diff --git a/vllm_ascend/ops/triton/mamba/casual_conv1d.py b/vllm_ascend/ops/triton/mamba/casual_conv1d.py
index bb8299237b3..be4c6053e9d 100644
--- a/vllm_ascend/ops/triton/mamba/casual_conv1d.py
+++ b/vllm_ascend/ops/triton/mamba/casual_conv1d.py
@@ -17,7 +17,7 @@
 PAD_SLOT_ID = -1
 
 
-def causal_conv1d_ref(
+def causal_conv1d_fn_native(
     x: torch.Tensor,
     weight: torch.Tensor,
     bias: Optional[torch.Tensor] = None,
@@ -32,6 +32,7 @@ def causal_conv1d_ref(
     bias: (dim,)
     initial_states: (batch, dim, width - 1)
     final_states_out: (batch, dim, width - 1)
+
     out: (batch, dim, seqlen)
     """
     if activation not in [None, "silu", "swish"]:
@@ -42,11 +43,7 @@ def causal_conv1d_ref(
     dim, width = weight.shape
 
     if initial_states is None:
-        out = F.conv1d(x,
-                       weight.unsqueeze(1),
-                       bias,
-                       padding=width - 1,
-                       groups=dim)
+        out = F.conv1d(x, weight.unsqueeze(1), bias, padding=width - 1, groups=dim)
     else:
         x = torch.cat([initial_states, x], dim=-1)
         out = F.conv1d(x, weight.unsqueeze(1), bias, padding=0, groups=dim)
@@ -55,7 +52,7 @@ def causal_conv1d_ref(
         final_states = F.pad(x, (width - 1 - x.shape[-1], 0)).to(
             dtype_in)  # (batch, dim, width - 1)
         if final_states_out is not None:
-            final_states_out[..., :(width - 1)].copy_(final_states)
+            final_states_out.copy_(final_states)
         else:
             final_states_out = final_states
     out = (out if activation is None else F.silu(out)).to(dtype=dtype_in)
@@ -109,24 +106,18 @@ def causal_conv1d_fn(
 
     out_ref = []
     out_ref_b = []
-    seqlens = query_start_loc[1:] - query_start_loc[:-1]
-    seqlens = seqlens.tolist()
-    splits = torch.split(x, seqlens, dim=-1)
-
-    for i in range(len(seqlens)):
-        x_s = splits[i]
-        if cache_indices[i] == PAD_SLOT_ID:
-            continue
-        out_ref_b.append(
-            causal_conv1d_ref(
-                x_s,
-                weight,
-                bias,
-                activation=activation,
-                return_final_states=True,
-                final_states_out=conv_states[cache_indices[i]].unsqueeze(0),
-                initial_states=conv_states[cache_indices[i]]
-                if has_initial_state[i] else None))
+    out_ref_b.append(
+        causal_conv1d_fn_native(
+            x,
+            weight,
+            bias,
+            activation=activation,
+            return_final_states=True,
+            final_states_out=conv_states[cache_indices[0]].unsqueeze(0),
+            initial_states=(conv_states[cache_indices[0]].unsqueeze(0)
+                if has_initial_state[0] else None),
+        ))
+
     out_ref.append(torch.cat([t[0] for t in out_ref_b], dim=-1))
     out_ref_tensor = torch.cat(out_ref, dim=0)
     return out_ref_tensor