80 строки
3.2 KiB
Python
80 строки
3.2 KiB
Python
# coding=utf-8
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"""PyTorch optimization."""
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import math
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import torch
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import LambdaLR
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class WarmupLinearSchedule(LambdaLR):
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def __init__(self, optimizer, warmup_steps, t_total, last_epoch=-1):
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self.warmup_step_size = warmup_steps
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self.t_total_size = t_total
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super(WarmupLinearSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch)
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def lr_lambda(self, step_size):
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if step_size < self.warmup_step_size:
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return float(step_size) / float(max(1, self.warmup_step_size))
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return max(float(self.t_total_size - step_size) / float(max(1.0, self.t_total_size - self.warmup_step_size)), 0.0)
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class AdamW(Optimizer):
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6, weight_decay=0.0, correct_bias=True):
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if lr < 0.0:
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raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[0]))
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[1]))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(eps))
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
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correct_bias=correct_bias)
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super(AdamW, self).__init__(params, defaults)
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def step(self, closure=None):
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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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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.data
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if grad.is_sparse:
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raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
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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['exp_avg'] = torch.zeros_like(p.data)
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state['exp_avg_sq'] = torch.zeros_like(p.data)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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beta_1, beta_2 = group['betas']
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state['step'] += 1
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exp_avg.mul_(beta_1).add_(1.0 - beta_1, grad)
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exp_avg_sq.mul_(beta_2).addcmul_(1.0 - beta_2, grad, grad)
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denom = exp_avg_sq.sqrt().add_(group['eps'])
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step_len = group['lr']
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if group['correct_bias']:
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bias_correction_1 = 1.0 - beta_1 ** state['step']
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bias_correction_2 = 1.0 - beta_2 ** state['step']
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step_len = step_len * math.sqrt(bias_correction_2) / bias_correction_1
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p.data.addcdiv_(-step_len, exp_avg, denom)
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if group['weight_decay'] > 0.0:
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p.data.add_(-group['lr'] * group['weight_decay'], p.data)
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return loss
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