training/optimizor/SAM.py

# borrowed from

import torch

import torch
import torch.nn as nn

def disable_running_stats(model):
    def _disable(module):
        if isinstance(module, nn.BatchNorm2d):
            module.backup_momentum = module.momentum
            module.momentum = 0

    model.apply(_disable)

def enable_running_stats(model):
    def _enable(module):
        if isinstance(module, nn.BatchNorm2d) and hasattr(module, "backup_momentum"):
            module.momentum = module.backup_momentum

    model.apply(_enable)

class SAM(torch.optim.Optimizer):
    def __init__(self, params, base_optimizer, rho=0.05, **kwargs):
        assert rho >= 0.0, f"Invalid rho, should be non-negative: {rho}"

        defaults = dict(rho=rho, **kwargs)
        super(SAM, self).__init__(params, defaults)

        self.base_optimizer = base_optimizer(self.param_groups, **kwargs)
        self.param_groups = self.base_optimizer.param_groups

    @torch.no_grad()
    def first_step(self, zero_grad=False):
        grad_norm = self._grad_norm()
        for group in self.param_groups:
            scale = group["rho"] / (grad_norm + 1e-12)

            for p in group["params"]:
                if p.grad is None: continue
                e_w = p.grad * scale.to(p)
                p.add_(e_w)  # climb to the local maximum "w + e(w)"
                self.state[p]["e_w"] = e_w

        if zero_grad: self.zero_grad()

    @torch.no_grad()
    def second_step(self, zero_grad=False):
        for group in self.param_groups:
            for p in group["params"]:
                if p.grad is None: continue
                p.sub_(self.state[p]["e_w"])  # get back to "w" from "w + e(w)"

        self.base_optimizer.step()  # do the actual "sharpness-aware" update

        if zero_grad: self.zero_grad()

    @torch.no_grad()
    def step(self, closure=None):
        assert closure is not None, "Sharpness Aware Minimization requires closure, but it was not provided"
        closure = torch.enable_grad()(closure)  # the closure should do a full forward-backward pass

        self.first_step(zero_grad=True)
        closure()
        self.second_step()

    def _grad_norm(self):
        shared_device = self.param_groups[0]["params"][0].device  # put everything on the same device, in case of model parallelism
        norm = torch.norm(
                    torch.stack([
                        p.grad.norm(p=2).to(shared_device)
                        for group in self.param_groups for p in group["params"]
                        if p.grad is not None
                    ]),
                    p=2
               )
        return norm