[FlashInfer v0.6.10] [RL] [DSv32] [GLM-5] Add --dsa-topk-backend and integrate FlashInfer and pytorch topk (#22851)

docs_new/docs/advanced_features/server_arguments.mdx

@@ -1206,6 +1206,12 @@ Please consult the documentation below and [server_args.py](https://github.com/s
       <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`fa3`</td>
       <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>flashmla_sparse</code>, <code>flashmla_kv</code>, <code>fa3</code>, <code>tilelang</code>, <code>aiter</code>, <code>trtllm</code></td>
     </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`--dsa-topk-backend`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Choose the DSA indexer top-k backend. The `torch` backend currently requires `SGLANG_DSA_FUSE_TOPK=false`.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`sgl-kernel`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>sgl-kernel</code>, <code>torch</code>, <code>flashinfer</code></td>
+    </tr>
     <tr>
       <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`--fp8-gemm-backend`</td>
       <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Choose the runner backend for Blockwise FP8 GEMM operations. Options: 'auto' (default, auto-selects based on hardware), 'deep_gemm' (JIT-compiled; enabled by default on NVIDIA Hopper (SM90) and Blackwell (SM100) when DeepGEMM is installed), 'flashinfer_trtllm' (FlashInfer TRTLLM backend; SM100/SM103 only), 'flashinfer_cutlass' (FlashInfer CUTLASS backend, SM120 only), 'flashinfer_deepgemm' (Hopper SM90 only, uses swapAB optimization for small M dimensions in decoding), 'cutlass' (optimal for Hopper/Blackwell GPUs and high-throughput), 'triton' (fallback, widely compatible), 'aiter' (ROCm only).</td>