| |
| |
|
|
| import math |
| import warnings |
| import torch |
| from torch.optim.optimizer import Optimizer |
|
|
| class RAdam(Optimizer): |
| r"""Implements RAdam optimization algorithm. |
| |
| Note: |
| Deprecated, please use version provided by PyTorch_. |
| |
| It has been proposed in `On the Variance of the Adaptive Learning |
| Rate and Beyond`. |
| https://arxiv.org/abs/1908.03265 |
| |
| Note: |
| Reference code: https://github.com/LiyuanLucasLiu/RAdam |
| """ |
|
|
| def __init__( |
| self, |
| params, |
| lr: float = 1e-3, |
| betas = (0.9, 0.999), |
| eps: float = 1e-8, |
| weight_decay: float = 0, |
| ) -> None: |
| warnings.warn( |
| "RAdam optimizer is deprecated, since it is included " |
| "in pytorch natively.", |
| DeprecationWarning, |
| stacklevel=2, |
| ) |
| 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) |
| ) |
|
|
| 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(RAdam, self).__init__(params, defaults) |
|
|
| def __setstate__(self, state): |
| super(RAdam, self).__setstate__(state) |
|
|
| 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: |
| lr = group["lr"] |
| weight_decay = group["weight_decay"] |
| beta1, beta2 = group["betas"] |
| eps = group["eps"] |
|
|
| for p in group["params"]: |
| if p.grad is None: |
| continue |
| grad = p.grad.data.float() |
| if grad.is_sparse: |
| msg = ( |
| "RAdam does not support sparse gradients, " |
| "please consider SparseAdam instead" |
| ) |
| raise RuntimeError(msg) |
|
|
| 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, memory_format=torch.preserve_format |
| ) |
| state["exp_avg_sq"] = torch.zeros_like( |
| p_data_fp32, memory_format=torch.preserve_format |
| ) |
| 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"] |
|
|
| 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 |
|
|
| |
| if N_sma >= 5: |
| step_size = ( |
| 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 = lr / (1 - beta1 ** state["step"]) |
| buffered[2] = step_size |
|
|
| if weight_decay != 0: |
| p_data_fp32.add_(p_data_fp32, alpha=-weight_decay * lr) |
|
|
| |
| if N_sma >= 5: |
| denom = exp_avg_sq.sqrt().add_(eps) |
| p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size) |
| else: |
| p_data_fp32.add_(exp_avg, alpha=-step_size) |
|
|
| p.data.copy_(p_data_fp32) |
|
|
| return loss |
