[MXFP8] [XPU] add a new compressed tensor schema and add a xpu mxfp8 gemm kernel (#38707)

Signed-off-by: Zhu, Zufang <zufang.zhu@intel.com>

vllm/model_executor/kernels/linear/__init__.py

@@ -71,6 +71,9 @@ from vllm.model_executor.kernels.linear.mxfp8.flashinfer import (
 from vllm.model_executor.kernels.linear.mxfp8.marlin import (
     MarlinMxfp8LinearKernel,
 )
+from vllm.model_executor.kernels.linear.mxfp8.xpu import (
+    XPUMxFp8LinearKernel,
+)
 from vllm.model_executor.kernels.linear.nvfp4 import (
     NvFp4LinearKernel,
     NvFp4LinearLayerConfig,
@@ -243,6 +246,10 @@ _POSSIBLE_MXFP8_KERNELS: dict[PlatformEnum, list[type[Mxfp8LinearKernel]]] = {
     PlatformEnum.ROCM: [
         EmulationMxfp8LinearKernel,
     ],
+    PlatformEnum.XPU: [
+        XPUMxFp8LinearKernel,
+        EmulationMxfp8LinearKernel,
+    ],
 }
 
 _POSSIBLE_NVFP4_KERNELS: dict[PlatformEnum, list[type[NvFp4LinearKernel]]] = {
@@ -742,6 +749,7 @@ __all__ = [
     "Mxfp8LinearLayerConfig",
     "FlashInferCutlassMxfp8LinearKernel",
     "MarlinMxfp8LinearKernel",
+    "XPUMxFp8LinearKernel",
     "EmulationMxfp8LinearKernel",
     "CutlassNvFp4LinearKernel",
     "EmulationNvFp4LinearKernel",

vllm/model_executor/kernels/linear/mxfp8/xpu.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
+    xpu_mxfp8_quantize as quant_mxfp8,
+)
+from vllm.model_executor.utils import replace_parameter
+from vllm.platforms import current_platform
+
+from .Mxfp8LinearKernel import Mxfp8LinearKernel, Mxfp8LinearLayerConfig
+
+
+class XPUMxFp8LinearKernel(Mxfp8LinearKernel):
+    """MXFP8 W8A8 GEMM on XPU."""
+
+    @classmethod
+    def is_supported(
+        cls, compute_capability: int | None = None
+    ) -> tuple[bool, str | None]:
+        if not current_platform.is_xpu():
+            return False, "XPUMxFp8 only support on XPU"
+        return True, None
+
+    @classmethod
+    def can_implement(cls, c: Mxfp8LinearLayerConfig) -> tuple[bool, str | None]:
+        return True, None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        weight_scale = layer.weight_scale.view(torch.float8_e8m0fnu)
+        weight_scale = weight_scale.t().contiguous()
+        replace_parameter(layer, "weight", layer.weight.t())
+        replace_parameter(layer, "weight_scale", weight_scale.data)
+
+    def apply_weights(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        out_dtype = x.dtype
+        x_fp8, x_scale = quant_mxfp8(x)
+        return torch.ops._xpu_C.fp8_gemm(
+            x_fp8,
+            layer.weight,
+            out_dtype,
+            x_scale,
+            layer.weight_scale,
+            bias,
+        )