vllm.model_executor.layers.quantization.kernels.scaled_mm.xpu ¶

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