87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
import math
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import torch
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from .optimizer import Optimizer
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class ASGD(Optimizer):
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"""Implements Averaged Stochastic Gradient Descent.
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It has been proposed in `Acceleration of stochastic approximation by
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averaging`_.
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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-2)
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lambd (float, optional): decay term (default: 1e-4)
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alpha (float, optional): power for eta update (default: 0.75)
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t0 (float, optional): point at which to start averaging (default: 1e6)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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.. _Acceleration of stochastic approximation by averaging:
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https://dl.acm.org/citation.cfm?id=131098
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"""
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def __init__(self, params, lr=1e-2, lambd=1e-4, alpha=0.75, t0=1e6, weight_decay=0):
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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 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(lr=lr, lambd=lambd, alpha=alpha, t0=t0,
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weight_decay=weight_decay)
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super(ASGD, self).__init__(params, defaults)
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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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for p in group['params']:
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if p.grad is None:
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continue
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grad = p.grad
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if grad.is_sparse:
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raise RuntimeError('ASGD does not support sparse gradients')
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = 0
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state['eta'] = group['lr']
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state['mu'] = 1
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state['ax'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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state['step'] += 1
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if group['weight_decay'] != 0:
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grad = grad.add(p, alpha=group['weight_decay'])
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# decay term
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p.mul_(1 - group['lambd'] * state['eta'])
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# update parameter
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p.add_(grad, alpha=-state['eta'])
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# averaging
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if state['mu'] != 1:
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state['ax'].add_(p.sub(state['ax']).mul(state['mu']))
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else:
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state['ax'].copy_(p)
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# update eta and mu
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state['eta'] = (group['lr'] /
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math.pow((1 + group['lambd'] * group['lr'] * state['step']), group['alpha']))
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state['mu'] = 1 / max(1, state['step'] - group['t0'])
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return loss
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