125 lines
4.5 KiB
Python
125 lines
4.5 KiB
Python
import torch
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from . import _functional as F
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from .optimizer import Optimizer, required
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class SGD(Optimizer):
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r"""Implements stochastic gradient descent (optionally with momentum).
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Nesterov momentum is based on the formula from
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`On the importance of initialization and momentum in deep learning`__.
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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): learning rate
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momentum (float, optional): momentum factor (default: 0)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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dampening (float, optional): dampening for momentum (default: 0)
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nesterov (bool, optional): enables Nesterov momentum (default: False)
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Example:
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>>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
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>>> optimizer.zero_grad()
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>>> loss_fn(model(input), target).backward()
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>>> optimizer.step()
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__ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf
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.. note::
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The implementation of SGD with Momentum/Nesterov subtly differs from
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Sutskever et. al. and implementations in some other frameworks.
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Considering the specific case of Momentum, the update can be written as
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.. math::
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\begin{aligned}
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v_{t+1} & = \mu * v_{t} + g_{t+1}, \\
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p_{t+1} & = p_{t} - \text{lr} * v_{t+1},
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\end{aligned}
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where :math:`p`, :math:`g`, :math:`v` and :math:`\mu` denote the
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parameters, gradient, velocity, and momentum respectively.
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This is in contrast to Sutskever et. al. and
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other frameworks which employ an update of the form
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.. math::
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\begin{aligned}
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v_{t+1} & = \mu * v_{t} + \text{lr} * g_{t+1}, \\
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p_{t+1} & = p_{t} - v_{t+1}.
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\end{aligned}
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The Nesterov version is analogously modified.
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"""
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def __init__(self, params, lr=required, momentum=0, dampening=0,
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weight_decay=0, nesterov=False):
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if lr is not required and lr < 0.0:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if momentum < 0.0:
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raise ValueError("Invalid momentum value: {}".format(momentum))
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if weight_decay < 0.0:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
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weight_decay=weight_decay, nesterov=nesterov)
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if nesterov and (momentum <= 0 or dampening != 0):
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raise ValueError("Nesterov momentum requires a momentum and zero dampening")
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super(SGD, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(SGD, self).__setstate__(state)
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for group in self.param_groups:
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group.setdefault('nesterov', 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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d_p_list = []
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momentum_buffer_list = []
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weight_decay = group['weight_decay']
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momentum = group['momentum']
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dampening = group['dampening']
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nesterov = group['nesterov']
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lr = group['lr']
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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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d_p_list.append(p.grad)
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state = self.state[p]
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if 'momentum_buffer' not in state:
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momentum_buffer_list.append(None)
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else:
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momentum_buffer_list.append(state['momentum_buffer'])
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F.sgd(params_with_grad,
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d_p_list,
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momentum_buffer_list,
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weight_decay,
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momentum,
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lr,
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dampening,
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nesterov)
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# update momentum_buffers in state
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for p, momentum_buffer in zip(params_with_grad, momentum_buffer_list):
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state = self.state[p]
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state['momentum_buffer'] = momentum_buffer
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return loss
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