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| # modified from https://github.com/LiyuanLucasLiu/RAdam | |
| import math | |
| import torch | |
| from torch.optim.optimizer import Optimizer | |
| class RAdam(Optimizer): | |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=True): | |
| 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])) | |
| self.degenerated_to_sgd = degenerated_to_sgd | |
| if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict): | |
| for param in params: | |
| if "betas" in param and (param["betas"][0] != betas[0] or param["betas"][1] != betas[1]): | |
| param["buffer"] = [[None, None, None] for _ in range(10)] | |
| defaults = dict( | |
| lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, buffer=[[None, None, None] for _ in range(10)] | |
| ) | |
| super().__init__(params, defaults) | |
| def __setstate__(self, state): # pylint: disable=useless-super-delegation | |
| super().__setstate__(state) | |
| def step(self, closure=None): | |
| 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.float() | |
| if grad.is_sparse: | |
| raise RuntimeError("RAdam does not support sparse gradients") | |
| p_data_fp32 = p.data.float() | |
| state = self.state[p] | |
| if len(state) == 0: | |
| state["step"] = 0 | |
| state["exp_avg"] = torch.zeros_like(p_data_fp32) | |
| state["exp_avg_sq"] = torch.zeros_like(p_data_fp32) | |
| else: | |
| state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32) | |
| state["exp_avg_sq"] = state["exp_avg_sq"].type_as(p_data_fp32) | |
| exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] | |
| beta1, beta2 = group["betas"] | |
| exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) | |
| exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) | |
| state["step"] += 1 | |
| buffered = group["buffer"][int(state["step"] % 10)] | |
| if state["step"] == buffered[0]: | |
| N_sma, step_size = buffered[1], buffered[2] | |
| else: | |
| buffered[0] = state["step"] | |
| beta2_t = beta2 ** state["step"] | |
| N_sma_max = 2 / (1 - beta2) - 1 | |
| N_sma = N_sma_max - 2 * state["step"] * beta2_t / (1 - beta2_t) | |
| buffered[1] = N_sma | |
| # more conservative since it's an approximated value | |
| if N_sma >= 5: | |
| step_size = math.sqrt( | |
| (1 - beta2_t) | |
| * (N_sma - 4) | |
| / (N_sma_max - 4) | |
| * (N_sma - 2) | |
| / N_sma | |
| * N_sma_max | |
| / (N_sma_max - 2) | |
| ) / (1 - beta1 ** state["step"]) | |
| elif self.degenerated_to_sgd: | |
| step_size = 1.0 / (1 - beta1 ** state["step"]) | |
| else: | |
| step_size = -1 | |
| buffered[2] = step_size | |
| # more conservative since it's an approximated value | |
| if N_sma >= 5: | |
| if group["weight_decay"] != 0: | |
| p_data_fp32.add_(p_data_fp32, alpha=-group["weight_decay"] * group["lr"]) | |
| denom = exp_avg_sq.sqrt().add_(group["eps"]) | |
| p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size * group["lr"]) | |
| p.data.copy_(p_data_fp32) | |
| elif step_size > 0: | |
| if group["weight_decay"] != 0: | |
| p_data_fp32.add_(p_data_fp32, alpha=-group["weight_decay"] * group["lr"]) | |
| p_data_fp32.add_(exp_avg, alpha=-step_size * group["lr"]) | |
| p.data.copy_(p_data_fp32) | |
| return loss | |