Fix GemmaRMSNorm gemma_weight buffer storage for Qwen3.5 (#26430)

python/sglang/srt/layers/layernorm.py

@@ -613,7 +613,9 @@ class GemmaRMSNorm(MultiPlatformOp):
         super().__init__()
         self.weight = nn.Parameter(torch.zeros(hidden_size))
         self.variance_epsilon = eps
-        self.register_buffer("gemma_weight", self.weight.data + 1.0, persistent=False)
+        self.register_buffer(
+            "gemma_weight", torch.ones_like(self.weight), persistent=False
+        )
         # (Chen-0210) Gemma weight = standard_weight + 1. Precompute once.
         # If TRTLLM allreduce fusion ever provides gemma-style norm
         # natively, this can be removed.
@@ -622,7 +624,8 @@ class GemmaRMSNorm(MultiPlatformOp):
     def _weight_loader(self, param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
         assert param.size() == loaded_weight.size()
         param.data.copy_(loaded_weight)
-        self.gemma_weight = param.data + 1.0
+        # Keep storage stable for CUDA graphs or fused paths that capture this buffer.
+        torch.add(param.data, 1.0, out=self.gemma_weight)
 
     def _forward_impl(
         self,