Use flashinfer_trtllm moe runner backend to gain around 10% perf on b200 fp8 dpsk (#11816)

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

@@ -232,7 +232,7 @@ class FusedMoE(torch.nn.Module):
             self.quant_method, ModelOptNvFp4FusedMoEMethod
         ) or (
             isinstance(self.quant_method, Fp8MoEMethod)
-            and self.quant_method.use_cutlass_fused_experts_fp8
+            and self.quant_method._should_use_cutlass_fused_experts()
         )
 
     def _load_per_tensor_weight_scale(

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

@@ -33,6 +33,7 @@ from sglang.srt.layers.amx_utils import _amx_process_weight_after_loading
 from sglang.srt.layers.moe import MoeRunner, MoeRunnerBackend, MoeRunnerConfig
 from sglang.srt.layers.moe.moe_runner.deep_gemm import DeepGemmMoeQuantInfo
 from sglang.srt.layers.moe.moe_runner.triton import TritonMoeQuantInfo
+from sglang.srt.layers.moe.utils import get_moe_runner_backend
 from sglang.srt.layers.parameter import (
     BlockQuantScaleParameter,
     ModelWeightParameter,
@@ -525,12 +526,6 @@ class Fp8MoEMethod(FusedMoEMethodBase):
         self.quant_config = quant_config
         self.block_quant = self.quant_config.weight_block_size is not None
         self.cutlass_fp8_supported = cutlass_fp8_supported()
-        self.use_cutlass_fused_experts_fp8 = (
-            get_bool_env_var("SGLANG_CUTLASS_MOE")
-            and self.cutlass_fp8_supported
-            and self.block_quant
-            and (is_sm100_supported() or is_sm90_supported())
-        )
 
     def create_weights(
         self,
@@ -638,58 +633,8 @@ class Fp8MoEMethod(FusedMoEMethodBase):
             layer.register_parameter("w13_weight_scale_inv", w13_weight_scale)
             layer.register_parameter("w2_weight_scale_inv", w2_weight_scale)
             assert self.quant_config.activation_scheme == "dynamic"
-            if self.use_cutlass_fused_experts_fp8:
-                self.ab_strides1 = torch.full(
-                    (num_experts,),
-                    hidden_size,
-                    device=w13_weight.device,
-                    dtype=torch.int64,
-                )
-                self.c_strides1 = torch.full(
-                    (num_experts,),
-                    2 * intermediate_size_per_partition,
-                    device=w13_weight.device,
-                    dtype=torch.int64,
-                )
-                self.ab_strides2 = torch.full(
-                    (num_experts,),
-                    intermediate_size_per_partition,
-                    device=w2_weight.device,
-                    dtype=torch.int64,
-                )
-                self.c_strides2 = torch.full(
-                    (num_experts,),
-                    hidden_size,
-                    device=w2_weight.device,
-                    dtype=torch.int64,
-                )
-                self.workspace = torch.empty(
-                    90000, device=w13_weight.device, dtype=torch.uint8
-                )
-                self.a_ptr = torch.empty(
-                    num_experts, device=w13_weight.device, dtype=torch.int64
-                )
-                self.b_ptr = torch.empty(
-                    num_experts, device=w13_weight.device, dtype=torch.int64
-                )
-                self.out_ptr = torch.empty(
-                    num_experts, device=w13_weight.device, dtype=torch.int64
-                )
-                self.a_scales_ptr = torch.empty(
-                    num_experts, device=w13_weight.device, dtype=torch.int64
-                )
-                self.b_scales_ptr = torch.empty(
-                    num_experts, device=w13_weight.device, dtype=torch.int64
-                )
-                self.expert_offsets = torch.empty(
-                    num_experts + 1, device=w13_weight.device, dtype=torch.int32
-                )
-                self.problem_sizes1 = torch.empty(
-                    num_experts, 3, device=w13_weight.device, dtype=torch.int32
-                )
-                self.problem_sizes2 = torch.empty(
-                    num_experts, 3, device=w13_weight.device, dtype=torch.int32
-                )
+            if self._should_use_cutlass_fused_experts():
+                self._ensure_cutlass_buffers_initialized(layer)
 
         else:
             # Allocate 2 scales for w1 and w3 respectively.
@@ -1079,7 +1024,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
             if ret is not None:
                 return StandardCombineInput(hidden_states=ret)
 
-        if self.use_cutlass_fused_experts_fp8:
+        if self._should_use_cutlass_fused_experts():
             from sglang.srt.layers.moe.cutlass_moe import cutlass_fused_experts_fp8
 
             topk_weights, topk_ids, _ = topk_output
@@ -1171,6 +1116,67 @@ class Fp8MoEMethod(FusedMoEMethodBase):
 
         return self.runner.run(dispatch_output, quant_info)
 
+    def _should_use_cutlass_fused_experts(self) -> bool:
+        """Decide whether to use Cutlass FP8 fused-experts path based on moe runner backend,
+        with env var override via `SGLANG_CUTLASS_MOE`.
+        """
+        backend = get_moe_runner_backend()
+        env_force = get_bool_env_var("SGLANG_CUTLASS_MOE")
+        # TODO: remove env var in the future, it should be handled by moe runner backend
+        if env_force:
+            return True
+        return (
+            backend.is_flashinfer_cutlass()
+            and self.cutlass_fp8_supported
+            and self.block_quant
+            and (is_sm100_supported() or is_sm90_supported())
+        )
+
+    def _ensure_cutlass_buffers_initialized(self, layer: Module) -> None:
+        if getattr(self, "_cutlass_buffers_ready", False):
+            return
+
+        device = layer.w13_weight.device
+        num_experts = layer.w13_weight.shape[0]
+        hidden_size = layer.w2_weight.shape[1]
+        intermediate_size_per_partition = layer.intermediate_size_per_partition
+
+        self.ab_strides1 = torch.full(
+            (num_experts,), hidden_size, device=device, dtype=torch.int64
+        )
+        self.c_strides1 = torch.full(
+            (num_experts,),
+            2 * intermediate_size_per_partition,
+            device=device,
+            dtype=torch.int64,
+        )
+        self.ab_strides2 = torch.full(
+            (num_experts,),
+            intermediate_size_per_partition,
+            device=device,
+            dtype=torch.int64,
+        )
+        self.c_strides2 = torch.full(
+            (num_experts,), hidden_size, device=device, dtype=torch.int64
+        )
+        self.workspace = torch.empty(90000, device=device, dtype=torch.uint8)
+        self.a_ptr = torch.empty(num_experts, device=device, dtype=torch.int64)
+        self.b_ptr = torch.empty(num_experts, device=device, dtype=torch.int64)
+        self.out_ptr = torch.empty(num_experts, device=device, dtype=torch.int64)
+        self.a_scales_ptr = torch.empty(num_experts, device=device, dtype=torch.int64)
+        self.b_scales_ptr = torch.empty(num_experts, device=device, dtype=torch.int64)
+        self.expert_offsets = torch.empty(
+            num_experts + 1, device=device, dtype=torch.int32
+        )
+        self.problem_sizes1 = torch.empty(
+            num_experts, 3, device=device, dtype=torch.int32
+        )
+        self.problem_sizes2 = torch.empty(
+            num_experts, 3, device=device, dtype=torch.int32
+        )
+
+        self._cutlass_buffers_ready = True
+
     def apply_with_router_logits(
         self,
         layer: torch.nn.Module,

python/sglang/srt/server_args.py

@@ -892,14 +892,19 @@ class ServerArgs:
                     logger.info(
                         "Enable FlashInfer AllReduce Fusion on sm100 for DeepseekV3ForCausalLM"
                     )
-                if (
-                    self.quantization == "modelopt_fp4"
-                    and self.moe_runner_backend == "auto"
-                ):
+                if self.moe_runner_backend == "auto":
                     self.moe_runner_backend = "flashinfer_trtllm"
                     logger.info(
-                        "Use flashinfer_trtllm as moe runner backend on sm100 for DeepseekV3ForCausalLM"
+                        "Use flashinfer_trtllm as MoE runner backend on sm100 for DeepseekV3ForCausalLM"
                     )
+                    if self.quantization is None:
+                        # Default DeepSeek V3/R1 native FP8 when not explicitly set,
+                        # Because we need this condition for an assertion in
+                        # flashinfer_trtllm MoE runner backend.
+                        self.quantization = "fp8"
+                        logger.info(
+                            "Quantization not specified, default to fp8 for DeepSeek on sm100"
+                        )
 
         elif model_arch in ["GptOssForCausalLM"]:
             if (