feat(moe)：新增 VLLM_USE_MOE_W16A16_TRTION 强制 Triton MoE

增加环境变量开关，禁用 Marlin W16A16 MoE 路径
强制 Triton 且权重已是 Marlin packed 时给出明确报错
Marlin 支持探测改为 best-effort（不再依赖 VLLM_USE_LIGHTOP）

vllm/envs.py

@@ -247,6 +247,7 @@ if TYPE_CHECKING:
     VLLM_V1_FAST_TOKEN_ID_COPY: bool = False
     VLLM_V1_USE_REDUCED_TOPK_TOPP_SAMPLER: bool = False
     VLLM_W8A8_BACKEND: int = 3
+    VLLM_USE_MOE_W16A16_TRITON: bool = False
 
 def get_default_cache_root():
     return os.getenv(
@@ -1704,6 +1705,10 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # blaslt: 3 (default)
     # rocblas: others
     "VLLM_W8A8_BACKEND": lambda: int(os.getenv("VLLM_W8A8_BACKEND", "3")),
+    # Force using Triton MoE path (disable Marlin W16A16 MoE).
+    "VLLM_USE_MOE_W16A16_TRITON":
+        lambda: (os.environ.get("VLLM_USE_MOE_W16A16_TRITON", "0").lower() in
+                 ("true", "1")),
 }
 
 # --8<-- [end:env-vars-definition]

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -1984,6 +1984,11 @@ def fused_experts_impl(
                      or getattr(w2, "marlin_w16a16_packed", False)
                      or _is_marlin_w16a16_packed(w1, w2))
         if is_packed:
+            if envs.VLLM_USE_MOE_W16A16_TRITON:
+                raise RuntimeError(
+                    "VLLM_USE_MOE_W16A16_TRITON=1 forces Triton MoE, but the MoE weights are "
+                    "packed in Marlin W16A16 layout. Please load unpacked weights or set "
+                    "VLLM_USE_MOE_W16A16_TRITON=0.")
             try:
                 from vllm.model_executor.layers.fused_moe.fuse_moe_w16a16_marlin import (  # noqa: E501
                     fused_experts_impl_w16a16_marlin)

vllm/model_executor/layers/fused_moe/layer.py

@@ -120,15 +120,17 @@ def _is_marlin_w16a16_moe_supported(
         return False
 
     try:
-        import lmslim.envs as lsenvs
-
         from lightop import get_moe_cuda_marlin_config_w16a16
 
-        device_name = lsenvs.LMSLIM_GPU_NAME
-        if not device_name:
+        props = torch.cuda.get_device_properties(torch.cuda.current_device())
+        arch_name = getattr(props, "gcnArchName", None)
+        if isinstance(arch_name, str) and arch_name:
+            arch_name = arch_name.split(":")[0]
+        else:
+            arch_name = getattr(props, "name", None)
+        if not isinstance(arch_name, str) or not arch_name:
             return False
-        num_cus = torch.cuda.get_device_properties(
-            torch.cuda.current_device()).multi_processor_count
+        arch_cu = props.multi_processor_count
         twoN = 2 * N
         for bs in _MARLIN_W16A16_MOE_PROBE_BATCH_SIZES:
             _, _, status = get_moe_cuda_marlin_config_w16a16(
@@ -139,8 +141,8 @@ def _is_marlin_w16a16_moe_supported(
                 K,
                 N,
                 top_k,
-                device_name,
-                num_cus,
+                arch_name,
+                arch_cu,
                 dtype,
             )
             if not status:
@@ -1304,7 +1306,9 @@ class FusedMoE(CustomOp):
         if quant_config is None:
             # Not considering quant for now, temporarily
             self._marlin_w16a16_moe_enabled = (
-                params_dtype == moe_in_dtype and not self.moe_config.has_bias
+                not envs.VLLM_USE_MOE_W16A16_TRITON
+                and params_dtype == moe_in_dtype
+                and not self.moe_config.has_bias
                 and self.activation == "silu"
                 and not self.apply_router_weight_on_input
                 and _is_marlin_w16a16_moe_supported(