[XPU] add scale transpose to prepare_fp8_moe_layer_for_xpu and bump up kernels (#43277)

Signed-off-by: mayuyuace <qiming1.zhang@intel.com>
Co-authored-by: Kunshang Ji <kunshang.ji@intel.com>

requirements/xpu.txt

@@ -17,4 +17,4 @@ torchaudio
 torchvision
 
 auto_round_lib>=0.13.0
-vllm_xpu_kernels @ https://github.com/vllm-project/vllm-xpu-kernels/releases/download/v0.1.8/vllm_xpu_kernels-0.1.8-cp38-abi3-manylinux_2_28_x86_64.whl
+vllm_xpu_kernels @ https://github.com/vllm-project/vllm-xpu-kernels/releases/download/v0.1.9/vllm_xpu_kernels-0.1.9-cp38-abi3-manylinux_2_28_x86_64.whl

vllm/model_executor/layers/fused_moe/experts/xpu_moe.py

@@ -29,9 +29,20 @@ if current_platform.is_xpu():
 
 def prepare_fp8_moe_layer_for_xpu(
     w13: torch.Tensor,
+    w13_scale: torch.Tensor,
     w2: torch.Tensor,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    return w13.transpose(-1, -2).contiguous(), w2.transpose(-1, -2).contiguous()
+    w2_scale: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    if w13_scale is not None and w13_scale.ndim == 3:
+        w13_scale = w13_scale.transpose(-1, -2).contiguous()
+    if w2_scale is not None and w2_scale.ndim == 3:
+        w2_scale = w2_scale.transpose(-1, -2).contiguous()
+    return (
+        w13.transpose(-1, -2).contiguous(),
+        w13_scale,
+        w2.transpose(-1, -2).contiguous(),
+        w2_scale,
+    )
 
 
 class XPUExperts(mk.FusedMoEExpertsModular):

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

@@ -483,7 +483,9 @@ def convert_to_fp8_moe_kernel_format(
             prepare_fp8_moe_layer_for_xpu,
         )
 
-        w13, w2 = prepare_fp8_moe_layer_for_xpu(w13, w2)
+        w13, w13_scale, w2, w2_scale = prepare_fp8_moe_layer_for_xpu(
+            w13, w13_scale, w2, w2_scale
+        )
     elif fp8_backend == Fp8MoeBackend.CPU:
         from vllm.model_executor.layers.fused_moe.experts.cpu_moe import (
             prepare_fp8_moe_layer_for_cpu,