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from typing import Callable, Iterable, Tuple
import math
import torch
from torch.optim import Optimizer
class AdamW(Optimizer):
def __init__(
self,
params: Iterable[torch.nn.parameter.Parameter],
lr: float = 1e-3,
betas: Tuple[float, float] = (0.9, 0.999),
eps: float = 1e-6,
weight_decay: float = 0.0,
correct_bias: bool = True,
):
if lr < 0.0:
raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[1]))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(eps))
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, correct_bias=correct_bias)
super().__init__(params, defaults)
def step(self, closure: Callable = 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
if grad.is_sparse:
raise RuntimeError("Adam does not support sparse gradients, please consider SparseAdam instead")
# Access state
state = self.state[p]
# Initialize state if not already done
if len(state) == 0:
state["step"] = 0
state["exp_avg"] = torch.zeros_like(p.data)
state["exp_avg_sq"] = torch.zeros_like(p.data)
# Hyperparameters
alpha = group["lr"]
beta1, beta2 = group["betas"]
eps = group["eps"]
weight_decay = group["weight_decay"]
correct_bias = group["correct_bias"]
# Retrieve state variables
exp_avg = state["exp_avg"]
exp_avg_sq = state["exp_avg_sq"]
step = state["step"]
# Update step
step += 1
state["step"] = step
# Update biased first and second moment estimates
exp_avg.mul_(beta1).add_(grad, alpha=(1 - beta1))
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=(1 - beta2))
# Compute bias-corrected moments
if correct_bias:
bias_correction1 = 1 - beta1 ** step
bias_correction2 = 1 - beta2 ** step
exp_avg_corr = exp_avg / bias_correction1
exp_avg_sq_corr = exp_avg_sq / bias_correction2
else:
exp_avg_corr = exp_avg
exp_avg_sq_corr = exp_avg_sq
# Update parameters
denom = exp_avg_sq_corr.sqrt().add_(eps)
step_size = alpha
p.data.addcdiv_(exp_avg_corr, denom, value=-step_size)
# Apply weight decay
if weight_decay != 0:
p.data.add_(p.data, alpha=-alpha * weight_decay)
return loss
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