| """RAdam Optimizer. |
| Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam |
| Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265 |
| """ |
| import math |
| import torch |
| from torch.optim.optimizer import Optimizer, required |
|
|
|
|
| class RAdam(Optimizer): |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): |
| defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) |
| self.buffer = [[None, None, None] for ind in range(10)] |
| super(RAdam, self).__init__(params, defaults) |
|
|
| def __setstate__(self, state): |
| super(RAdam, self).__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_(1 - beta2, grad, grad) |
| exp_avg.mul_(beta1).add_(1 - beta1, grad) |
|
|
| state["step"] += 1 |
| buffered = self.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 |
|
|
| |
| if N_sma >= 5: |
| step_size = ( |
| group["lr"] |
| * 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"]) |
| ) |
| else: |
| step_size = group["lr"] / (1 - beta1 ** state["step"]) |
| buffered[2] = step_size |
|
|
| if group["weight_decay"] != 0: |
| p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32) |
|
|
| |
| if N_sma >= 5: |
| denom = exp_avg_sq.sqrt().add_(group["eps"]) |
| p_data_fp32.addcdiv_(-step_size, exp_avg, denom) |
| else: |
| p_data_fp32.add_(-step_size, exp_avg) |
|
|
| p.data.copy_(p_data_fp32) |
|
|
| return loss |
|
|
|
|
| class PlainRAdam(Optimizer): |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): |
| defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) |
|
|
| super(PlainRAdam, self).__init__(params, defaults) |
|
|
| def __setstate__(self, state): |
| super(PlainRAdam, self).__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_(1 - beta2, grad, grad) |
| exp_avg.mul_(beta1).add_(1 - beta1, grad) |
|
|
| state["step"] += 1 |
| 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) |
|
|
| if group["weight_decay"] != 0: |
| p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32) |
|
|
| |
| if N_sma >= 5: |
| step_size = ( |
| group["lr"] |
| * 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"]) |
| ) |
| denom = exp_avg_sq.sqrt().add_(group["eps"]) |
| p_data_fp32.addcdiv_(-step_size, exp_avg, denom) |
| else: |
| step_size = group["lr"] / (1 - beta1 ** state["step"]) |
| p_data_fp32.add_(-step_size, exp_avg) |
|
|
| p.data.copy_(p_data_fp32) |
|
|
| return loss |
|
|
