[4/N] MoE Refactor: Unified Triton Kernel for FusedMoE and EPMoE (#8515)

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

@@ -172,7 +172,6 @@ class Fp8Config(QuantizationConfig):
         self, layer: torch.nn.Module, prefix: str
     ) -> Optional[QuantizeMethodBase]:
         from sglang.srt.layers.linear import LinearBase
-        from sglang.srt.layers.moe.ep_moe.layer import EPMoE
         from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
 
         if isinstance(layer, LinearBase):
@@ -181,8 +180,6 @@ class Fp8Config(QuantizationConfig):
             return Fp8LinearMethod(self)
         elif isinstance(layer, FusedMoE):
             return Fp8MoEMethod(self)
-        elif isinstance(layer, EPMoE):
-            return Fp8EPMoEMethod(self)
         return None
 
     def get_scaled_act_names(self) -> List[str]:
@@ -984,23 +981,8 @@ class Fp8MoEMethod(FusedMoEMethodBase):
         no_combine: bool = False,
         routed_scaling_factor: Optional[float] = None,
     ) -> torch.Tensor:
-        from sglang.srt.layers.moe.ep_moe.layer import EPMoE
         from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
 
-        if isinstance(layer, EPMoE):
-            layer.w13_weight_scale = (
-                layer.w13_weight_scale_inv
-                if self.block_quant
-                else layer.w13_weight_scale
-            )
-            layer.w2_weight_scale = (
-                layer.w2_weight_scale_inv if self.block_quant else layer.w2_weight_scale
-            )
-            return layer.run_moe(
-                hidden_states=x,
-                topk_output=topk_output,
-            )
-
         if use_intel_amx_backend(layer):
             from sglang.srt.layers.moe.topk import apply_topk_weights_cpu
 

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

@@ -204,14 +204,6 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         routed_scaling_factor: Optional[float] = None,
     ) -> torch.Tensor:
 
-        from sglang.srt.layers.moe.ep_moe.layer import EPMoE
-
-        if isinstance(layer, EPMoE):
-            return layer.run_moe(
-                hidden_states=x,
-                topk_output=topk_output,
-            )
-
         return self.forward(
             x=x,
             layer=layer,

python/sglang/srt/layers/quantization/w4afp8.py

@@ -276,6 +276,7 @@ class W4AFp8MoEMethod(FusedMoEMethodBase):
         layer: EPMoE,
         hidden_states: torch.Tensor,
         topk_output: TopKOutput,
+        **kwargs,
     ) -> torch.Tensor:
 
         # TODO(ch-wan): move it out of this class