minor fix

utils/lion.py

@@ -0,0 +1,67 @@
+"""PyTorch implementation of the Lion optimizer."""
+import torch
+from torch.optim.optimizer import Optimizer
+
+
+class Lion(Optimizer):
+    r"""Implements Lion algorithm."""
+
+    def __init__(self, params, lr=1e-4, betas=(0.9, 0.99), weight_decay=0.0):
+        """Initialize the hyperparameters.
+        Args:
+          params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+          lr (float, optional): learning rate (default: 1e-4)
+          betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.99))
+          weight_decay (float, optional): weight decay coefficient (default: 0)
+        """
+
+        if not 0.0 <= lr:
+            raise ValueError('Invalid learning rate: {}'.format(lr))
+        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]))
+        defaults = dict(lr=lr, betas=betas, weight_decay=weight_decay)
+        super().__init__(params, defaults)
+
+    @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.
+        Returns:
+          the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+
+                # Perform stepweight decay
+                p.data.mul_(1 - group['lr'] * group['weight_decay'])
+
+                grad = p.grad
+                state = self.state[p]
+                # State initialization
+                if len(state) == 0:
+                    # Exponential moving average of gradient values
+                    state['exp_avg'] = torch.zeros_like(p)
+
+                exp_avg = state['exp_avg']
+                beta1, beta2 = group['betas']
+
+                # Weight update
+                update = exp_avg * beta1 + grad * (1 - beta1)
+                p.add_(torch.sign(update), alpha=-group['lr'])
+                # Decay the momentum running average coefficient
+                exp_avg.mul_(beta2).add_(grad, alpha=1 - beta2)
+
+        return loss