in pytext/optimizer/adabelief.py [0:0]
def step(self, closure=None, **kwargs):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
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(
"AdaBelief does not support sparse gradients, please consider SparseAdam instead"
)
amsgrad = group["amsgrad"]
state = self.state[p]
beta1, beta2 = group["betas"]
# State initialization
if len(state) == 0:
state["rho_inf"] = 2.0 / (1.0 - beta2) - 1.0
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
# Exponential moving average of squared gradient values
state["exp_avg_var"] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state["max_exp_avg_var"] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
# get current state variable
exp_avg, exp_avg_var = state["exp_avg"], state["exp_avg_var"]
state["step"] += 1
bias_correction1 = 1 - beta1 ** state["step"]
bias_correction2 = 1 - beta2 ** state["step"]
# perform weight decay, check if decoupled weight decay
if self.weight_decouple:
if not self.fixed_decay:
p.data.mul_(1.0 - group["lr"] * group["weight_decay"])
else:
p.data.mul_(1.0 - group["weight_decay"])
else:
if group["weight_decay"] != 0:
grad.add_(group["weight_decay"], p.data)
# Update first and second moment running average
exp_avg.mul_(beta1).add_(1 - beta1, grad)
grad_residual = grad - exp_avg
exp_avg_var.mul_(beta2).addcmul_(
1 - beta2, grad_residual, grad_residual
)
if amsgrad:
max_exp_avg_var = state["max_exp_avg_var"]
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_var, exp_avg_var, out=max_exp_avg_var)
# Use the max. for normalizing running avg. of gradient
denom = (
max_exp_avg_var.add_(group["eps"]).sqrt()
/ math.sqrt(bias_correction2)
).add_(group["eps"])
else:
denom = (
exp_avg_var.add_(group["eps"]).sqrt()
/ math.sqrt(bias_correction2)
).add_(group["eps"])
if not self.rectify:
# Default update
step_size = group["lr"] / bias_correction1
p.data.addcdiv_(-step_size, exp_avg, denom)
else: # Rectified update
# calculate rho_t
state["rho_t"] = state["rho_inf"] - 2 * state[
"step"
] * beta2 ** state["step"] / (1.0 - beta2 ** state["step"])
if (
state["rho_t"] > 4
): # perform Adam style update if variance is small
rho_inf, rho_t = state["rho_inf"], state["rho_t"]
rt = (
(rho_t - 4.0)
* (rho_t - 2.0)
* rho_inf
/ (rho_inf - 4.0)
/ (rho_inf - 2.0)
/ rho_t
)
rt = math.sqrt(rt)
step_size = rt * group["lr"] / bias_correction1
p.data.addcdiv_(-step_size, exp_avg, denom)
else: # perform SGD style update
p.data.add_(-group["lr"], exp_avg)
return loss