XPU: remove SGLANG_USE_SGL_XPU flag (#34492)

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

@@ -30,7 +30,6 @@ from sglang.srt.utils import (
     is_npu,
     is_xpu,
     print_info_once,
-    use_intel_xpu_backend,
 )
 from sglang.srt.utils.multi_stream_utils import (
     maybe_execute_in_parallel,
@@ -1266,7 +1265,7 @@ class VisionAttention(nn.Module):
         elif _is_cpu and _is_cpu_amx_available:
             backend = "amx_attn"
         elif _is_xpu:
-            backend = "triton_attn" if not use_intel_xpu_backend() else "xpu_attn"
+            backend = "xpu_attn"
         else:
             backend = "sdpa"
         if backend == "fa3" and is_blackwell_supported():

python/sglang/srt/layers/moe/moe_runner/triton_utils/fused_moe.py

@@ -28,7 +28,7 @@ from sglang.srt.distributed.device_communicators.pynccl_allocator import (
 )
 from sglang.srt.layers.dp_attention import is_allocation_symmetric
 from sglang.srt.layers.moe.moe_runner import MoeRunnerConfig
-from sglang.srt.layers.moe.utils import get_moe_padding_size
+from sglang.srt.layers.moe.utils import get_moe_padding_size, get_moe_runner_backend
 from sglang.srt.runtime_context import get_exec
 from sglang.srt.utils import (
     cpu_has_amx_support,
@@ -38,7 +38,6 @@ from sglang.srt.utils import (
     is_hip,
     is_musa,
     is_xpu,
-    use_intel_xpu_backend,
 )
 from sglang.srt.utils.custom_op import register_custom_op
 
@@ -54,7 +53,6 @@ _is_cpu_amx_available = cpu_has_amx_support()
 _is_cpu = is_cpu()
 _use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
 _is_xpu = is_xpu()
-_use_sgl_xpu = use_intel_xpu_backend()
 _is_musa = is_musa()
 
 
@@ -1133,7 +1131,7 @@ def fused_moe(
     Returns:
     - torch.Tensor: The output tensor after applying the MoE layer.
     """
-    if _use_sgl_xpu:
+    if _is_xpu and not get_moe_runner_backend().is_triton():
         topk_weight, topk_ids, _ = topk_output
         from sgl_kernel import fused_experts as sgl_fused_experts
 

python/sglang/srt/layers/moe/utils.py

@@ -119,6 +119,7 @@ class MoeRunnerBackend(Enum):
     EXPERIMENTAL_SGL_MARLIN = "experimental_sgl_marlin"
     AITER = "aiter"
     HPC_OPS = "hpc_ops"
+    INTEL_XPU = "intel_xpu"
 
     def is_auto(self):
         return self == MoeRunnerBackend.AUTO
@@ -182,6 +183,9 @@ class MoeRunnerBackend(Enum):
     def is_aiter(self):
         return self == MoeRunnerBackend.AITER
 
+    def is_intel_xpu(self):
+        return self == MoeRunnerBackend.INTEL_XPU
+
 
 class DeepEPv2Fp8ScaleFormat(NamedTuple):
     """DeepGEMM FP8 activation-scale layout expected from DeepEP v2."""

python/sglang/srt/layers/quantization/fp8.py

@@ -95,12 +95,12 @@ from sglang.srt.utils import (
     is_sm90_supported,
     is_sm100_supported,
     is_sm120_supported,
+    is_xpu,
     log_info_on_rank0,
     mxfp8_block_convert_required,
     print_warning_once,
     set_weight_attrs,
     use_intel_amx_backend,
-    use_intel_xpu_backend,
 )
 
 if TYPE_CHECKING:
@@ -2435,10 +2435,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
             if quant_info is not None:
                 return self.runner.run(dispatch_output, quant_info)
 
-        if use_intel_xpu_backend() and not (
-            getattr(self, "runner", None) is not None
-            and self.runner.runner_backend.is_triton()
-        ):
+        if is_xpu() and not get_moe_runner_backend().is_triton():
             # sgl-kernel-xpu path
             from sgl_kernel import fused_experts
 

python/sglang/srt/layers/quantization/unquant.py

@@ -42,9 +42,9 @@ from sglang.srt.utils import (
     is_cuda,
     is_hip,
     is_npu,
+    is_xpu,
     set_weight_attrs,
     use_intel_amx_backend,
-    use_intel_xpu_backend,
 )
 from sglang.srt.utils.custom_op import register_custom_op
 
@@ -343,18 +343,20 @@ class UnquantizedLinearMethod(LinearMethodBase):
 def _use_xpu_moe_ld_padding(use_triton_kernels: bool) -> bool:
     """Whether MoE expert weights should get a padded row stride for XPU.
 
-    use_intel_xpu_backend() only tells us an XPU exists on this machine, not
-    that the weights being created land on it -- the env var can be set while
-    serving on CPU/CUDA. create_weights takes no device argument and allocates
-    under the model loader's ambient device context, so check that context too:
-    padding a non-XPU weight would make it non-contiguous for no benefit, and
-    other backends' MoE kernels expect contiguous expert tensors.
+    is_xpu() only tells us an XPU exists on this machine, not that the weights
+    being created land on it -- this can be true while serving on CPU/CUDA.
+    create_weights takes no device argument and allocates under the model
+    loader's ambient device context, so check that context too: padding a
+    non-XPU weight would make it non-contiguous for no benefit, and other
+    backends' MoE kernels expect contiguous expert tensors.
 
     The Triton path stores B transposed and does not read a row stride, so it
-    is excluded even on XPU.
+    is excluded even on XPU (either via --moe-runner-backend triton or the
+    triton_kernels build).
     """
     return (
-        use_intel_xpu_backend()
+        is_xpu()
+        and not get_moe_runner_backend().is_triton()
         and torch.get_default_device().type == "xpu"
         and not use_triton_kernels
     )
@@ -948,7 +950,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, BaseFusedOp):
         ], f"activation = {moe_runner_config.activation} is not supported."
 
         backend = self.runner.runner_backend
-        if use_intel_xpu_backend():
+        if not get_moe_runner_backend().is_triton():
             # sgl-kernel-xpu path
             from sgl_kernel import fused_experts
 
@@ -974,7 +976,8 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, BaseFusedOp):
             assert (
                 moe_runner_config.activation == "silu"
             ), f"activation = {moe_runner_config.activation} is not supported \
-            for Triton PATH, please set ENV SGLANG_USE_SGL_XPU=1."
+            for Triton PATH, please drop --moe-runner-backend triton to use \
+            the sgl-kernel-xpu path, which supports more activations."
 
             quant_info = self.get_triton_quant_info(layer)
             return self.runner.run(dispatch_output, quant_info)

python/sglang/srt/server_args.py

@@ -305,6 +305,7 @@ MOE_RUNNER_BACKEND_CHOICES = [
     "experimental_sgl_marlin",
     "hpc_ops",  # HPC-Ops (https://github.com/Tencent/hpc-ops), FP8 MoE on Hopper (SM90) only
     "megamoe",
+    "intel_xpu",
 ]
 add_moe_runner_backend_choices = MOE_RUNNER_BACKEND_CHOICES.extend
 

python/sglang/srt/utils/common.py

@@ -354,10 +354,6 @@ def xpu_has_xmx_support():
     return False
 
 
-def use_intel_xpu_backend():
-    return get_bool_env_var("SGLANG_USE_SGL_XPU") and is_xpu()
-
-
 @lru_cache(maxsize=1)
 def is_flashinfer_available():
     """