121 lines
4.9 KiB
Python
121 lines
4.9 KiB
Python
import torch
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from . import _functional as F
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from .optimizer import Optimizer
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class Adam(Optimizer):
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r"""Implements Adam algorithm.
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It has been proposed in `Adam: A Method for Stochastic Optimization`_.
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The implementation of the L2 penalty follows changes proposed in
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`Decoupled Weight Decay Regularization`_.
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Args:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate (default: 1e-3)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square (default: (0.9, 0.999))
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-8)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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amsgrad (boolean, optional): whether to use the AMSGrad variant of this
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algorithm from the paper `On the Convergence of Adam and Beyond`_
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(default: False)
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.. _Adam\: A Method for Stochastic Optimization:
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https://arxiv.org/abs/1412.6980
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.. _Decoupled Weight Decay Regularization:
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https://arxiv.org/abs/1711.05101
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.. _On the Convergence of Adam and Beyond:
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https://openreview.net/forum?id=ryQu7f-RZ
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"""
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
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weight_decay=0, amsgrad=False):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
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if not 0.0 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay, amsgrad=amsgrad)
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super(Adam, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(Adam, self).__setstate__(state)
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for group in self.param_groups:
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group.setdefault('amsgrad', False)
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@torch.no_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Args:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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params_with_grad = []
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grads = []
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exp_avgs = []
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exp_avg_sqs = []
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state_sums = []
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max_exp_avg_sqs = []
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state_steps = []
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for p in group['params']:
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if p.grad is not None:
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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
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grads.append(p.grad)
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state = self.state[p]
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# Lazy state initialization
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if len(state) == 0:
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state['step'] = 0
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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if group['amsgrad']:
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# Maintains max of all exp. moving avg. of sq. grad. values
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state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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exp_avgs.append(state['exp_avg'])
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exp_avg_sqs.append(state['exp_avg_sq'])
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if group['amsgrad']:
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max_exp_avg_sqs.append(state['max_exp_avg_sq'])
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# update the steps for each param group update
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state['step'] += 1
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# record the step after step update
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state_steps.append(state['step'])
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beta1, beta2 = group['betas']
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F.adam(params_with_grad,
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grads,
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exp_avgs,
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exp_avg_sqs,
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max_exp_avg_sqs,
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state_steps,
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group['amsgrad'],
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beta1,
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beta2,
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group['lr'],
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group['weight_decay'],
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group['eps'])
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return loss
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