| | |
| |
|
| | 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): |
| | 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 |
| |
|
| | |
| | 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 |
| |
|
| | |
| | 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 |
| |
|
