diff --git a/optimum/exporters/ipex/modeling_utils.py b/optimum/exporters/ipex/modeling_utils.py
index 039d5201b2..91f406d3c5 100755
--- a/optimum/exporters/ipex/modeling_utils.py
+++ b/optimum/exporters/ipex/modeling_utils.py
@@ -591,9 +591,9 @@ def postprocess_attention_output(self, attn_output):
 
     def varlen_attn(self, query, key, value, past_key_value, input_lens):
         # prefill, remove padding
-        attn_output = torch.empty_like(query)
-        seq_len_tensor = torch.cat((input_lens.new_tensor([0]), input_lens.cumsum(-1).int()))
         if past_key_value and is_ipex_version(">=", _IPEX_MINIMUM_VERSION_FOR_FLASH_VARLEN_ATTN):
+            attn_output = torch.empty_like(query)
+            seq_len_tensor = torch.cat((input_lens.new_tensor([0]), input_lens.cumsum(-1).int()))
             PagedAttention.flash_attn_varlen_func(
                 attn_output,
                 query,
@@ -609,21 +609,12 @@ def varlen_attn(self, query, key, value, past_key_value, input_lens):
                 None,
             )
         else:
-            varlen_attention(
-                query.contiguous() if query.device.type == "xpu" else query,
-                key.contiguous() if key.device.type == "xpu" else key,
-                value.contiguous() if value.device.type == "xpu" else value,
-                attn_output,
-                seq_len_tensor,
-                seq_len_tensor,
-                input_lens.max(),
-                input_lens.max(),
-                0.0,
-                1.0 / math.sqrt(self.head_dim),
-                False,
-                True,
-                False,
-                None,
+            attn_output = torch.nn.functional.scaled_dot_product_attention(
+                query,
+                key,
+                value,
+                attn_mask=None,
+                is_causal=True,
             )
         return attn_output