/usr/local/lib64/python3.6/site-packages/torch/optim/_multi_tensor
Edit: /usr/local/lib64/python3.6/site-packages/torch/optim/_multi_tensor/adam.py (7140B)
import math
import torch
from ..optimizer import Optimizer
from collections import defaultdict
class Adam(Optimizer):
r"""Implements Adam algorithm with multi tensor APIs.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
The implementation of the L2 penalty follows changes proposed in
`Decoupled Weight Decay Regularization`_.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False)
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _Decoupled Weight Decay Regularization:
https://arxiv.org/abs/1711.05101
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, amsgrad=False):
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]))
if not 0.0 <= weight_decay:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay, amsgrad=amsgrad)
super(Adam, self).__init__(params, defaults)
def __setstate__(self, state):
super(Adam, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
@torch.no_grad()
def step(self, closure=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
amsgrad = group['amsgrad']
grads = []
states = []
exp_avg = []
exp_avg_sq = []
max_exp_avg_sq = []
params_with_grad = []
for p in group['params']:
if p.grad is not None:
if p.grad.is_sparse:
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
params_with_grad.append(p)
grads.append(p.grad)
for p in params_with_grad:
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
exp_avg.append(state['exp_avg'])
exp_avg_sq.append(state['exp_avg_sq'])
if amsgrad:
max_exp_avg_sq.append(state['max_exp_avg_sq'])
state['step'] += 1
states.append(state)
beta1, beta2 = group['betas']
bias_correction1 = [1 - beta1 ** state['step'] for state in states]
bias_correction2 = [1 - beta2 ** state['step'] for state in states]
if group['weight_decay'] != 0:
grads = torch._foreach_add(grads, params_with_grad, alpha=group['weight_decay'])
#
# Decay the first and second moment running average coefficient
#
torch._foreach_mul_(exp_avg, beta1)
torch._foreach_add_(exp_avg, grads, alpha=1 - beta1)
torch._foreach_mul_(exp_avg_sq, beta2)
torch._foreach_addcmul_(exp_avg_sq, grads, grads, 1 - beta2)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
max_exp_avg_sq = torch._foreach_maximum(max_exp_avg_sq, exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
max_exp_avg_sq_sqrt = torch._foreach_sqrt(max_exp_avg_sq)
bias_correction_sqrt = [math.sqrt(bc) for bc in bias_correction2]
torch._foreach_div_(max_exp_avg_sq_sqrt, bias_correction_sqrt)
denom = torch._foreach_add(max_exp_avg_sq_sqrt, group['eps'])
else:
exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sq)
bias_correction_sqrt = [math.sqrt(bc) for bc in bias_correction2]
torch._foreach_div_(exp_avg_sq_sqrt, bias_correction_sqrt)
denom = torch._foreach_add(exp_avg_sq_sqrt, group['eps'])
step_size = [(group['lr'] / bc) * -1 for bc in bias_correction1]
torch._foreach_addcdiv_(params_with_grad, exp_avg, denom, step_size)
return loss
# TODO: refactor to a base class once foreach ops are in a good shape.
def zero_grad(self, set_to_none: bool = False):
per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))
for group in self.param_groups:
for p in group['params']:
if p.grad is not None:
if set_to_none:
p.grad = None
else:
if p.grad.grad_fn is not None:
p.grad.detach_()
else:
p.grad.requires_grad_(False)
if p.grad.is_sparse:
p.grad.zero_()
else:
per_device_and_dtype_grads[p.grad.device][p.grad.dtype].append(p.grad)
for _, per_dtype_grads in per_device_and_dtype_grads.items():
for grads in per_dtype_grads.values():
torch._foreach_zero_(grads)