Add Adan optimizer

timm/optim/adan.py

@@ -0,0 +1,124 @@
+""" Adan Optimizer
+
+Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J]. arXiv preprint arXiv:2208.06677, 2022.
+    https://arxiv.org/abs/2208.06677
+
+Implementation adapted from https://github.com/sail-sg/Adan
+"""
+
+import math
+
+import torch
+
+from torch.optim import Optimizer
+
+
+class Adan(Optimizer):
+    """
+    Implements a pytorch variant of Adan
+    Adan was proposed in
+    Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J]. arXiv preprint arXiv:2208.06677, 2022.
+    https://arxiv.org/abs/2208.06677
+    Arguments:
+        params (iterable): iterable of parameters to optimize or dicts defining parameter groups.
+        lr (float, optional): learning rate. (default: 1e-3)
+        betas (Tuple[float, float, flot], optional): coefficients used for computing
+            running averages of gradient and its norm. (default: (0.98, 0.92, 0.99))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability. (default: 1e-8)
+        weight_decay (float, optional): decoupled weight decay (L2 penalty) (default: 0)
+        no_prox (bool): how to perform the decoupled weight decay (default: False)
+    """
+
+    def __init__(
+            self,
+            params,
+            lr=1e-3,
+            betas=(0.98, 0.92, 0.99),
+            eps=1e-8,
+            weight_decay=0.0,
+            no_prox=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 <= betas[2] < 1.0:
+            raise ValueError("Invalid beta parameter at index 2: {}".format(betas[2]))
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, no_prox=no_prox)
+        super(Adan, self).__init__(params, defaults)
+
+    @torch.no_grad()
+    def restart_opt(self):
+        for group in self.param_groups:
+            group['step'] = 0
+            for p in group['params']:
+                if p.requires_grad:
+                    state = self.state[p]
+                    # State initialization
+
+                    # 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)
+                    # Exponential moving average of gradient difference
+                    state['exp_avg_diff'] = torch.zeros_like(p)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """ Performs a single optimization step.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            beta1, beta2, beta3 = group['betas']
+            # assume same step across group now to simplify things
+            # per parameter step can be easily support by making it tensor, or pass list into kernel
+            if 'step' in group:
+                group['step'] += 1
+            else:
+                group['step'] = 1
+
+            bias_correction1 = 1.0 - beta1 ** group['step']
+            bias_correction2 = 1.0 - beta2 ** group['step']
+            bias_correction3 = 1.0 - beta3 ** group['step']
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad
+
+                state = self.state[p]
+                if len(state) == 0:
+                    state['exp_avg'] = torch.zeros_like(p)
+                    state['exp_avg_diff'] = torch.zeros_like(p)
+                    state['exp_avg_sq'] = torch.zeros_like(p)
+                    state['pre_grad'] = grad.clone()
+
+                exp_avg, exp_avg_sq, exp_avg_diff = state['exp_avg'], state['exp_avg_diff'], state['exp_avg_sq']
+                grad_diff = grad - state['pre_grad']
+
+                exp_avg.lerp_(grad, 1. - beta1)  # m_t
+                exp_avg_diff.lerp_(grad_diff, 1. - beta2)  # diff_t (v)
+                update = grad + beta2 * grad_diff
+                exp_avg_sq.mul_(beta3).addcmul_(update, update, value=1. - beta3)  # n_t
+
+                denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction3)).add_(group['eps'])
+                update = (exp_avg / bias_correction1 + beta2 * exp_avg_diff / bias_correction2).div_(denom)
+                if group['no_prox']:
+                    p.data.mul_(1 - group['lr'] * group['weight_decay'])
+                    p.add_(update, alpha=-group['lr'])
+                else:
+                    p.add_(update, alpha=-group['lr'])
+                    p.data.div_(1 + group['lr'] * group['weight_decay'])
+
+                state['pre_grad'].copy_(grad)
+
+        return loss

timm/optim/optim_factory.py

@@ -15,6 +15,7 @@ from .adabelief import AdaBelief
 from .adafactor import Adafactor
 from .adahessian import Adahessian
 from .adamp import AdamP
+from .adan import Adan
 from .lamb import Lamb
 from .lars import Lars
 from .lookahead import Lookahead
@@ -285,6 +286,10 @@ def create_optimizer_v2(
         optimizer = optim.Adagrad(parameters, **opt_args)
     elif opt_lower == 'adafactor':
         optimizer = Adafactor(parameters, **opt_args)
+    elif opt_lower == 'adanp':
+        optimizer = Adan(parameters, no_prox=False, **opt_args)
+    elif opt_lower == 'adanw':
+        optimizer = Adan(parameters, no_prox=True, **opt_args)
     elif opt_lower == 'lamb':
         optimizer = Lamb(parameters, **opt_args)
     elif opt_lower == 'lambc':