| """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 |
|
|
