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96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
""" Layer-wise adaptive rate scaling for SGD in PyTorch! """
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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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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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eta (float, optional): LARS coefficient
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max_epoch: maximum training epoch to determine polynomial LR decay.
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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, 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=.9,
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weight_decay=.0005,
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eta=0.001):
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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, weight_decay=weight_decay, eta=eta)
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super(LARS, self).__init__(params, defaults)
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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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epoch: current epoch to calculate polynomial LR decay schedule.
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if None, uses self.epoch and increments it.
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"""
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loss = None
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if closure is not None:
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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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eta = group['eta']
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lr = group['lr']
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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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param_state = self.state[p]
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d_p = p.grad.data
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weight_norm = torch.norm(p.data)
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grad_norm = torch.norm(d_p)
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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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# Update the momentum term
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actual_lr = local_lr * lr
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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.zeros_like(p.data)
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else:
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buf = param_state['momentum_buffer']
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buf.mul_(momentum).add_(actual_lr, d_p + weight_decay * p.data)
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p.data.add_(-buf)
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return loss
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