[XPU][4/N] add mxfp4 moe model support (#33679)

Signed-off-by: Kunshang Ji <kunshang.ji@intel.com>

vllm/model_executor/layers/quantization/mxfp4.py

@@ -215,7 +215,7 @@ class Mxfp4Config(QuantizationConfig):
             return UnquantizedLinearMethod()
         elif isinstance(layer, FusedMoE):
             if current_platform.is_xpu():
-                return IpexMxfp4MoEMethod(layer.moe_config)
+                return XpuMxfp4MoEMethod(layer.moe_config)
             else:
                 quant_method = Mxfp4MoEMethod(layer.moe_config)
                 quant_method.marlin_input_dtype = get_marlin_input_dtype(prefix)
@@ -1096,7 +1096,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
             raise ValueError(f"Unsupported backend: {self.mxfp4_backend}")
 
 
-class IpexMxfp4MoEMethod(Mxfp4MoEMethod):
+class XpuMxfp4MoEMethod(Mxfp4MoEMethod):
     def __init__(self, moe_config: FusedMoEConfig):
         super().__init__(moe_config)
         self.moe_config = moe_config
@@ -1121,21 +1121,7 @@ class IpexMxfp4MoEMethod(Mxfp4MoEMethod):
         self.original_hidden_size = hidden_size
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
-        import intel_extension_for_pytorch as ipex
-
-        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,
-            w1_scale_inv=layer.w13_weight_scale,
-            w2_scale_inv=layer.w2_weight_scale,
-            w13_bias=layer.w13_bias,
-            w2_bias=layer.w2_bias,
-            is_mxfp4=True,
-            experts_start_id=ep_rank_start,
-        )
+        pass
 
     @property
     def is_monolithic(self) -> bool:
@@ -1148,19 +1134,55 @@ class IpexMxfp4MoEMethod(Mxfp4MoEMethod):
         router_logits: torch.Tensor,
     ) -> torch.Tensor:
         assert layer.activation == "swigluoai", (
-            "Only swiglu_oai activation is supported for IPEX MXFP4 MoE"
+            "Only swiglu_oai activation is supported for XPU MXFP4 MoE"
         )
-        hidden_size_pad = round_up(self.original_hidden_size, 128)
-        x_pad = torch.nn.functional.pad(x, (0, hidden_size_pad - x.size(-1)))
-        hidden_states = layer.ipex_fusion(
-            x_pad,
-            layer.use_grouped_topk,
-            layer.top_k,
-            router_logits,
-            layer.renormalize,
-            layer.topk_group,
-            layer.num_expert_group,
-            activation="swiglu_oai",
+        from vllm_xpu_kernels.fused_moe_interface import xpu_fused_moe
+
+        M, _ = x.size()
+        routing_weights = torch.empty(
+            M, layer.top_k, dtype=torch.float32, device=x.device
+        )
+        selected_experts = torch.empty(
+            M, layer.top_k, dtype=torch.int32, device=x.device
+        )
+        token_expert_indices = torch.empty(
+            M, layer.top_k, dtype=torch.int32, device=x.device
+        )
+
+        if layer.use_grouped_topk:
+            routing_weights, selected_experts = torch.ops._moe_C.fused_grouped_topk(
+                x,
+                router_logits,
+                layer.top_k,
+                layer.renormalize,
+                n_expert_group=layer.num_expert_group,
+                n_topk_group=layer.topk_group,
+                scoring_func=layer.scoring_func,
+                routed_scaling_factor=layer.routed_scaling_factor,
+                bias=layer.e_score_correction_bias,
+            )
+        else:
+            torch.ops._moe_C.topk_softmax(
+                routing_weights,
+                selected_experts,
+                token_expert_indices,
+                router_logits,
+                layer.renormalize,
+                layer.e_score_correction_bias,
+            )
+
+        return xpu_fused_moe(
+            hidden_states=x,
+            w13=layer.w13_weight,
+            w13_bias=layer.w13_bias if self.moe.has_bias else None,
+            w13_scales=layer.w13_weight_scale,
+            w2=layer.w2_weight,
+            w2_bias=layer.w2_bias if self.moe.has_bias else None,
+            w2_scales=layer.w2_weight_scale,
+            topk_weights=routing_weights,
+            topk_ids=selected_experts,
+            n_experts_per_token=layer.top_k,
+            activation=layer.activation,
+            num_experts=layer.local_num_experts,
+            is_mxfp4=True,
         )
-        hidden_states = hidden_states[..., : self.original_hidden_size].contiguous()
-        return hidden_states