| import math |
| import torch |
| from torch import Tensor |
| from .optimizer import Optimizer, _use_grad_for_differentiable |
| from typing import List, Optional |
| |
| __all__ = ['AdamW', 'adamw'] |
| |
| class AdamW(Optimizer): |
| r"""Implements AdamW algorithm. |
| |
| .. 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)}, |
| \: \epsilon \text{ (epsilon)} \\ |
| &\hspace{13mm} \lambda \text{(weight decay)}, \: \textit{amsgrad}, |
| \: \textit{maximize} \\ |
| &\textbf{initialize} : m_0 \leftarrow 0 \text{ (first moment)}, v_0 \leftarrow 0 |
| \text{ ( second moment)}, \: \widehat{v_0}^{max}\leftarrow 0 \\[-1.ex] |
| &\rule{110mm}{0.4pt} \\ |
| &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ |
| |
| &\hspace{5mm}\textbf{if} \: \textit{maximize}: \\ |
| &\hspace{10mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\ |
| &\hspace{5mm}\textbf{else} \\ |
| &\hspace{10mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ |
| &\hspace{5mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1} \\ |
| &\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ |
| &\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\ |
| &\hspace{5mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\ |
| &\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\ |
| &\hspace{5mm}\textbf{if} \: amsgrad \\ |
| &\hspace{10mm}\widehat{v_t}^{max} \leftarrow \mathrm{max}(\widehat{v_t}^{max}, |
| \widehat{v_t}) \\ |
| &\hspace{10mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/ |
| \big(\sqrt{\widehat{v_t}^{max}} + \epsilon \big) \\ |
| &\hspace{5mm}\textbf{else} \\ |
| &\hspace{10mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/ |
| \big(\sqrt{\widehat{v_t}} + \epsilon \big) \\ |
| &\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 `Decoupled Weight Decay Regularization`_. |
| |
| Args: |
| params (iterable): iterable of parameters to optimize or dicts defining |
| parameter groups |
| lr (float, optional): learning rate (default: 1e-3) |
| betas (Tuple[float, float], optional): coefficients used for computing |
| running averages of gradient and its square (default: (0.9, 0.999)) |
| eps (float, optional): term added to the denominator to improve |
| numerical stability (default: 1e-8) |
| weight_decay (float, optional): weight decay coefficient (default: 1e-2) |
| amsgrad (bool, optional): whether to use the AMSGrad variant of this |
| algorithm from the paper `On the Convergence of Adam and Beyond`_ |
| (default: False) |
| maximize (bool, optional): maximize the params based on the objective, instead of |
| minimizing (default: False) |
| foreach (bool, optional): whether foreach implementation of optimizer |
| is used (default: None) |
| capturable (bool, optional): whether this instance is safe to capture in a CUDA graph. |
| Passing True can impair ungraphed performance, so if you don't intend to |
| graph capture this instance, leave it False (default: False) |
| |
| .. _Decoupled Weight Decay Regularization: |
| https://arxiv.org/abs/1711.05101 |
| .. _On the Convergence of Adam and Beyond: |
| https://openreview.net/forum?id=ryQu7f-RZ |
| """ |
| |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, |
| weight_decay=1e-2, amsgrad=False, *, maximize: bool = False, |
| foreach: Optional[bool] = None, |
| capturable: bool = False, |
| differentiable: 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, amsgrad=amsgrad, |
| foreach=foreach, maximize=maximize, capturable=capturable, |
| differentiable=differentiable) |
| super(AdamW, self).__init__(params, defaults) |
| |
| def __setstate__(self, state): |
| super().__setstate__(state) |
| for group in self.param_groups: |
| group.setdefault('amsgrad', False) |
| group.setdefault('maximize', False) |
| group.setdefault('foreach', None) |
| group.setdefault('capturable', False) |
| group.setdefault('differentiable', 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'])) |
| |
| @_use_grad_for_differentiable |
| def step(self, closure=None): |
| """Performs a single optimization step. |
| |
| Args: |
| closure (Callable, optional): A closure that reevaluates the model |
| and returns the loss. |
| """ |
| self._cuda_graph_capture_health_check() |
| |
| 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_avg_sqs = [] |
| max_exp_avg_sqs = [] |
| state_steps = [] |
| amsgrad = group['amsgrad'] |
| beta1, beta2 = group['betas'] |
| differentiable = group['differentiable'] |
| |
| for p in group['params']: |
| if p.grad is None: |
| continue |
| params_with_grad.append(p) |
| if p.grad.is_sparse: |
| raise RuntimeError('AdamW does not support sparse gradients') |
| grads.append(p.grad) |
| |
| state = self.state[p] |
| |
| # State initialization |
| if len(state) == 0: |
| state['step'] = torch.zeros((1,), dtype=torch.float, device=p.device) \ |
| if self.defaults['capturable'] else torch.tensor(0.) |
| # Exponential moving average of gradient values |
| state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
| # Exponential moving average of squared gradient values |
| state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
| if amsgrad: |
| # Maintains max of all exp. moving avg. of sq. grad. values |
| state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
| |
| exp_avgs.append(state['exp_avg']) |
| exp_avg_sqs.append(state['exp_avg_sq']) |
| |
| if amsgrad: |
| max_exp_avg_sqs.append(state['max_exp_avg_sq']) |
| |
| state_steps.append(state['step']) |
| |
| adamw(params_with_grad, |
| grads, |
| exp_avgs, |
| exp_avg_sqs, |
| max_exp_avg_sqs, |
| state_steps, |
| amsgrad=amsgrad, |
| beta1=beta1, |
| beta2=beta2, |
| lr=group['lr'], |
| weight_decay=group['weight_decay'], |
| eps=group['eps'], |
| maximize=group['maximize'], |
| foreach=group['foreach'], |
| capturable=group['capturable'], |
| differentiable=group['differentiable']) |
| |
| return loss |
| |
| |
| def adamw(params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_avg_sqs: List[Tensor], |
| max_exp_avg_sqs: 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, |
| capturable: bool = False, |
| differentiable: bool = False, |
| *, |
| amsgrad: bool, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float, |
| eps: float, |
| maximize: bool): |
| r"""Functional API that performs AdamW algorithm computation. |
| |
| See :class:`~torch.optim.AdamW` 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_adamw |
| else: |
| func = _single_tensor_adamw |
| |
| func(params, |
| grads, |
| exp_avgs, |
| exp_avg_sqs, |
| max_exp_avg_sqs, |
| state_steps, |
| amsgrad=amsgrad, |
| beta1=beta1, |
| beta2=beta2, |
| lr=lr, |
| weight_decay=weight_decay, |
| eps=eps, |
| maximize=maximize, |
| capturable=capturable, |
| differentiable=differentiable) |
| |
| |
| def _single_tensor_adamw(params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_avg_sqs: List[Tensor], |
| max_exp_avg_sqs: List[Tensor], |
| state_steps: List[Tensor], |
| *, |
| amsgrad: bool, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float, |
| eps: float, |
| maximize: bool, |
| capturable: bool, |
| differentiable: bool): |
| |
| for i, param in enumerate(params): |
| grad = grads[i] if not maximize else -grads[i] |
| exp_avg = exp_avgs[i] |
| exp_avg_sq = exp_avg_sqs[i] |
| step_t = state_steps[i] |
| |
| if capturable: |
| assert param.is_cuda and step_t.is_cuda, "If capturable=True, params and state_steps must be CUDA tensors." |
| |
| if torch.is_complex(param): |
| grad = torch.view_as_real(grad) |
| exp_avg = torch.view_as_real(exp_avg) |
| exp_avg_sq = torch.view_as_real(exp_avg_sq) |
| param = torch.view_as_real(param) |
| |
| # update step |
| step_t += 1 |
| |
| # Perform stepweight decay |
| param.mul_(1 - lr * weight_decay) |
| |
| # Decay the first and second moment running average coefficient |
| exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) |
| exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) |
| |
| if capturable or differentiable: |
| step = step_t |
| |
| # 1 - beta1 ** step can't be captured in a CUDA graph, even if step is a CUDA tensor |
| # (incurs "RuntimeError: CUDA error: operation not permitted when stream is capturing") |
| bias_correction1 = 1 - torch.pow(beta1, step) |
| bias_correction2 = 1 - torch.pow(beta2, step) |
| |
| step_size = lr / bias_correction1 |
| step_size_neg = step_size.neg() |
| |
| bias_correction2_sqrt = bias_correction2.sqrt() |
| |
| if amsgrad: |
| # Maintains the maximum of all 2nd moment running avg. till now |
| if differentiable: |
| max_exp_avg_sqs_i = max_exp_avg_sqs[i].clone() |
| else: |
| max_exp_avg_sqs_i = max_exp_avg_sqs[i] |
| max_exp_avg_sqs[i].copy_(torch.maximum(max_exp_avg_sqs_i, exp_avg_sq)) |
| # Uses the max. for normalizing running avg. of gradient |
| # Folds in (admittedly ugly) 1-elem step_size math here to avoid extra param-set-sized read+write |
| # (can't fold it into addcdiv_ below because addcdiv_ requires value is a Number, not a Tensor) |
| denom = (max_exp_avg_sqs[i].sqrt() / (bias_correction2_sqrt * step_size_neg)).add_(eps / step_size_neg) |
| else: |
| denom = (exp_avg_sq.sqrt() / (bias_correction2_sqrt * step_size_neg)).add_(eps / step_size_neg) |
| |
| param.addcdiv_(exp_avg, denom) |
| else: |
| step = step_t.item() |
| |
| bias_correction1 = 1 - beta1 ** step |
| bias_correction2 = 1 - beta2 ** step |
| |
| step_size = lr / bias_correction1 |
| |
| bias_correction2_sqrt = math.sqrt(bias_correction2) |
| |
| if amsgrad: |
| # Maintains the maximum of all 2nd moment running avg. till now |
| torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i]) |
| # Use the max. for normalizing running avg. of gradient |
| denom = (max_exp_avg_sqs[i].sqrt() / bias_correction2_sqrt).add_(eps) |
| else: |
| denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps) |
| |
| param.addcdiv_(exp_avg, denom, value=-step_size) |
| |
| |
| def _multi_tensor_adamw(params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_avg_sqs: List[Tensor], |
| max_exp_avg_sqs: List[Tensor], |
| state_steps: List[Tensor], |
| *, |
| amsgrad: bool, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float, |
| eps: float, |
| maximize: bool, |
| capturable: bool, |
| differentiable: bool): |
| if len(params) == 0: |
| return |
| |
| if capturable: |
| assert all(p.is_cuda and step.is_cuda for p, step in zip(params, state_steps)), \ |
| "If capturable=True, params and state_steps must be CUDA tensors." |
| |
| if maximize: |
| grads = torch._foreach_neg(tuple(grads)) # type: ignore[assignment] |
| |
| assert not differentiable, "_foreach ops don't support autograd" |
| |
| 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_avg_sqs = [torch.view_as_real(x) if torch.is_complex(x) else x for x in exp_avg_sqs] |
| params = [torch.view_as_real(x) if torch.is_complex(x) else x for x in params] |
| |
| # update steps |
| torch._foreach_add_(state_steps, 1) |
| |
| # Perform stepweight decay |
| torch._foreach_mul_(params, 1 - lr * weight_decay) |
| |
| # Decay the first and second moment running average coefficient |
| torch._foreach_mul_(exp_avgs, beta1) |
| torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1) |
| |
| torch._foreach_mul_(exp_avg_sqs, beta2) |
| torch._foreach_addcmul_(exp_avg_sqs, grads, grads, 1 - beta2) |
| |
| if capturable: |
| # TODO: use foreach_pow if/when foreach_pow is added |
| bias_correction1 = [torch.pow(beta1, step) for step in state_steps] |
| bias_correction2 = [torch.pow(beta2, step) for step in state_steps] |
| # foreach_sub doesn't allow a scalar as the first arg |
| torch._foreach_sub_(bias_correction1, 1) |
| torch._foreach_sub_(bias_correction2, 1) |
| torch._foreach_neg_(bias_correction1) |
| torch._foreach_neg_(bias_correction2) |
| |
| # foreach_div doesn't allow a scalar as the first arg |
| step_size = torch._foreach_div(bias_correction1, lr) |
| torch._foreach_reciprocal_(step_size) |
| torch._foreach_neg_(step_size) |
| |
| bias_correction2_sqrt = torch._foreach_sqrt(bias_correction2) |
| |
| if amsgrad: |
| # Maintains the maximum of all 2nd moment running avg. till now |
| torch._foreach_maximum_(max_exp_avg_sqs, exp_avg_sqs) |
| |
| # Use the max. for normalizing running avg. of gradient |
| max_exp_avg_sq_sqrt = torch._foreach_sqrt(max_exp_avg_sqs) |
| # Folds in (admittedly ugly) 1-elem step_size math here to avoid extra param-set-sized read+write |
| # (can't fold it into addcdiv_ below because addcdiv_ requires value is a Number, not a Tensor) |
| torch._foreach_div_(max_exp_avg_sq_sqrt, torch._foreach_mul(bias_correction2_sqrt, step_size)) |
| eps_over_step_size = torch._foreach_div(step_size, eps) |
| torch._foreach_reciprocal_(eps_over_step_size) |
| denom = torch._foreach_add(max_exp_avg_sq_sqrt, eps_over_step_size) |
| else: |
| exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs) |
| torch._foreach_div_(exp_avg_sq_sqrt, torch._foreach_mul(bias_correction2_sqrt, step_size)) |
| eps_over_step_size = torch._foreach_div(step_size, eps) |
| torch._foreach_reciprocal_(eps_over_step_size) |
| denom = torch._foreach_add(exp_avg_sq_sqrt, eps_over_step_size) |
| |
| torch._foreach_addcdiv_(params, exp_avgs, denom) |
| else: |
| bias_correction1 = [1 - beta1 ** step.item() for step in state_steps] |
| bias_correction2 = [1 - beta2 ** step.item() for step in state_steps] |
| |
| step_size = [(lr / bc) * -1 for bc in bias_correction1] |
| |
| bias_correction2_sqrt = [math.sqrt(bc) for bc in bias_correction2] |
| |
| if amsgrad: |
| # Maintains the maximum of all 2nd moment running avg. till now |
| torch._foreach_maximum_(max_exp_avg_sqs, exp_avg_sqs) |
| |
| # Use the max. for normalizing running avg. of gradient |
| max_exp_avg_sq_sqrt = torch._foreach_sqrt(max_exp_avg_sqs) |
| torch._foreach_div_(max_exp_avg_sq_sqrt, bias_correction2_sqrt) |
| denom = torch._foreach_add(max_exp_avg_sq_sqrt, eps) |
| else: |
| exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs) |
| torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt) |
| denom = torch._foreach_add(exp_avg_sq_sqrt, eps) |
| |
| torch._foreach_addcdiv_(params, exp_avgs, denom, step_size) |
