Migrated project

colossalai/nn/optimizer/lars.py

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+"""Adapted from https://github.com/NUS-HPC-AI-Lab/LARS-ImageNet-PyTorch/blob/main/lars.py"""
+
+from typing import Iterable
+
+import torch
+from torch.optim import Optimizer
+
+from colossalai.registry import OPTIMIZERS
+
+
+@OPTIMIZERS.register_module
+class Lars(Optimizer):
+    r"""Implements the LARS optimizer from `"Large batch training of convolutional networks"
+    <https://arxiv.org/pdf/1708.03888.pdf>`_.
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, optional): learning rate
+        momentum (float, optional): momentum factor (default: 0)
+        eeta (float, optional): LARS coefficient as used in the paper (default: 1e-3)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+    """
+
+    def __init__(
+            self,
+            params: Iterable[torch.nn.Parameter],
+            lr=1e-3,
+            momentum=0,
+            eeta=1e-3,
+            weight_decay=0,
+            epsilon=0.0
+    ) -> None:
+        if not isinstance(lr, float) or lr < 0.0:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if momentum < 0.0:
+            raise ValueError("Invalid momentum value: {}".format(momentum))
+        if weight_decay < 0.0:
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+        if eeta <= 0 or eeta > 1:
+            raise ValueError("Invalid eeta value: {}".format(eeta))
+        if epsilon < 0:
+            raise ValueError("Invalid epsilon value: {}".format(epsilon))
+        defaults = dict(lr=lr, momentum=momentum,
+                        weight_decay=weight_decay, eeta=eeta, epsilon=epsilon, lars=True)
+
+        super().__init__(params, defaults)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+        Arguments:
+            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:
+            weight_decay = group['weight_decay']
+            momentum = group['momentum']
+            eeta = group['eeta']
+            lr = group['lr']
+            lars = group['lars']
+            eps = group['epsilon']
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                decayed_grad = p.grad
+                scaled_lr = lr
+                if lars:
+                    w_norm = torch.norm(p)
+                    g_norm = torch.norm(p.grad)
+                    trust_ratio = torch.where(
+                        w_norm > 0 and g_norm > 0,
+                        eeta * w_norm / (g_norm + weight_decay * w_norm + eps),
+                        torch.ones_like(w_norm)
+                    )
+                    trust_ratio.clamp_(0.0, 50)
+                    scaled_lr *= trust_ratio.item()
+                    if weight_decay != 0:
+                        decayed_grad = decayed_grad.add(p, alpha=weight_decay)
+                decayed_grad = torch.clamp(decayed_grad, -10.0, 10.0)
+
+                if momentum != 0:
+                    param_state = self.state[p]
+                    if 'momentum_buffer' not in param_state:
+                        buf = param_state['momentum_buffer'] = torch.clone(
+                            decayed_grad).detach()
+                    else:
+                        buf = param_state['momentum_buffer']
+                        buf.mul_(momentum).add_(decayed_grad)
+                    decayed_grad = buf
+
+                p.add_(decayed_grad, alpha=-scaled_lr)
+
+        return loss