write an essay on why that matters.

hivemind/optim/grad_scaler.py

@@ -56,8 +56,8 @@ class GradScaler(TorchGradScaler):
                 self._inner_optimizer_states[id(optimizer.opt)] = deepcopy(self._per_optimizer_states[id(optimizer)])
                 # note: we store unscaled optimizer state in a separate dict and not in _per_optimizer_states in order
                 # to avoid an edge case where full DPU peer encounters overflow in local gradients while averaging
-                # gradients (i.e. after global unscale but before global step). In that case, the next call to .update
-                # on user side would reset *all* optimizer states and cause .step to unscale gradients the second time.
+                # offloaded gradients (i.e. after global unscale but before global step). Due to overflow, next call to
+                # .update on user side would reset *all* optimizer states and cause .step to unscale gradients twice.
                 # Offloaded optimizer is not affected by overflow in on-device gradients and should not be reset.
                 return True
             else: