[XPU][6/N] add xpu scaled_mm kernel (#34117)

Signed-off-by: Zhu, Zufang <zufang.zhu@intel.com>
(cherry picked from commit 9bdb06b436)

.buildkite/scripts/hardware_ci/run-xpu-test.sh

@@ -39,6 +39,7 @@ docker run \
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --quantization fp8
     python3 examples/offline_inference/basic/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4  --block-size 64 --enforce-eager
     python3 examples/offline_inference/basic/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2
     python3 examples/offline_inference/basic/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel

vllm/model_executor/layers/quantization/fp8.py

@@ -180,18 +180,9 @@ class Fp8Config(QuantizationConfig):
             weight_block_size=weight_block_size,
         )
 
-    def get_xpu_quant_method(
-        self, layer: torch.nn.Module, prefix: str
-    ) -> "QuantizeMethodBase | None":
-        raise NotImplementedError(
-            "FP8 quantization is not supported during xpu kernel migration."
-        )
-
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
     ) -> "QuantizeMethodBase | None":
-        if current_platform.is_xpu():
-            return self.get_xpu_quant_method(layer, prefix)
         if isinstance(layer, LinearBase):
             if is_layer_skipped(
                 prefix=prefix,
@@ -300,7 +291,7 @@ class Fp8LinearMethod(LinearMethodBase):
             or envs.VLLM_TEST_FORCE_FP8_MARLIN
         )
         # Disable marlin for rocm
-        if current_platform.is_rocm():
+        if current_platform.is_rocm() or current_platform.is_xpu():
             self.use_marlin = False
         if vllm_is_batch_invariant():
             self.use_marlin = False

vllm/model_executor/layers/quantization/kernels/scaled_mm/__init__.py

@@ -39,6 +39,9 @@ from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKer
 from vllm.model_executor.layers.quantization.kernels.scaled_mm.triton import (
     TritonInt8ScaledMMLinearKernel,
 )
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.xpu import (
+    XPUFP8ScaledMMLinearKernel,
+)
 from vllm.model_executor.layers.quantization.utils.quant_utils import QuantKey
 from vllm.platforms import PlatformEnum, current_platform
 
@@ -72,6 +75,9 @@ _POSSIBLE_FP8_KERNELS: dict[PlatformEnum, list[type[FP8ScaledMMLinearKernel]]] =
         PerTensorTorchFP8ScaledMMLinearKernel,
         ChannelWiseTorchFP8ScaledMMLinearKernel,
     ],
+    PlatformEnum.XPU: [
+        XPUFP8ScaledMMLinearKernel,
+    ],
 }
 
 _KernelT = TypeVar("_KernelT", bound=ScaledMMLinearKernel)

vllm/model_executor/layers/quantization/kernels/scaled_mm/xpu.py

@@ -0,0 +1,59 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Sequence
+
+import torch
+
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKernel import (  # noqa: E501
+    FP8ScaledMMLinearKernel,
+    FP8ScaledMMLinearLayerConfig,
+)
+from vllm.platforms import current_platform
+
+
+class XPUFP8ScaledMMLinearKernel(FP8ScaledMMLinearKernel):
+    @classmethod
+    def is_supported(
+        cls, compute_capability: int | None = None
+    ) -> tuple[bool, str | None]:
+        if not current_platform.is_xpu():
+            return False, "XPUFP8ScaledMM only support on XPU"
+        return True, None
+
+    @classmethod
+    def can_implement(cls, c: FP8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+        if c.weight_quant_key.dtype not in {torch.float8_e5m2, torch.float8_e4m3fn}:
+            return False, "XPUFP8ScaledMM only support FP8 weight dtype"
+        return True, None
+
+    def __init__(
+        self, c: FP8ScaledMMLinearLayerConfig, layer_param_names: Sequence[str]
+    ) -> None:
+        assert self.can_implement(c)[0]
+        assert self.is_supported()[0]
+        self.config = c
+        self.layer_param_names = layer_param_names
+
+    def apply_weights(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        weight = layer.weight
+        weight_scale = layer.weight_scale
+        return torch.ops._xpu_C.fp8_gemm_w8a16(x, weight, weight_scale, bias)
+
+    def apply_scaled_mm(
+        self,
+        *,
+        A: torch.Tensor,
+        B: torch.Tensor,
+        out_dtype: torch.dtype,
+        As: torch.Tensor,
+        Bs: torch.Tensor,
+        bias: torch.Tensor | None,
+        output_shape: list,
+    ) -> torch.Tensor:
+        pass