Fix ConvNext V2 paramater naming issue (#23122)

Fixes the parameter naming issue in ConvNextV2GRN module

src/transformers/models/convnextv2/convert_convnextv2_to_pytorch.py

@@ -99,6 +99,10 @@ def rename_key(name):
     if "stages" in name and "downsampling_layer" not in name:
         # stages.0.0. for instance should be renamed to stages.0.layers.0.
         name = name[: len("stages.0")] + ".layers" + name[len("stages.0") :]
+    if "gamma" in name:
+        name = name.replace("gamma", "weight")
+    if "beta" in name:
+        name = name.replace("beta", "bias")
     if "stages" in name:
         name = name.replace("stages", "encoder.stages")
     if "norm" in name:

src/transformers/models/convnextv2/modeling_convnextv2.py

@@ -100,14 +100,14 @@ class ConvNextV2GRN(nn.Module):
 
     def __init__(self, dim: int):
         super().__init__()
-        self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))
-        self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))
+        self.weight = nn.Parameter(torch.zeros(1, 1, 1, dim))
+        self.bias = nn.Parameter(torch.zeros(1, 1, 1, dim))
 
     def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
         # Compute and normalize global spatial feature maps
         global_features = torch.norm(hidden_states, p=2, dim=(1, 2), keepdim=True)
         norm_features = global_features / (global_features.mean(dim=-1, keepdim=True) + 1e-6)
-        hidden_states = self.gamma * (hidden_states * norm_features) + self.beta + hidden_states
+        hidden_states = self.weight * (hidden_states * norm_features) + self.bias + hidden_states
 
         return hidden_states