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| import torch | |
| from torch.optim import Optimizer | |
| class PerPointAdam(Optimizer): | |
| """Implements Adam optimizer with per-point learning rates. | |
| Allows unique learning rates for each point in specified parameter tensors, | |
| useful for point cloud optimization. | |
| Args: | |
| params: Iterable of parameters to optimize or parameter groups | |
| lr (float, optional): Default learning rate (default: 1e-3) | |
| betas (tuple, optional): Coefficients for moving averages (default: (0.9, 0.999)) | |
| eps (float, optional): Term for numerical stability (default: 1e-8) | |
| weight_decay (float, optional): Weight decay (L2 penalty) (default: 0) | |
| """ | |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): | |
| if not all(0.0 <= x for x in [lr, eps, weight_decay]): | |
| raise ValueError(f"Invalid learning parameters: lr={lr}, eps={eps}, weight_decay={weight_decay}") | |
| if not all(0.0 <= beta < 1.0 for beta in betas): | |
| raise ValueError(f"Invalid beta parameters: {betas}") | |
| defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, per_point_lr=None) | |
| super().__init__(params, defaults) | |
| def _adjust_per_point_lr(self, per_point_lr, grad, mask): | |
| """Adjusts per-point learning rates based on gradient magnitudes.""" | |
| grad_magnitude = grad.norm(dim=-1) | |
| scaling_factor = torch.ones_like(grad_magnitude) | |
| grad_sigmoid = torch.sigmoid(grad_magnitude[mask]) | |
| scaling_factor[mask] = 0.99 + (grad_sigmoid * 0.02) | |
| return per_point_lr * scaling_factor.unsqueeze(1) | |
| def step(self, closure=None): | |
| """Performs a single optimization step.""" | |
| loss = closure() if closure is not None else None | |
| for group in self.param_groups: | |
| per_point_lr = group.get('per_point_lr') | |
| for p in group['params']: | |
| if p.grad is None: | |
| continue | |
| grad = p.grad.data | |
| if grad.is_sparse: | |
| raise RuntimeError('PerPointAdam does not support sparse gradients') | |
| # Initialize state if needed | |
| 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) | |
| # Get state values | |
| exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] | |
| beta1, beta2 = group['betas'] | |
| state['step'] += 1 | |
| # Apply weight decay if specified | |
| if group['weight_decay'] != 0: | |
| grad = grad.add(p.data, alpha=group['weight_decay']) | |
| # Compute mask for non-zero gradients | |
| grad_norm = grad.norm() | |
| mask = grad_norm > 0 | |
| # Update momentum terms | |
| exp_avg.masked_scatter_(mask, | |
| exp_avg[mask].mul_(beta1).add_(grad[mask], alpha=1 - beta1)) | |
| exp_avg_sq.masked_scatter_(mask, | |
| exp_avg_sq[mask].mul_(beta2).addcmul_(grad[mask], grad[mask], value=1 - beta2)) | |
| # Compute bias corrections | |
| bias_correction1 = 1 - beta1 ** state['step'] | |
| bias_correction2 = 1 - beta2 ** state['step'] | |
| # Compute step size | |
| denom = exp_avg_sq.sqrt().add_(group['eps']) | |
| step_size = group['lr'] * (bias_correction2 ** 0.5 / bias_correction1) | |
| # Apply updates | |
| if per_point_lr is not None: | |
| if not isinstance(per_point_lr, torch.Tensor): | |
| raise TypeError("per_point_lr must be a torch.Tensor") | |
| if per_point_lr.device != p.data.device: | |
| raise ValueError("per_point_lr must be on the same device as parameter") | |
| expected_shape = p.data.shape[:1] + (1,) * (p.data.dim() - 1) | |
| if per_point_lr.shape != expected_shape: | |
| raise ValueError(f"{group['name']}: Invalid per_point_lr shape. Expected {expected_shape}, got {per_point_lr.shape}") | |
| scaled_step_size = step_size * per_point_lr | |
| p.data.add_(-scaled_step_size * (exp_avg / denom)) | |
| per_point_lr = self._adjust_per_point_lr(per_point_lr, grad, mask) | |
| else: | |
| p.data.addcdiv_(exp_avg, denom, value=-step_size) | |
| return loss |