_like(self.center))
self.beta1 = Parameter(0.9, device=device)
self.beta2 = Parameter(0.999, device=device)
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def step(self):
"""One iteration of the ESMC algorithm.
This function will sample a population, evaluate their fitness, and then
update the center and standard deviation of the algorithm using the
sampled population.
"""
device = self.center.device
z_plus = torch.randn(int(self.pop_size / 2), self.dim, device=device)
z = torch.cat([torch.zeros(1, self.dim, device=device), z_plus, -1.0 * z_plus])
population = self.center + z * self.sigma.reshape(1, self.dim)
fitness = self.evaluate(population)
noise = (population - self.center) / self.sigma
bline_fitness = fitness[0]
noise = noise[1:]
fitness = fitness[1:]
noise_1 = noise[: int((self.pop_size - 1) / 2)]
fit_1 = fitness[: int((self.pop_size - 1) / 2)]
fit_2 = fitness[int((self.pop_size - 1) / 2) :]
fit_diff = torch.minimum(fit_1, bline_fitness) - torch.minimum(fit_2, bline_fitness)
fit_diff_noise = noise_1.T @ fit_diff
theta_grad = 1.0 / int((self.pop_size - 1) / 2) * fit_diff_noise
if self.optimizer is None:
center = self.center - self.lr * theta_grad
else:
center, self.exp_avg, self.exp_avg_sq = adam_single_tensor(
self.center,
theta_grad,
self.exp_avg,
self.exp_avg_sq,
self.beta1,
self.beta2,
self.lr,
)
self.center = center
sigma = torch.maximum(self.sigma * self.sigma_decay, self.sigma_limit)
self.sigma = sigma
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def record_step(self):
return {"center": self.center, "sigma": self.sigma}
EvoX