vllm.model_executor.layers.quantization.quark.schemes.quark_scheme ¶

class QuarkScheme(ABC):
    """
    Abstract class used to describe the weight creation and forward pass
    of different quantization schemes supported by Quark.
    """

    @classmethod
    @abstractmethod
    def get_min_capability(cls) -> int:
        """
        Get minimum device capability.
        """
        raise NotImplementedError

    @abstractmethod
    def create_weights(self, *args, **kwargs):
        """
        Weight creation for the particular scheme. Inputs to this function

        """
        raise NotImplementedError

    @abstractmethod
    def apply_weights(
        self, layer: torch.nn.Module, x: torch.Tensor, bias: torch.Tensor | None
    ):
        """
        Run the forward pass for the particular scheme. This is where
        scheme-specific dequant/quant steps/kernels should be applied.

        Args:
            layer: torch.nn.Module with the registered weights and
                other parameters relevant to the particular scheme.
            x: input to the layer
            bias: bias parameter
        """
        raise NotImplementedError

    @abstractmethod
    def process_weights_after_loading(self, layer: torch.nn.Module):
        """
        Called after weight loading is complete for any cleanup that
        needs to occur.
        """
        raise NotImplementedError