| import torch |
| from torch import Tensor |
| |
| from .optimizer import Optimizer |
| from typing import List, Optional |
| |
| __all__ = ['Adamax', 'adamax'] |
| |
| class Adamax(Optimizer): |
| r"""Implements Adamax algorithm (a variant of Adam based on infinity norm). |
| |
| .. math:: |
| \begin{aligned} |
| &\rule{110mm}{0.4pt} \\ |
| &\textbf{input} : \gamma \text{ (lr)}, \beta_1, \beta_2 |
| \text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)}, |
| \: \lambda \text{ (weight decay)}, \\ |
| &\hspace{13mm} \epsilon \text{ (epsilon)} \\ |
| &\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)}, |
| u_0 \leftarrow 0 \text{ ( infinity norm)} \\[-1.ex] |
| &\rule{110mm}{0.4pt} \\ |
| &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ |
| &\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ |
| &\hspace{5mm}if \: \lambda \neq 0 \\ |
| &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ |
| &\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ |
| &\hspace{5mm}u_t \leftarrow \mathrm{max}(\beta_2 u_{t-1}, |g_{t}|+\epsilon) \\ |
| &\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \frac{\gamma m_t}{(1-\beta^t_1) u_t} \\ |
| &\rule{110mm}{0.4pt} \\[-1.ex] |
| &\bf{return} \: \theta_t \\[-1.ex] |
| &\rule{110mm}{0.4pt} \\[-1.ex] |
| \end{aligned} |
| |
| For further details regarding the algorithm we refer to `Adam: A Method for Stochastic Optimization`_. |
| |
| Args: |
| params (iterable): iterable of parameters to optimize or dicts defining |
| parameter groups |
| lr (float, optional): learning rate (default: 2e-3) |
| betas (Tuple[float, float], optional): coefficients used for computing |
| running averages of gradient and its square |
| eps (float, optional): term added to the denominator to improve |
| numerical stability (default: 1e-8) |
| weight_decay (float, optional): weight decay (L2 penalty) (default: 0) |
| foreach (bool, optional): whether foreach implementation of optimizer is used (default: None) |
| maximize (bool, optional): maximize the params based on the objective, instead of |
| minimizing (default: False) |
| |
| .. _Adam\: A Method for Stochastic Optimization: |
| https://arxiv.org/abs/1412.6980 |
| """ |
| |
| def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, |
| weight_decay=0, foreach: Optional[bool] = None, *, maximize: bool = False): |
| 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])) |
| if not 0.0 <= weight_decay: |
| raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) |
| |
| defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, |
| foreach=foreach, maximize=maximize) |
| super(Adamax, self).__init__(params, defaults) |
| |
| def __setstate__(self, state): |
| super().__setstate__(state) |
| for group in self.param_groups: |
| group.setdefault('foreach', None) |
| group.setdefault('maximize', False) |
| state_values = list(self.state.values()) |
| step_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['step']) |
| if not step_is_tensor: |
| for s in state_values: |
| s['step'] = torch.tensor(float(s['step'])) |
| |
| @torch.no_grad() |
| def step(self, closure=None): |
| """Performs a single optimization step. |
| |
| Args: |
| closure (Callable, optional): A closure that reevaluates the model |
| and returns the loss. |
| """ |
| loss = None |
| if closure is not None: |
| with torch.enable_grad(): |
| loss = closure() |
| |
| for group in self.param_groups: |
| params_with_grad = [] |
| grads = [] |
| exp_avgs = [] |
| exp_infs = [] |
| state_steps = [] |
| |
| beta1, beta2 = group['betas'] |
| eps = group['eps'] |
| lr = group['lr'] |
| weight_decay = group['weight_decay'] |
| foreach = group['foreach'] |
| maximize = group['maximize'] |
| |
| for p in group['params']: |
| if p.grad is None: |
| continue |
| params_with_grad.append(p) |
| if p.grad.is_sparse: |
| raise RuntimeError('Adamax does not support sparse gradients') |
| grads.append(p.grad) |
| |
| state = self.state[p] |
| |
| # State initialization |
| if len(state) == 0: |
| state['step'] = torch.tensor(0.) |
| state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
| state['exp_inf'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
| |
| exp_avgs.append(state['exp_avg']) |
| exp_infs.append(state['exp_inf']) |
| state_steps.append(state['step']) |
| |
| adamax(params_with_grad, |
| grads, |
| exp_avgs, |
| exp_infs, |
| state_steps, |
| eps=eps, |
| beta1=beta1, |
| beta2=beta2, |
| lr=lr, |
| weight_decay=weight_decay, |
| foreach=foreach, |
| maximize=maximize) |
| |
| return loss |
| |
| |
| def adamax(params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_infs: List[Tensor], |
| state_steps: List[Tensor], |
| # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627 |
| # setting this as kwarg for now as functional API is compiled by torch/distributed/optim |
| foreach: bool = None, |
| maximize: bool = False, |
| *, |
| eps: float, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float): |
| r"""Functional API that performs adamax algorithm computation. |
| |
| See :class:`~torch.optim.Adamax` for details. |
| """ |
| |
| if not all(isinstance(t, torch.Tensor) for t in state_steps): |
| raise RuntimeError("API has changed, `state_steps` argument must contain a list of singleton tensors") |
| |
| if foreach is None: |
| # Placeholder for more complex foreach logic to be added when value is not set |
| foreach = False |
| |
| if foreach and torch.jit.is_scripting(): |
| raise RuntimeError('torch.jit.script not supported with foreach optimizers') |
| |
| if foreach and not torch.jit.is_scripting(): |
| func = _multi_tensor_adamax |
| else: |
| func = _single_tensor_adamax |
| |
| func(params, |
| grads, |
| exp_avgs, |
| exp_infs, |
| state_steps, |
| eps=eps, |
| beta1=beta1, |
| beta2=beta2, |
| lr=lr, |
| weight_decay=weight_decay, |
| maximize=maximize) |
| |
| |
| def _single_tensor_adamax(params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_infs: List[Tensor], |
| state_steps: List[Tensor], |
| *, |
| eps: float, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float, |
| maximize: bool): |
| |
| for i, param in enumerate(params): |
| grad = grads[i] |
| grad = grad if not maximize else -grad |
| exp_avg = exp_avgs[i] |
| exp_inf = exp_infs[i] |
| step_t = state_steps[i] |
| # update step |
| step_t += 1 |
| step = step_t.item() |
| |
| if weight_decay != 0: |
| grad = grad.add(param, alpha=weight_decay) |
| |
| if torch.is_complex(param): |
| param = torch.view_as_real(param) |
| grad = torch.view_as_real(grad) |
| exp_avg = torch.view_as_real(exp_avg) |
| exp_inf = torch.view_as_real(exp_inf) |
| |
| # Update biased first moment estimate. |
| exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) |
| # Update the exponentially weighted infinity norm. |
| norm_buf = torch.cat([ |
| exp_inf.mul_(beta2).unsqueeze(0), |
| grad.abs().add_(eps).unsqueeze_(0) |
| ], 0) |
| torch.amax(norm_buf, 0, keepdim=False, out=exp_inf) |
| |
| bias_correction = 1 - beta1 ** step |
| clr = lr / bias_correction |
| |
| param.addcdiv_(exp_avg, exp_inf, value=-clr) |
| |
| |
| def _multi_tensor_adamax(params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_infs: List[Tensor], |
| state_steps: List[Tensor], |
| *, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float, |
| eps: float, |
| maximize: bool): |
| |
| if len(params) == 0: |
| return |
| |
| if maximize: |
| grads = torch._foreach_neg(grads) |
| |
| params = [torch.view_as_real(x) if torch.is_complex(x) else x for x in params] |
| grads = [torch.view_as_real(x) if torch.is_complex(x) else x for x in grads] |
| exp_avgs = [torch.view_as_real(x) if torch.is_complex(x) else x for x in exp_avgs] |
| exp_infs = [torch.view_as_real(x) if torch.is_complex(x) else x for x in exp_infs] |
| |
| # Update steps |
| torch._foreach_add_(state_steps, 1) |
| |
| if weight_decay != 0: |
| torch._foreach_add_(grads, params, alpha=weight_decay) |
| |
| # Update biased first moment estimate. |
| torch._foreach_mul_(exp_avgs, beta1) |
| torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1) |
| |
| # Update the exponentially weighted infinity norm. |
| torch._foreach_mul_(exp_infs, beta2) |
| |
| for exp_inf, grad in zip(exp_infs, grads): |
| norm_buf = torch.cat([ |
| exp_inf.unsqueeze(0), |
| grad.abs().add_(eps).unsqueeze_(0) |
| ], 0) |
| torch.max(norm_buf, 0, keepdim=False, out=(exp_inf, exp_inf.new().long())) |
| |
| bias_corrections = [1 - beta1 ** step.item() for step in state_steps] |
| clr = [-1 * (lr / bias_correction) for bias_correction in bias_corrections] |
| torch._foreach_addcdiv_(params, exp_avgs, exp_infs, clr) |
