[Perf][DSA] Pass topk_length to flash_mla_sparse_fwd in the sparse attention path (#31128)

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

@@ -2205,6 +2205,7 @@ class DeepseekSparseAttnBackend(
                 page_table_1=page_table_1,
                 sm_scale=layer.scaling,
                 v_head_dim=layer.v_head_dim,
+                topk_length=metadata.dsa_cache_seqlens_int32,
             )
         elif dsa_impl == "flashinfer_sparse_mla":
             if q_rope is not None:
@@ -2361,6 +2362,7 @@ class DeepseekSparseAttnBackend(
                 page_table_1=page_table_1,
                 sm_scale=layer.scaling,
                 v_head_dim=layer.v_head_dim,
+                topk_length=metadata.dsa_cache_seqlens_int32,
             )
         elif self.dsa_decode_impl == "flashinfer_sparse_mla":
             if q_all is None:
@@ -2475,6 +2477,7 @@ class DeepseekSparseAttnBackend(
         v_head_dim: int,
         page_table_1: torch.Tensor,
         sm_scale: float,
+        topk_length: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         from sgl_kernel.flash_mla import flash_mla_sparse_fwd
 
@@ -2503,12 +2506,25 @@ class DeepseekSparseAttnBackend(
         # indices shape must be (s_q, h_kv=1, topk), keep h_kv=1 unchanged
         indices_input = page_table_1.unsqueeze(1)
 
+        # topk_length is the per-row count of valid indices
+        # (`dsa_cache_seqlens_int32` = seqlens clipped to `index_topk`). Rows
+        # whose context is shorter than `index_topk` have their indices
+        # tail-padded with -1; passing the valid length lets the kernel skip
+        # the padded tail instead of scanning the full topk width. The output
+        # is unchanged: the kernel masks -1 indices either way.
+        if topk_length is not None and topk_length.shape[0] != num_tokens:
+            # Metadata rows are expected to match q rows (the DP/CP padding
+            # helpers keep them aligned); fall back to full-width compute if
+            # they ever diverge.
+            topk_length = None
+
         o, _, _ = flash_mla_sparse_fwd(
             q=q_input,
             kv=kv_cache,
             indices=indices_input,
             sm_scale=sm_scale,
             d_v=v_head_dim,
+            topk_length=topk_length,
         )
 
         # Trim output back to original num_heads if we padded