diff --git a/python/pyproject.toml b/python/pyproject.toml
index 13b3d9952..7a0f23e4f 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -52,7 +52,7 @@ runtime_common = [
 
 srt = [
     "sglang[runtime_common]",
-    "sgl-kernel==0.2.1",
+    "sgl-kernel==0.2.2",
     "torch==2.7.1",
     "torchaudio==2.7.1",
     "torchvision==0.22.1",
diff --git a/python/sglang/srt/entrypoints/engine.py b/python/sglang/srt/entrypoints/engine.py
index f56c40133..5bb67d1e0 100644
--- a/python/sglang/srt/entrypoints/engine.py
+++ b/python/sglang/srt/entrypoints/engine.py
@@ -650,7 +650,7 @@ def _set_envs_and_config(server_args: ServerArgs):
     if _is_cuda:
         assert_pkg_version(
             "sgl-kernel",
-            "0.2.1",
+            "0.2.2",
             "Please reinstall the latest version with `pip install sgl-kernel --force-reinstall`",
         )
 
diff --git a/python/sglang/srt/layers/moe/topk.py b/python/sglang/srt/layers/moe/topk.py
index 908927b88..3c6d9eae9 100644
--- a/python/sglang/srt/layers/moe/topk.py
+++ b/python/sglang/srt/layers/moe/topk.py
@@ -108,38 +108,14 @@ def fused_topk(
         M, topk, dtype=torch.float32, device=hidden_states.device
     )
     topk_ids = torch.empty(M, topk, dtype=torch.int32, device=hidden_states.device)
-    token_expert_indicies = torch.empty(
-        M, topk, dtype=torch.int32, device=hidden_states.device
-    )
 
     topk_softmax(
         topk_weights,
         topk_ids,
-        token_expert_indicies,
         gating_output.float(),
-    )
-    del token_expert_indicies
-
-    return _fused_topk_postprocess(
-        topk_weights=topk_weights,
-        topk_ids=topk_ids,
-        renormalize=renormalize,
-        expert_location_dispatch_info=expert_location_dispatch_info,
-        num_token_non_padded=num_token_non_padded,
+        renormalize,
     )
 
-
-@torch.compile(dynamic=True, backend=get_compiler_backend())
-def _fused_topk_postprocess(
-    topk_weights,
-    topk_ids,
-    renormalize,
-    expert_location_dispatch_info,
-    num_token_non_padded,
-):
-    if renormalize:
-        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
-    topk_ids = topk_ids_logical_to_physical(topk_ids, expert_location_dispatch_info)
     _mask_topk_ids_padded_region(topk_ids, num_token_non_padded)
     return topk_weights, topk_ids