[XPU] Enable Expert parallel for MoE models (#28263)

Signed-off-by: Yan Ma <yan.ma@intel.com>
Signed-off-by: Kunshang Ji <kunshang.ji@intel.com>

vllm/model_executor/layers/fused_moe/layer.py

@@ -642,10 +642,12 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         if current_platform.is_xpu():
             import intel_extension_for_pytorch as ipex
 
+            ep_rank_start = self.moe.ep_rank * self.moe.num_local_experts
             layer.ipex_fusion = ipex.llm.modules.GatedMLPMOE(
                 layer.w13_weight,
                 layer.w2_weight,
                 use_prepack=True,
+                experts_start_id=ep_rank_start,
             )
         elif current_platform.is_cpu():
             from vllm.model_executor.layers.fused_moe import cpu_fused_moe

vllm/model_executor/layers/quantization/ipex_quant.py

@@ -399,6 +399,7 @@ class XPUFp8MoEMethod(FusedMoEMethodBase):
             layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
         import intel_extension_for_pytorch as ipex
 
+        ep_rank_start = self.moe.ep_rank * self.moe.num_local_experts
         layer.ipex_fusion = ipex.llm.modules.GatedMLPMOE(
             layer.w13_weight,
             layer.w2_weight,
@@ -407,6 +408,7 @@ class XPUFp8MoEMethod(FusedMoEMethodBase):
             a1_scale_inv=layer.w13_input_scale,
             a2_scale_inv=layer.w2_input_scale,
             use_prepack=True,
+            experts_start_id=ep_rank_start,
         )
 
     def get_fused_moe_quant_config(

vllm/model_executor/layers/quantization/mxfp4.py

@@ -1113,6 +1113,7 @@ class IpexMxfp4MoEMethod(Mxfp4MoEMethod):
 
         layer.w13_weight.data = layer.w13_weight.data.view(torch.int32)
         layer.w2_weight.data = layer.w2_weight.data.view(torch.int32)
+        ep_rank_start = self.moe_config.ep_rank * self.moe_config.num_local_experts
         layer.ipex_fusion = ipex.llm.modules.GatedMLPMOE(
             layer.w13_weight,
             layer.w2_weight,
@@ -1121,6 +1122,7 @@ class IpexMxfp4MoEMethod(Mxfp4MoEMethod):
             w13_bias=layer.w13_bias,
             w2_bias=layer.w2_bias,
             is_mxfp4=True,
+            experts_start_id=ep_rank_start,
         )
 
     def apply(