[XPU] fix MoE triton backend in online fp8 quantization (#40109)

Signed-off-by: Yan Ma <yan.ma@intel.com>

vllm/model_executor/layers/fused_moe/oracle/fp8.py

@@ -471,6 +471,12 @@ def convert_to_fp8_moe_kernel_format(
             w2_input_scale=w2_input_scale,
             is_trtllm=(fp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM),
         )
+    elif fp8_backend == Fp8MoeBackend.XPU:
+        from vllm.model_executor.layers.fused_moe.xpu_fused_moe import (
+            prepare_fp8_moe_layer_for_xpu,
+        )
+
+        w13, w2 = prepare_fp8_moe_layer_for_xpu(w13, w2)
     else:
         if fp8_backend not in [
             Fp8MoeBackend.TRITON,

vllm/model_executor/layers/fused_moe/xpu_fused_moe.py

@@ -24,6 +24,13 @@ if current_platform.is_xpu():
     from vllm_xpu_kernels.fused_moe_interface import xpu_fused_moe
 
 
+def prepare_fp8_moe_layer_for_xpu(
+    w13: torch.Tensor,
+    w2: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    return w13.transpose(-1, -2).contiguous(), w2.transpose(-1, -2).contiguous()
+
+
 class XPUExperts(mk.FusedMoEExpertsModular):
     def __init__(
         self,

vllm/model_executor/layers/quantization/fp8.py

@@ -1019,10 +1019,6 @@ class Fp8OnlineMoEMethod(Fp8MoEMethod):
                 layer.w2_weight[expert, :, :]
             )
 
-        if current_platform.is_xpu():
-            w13.data = w13.transpose(-1, -2).contiguous()
-            w2.data = w2.transpose(-1, -2).contiguous()
-
         # Shuffle weights to runtime format and setup kernel.
         self._setup_kernel(
             layer,