adamw.py

import math
import torch
from torch.optim.optimizer import Optimizer

class AdamW(Optimizer):
    def __init__(self, params, lr=1e-4, betas=(0.9, 0.999), eps=1e-5, weight_decay=1e-5, hypergrad=0, partial=1):
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, hypergrad=hypergrad, partial=partial)
        super().__init__(params, defaults)
    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()
        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
                state = self.state[p]
                if len(state) == 0:
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p.data)
                    state['exp_avg_sq'] = torch.zeros_like(p.data)
                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta1, beta2 = group['betas']
                state['step'] += 1

                if group['hypergrad'] > 0 and state['step'] > 1:
                    prev_bias_correction1 = 1 - beta1 ** (state['step'] - 1)
                    prev_bias_correction2 = 1 - beta2 ** (state['step'] - 1)
                    h = torch.dot(grad.view(-1), torch.div(exp_avg, exp_avg_sq.sqrt().add_(group['eps'])).view(-1)) * math.sqrt(prev_bias_correction2) / prev_bias_correction1
                    group['lr'] += group['hypergrad'] * h

                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                denom = exp_avg_sq.sqrt().add_(group['eps'])
                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']
                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

                if group['weight_decay'] != 0:
                    decayed_weights = torch.mul(p.data, group['weight_decay'])
                    p.data.addcdiv_(-step_size, exp_avg, denom**group['partial'])
                    p.data.sub_(decayed_weights)
                else:
                    p.data.addcdiv_(-step_size, exp_avg, denom**group['partial'])

        return loss