diff --git a/python/sglang/test/bench_one_batch_server_internal.py b/python/sglang/test/bench_one_batch_server_internal.py
index 0deeb0b01..2a15f4835 100644
--- a/python/sglang/test/bench_one_batch_server_internal.py
+++ b/python/sglang/test/bench_one_batch_server_internal.py
@@ -35,7 +35,10 @@ def get_cache_tokens_from_metrics(url: str) -> Optional[tuple]:
     """
     try:
         response = requests.get(url + "/metrics", timeout=5)
-        response.raise_for_status()
+        try:
+            response.raise_for_status()
+        except requests.exceptions.HTTPError:
+            return None
 
         # Parse Prometheus text format
         # Looking for: sglang:cached_tokens_total{...} <value>
diff --git a/sgl-kernel/python/sgl_kernel/elementwise.py b/sgl-kernel/python/sgl_kernel/elementwise.py
index 62a3f646c..48fa5584c 100644
--- a/sgl-kernel/python/sgl_kernel/elementwise.py
+++ b/sgl-kernel/python/sgl_kernel/elementwise.py
@@ -113,14 +113,13 @@ def rmsnorm(
     # See: https://github.com/flashinfer-ai/flashinfer/issues/2734
     #      https://github.com/flashinfer-ai/flashinfer/pull/2733
     if (
-        input.device.type == "musa"
-        or not _has_flashinfer
-        or input.dtype not in _FLASHINFER_NORM_SUPPORTED_DTYPES
-        or torch.compiler.is_dynamo_compiling()
+        _has_flashinfer
+        and input.dtype in _FLASHINFER_NORM_SUPPORTED_DTYPES
+        and not torch.compiler.is_dynamo_compiling()
     ):
-        return _rmsnorm_internal(input, weight, eps, out, enable_pdl)
-    else:
         return _flashinfer_norm.rmsnorm(input, weight, eps, out, enable_pdl)
+    else:
+        return _rmsnorm_internal(input, weight, eps, out, enable_pdl)
 
 
 def fused_add_rmsnorm(
@@ -153,16 +152,14 @@ def fused_add_rmsnorm(
         <https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#programmatic-dependent-launch-and-synchronization>`_
         If None, will be automatically enabled on Hopper architecture.
     """
-    # See is_dynamo_compiling() comment in rmsnorm() above.
     if (
-        input.device.type == "musa"
-        or not _has_flashinfer
-        or input.dtype not in _FLASHINFER_NORM_SUPPORTED_DTYPES
-        or torch.compiler.is_dynamo_compiling()
+        _has_flashinfer
+        and input.dtype in _FLASHINFER_NORM_SUPPORTED_DTYPES
+        and not torch.compiler.is_dynamo_compiling()
     ):
-        _fused_add_rmsnorm_internal(input, residual, weight, eps, enable_pdl)
-    else:
         _flashinfer_norm.fused_add_rmsnorm(input, residual, weight, eps, enable_pdl)
+    else:
+        _fused_add_rmsnorm_internal(input, residual, weight, eps, enable_pdl)
 
 
 def gemma_rmsnorm(
@@ -196,16 +193,14 @@ def gemma_rmsnorm(
     output: torch.Tensor
         Gemma Normalized tensor, shape (batch_size, hidden_size).
     """
-    # See is_dynamo_compiling() comment in rmsnorm() above.
     if (
-        input.device.type == "musa"
-        or not _has_flashinfer
-        or input.dtype not in _FLASHINFER_NORM_SUPPORTED_DTYPES
-        or torch.compiler.is_dynamo_compiling()
+        _has_flashinfer
+        and input.dtype in _FLASHINFER_NORM_SUPPORTED_DTYPES
+        and not torch.compiler.is_dynamo_compiling()
     ):
-        return _gemma_rmsnorm_internal(input, weight, eps, out, enable_pdl)
-    else:
         return _flashinfer_norm.gemma_rmsnorm(input, weight, eps, out, enable_pdl)
+    else:
+        return _gemma_rmsnorm_internal(input, weight, eps, out, enable_pdl)
 
 
 def gemma_fused_add_rmsnorm(
@@ -238,18 +233,16 @@ def gemma_fused_add_rmsnorm(
         <https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#programmatic-dependent-launch-and-synchronization>`_
         If None, will be automatically enabled on Hopper architecture.
     """
-    # See is_dynamo_compiling() comment in rmsnorm() above.
     if (
-        input.device.type == "musa"
-        or not _has_flashinfer
-        or input.dtype not in _FLASHINFER_NORM_SUPPORTED_DTYPES
-        or torch.compiler.is_dynamo_compiling()
+        _has_flashinfer
+        and input.dtype in _FLASHINFER_NORM_SUPPORTED_DTYPES
+        and not torch.compiler.is_dynamo_compiling()
     ):
-        _gemma_fused_add_rmsnorm_internal(input, residual, weight, eps, enable_pdl)
-    else:
         _flashinfer_norm.gemma_fused_add_rmsnorm(
             input, residual, weight, eps, enable_pdl
         )
+    else:
+        _gemma_fused_add_rmsnorm_internal(input, residual, weight, eps, enable_pdl)
 
 
 def _check_shape(input: torch.Tensor, output: torch.Tensor) -> None: