Replcae torch.norm with torch.linalg.vector_norm in L1-norm

torch.norm is deprecated and may be removed in future PyTorch releases

Author: namgyu-youn

modelopt/torch/nas/modules/conv.py

@@ -139,7 +139,9 @@ def _estimate_importance(self) -> TracedHp.Importance:
             return None
         weight = self._parameters["weight"]  # retrieve full weight tensor
         c_in = weight.shape[1]
-        return torch.norm(torch.reshape(weight.detach().transpose(0, 1), (c_in, -1)), dim=1)
+        return torch.linalg.vector_norm(
+            torch.reshape(weight.detach().transpose(0, 1), (c_in, -1)), dim=1
+        )
 
     def _setup(self):
         # only support ungrouped conv or grouped conv with in_channels == out_channels
@@ -249,4 +251,6 @@ def _estimate_importance(self) -> TracedHp.Importance:
             return None
         weight = self._parameters["weight"]  # retrieve full weight tensor
         c_in = weight.shape[0]
-        return torch.norm(torch.reshape(weight.detach(), (c_in, -1)), dim=1)
+        return torch.linalg.vector_norm(
+            torch.reshape(weight.detach().transpose(0, 1), (c_in, -1)), dim=1
+        )

modelopt/torch/nas/modules/linear.py

@@ -41,7 +41,7 @@ def _get_bias(mod: "_DynamicLinear", bias: torch.Tensor | None) -> torch.Tensor
         return get_sliced_tensor(mod, bias, "out_features")
 
     def _estimate_importance(self) -> TracedHp.Importance:
-        return self._parameters["weight"].detach().norm(dim=0)
+        return torch.linalg.vector_norm(self._parameters["weight"].detach(), dim=0)
 
     def _setup(self):
         # register hyperparameters