diff --git a/python/sglang/srt/layers/attention/dsa/utils.py b/python/sglang/srt/layers/attention/dsa/utils.py
index 92755d103..7c2c2d0f4 100644
--- a/python/sglang/srt/layers/attention/dsa/utils.py
+++ b/python/sglang/srt/layers/attention/dsa/utils.py
@@ -196,7 +196,18 @@ def dsa_cp_round_robin_split_data(input_: Union[torch.Tensor, List]):
         return input_[indices]
 
     # for torch device tensor
-    return input_.view(-1, cp_size, *input_.shape[1:])[:, cp_rank].contiguous()
+    shard = input_.view(-1, cp_size, *input_.shape[1:])[:, cp_rank]
+    # .contiguous() is not sufficient here. When tokens == cp_size every rank's
+    # shard has a single row, and a size-1 outer dimension imposes no contiguity
+    # constraint, so is_contiguous() is True whatever stride(0) is and
+    # .contiguous() becomes a no-op. The shard then keeps the cp_size-inflated
+    # row pitch (cp_size * row_numel instead of row_numel), which any kernel that
+    # takes its row pitch from stride(0) will read as an oversized tensor.
+    # Compare the pitch against the parent's explicitly, so the copy happens
+    # exactly when the shard really is strided -- and not at all for cp_size == 1.
+    if shard.stride(0) != input_.stride(0):
+        shard = shard.clone(memory_format=torch.contiguous_format)
+    return shard
 
 
 def cal_padded_tokens(forward_batch: "ForwardBatch"):