diff --git a/python/llm/src/ipex_llm/transformers/models/minicpmv.py b/python/llm/src/ipex_llm/transformers/models/minicpmv.py
index 0bb0b643357..7703f9f3158 100644
--- a/python/llm/src/ipex_llm/transformers/models/minicpmv.py
+++ b/python/llm/src/ipex_llm/transformers/models/minicpmv.py
@@ -53,28 +53,39 @@ def siglip_attention_forward(
     qkv = qkv.transpose(1, 2)
     query_states, key_states, value_states = qkv.chunk(3, dim=1)
 
-    query_states, key_states, value_states = padding_qkv_hd(
-        query_states, key_states, value_states,
-        72, 80
-    )
-
-    if use_sdp_non_causal(query_states.size(-1), query_states.device, query_states.dtype):
+    from ipex_llm.transformers.utils import get_xpu_device_type
+    if (
+        self.head_dim == 72
+        and get_xpu_device_type(query_states) in ["arc", "flex"] and
+        query_states.dtype in [torch.float, torch.half]
+    ):
         import xe_addons
         attn_weights = None
-        attn_output = xe_addons.sdp_non_causal(query_states, key_states.contiguous(),
-                                               value_states.contiguous(), attention_mask)
+        attn_output = xe_addons.siglip_sdp_non_causal(query_states, key_states,
+                                                      value_states, attention_softmax)
     else:
-        attn_weights = torch.matmul(query_states * self.scale, key_states.transpose(2, 3))
-        if attention_mask is not None:
-            attn_weights = attn_weights + attention_mask
+        query_states, key_states, value_states = padding_qkv_hd(
+            query_states, key_states, value_states,
+            72, 80
+        )
+
+        if use_sdp_non_causal(query_states.size(-1), query_states.device, query_states.dtype):
+            import xe_addons
+            attn_weights = None
+            attn_output = xe_addons.sdp_non_causal(query_states, key_states.contiguous(),
+                                                   value_states.contiguous(), attention_mask)
+        else:
+            attn_weights = torch.matmul(query_states * self.scale, key_states.transpose(2, 3))
+            if attention_mask is not None:
+                attn_weights = attn_weights + attention_mask
 
-        attn_weights = attention_softmax(attn_weights)
+            attn_weights = attention_softmax(attn_weights)
 
-        attn_weights = torch.nn.functional.dropout(attn_weights,
-                                                   p=self.dropout, training=self.training)
-        attn_output = torch.matmul(attn_weights, value_states)
+            attn_weights = torch.nn.functional.dropout(attn_weights,
+                                                       p=self.dropout, training=self.training)
+            attn_output = torch.matmul(attn_weights, value_states)
 
-    attn_output = attn_output[:, :, :, :self.head_dim]
+        attn_output = attn_output[:, :, :, :self.head_dim]
 
     attn_output = attn_output.transpose(1, 2).contiguous()
     attn_output = attn_output.reshape(bsz, q_len, self.embed_dim)