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| import math | |
| import torch | |
| from torch.optim.optimizer import Optimizer | |
| class DenseSparseAdam(Optimizer): | |
| """ | |
| """ | |
| def __init__(self, params, lr=3e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.0): | |
| if not 0.0 <= lr: | |
| raise ValueError("Invalid learning rate: {}".format(lr)) | |
| if not 0.0 <= eps: | |
| raise ValueError("Invalid epsilon value: {}".format(eps)) | |
| 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) | |
| super(DenseSparseAdam, self).__init__(params, defaults) | |
| def step(self, closure=None): | |
| """ | |
| Performs a single optimization step. | |
| Parameters | |
| ---------- | |
| closure : ``callable``, optional. | |
| A closure that reevaluates the model and returns the loss. | |
| """ | |
| 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 | |
| state = self.state[p] | |
| # State initialization | |
| if 'step' not in state: | |
| state['step'] = 0 | |
| if 'exp_avg' not in state: | |
| # Exponential moving average of gradient values | |
| state['exp_avg'] = torch.zeros_like(p.data) | |
| if 'exp_avg_sq' not in state: | |
| # Exponential moving average of squared gradient values | |
| state['exp_avg_sq'] = torch.zeros_like(p.data) | |
| state['step'] += 1 | |
| exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] | |
| beta1, beta2 = group['betas'] | |
| weight_decay = group['weight_decay'] | |
| if grad.is_sparse: | |
| grad = grad.coalesce() # the update is non-linear so indices must be unique | |
| grad_indices = grad._indices() | |
| grad_values = grad._values() | |
| size = grad.size() | |
| def make_sparse(values): | |
| constructor = grad.new | |
| if grad_indices.dim() == 0 or values.dim() == 0: | |
| return constructor().resize_as_(grad) | |
| return constructor(grad_indices, values, size) | |
| # Decay the first and second moment running average coefficient | |
| # old <- b * old + (1 - b) * new | |
| # <==> old += (1 - b) * (new - old) | |
| old_exp_avg_values = exp_avg.sparse_mask(grad)._values() | |
| exp_avg_update_values = grad_values.sub(old_exp_avg_values).mul_(1 - beta1) | |
| exp_avg.add_(make_sparse(exp_avg_update_values)) | |
| old_exp_avg_sq_values = exp_avg_sq.sparse_mask(grad)._values() | |
| exp_avg_sq_update_values = grad_values.pow(2).sub_(old_exp_avg_sq_values).mul_(1 - beta2) | |
| exp_avg_sq.add_(make_sparse(exp_avg_sq_update_values)) | |
| # Dense addition again is intended, avoiding another sparse_mask | |
| numer = exp_avg_update_values.add_(old_exp_avg_values) | |
| exp_avg_sq_update_values.add_(old_exp_avg_sq_values) | |
| denom = exp_avg_sq_update_values.sqrt_().add_(group['eps']) | |
| del exp_avg_update_values, exp_avg_sq_update_values | |
| bias_correction1 = 1 - beta1 ** state['step'] | |
| bias_correction2 = 1 - beta2 ** state['step'] | |
| step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1 | |
| p.data.add_(make_sparse(-step_size * numer.div_(denom))) | |
| if weight_decay > 0.0: | |
| p.data.add_(-group['lr'] * weight_decay, p.data.sparse_mask(grad)) | |
| else: | |
| # Decay the first and second moment running average coefficient | |
| # exp_avg.mul_(beta1).add_(1 - beta1, grad) | |
| exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) | |
| exp_avg_sq.mul_(beta2).addcmul_(grad, grad,value=1 - beta2) | |
| 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 | |
| p.data.addcdiv_(exp_avg, denom, value=-step_size) | |
| if weight_decay > 0.0: | |
| p.data.add_(-group['lr'] * weight_decay, p.data) | |
| return loss | |