118 lines
4.3 KiB
Python
118 lines
4.3 KiB
Python
import torch
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from torch.optim.optimizer import Optimizer, required
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from torch.optim import *
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from .larc import LARC
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class LARS(Optimizer):
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r"""Implements layer-wise adaptive rate scaling for SGD.
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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): base learning rate (\gamma_0)
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momentum (float, optional): momentum factor (default: 0) ("m")
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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("\beta")
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dampening (float, optional): dampening for momentum (default: 0)
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eta (float, optional): LARS coefficient
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nesterov (bool, optional): enables Nesterov momentum (default: False)
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Based on Algorithm 1 of the following paper by You, Gitman, and Ginsburg.
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Large Batch Training of Convolutional Networks:
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https://arxiv.org/abs/1708.03888
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Example:
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>>> optimizer = LARS(model.parameters(), lr=0.1, momentum=0.9,
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>>> weight_decay=1e-4, eta=1e-3)
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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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"""
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def __init__(self,
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params,
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lr=required,
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momentum=0,
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dampening=0,
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weight_decay=0,
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eta=0.001,
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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(
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"Invalid weight_decay value: {}".format(weight_decay))
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if eta < 0.0:
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raise ValueError("Invalid LARS coefficient value: {}".format(eta))
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defaults = dict(
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lr=lr, momentum=momentum, dampening=dampening,
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weight_decay=weight_decay, nesterov=nesterov, eta=eta)
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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(LARS, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(LARS, 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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Arguments:
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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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weight_decay = group['weight_decay']
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momentum = group['momentum']
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dampening = group['dampening']
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eta = group['eta']
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nesterov = group['nesterov']
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lr = group['lr']
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lars_exclude = group.get('lars_exclude', False)
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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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d_p = p.grad
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if lars_exclude:
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local_lr = 1.
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else:
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weight_norm = torch.norm(p).item()
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grad_norm = torch.norm(d_p).item()
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# Compute local learning rate for this layer
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local_lr = eta * weight_norm / \
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(grad_norm + weight_decay * weight_norm)
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actual_lr = local_lr * lr
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d_p = d_p.add(p, alpha=weight_decay).mul(actual_lr)
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if momentum != 0:
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param_state = self.state[p]
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if 'momentum_buffer' not in param_state:
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buf = param_state['momentum_buffer'] = \
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torch.clone(d_p).detach()
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else:
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buf = param_state['momentum_buffer']
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buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
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if nesterov:
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d_p = d_p.add(buf, alpha=momentum)
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else:
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d_p = buf
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p.add_(-d_p)
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return loss
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