|
|
|
|
|
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 |
|
|
