feat(scalarization): add GradNormScalarizer implementation

gradnorm.py

@@ -0,0 +1,33 @@
+import torch
+from torch import Tensor, nn
+from ._scalarizer_base import Scalarizer
+
+
+class GradNormScalarizer(Scalarizer):
+    def __init__(self, num_tasks: int, alpha: float = 1.5) -> None:
+        super().__init__()
+        self.num_tasks = num_tasks
+        self.weights = nn.Parameter(torch.ones(num_tasks))
+        self.alpha = alpha
+        self.register_buffer("initial_losses", None)
+
+    def forward(self, values: Tensor, model: nn.Module = None) -> Tensor:
+        if self.initial_losses is None:
+            self.initial_losses = values.detach().clone()
+
+        if model is not None:
+            norms = self._compute_gradient_norms(values, model)
+            loss_ratios = values / self.initial_losses
+            target_norm = torch.mean(norms) * (loss_ratios**self.alpha)
+            # Added 1e-8 epsilon for numerical stability
+            self.weights.data = target_norm / (norms + 1e-8)
+
+        return (values * self.weights).sum()
+
+    def _compute_gradient_norms(self, values: Tensor, model: nn.Module) -> Tensor:
+        norms = []
+        for loss in values:
+            grads = torch.autograd.grad(loss, model.parameters(), retain_graph=True)
+            norm = torch.norm(torch.cat([g.view(-1) for g in grads]))
+            norms.append(norm)
+        return torch.stack(norms)