| |
| |
|
|
| import math |
| import torch |
| import torch.nn as nn |
| from torch.optim.optimizer import Optimizer |
|
|
| class Yogi(Optimizer): |
| r"""Implements Yogi Optimizer Algorithm. |
| It has been proposed in `Adaptive methods for Nonconvex Optimization`. |
| |
| https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization # noqa |
| |
| Note: |
| Reference code: https://github.com/4rtemi5/Yogi-Optimizer_Keras |
| """ |
|
|
| def __init__( |
| self, |
| params, |
| lr: float = 1e-2, |
| betas = (0.9, 0.999), |
| eps: float = 1e-3, |
| initial_accumulator: float = 1e-6, |
| weight_decay: float = 0, |
| ) -> None: |
| if lr <= 0.0: |
| raise ValueError("Invalid learning rate: {}".format(lr)) |
| if eps < 0.0: |
| raise ValueError("Invalid epsilon value: {}".format(eps)) |
| if not 0.0 <= betas[0] < 1.0: |
| raise ValueError( |
| "Invalid beta parameter at index 0: {}".format(betas[0]) |
| ) |
| if not 0.0 <= betas[1] < 1.0: |
| raise ValueError( |
| "Invalid beta parameter at index 1: {}".format(betas[1]) |
| ) |
| if weight_decay < 0: |
| raise ValueError( |
| "Invalid weight_decay value: {}".format(weight_decay) |
| ) |
|
|
| defaults = dict( |
| lr=lr, |
| betas=betas, |
| eps=eps, |
| initial_accumulator=initial_accumulator, |
| weight_decay=weight_decay, |
| ) |
| super(Yogi, self).__init__(params, defaults) |
|
|
| def step(self, closure = None): |
| r"""Performs a single optimization step. |
| |
| Arguments: |
| closure: A closure that reevaluates the model and returns the loss. |
| """ |
| loss = None |
| if closure is not None: |
| loss = closure() |
|
|
| for group in self.param_groups: |
| for p in group["params"]: |
| if p.grad is None: |
| continue |
| grad = p.grad.data |
| if grad.is_sparse: |
| raise RuntimeError( |
| "Yogi does not support sparse gradients, " |
| "please consider SparseAdam instead" |
| ) |
|
|
| state = self.state[p] |
|
|
| |
| |
| |
| |
| |
| if len(state) == 0: |
| state["step"] = 0 |
| |
| state["exp_avg"] = nn.init.constant_( |
| torch.empty_like( |
| p.data, memory_format=torch.preserve_format |
| ), |
| group["initial_accumulator"], |
| ) |
| |
| state["exp_avg_sq"] = nn.init.constant_( |
| torch.empty_like( |
| p.data, memory_format=torch.preserve_format |
| ), |
| group["initial_accumulator"], |
| ) |
|
|
| exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] |
| beta1, beta2 = group["betas"] |
|
|
| state["step"] += 1 |
| bias_correction1 = 1 - beta1 ** state["step"] |
| bias_correction2 = 1 - beta2 ** state["step"] |
|
|
| if group["weight_decay"] != 0: |
| grad = grad.add(p.data, alpha=group["weight_decay"]) |
|
|
| |
| exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) |
|
|
| grad_squared = grad.mul(grad) |
|
|
| exp_avg_sq.addcmul_( |
| torch.sign(exp_avg_sq - grad_squared), |
| grad_squared, |
| value=-(1 - beta2), |
| ) |
|
|
| denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_( |
| group["eps"] |
| ) |
| step_size = group["lr"] / bias_correction1 |
| p.data.addcdiv_(exp_avg, denom, value=-step_size) |
|
|
| return loss |
