update norm cpu kernel

ktransformers/operators/layernorm.py

@@ -163,3 +163,34 @@ class KQwen3MoeRMSNorm(Qwen3MoeRMSNorm, BaseInjectedModule):
         variance = hidden_states.pow(2).mean(-1, keepdim=True)
         hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
         return self.weight * hidden_states.to(input_dtype)
+
+class DeepseekV3RMSNormTorch(DeepseekV3RMSNorm, BaseInjectedModule):
+    def __init__(self,
+                key: str,
+                gguf_loader : GGUFLoader,
+                config: PretrainedConfig,
+                orig_module: nn.Module,
+                prefill_device: str = "cuda",
+                generate_device: str = "cuda",
+                **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
+        self.orig_module.__init__(orig_module.hidden_size,
+            orig_module.variance_epsilon)
+
+    def forward(
+        self, 
+        x,
+        batch_size_tensor: torch.Tensor = None,
+        residual: Optional[torch.Tensor] = None,
+    )-> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        if residual is not None:
+            x = x + residual
+            residual = x
+        # range batch_size_tensor for x
+        input_dtype = x.dtype
+        x = x.to(torch.float32)
+        variance = x.pow(2).mean(-1, keepdim=True)
+        x = x * torch.rsqrt(variance + self.variance_epsilon)
+        if residual is not None:
+            return self.weight * x.to(input_dtype), residual
+        return self.weight * x.to(input_dtype)
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