| 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-6,
|
| weight_decay=0,
|
| degenerated_to_sgd=True,
|
| ):
|
| if not 0.0 <= lr:
|
| raise ValueError("Invalid learning rate: {}".format(lr))
|
| if not 0.0 <= eps:
|
| 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(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 = 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_(-group["weight_decay"] * group["lr"], p_data_fp32)
|
| denom = exp_avg_sq.sqrt().add_(group["eps"])
|
| p_data_fp32.addcdiv_(-step_size * group["lr"], exp_avg, denom)
|
| p.data.copy_(p_data_fp32)
|
| elif step_size > 0:
|
| if group["weight_decay"] != 0:
|
| p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32)
|
| p_data_fp32.add_(-step_size * group["lr"], 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,
|
| degenerated_to_sgd=True,
|
| ):
|
| if not 0.0 <= lr:
|
| raise ValueError("Invalid learning rate: {}".format(lr))
|
| if not 0.0 <= eps:
|
| 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
|
| 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 N_sma >= 5:
|
| if group["weight_decay"] != 0:
|
| p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32)
|
| 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)
|
| p.data.copy_(p_data_fp32)
|
| elif self.degenerated_to_sgd:
|
| if group["weight_decay"] != 0:
|
| p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32)
|
| step_size = group["lr"] / (1 - beta1 ** state["step"])
|
| p_data_fp32.add_(-step_size, exp_avg)
|
| p.data.copy_(p_data_fp32)
|
|
|
| return loss
|
|
|
|
|
| class AdamW(Optimizer):
|
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, warmup=0):
|
| if not 0.0 <= lr:
|
| raise ValueError("Invalid learning rate: {}".format(lr))
|
| if not 0.0 <= eps:
|
| 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]))
|
|
|
| defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, warmup=warmup)
|
| super(AdamW, self).__init__(params, defaults)
|
|
|
| def __setstate__(self, state):
|
| super(AdamW, 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("Adam does not support sparse gradients, please consider SparseAdam instead")
|
|
|
| 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"]
|
|
|
| state["step"] += 1
|
|
|
| exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
|
| exp_avg.mul_(beta1).add_(1 - beta1, grad)
|
|
|
| denom = exp_avg_sq.sqrt().add_(group["eps"])
|
| bias_correction1 = 1 - beta1 ** state["step"]
|
| bias_correction2 = 1 - beta2 ** state["step"]
|
|
|
| if group["warmup"] > state["step"]:
|
| scheduled_lr = 1e-8 + state["step"] * group["lr"] / group["warmup"]
|
| else:
|
| scheduled_lr = group["lr"]
|
|
|
| step_size = scheduled_lr * math.sqrt(bias_correction2) / bias_correction1
|
|
|
| if group["weight_decay"] != 0:
|
| p_data_fp32.add_(-group["weight_decay"] * scheduled_lr, p_data_fp32)
|
|
|
| p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
|
|
|
| p.data.copy_(p_data_fp32)
|
|
|
| return loss
|
|
|
