[NPU] qwen3_vl encoder support graph

python/sglang/srt/layers/attention/vision.py

@@ -678,28 +678,33 @@ class VisionAscendAttention(nn.Module):
         Returns:
              [b * s, h, head_size]
         """
-        cu_seqlens = resolve_seqlens(cu_seqlens, bsz, seq_len, device="cpu")
+        if envs.SGLANG_VIT_ENABLE_CUDA_GRAPH.get():
+            if "output_ws" not in kwargs:
+                raise RuntimeError("output_ws should be prepared for npu-graph mode")
+            output = kwargs["output_ws"]
+            # graph mode: runner already passes seq_lens (int32 on CPU)
+            seq_len_arg = cu_seqlens
+        else:
+            cu_seqlens = resolve_seqlens(cu_seqlens, bsz, seq_len, device="cpu")
+            seq_lens = cu_seqlens[1:] - cu_seqlens[:-1]
+            if seq_lens.is_npu:
+                seq_lens = seq_lens.to("cpu")
+            output = torch.empty_like(q)
+            seq_len_arg = seq_lens.to(torch.int32)
 
-        seq_lens = cu_seqlens[1:] - cu_seqlens[:-1]
-        if seq_lens.is_npu:
-            # cu_seqlens must be on cpu because of operator restriction
-            seq_lens = seq_lens.to("cpu")
         _, num_heads, head_size = q.shape
         num_kv_heads = k.shape[1]
-        output = torch.empty_like(q)
 
-        # operator requires pta version >= 2.5.1
         torch_npu._npu_flash_attention_unpad(
             query=q,
             key=k,
             value=v,
-            seq_len=seq_lens.to(torch.int32),
+            seq_len=seq_len_arg,
             scale_value=head_size**-0.5,
             num_heads=num_heads,
             num_kv_heads=num_kv_heads,
             out=output,
         )
-
         return output