Source code for lmflow.optim.adam
#!/usr/bin/env python
import torch
from torch.optim.optimizer import Optimizer
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class Adam(Optimizer):
def __init__(self, params, lr=0.001, betas=(0.9, 0.999), eps=1e-8):
defaults = dict(lr=lr, betas=betas, eps=eps)
super().__init__(params, defaults)
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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
state = self.state[p]
if len(state) == 0:
state["step"] = 0
state["exp_avg"] = torch.zeros_like(p.data)
state["exp_avg_sq"] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
beta1, beta2 = group["betas"]
state["step"] += 1
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
bias_correction1 = 1 - beta1 ** state["step"]
bias_correction2 = 1 - beta2 ** state["step"]
step_size = group["lr"] * (bias_correction2**0.5) / bias_correction1
denom = exp_avg_sq.sqrt().add_(group["eps"])
p.data.addcdiv_(-step_size, exp_avg, denom)
return loss