diff --git a/mmdeploy/mmcv/ops/nms.py b/mmdeploy/mmcv/ops/nms.py
index dc22c55380..6e71b7cc22 100644
--- a/mmdeploy/mmcv/ops/nms.py
+++ b/mmdeploy/mmcv/ops/nms.py
@@ -610,7 +610,6 @@ def multiclass_nms__torchscript(boxes: Tensor,
 
     Use batched_nms from torchvision instead of custom nms.
     """
-    assert not output_index, 'output_index is not supported on this backend.'
     # TODO: simplify inference for non-batch model
     from torchvision.ops import batched_nms
     batch_size = scores.shape[0]
@@ -618,11 +617,12 @@ def multiclass_nms__torchscript(boxes: Tensor,
     num_classes = scores.shape[2]
     box_per_cls = len(boxes.shape) == 4
     scores = torch.where(scores > score_threshold, scores, scores.new_zeros(1))
-
+    pre_topk_inds = None
     # pre-topk
     if pre_top_k > 0:
         max_scores, _ = scores.max(-1)
         _, topk_inds = max_scores.topk(pre_top_k)
+        pre_topk_inds = topk_inds
         batch_inds = torch.arange(batch_size).view(-1, 1).long()
         boxes = boxes[batch_inds, topk_inds, ...]
         scores = scores[batch_inds, topk_inds, :]
@@ -646,10 +646,14 @@ def multiclass_nms__torchscript(boxes: Tensor,
 
     keeps = torch.cat(keeps)
     scores = scores.permute(0, 2, 1)
-    dets, labels = _select_nms_index(
-        scores, boxes, keeps, batch_size, keep_top_k=keep_top_k)
-
-    return dets, labels
+    return _select_nms_index(
+        scores,
+        boxes,
+        keeps,
+        batch_size,
+        keep_top_k=keep_top_k,
+        pre_inds=pre_topk_inds,
+        output_index=output_index)
 
 
 class AscendBatchNMSOp(torch.autograd.Function):
diff --git a/mmdeploy/pytorch/functions/multi_head_attention_forward.py b/mmdeploy/pytorch/functions/multi_head_attention_forward.py
index 50f3f6c1ea..97d9ad327a 100644
--- a/mmdeploy/pytorch/functions/multi_head_attention_forward.py
+++ b/mmdeploy/pytorch/functions/multi_head_attention_forward.py
@@ -53,3 +53,29 @@ def _scaled_dot_product_attention__tensorrt(q: Tensor,
                                             **kwargs) -> Tuple[Tensor, Tensor]:
     """Rewrite for custom ops."""
     return ScaledDotProductAttentionTRT.apply(q, k, v, attn_mask)
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    func_name='torch.nn.functional.scaled_dot_product_attention',
+    backend=Backend.DEFAULT.value)
+def scaled_dot_product_attention__default(query,
+                                          key,
+                                          value,
+                                          attn_mask=None,
+                                          dropout_p=0.,
+                                          scale=None,
+                                          is_causal=False):
+    """Rewrite to export to onnx on torch>=2.0.0."""
+    scale = scale or query.size(-1)**0.5
+    if is_causal and attn_mask is not None:
+        attn_mask = torch.ones(
+            query.size(-2), key.size(-2), dtype=torch.bool).tril(diagonal=0)
+    if attn_mask is not None and attn_mask.dtype == torch.bool:
+        attn_mask = attn_mask.masked_fill(not attn_mask, -float('inf'))
+
+    attn_weight = query @ key.transpose(-2, -1) / scale
+    if attn_mask is not None:
+        attn_weight += attn_mask
+    attn_weight = torch.softmax(attn_weight, dim=-1)
+    attn_weight = torch.dropout(attn_weight, dropout_p, True)
+    return attn_weight @ value