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 ASEBO(Algorithm):
"""The implementation of the ASEBO algorithm.
Reference:
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization
(https://arxiv.org/abs/1903.04268)
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,
lr: float = 0.05,
lr_decay: float = 1.0,
lr_limit: float = 0.001,
sigma: float = 0.03,
sigma_decay: float = 1.0,
sigma_limit: float = 0.01,
subspace_dims: int | None = None,
device: torch.device | None = None,
):
"""Initialize the ARS 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 optimizer: The optimizer to use. Defaults to None. Currently, only "adam" or None is supported.
:param lr: The learning rate for the optimizer. Defaults to 0.05.
:param lr_decay: The decay factor for the learning rate. Defaults to 1.0.
:param lr_limit: The minimum value for the learning rate. Defaults to 0.001.
:param sigma: The standard deviation of the noise. Defaults to 0.03.
: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 subspace_dims: The dimension of the subspace. Defaults to None.
:param device: The device to use for the tensors. Defaults to None.
"""
super().__init__()
assert pop_size > 1
dim = center_init.shape[0]
if subspace_dims is None:
subspace_dims = dim
# set hyperparameters
self.lr = Parameter(lr, device=device)
self.lr_decay = Parameter(lr_decay, device=device)
self.lr_limit = Parameter(lr_limit, 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
self.subspace_dims = subspace_dims
# setup
center_init.to(device=device)
self.center = Mutable(center_init)
self.grad_subspace = Mutable(torch.zeros(self.subspace_dims, self.dim, device=device))
self.UUT = Mutable(torch.zeros(self.dim, self.dim, device=device))
self.UUT_ort = Mutable(torch.zeros(self.dim, self.dim, device=device))
self.sigma = Mutable(torch.tensor(sigma, device=device))
self.alpha = Mutable(torch.tensor(0.1, device=device))
self.gen_counter = Mutable(torch.tensor(0.0, device=device))
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):
"""
The main step of the ASEBO algorithm.
This function first computes the subspace spanned by the gradient of the fitness function
and then projects the gradient onto the subspace. It then computes the step direction
using the projected gradient and updates the center and standard deviation of the
search distribution.
"""
device = self.center.device
X = self.grad_subspace
X = X - torch.mean(X, dim=0)
U, S, Vt = torch.svd(X, some=True)
max_abs_cols = torch.argmax(torch.abs(U), dim=0)
signs = torch.sign(U[max_abs_cols, :])
U = U * signs
Vt = Vt * signs
U = Vt[: int(self.pop_size / 2)]
UUT = torch.matmul(U.T, U)
U_ort = Vt[int(self.pop_size / 2) :]
UUT_ort = torch.matmul(U_ort.T, U_ort)
UUT = torch.where(self.gen_counter > self.subspace_dims, UUT, torch.zeros(self.dim, self.dim, device=device))
cov = (
self.sigma * (self.alpha / self.dim) * torch.eye(self.dim, device=device)
+ ((1 - self.alpha) / int(self.pop_size / 2)) * UUT
)
chol = torch.linalg.cholesky(cov)
noise = torch.randn(self.dim, int(self.pop_size / 2), device=device)
z_plus = torch.swapaxes(chol @ noise, 0, 1)
z_plus = z_plus / torch.linalg.norm(z_plus, dim=-1)[:, None]
z = torch.cat([z_plus, -1.0 * z_plus])
population = self.center + z
self.gen_counter = self.gen_counter + 1
fitness = self.evaluate(population)
noise = (population - self.center) / self.sigma
noise_1 = noise[: int(self.pop_size / 2)]
fit_1 = fitness[: int(self.pop_size / 2)]
fit_2 = fitness[int(self.pop_size / 2) :]
fit_diff_noise = noise_1.T @ (fit_1 - fit_2)
theta_grad = 1.0 / 2.0 * fit_diff_noise
alpha = torch.linalg.norm(theta_grad @ UUT_ort) / torch.linalg.norm(theta_grad @ self.UUT)
alpha = torch.where(self.gen_counter > self.subspace_dims, alpha, 1.0)
self.grad_subspace = torch.cat([self.grad_subspace, theta_grad[None, :]])[1:, :]
theta_grad /= torch.linalg.norm(theta_grad) / self.dim + 1e-8
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,
)
sigma = self.sigma * self.sigma_decay
sigma = torch.maximum(sigma, self.sigma_limit)
self.center = center
self.sigma = sigma
self.alpha = alpha
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def record_step(self):
return {
"center": self.center,
"sigma": self.sigma,
"alpha": self.alpha,
}
EvoX