fix(dsa): align FA3 metadata with unpadded queries

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

@@ -2581,6 +2581,28 @@ def _forward_fa3(
         logit_cap: float,
         page_size: int,
     ) -> torch.Tensor:
+        # Sparse DSA represents each query token as a one-token FA3 sequence.
+        # MoE synchronization can pad length metadata, while the prefill graph
+        # attention bridge narrows Q and the page table to real query tokens.
+        # Remove only trailing metadata padding to match that narrowed layout.
+        if max_seqlen_q == 1 and q_nope.ndim == 3:
+            num_queries = q_nope.shape[0]
+            if cache_seqlens.shape[0] > num_queries:
+                if (
+                    page_table.shape[0] != num_queries
+                    or cu_seqlens_q.shape[0] != cache_seqlens.shape[0] + 1
+                    or cu_seqlens_k.shape[0] != cache_seqlens.shape[0] + 1
+                ):
+                    raise ValueError(
+                        "Inconsistent padded DSA FA3 metadata: "
+                        f"q={tuple(q_nope.shape)}, page_table={tuple(page_table.shape)}, "
+                        f"cache_seqlens={tuple(cache_seqlens.shape)}, "
+                        f"cu_q={tuple(cu_seqlens_q.shape)}, "
+                        f"cu_k={tuple(cu_seqlens_k.shape)}"
+                    )
+                cache_seqlens = cache_seqlens[:num_queries]
+                cu_seqlens_q = cu_seqlens_q[: num_queries + 1]
+                cu_seqlens_k = cu_seqlens_k[: num_queries + 1]
         k_rope_cache = kv_cache[:, :, v_head_dim:]
         c_kv_cache = kv_cache[:, :, :v_head_dim]
         qk_rope_dim = k_rope_cache.shape[-1]