diff --git a/torch_npu/meta/_meta_registrations.py b/torch_npu/meta/_meta_registrations.py
index 93db00131f7c4be7fddc64050e96734ac6110c9b..0a2e6be69b7887154ae704b8e0b9a31a1801ee29 100644
--- a/torch_npu/meta/_meta_registrations.py
+++ b/torch_npu/meta/_meta_registrations.py
@@ -1036,6 +1036,50 @@ def npu_prefetch_meta(self, dependency, max_size, offset=0):
     )
 
 
+@impl(m, "npu_dequant_swiglu_quant")
+def npu_dequant_swiglu_quant_meta(x, weight_scale, activation_scale, bias, quant_scale,
+                                  quant_offset, group_index, activate_left=False, quant_mode=0):
+    y_size = []
+    scale_size = []
+    for i in range(x.dim() - 1):
+        y_size.append(x.size(i))
+        scale_size.append(x.size(i))
+    y_size.append(math.floor(x.size(x.dim() - 1) / 2))
+    return (torch.empty(y_size, dtype=torch.int8, device=x.device),
+            torch.empty(scale_size, dtype=torch.float32, device=x.device))
+
+
+@impl(m, "npu_single_rope")
+def npu_single_rope_meta(x, cos, sin):
+    return torch.empty_like(x)
+
+
+@impl(m, "npu_kv_rmsnorm_rope_cache")
+def npu_kv_rmsnorm_rope_cache_meta(x, gamma, cos, sin, index, k_cache, v_cache, epsilon=1e-5):
+    return (k_cache, v_cache)
+
+
+@impl(m, "npu_moe_gating_top_k")
+def npu_moe_gating_top_k_meta(x, bias=None, k=1, k_group=1, group_count=1, group_select_mode=0, renorm=0, norm_type=0, y2_flag=False, routed_scaling_factor=1.0, eps=1e-20):
+    x_dim = x.dim()
+    torch._check(
+        x_dim == 2,
+        lambda: "the x shape support only 2d)" + ops_error(ErrCode.VALUE),
+    )
+    if bias is not None:
+        bias_dim = bias.dim()
+        torch._check(
+            bias_dim == 1,
+            lambda: "the bias shape support only 1d)" + ops_error(ErrCode.VALUE),
+        )
+    y_dim_list = [x.size(0), k]
+    expert_idx_dim_list = [x.size(0), k]
+    y2_dim_list = [x.size(0), x.size(1)]
+    return (x.new_empty(tuple(y_dim_list), dtype=x.dtype),
+            x.new_empty(tuple(expert_idx_dim_list), dtype=torch.int32),
+            x.new_empty(tuple(y_dim_list), dtype=torch.float32))
+
+
 @impl(m, "npu_swiglu")
 def npu_swiglu_meta(x, dim=-1):
     output_size = []