adaboundw

torch_optimizer/adabound.py

@@ -175,3 +175,177 @@ def step(self, closure: OptLossClosure = None) -> OptFloat:
 
                 p.data.add_(-step_size)
         return loss
+
+
+
+class AdaBound(Optimizer):
+    r"""Implements AdaBound algorithm with Decoupled Weight Decay (arxiv.org/abs/1711.05101)
+
+    It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of
+    Learning Rate`__.
+
+    Arguments:
+        params: iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr: learning rate (default: 1e-3)
+        betas: coefficients used for computing running averages of gradient
+            and its square (default: (0.9, 0.999))
+        final_lr: final (SGD) learning rate (default: 0.1)
+        gamma: convergence speed of the bound functions
+            (default: 1e-3)
+        eps: term added to the denominator to improve numerical stability
+            (default: 1e-8)
+        weight_decay: weight decay (L2 penalty) (default: 0)
+        amsbound: whether to use the AMSBound variant of this algorithm
+
+    Example:
+        >>> import torch_optimizer as optim
+        >>> optimizer = optim.AdaBoundW(model.parameters(), lr=0.1, weight_decay=0.001)
+        >>> optimizer.zero_grad()
+        >>> loss_fn(model(input), target).backward()
+        >>> optimizer.step()
+
+    __ https://arxiv.org/abs/1902.09843
+
+    Note:
+        Reference code: https://github.com/Luolc/AdaBound
+    """
+
+    def __init__(
+        self,
+        params: Params,
+        lr: float = 1e-3,
+        betas: Betas2 = (0.9, 0.999),
+        final_lr: float = 0.1,
+        gamma: float = 1e-3,
+        eps: float = 1e-8,
+        weight_decay: float = 0,
+        amsbound: bool = False,
+    ) -> None:
+        if lr <= 0.0:
+            raise ValueError('Invalid learning rate: {}'.format(lr))
+        if eps < 0.0:
+            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 final_lr < 0.0:
+            raise ValueError(
+                'Invalid final learning rate: {}'.format(final_lr)
+            )
+        if not 0.0 <= gamma < 1.0:
+            raise ValueError('Invalid gamma parameter: {}'.format(gamma))
+        if weight_decay < 0:
+            raise ValueError(
+                'Invalid weight_decay value: {}'.format(weight_decay)
+            )
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            final_lr=final_lr,
+            gamma=gamma,
+            eps=eps,
+            weight_decay=weight_decay,
+            amsbound=amsbound,
+        )
+        super(AdaBoundW, self).__init__(params, defaults)
+        self.base_lrs = [group['lr'] for group in self.param_groups]
+
+    def __setstate__(self, state: State) -> None:
+        super(AdaBound, self).__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault('amsbound', False)
+
+    def step(self, closure: OptLossClosure = None) -> OptFloat:
+        r"""Performs a single optimization step.
+
+        Arguments:
+            closure: A closure that reevaluates the model and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group, base_lr in zip(self.param_groups, self.base_lrs):
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+
+                # Perform stepweight decay
+                p.mul_(1 - base_lr * group['weight_decay'])
+
+                grad = p.grad.data
+                if grad.is_sparse:
+                    msg = (
+                        'AdaBound does not support sparse gradients, '
+                        'please consider SparseAdam instead'
+                    )
+                    raise RuntimeError(msg)
+                amsbound = group['amsbound']
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state['step'] = 0
+                    # Exponential moving average of gradient values
+                    state['exp_avg'] = torch.zeros_like(p)
+                    # Exponential moving average of squared gradient values
+                    state['exp_avg_sq'] = torch.zeros_like(p)
+                    if amsbound:
+                        # Maintains max of all exp. moving avg. of
+                        # sq. grad. values
+                        state['max_exp_avg_sq'] = torch.zeros_like(p)
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                if amsbound:
+                    max_exp_avg_sq = state['max_exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                state['step'] += 1
+
+                # if group['weight_decay'] != 0:
+                #     grad = grad.add(group['weight_decay'], p.data)
+
+                # Decay the first and second moment running average coefficient
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                if amsbound:
+                    # Maintains the maximum of all 2nd moment running
+                    # avg. till now
+                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
+                    # Use the max. for normalizing running avg. of gradient
+                    denom = max_exp_avg_sq.sqrt().add_(group['eps'])
+                else:
+                    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
+                )
+
+                # Applies bounds on actual learning rate
+                # lr_scheduler cannot affect final_lr, this is a workaround
+                # to apply lr decay
+                final_lr = group['final_lr'] * group['lr'] / base_lr
+                lower_bound = final_lr * (
+                    1 - 1 / (group['gamma'] * state['step'] + 1)
+                )
+                upper_bound = final_lr * (
+                    1 + 1 / (group['gamma'] * state['step'])
+                )
+                step_size = torch.full_like(denom, step_size)
+                step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(
+                    exp_avg
+                )
+
+                p.data.add_(-step_size)
+        return loss