| """NovoGrad Optimizer. | |
| Original impl by Masashi Kimura (Convergence Lab): https://github.com/convergence-lab/novograd | |
| Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks` | |
| - https://arxiv.org/abs/1905.11286 | |
| """ | |
| import torch | |
| from torch.optim.optimizer import Optimizer | |
| import math | |
| class NovoGrad(Optimizer): | |
| def __init__( | |
| self, | |
| params, | |
| grad_averaging=False, | |
| lr=0.1, | |
| betas=(0.95, 0.98), | |
| eps=1e-8, | |
| weight_decay=0, | |
| ): | |
| defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) | |
| super(NovoGrad, self).__init__(params, defaults) | |
| self._lr = lr | |
| self._beta1 = betas[0] | |
| self._beta2 = betas[1] | |
| self._eps = eps | |
| self._wd = weight_decay | |
| self._grad_averaging = grad_averaging | |
| self._momentum_initialized = False | |
| def step(self, closure=None): | |
| loss = None | |
| if closure is not None: | |
| loss = closure() | |
| if not self._momentum_initialized: | |
| for group in self.param_groups: | |
| for p in group["params"]: | |
| if p.grad is None: | |
| continue | |
| state = self.state[p] | |
| grad = p.grad.data | |
| if grad.is_sparse: | |
| raise RuntimeError("NovoGrad does not support sparse gradients") | |
| v = torch.norm(grad) ** 2 | |
| m = grad / (torch.sqrt(v) + self._eps) + self._wd * p.data | |
| state["step"] = 0 | |
| state["v"] = v | |
| state["m"] = m | |
| state["grad_ema"] = None | |
| self._momentum_initialized = True | |
| for group in self.param_groups: | |
| for p in group["params"]: | |
| if p.grad is None: | |
| continue | |
| state = self.state[p] | |
| state["step"] += 1 | |
| step, v, m = state["step"], state["v"], state["m"] | |
| grad_ema = state["grad_ema"] | |
| grad = p.grad.data | |
| g2 = torch.norm(grad) ** 2 | |
| grad_ema = ( | |
| g2 | |
| if grad_ema is None | |
| else grad_ema * self._beta2 + g2 * (1.0 - self._beta2) | |
| ) | |
| grad *= 1.0 / (torch.sqrt(grad_ema) + self._eps) | |
| if self._grad_averaging: | |
| grad *= 1.0 - self._beta1 | |
| g2 = torch.norm(grad) ** 2 | |
| v = self._beta2 * v + (1.0 - self._beta2) * g2 | |
| m = self._beta1 * m + ( | |
| grad / (torch.sqrt(v) + self._eps) + self._wd * p.data | |
| ) | |
| bias_correction1 = 1 - self._beta1 ** step | |
| bias_correction2 = 1 - self._beta2 ** step | |
| step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1 | |
| state["v"], state["m"] = v, m | |
| state["grad_ema"] = grad_ema | |
| p.data.add_(-step_size, m) | |
| return loss | |