diff --git a/export/modeling_qwen2.py b/export/modeling_qwen2.py
index 53cdd8d..fcb6de9 100644
--- a/export/modeling_qwen2.py
+++ b/export/modeling_qwen2.py
@@ -682,27 +682,27 @@ def forward(
         key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
         value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
 
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
+        kv_seq_len = key_states.shape[-2] + past_key_value.shape[2]
+        # if past_key_value is not None:
             # kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-            kv_seq_len += past_key_value.shape[1]
+            # kv_seq_len += past_key_value.shape[2]
         cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
 
         query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
         output_cache = (key_states, value_states)
-        if past_key_value is not None:
-            # cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            # key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-            cache_key = past_key_value[
-                :,
-                self.layer_idx * 2 * self.num_key_value_heads: (self.layer_idx * 2 + 1) * self.num_key_value_heads
-            ]
-            cache_value = past_key_value[
-                :,
-                (self.layer_idx * 2 + 1) * self.num_key_value_heads: (self.layer_idx * 2 + 2) * self.num_key_value_heads
-            ]
-            key_states = torch.cat((cache_key, key_states), dim=2)
-            value_states = torch.cat((cache_value, value_states), dim=2)
+        # if past_key_value is not None:
+        # cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+        # key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+        cache_key = past_key_value[
+            :,
+            self.layer_idx * 2 * self.num_key_value_heads: (self.layer_idx * 2 + 1) * self.num_key_value_heads
+        ]
+        cache_value = past_key_value[
+            :,
+            (self.layer_idx * 2 + 1) * self.num_key_value_heads: (self.layer_idx * 2 + 2) * self.num_key_value_heads
+        ]
+        key_states = torch.cat((cache_key, key_states), dim=2)
+        value_states = torch.cat((cache_value, value_states), dim=2)
 
         key_states = repeat_kv(key_states, self.num_key_value_groups)
         value_states = repeat_kv(value_states, self.num_key_value_groups)