Update Ln-norm logic for upcoming PyTorch update (#206)

Co-authored-by: namgyu-youn <yynk2012@gmail.com>

Author: kevalmorabia97

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 +251,4 @@ 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(), (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

modelopt/torch/nas/plugins/megatron.py

@@ -613,20 +613,29 @@ def _linear_proj_forward_hook(self, module, input, output):
     def _estimate_all_head_importance(self) -> TracedHp.Importance:
         """Return the importance for num_attention_heads (num_heads_per_group * num_query_groups)."""
         assert self._activations is not None, "No activations collected for importance estimation."
-        attn_head_importance = self._activations.view(
-            self.get_hparam("num_heads_per_group").max * self.get_hparam("num_query_groups").max,
-            self.config.kv_channels,
-        ).norm(p=2, dim=1)
+        attn_head_importance = torch.linalg.vector_norm(
+            self._activations.view(
+                self.get_hparam("num_heads_per_group").max
+                * self.get_hparam("num_query_groups").max,
+                self.config.kv_channels,
+            ),
+            ord=2,
+            dim=1,
+        )
         return attn_head_importance
 
     def _estimate_query_group_importance(self) -> TracedHp.Importance:
         """Return the importance of the ``num_query_groups`` hparam."""
         assert self._activations is not None, "No activations collected for importance estimation."
-        group_importance = self._activations.view(
-            self.get_hparam("num_heads_per_group").max,
-            self.get_hparam("num_query_groups").max,
-            self.config.kv_channels,
-        ).norm(p=2, dim=(0, 2))
+        group_importance = torch.linalg.vector_norm(
+            self._activations.view(
+                self.get_hparam("num_heads_per_group").max,
+                self.get_hparam("num_query_groups").max,
+                self.config.kv_channels,
+            ),
+            ord=2,
+            dim=(0, 2),
+        )
         return group_importance
 
     def export(self) -> torch.nn.Module:

modelopt/torch/nas/plugins/transformers.py

@@ -122,7 +122,9 @@ def configure_qkv_out(self, q_name: str, k_name: str, v_name: str, out_name: str
         out.in_features = hp_hidden_dim
 
         assert isinstance(out, nn.Linear)
-        hp_hidden_dim.register_importance(lambda: out._parameters["weight"].detach().norm(dim=0))
+        hp_hidden_dim.register_importance(
+            lambda: torch.linalg.vector_norm(out._parameters["weight"].detach(), dim=0)
+        )
 
     def modify(
         self, *, n_heads_ratio: tuple[float, ...] | None = None, n_heads_divisor: int = 1