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

class QuarkOCP_MX(QuarkScheme):
    def __init__(
        self, weight_quant_spec: dict[str, Any], input_quant_spec: dict[str, Any]
    ):
        self.out_dtype = torch.get_default_dtype()
        self.qscheme = "per_group"
        self.weight_quant_spec = weight_quant_spec
        self.input_quant_spec = input_quant_spec

        self.weight_dtype = weight_quant_spec["dtype"].replace("fp", "mxfp")
        self.input_dtype = input_quant_spec["dtype"].replace("fp", "mxfp")

        self.ocp_mx_scheme = OCP_MX_Scheme.from_quant_dtype(
            self.input_dtype, self.weight_dtype
        )

        if self.weight_dtype == "mxfp4":
            self.packed_factor: Union[int, Fraction] = 2
            self.dequant_func = dequant_mxfp4
        else:
            self.packed_factor = Fraction(numerator=8, denominator=6)
            self.dequant_func = partial(
                dequant_mxfp6, quant_dtype=self.weight_dtype.replace("mx", "")
            )

        if self.input_dtype == "mxfp4":
            self.quant_dequant_func = quant_dequant_mxfp4
        else:
            self.quant_dequant_func = partial(
                quant_dequant_mxfp6, quant_dtype=self.input_dtype.replace("mx", "")
            )

        self.static_input_scales = not input_quant_spec.get("is_dynamic")

        if self.static_input_scales:
            raise NotImplementedError(
                "QuarkOCP_MX with static input scales is currently not "
                "implemented. Please open an issue."
            )

        # TODO: integrate (or test) mixed-precision kernel.
        self.emulate = not current_platform.supports_mx() or (
            self.input_dtype != "mxfp4" or self.weight_dtype != "mxfp4"
        )

        self.rocm_use_aiter_fp4_asm_gemm = is_rocm_aiter_fp4_asm_gemm_enabled()

        if not self.emulate and (dynamic_mxfp4_quant is None or gemm_afp4wfp4 is None):
            # Currently need these kernels if not emulating
            raise NotImplementedError(
                f"{self.__class__.__name__} requires AITER to be installed "
                "for non-emulation mode! Please refer to "
                "https://github.com/ROCm/aiter for installation details."
            )

        if not current_platform.supports_mx():
            logger.warning_once(
                "The current platform does not support native MXFP4/MXFP6 "
                "computation. Simulated weight dequantization and activation "
                "QDQ (quantize and dequantize) will be used, with the linear "
                "layers computed in high precision."
            )

        if current_platform.supports_mx() and (
            self.input_dtype != "mxfp4" or self.weight_dtype != "mxfp4"
        ):
            logger.warning_once(
                "The current platform supports native MXFP4/MXFP6 "
                f"computation, but kernels for input_dtype={self.input_dtype} "
                f"and weight_dtype={self.weight_dtype} are not yet integrated "
                "in vLLM. Simulated weight dequantization and activation "
                "QDQ (quantize and dequantize) will be used, with the linear "
                "layers computed in high precision."
            )

    def get_packed_dim(self, dim: int, quant_dtype: str):
        if quant_dtype == "mxfp4":
            assert dim % 2 == 0
            return dim // 2
        elif quant_dtype in {"mxfp6_e3m2", "mxfp6_e2m3"}:
            # FP6 packs 4 * 6 = 24 bits on 3 bytes.
            assert (dim * 3) % 4 == 0
            return (dim * 3) // 4
        else:
            raise NotImplementedError(
                "Unsupported quant_dtype in QuarkOCP_MX.get_packed_dim, "
                f"got quant_dtype={quant_dtype}. Something is wrong, please "
                "open an issue."
            )

    @classmethod
    def get_min_capability(cls) -> int:
        return 70

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        layer.weight = torch.nn.Parameter(layer.weight.data, requires_grad=False)

        if self.emulate:
            layer.weight_scale = torch.nn.Parameter(
                layer.weight_scale.data, requires_grad=False
            )
        else:
            if self.rocm_use_aiter_fp4_asm_gemm:
                # shuffle weight scale
                weight_scale_shuffle = layer.weight_scale.data
                sm, sn = weight_scale_shuffle.shape
                weight_scale_shuffle = weight_scale_shuffle.view(
                    sm // 32, 2, 16, sn // 8, 2, 4, 1
                )
                weight_scale_shuffle = weight_scale_shuffle.permute(
                    0, 3, 5, 2, 4, 1, 6
                ).contiguous()
                weight_scale_shuffle = weight_scale_shuffle.view(sm, sn)
                layer.weight_scale = torch.nn.Parameter(
                    weight_scale_shuffle, requires_grad=False
                )

                # shuffle weight
                weight_shuffle = layer.weight.data
                weight_shuffle = shuffle_weight(weight_shuffle, layout=(16, 16))
                layer.weight = torch.nn.Parameter(weight_shuffle, requires_grad=False)
            else:
                layer.weight_scale = torch.nn.Parameter(
                    layer.weight_scale.data.T.contiguous(), requires_grad=False
                )

    def create_weights(
        self,
        layer: torch.nn.Module,
        output_partition_sizes: list[int],
        input_size_per_partition: int,
        params_dtype: torch.dtype,
        weight_loader: Callable,
        **kwargs,
    ):
        output_size_per_partition = sum(output_partition_sizes)
        layer.logical_widths = output_partition_sizes

        # WEIGHT
        weight = PackedvLLMParameter(
            data=torch.empty(
                output_size_per_partition,
                self.get_packed_dim(input_size_per_partition, self.weight_dtype),
                dtype=torch.uint8,
            ),
            input_dim=1,
            output_dim=0,
            packed_dim=1,
            packed_factor=self.packed_factor,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight", weight)

        # WEIGHT SCALE
        weight_scale = GroupQuantScaleParameter(
            data=torch.empty(
                output_size_per_partition,
                input_size_per_partition // OCP_MX_BLOCK_SIZE,
                dtype=torch.uint8,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight_scale", weight_scale)

    def apply_weights(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        if self.emulate:
            dq_w = self.dequant_func(layer.weight, layer.weight_scale, x.dtype)
            qdq_x = self.quant_dequant_func(x)
            return F.linear(qdq_x, dq_w, bias)
        else:
            return torch.ops.vllm.gemm_with_dynamic_quant(
                x,
                layer.weight,
                layer.weight_scale,
                self.rocm_use_aiter_fp4_asm_gemm,
                self.out_dtype,
            )

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: Optional[torch.Tensor]
    ):
        """
        Run the forward pass for the particular scheme. This is where
        scheme-specific dequant/quant steps/kernels should be applied.

        :param layer: torch.nn.Module with the registered weights and
            other parameters relevant to the particular scheme.
        :param x: input to the layer
        :param 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

class QuarkW8A8Fp8(QuarkScheme):
    def __init__(
        self, weight_config: dict[str, Any], input_config: Optional[dict[str, Any]]
    ):
        self.weight_qscheme = cast(str, weight_config.get("qscheme"))
        self.is_static_input_scheme: bool = False
        self.input_qscheme: Optional[str] = None
        if input_config is not None:
            self.is_static_input_scheme = not cast(bool, input_config.get("is_dynamic"))
            self.input_qscheme = cast(str, input_config.get("qscheme"))

        per_token = (
            not self.is_static_input_scheme and self.input_qscheme == "per_channel"
        )
        self.act_quant_group_shape = (
            GroupShape.PER_TOKEN if per_token else GroupShape.PER_TENSOR
        )
        self.fp8_linear = Fp8LinearOp(
            act_quant_static=self.is_static_input_scheme,
            act_quant_group_shape=self.act_quant_group_shape,
        )
        self.out_dtype = torch.get_default_dtype()

    @classmethod
    def get_min_capability(cls) -> int:
        # lovelace and up
        return 89

    def process_weights_after_loading(self, layer) -> None:
        # If per tensor, when we have a fused module (e.g. QKV) with per
        # tensor scales (thus N scales being passed to the kernel),
        # requantize so we can always run per tensor
        if self.weight_qscheme == "per_tensor":
            if current_platform.is_fp8_fnuz():
                input_scale = getattr(layer, "input_scale", None)
                weight, max_w_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
                    weight=layer.weight,
                    weight_scale=layer.weight_scale,
                    input_scale=input_scale,
                )
                if input_scale is not None:
                    layer.input_scale = Parameter(input_scale, requires_grad=False)
            else:
                max_w_scale = layer.weight_scale
                weight = layer.weight

            max_w_scale, weight = requantize_with_max_scale(
                weight=weight,
                weight_scale=max_w_scale,
                logical_widths=layer.logical_widths,
            )

            layer.weight = Parameter(weight.t(), requires_grad=False)
            layer.weight_scale = Parameter(max_w_scale, requires_grad=False)

        # If channelwise, scales are already lined up, so just transpose.
        elif self.weight_qscheme == "per_channel":
            weight = layer.weight

            if current_platform.is_fp8_fnuz():
                input_scale = getattr(layer, "input_scale", None)
                weight, weight_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
                    weight=weight,
                    weight_scale=layer.weight_scale,
                    input_scale=input_scale,
                )
                if input_scale is not None:
                    layer.input_scale = Parameter(input_scale, requires_grad=False)
            else:
                weight_scale = layer.weight_scale.data
            if self.act_quant_group_shape == GroupShape.PER_TOKEN:
                weight_scale = weight_scale.view(-1, 1)
            layer.weight = Parameter(weight.t(), requires_grad=False)
            # required by torch.compile to be torch.nn.Parameter
            layer.weight_scale = Parameter(weight_scale, requires_grad=False)

        else:
            raise ValueError(f"Unknown quantization scheme {self.weight_qscheme}")

        # INPUT SCALE
        if self.is_static_input_scheme:
            layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False)
        else:
            layer.input_scale = None

    def create_weights(
        self,
        layer: torch.nn.Module,
        output_partition_sizes: list[int],
        input_size_per_partition: int,
        params_dtype: torch.dtype,
        weight_loader: Callable,
        **kwargs,
    ):
        output_size_per_partition = sum(output_partition_sizes)
        layer.logical_widths = output_partition_sizes

        # WEIGHT
        weight = ModelWeightParameter(
            data=torch.empty(
                output_size_per_partition,
                input_size_per_partition,
                dtype=torch.float8_e4m3fn,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight", weight)

        # WEIGHT SCALE
        # TODO: update create_xxx_parameter functions to return
        # the newly added parameters
        if self.weight_qscheme == "per_channel":
            weight_scale = ChannelQuantScaleParameter(
                data=torch.empty((sum(output_partition_sizes)), dtype=torch.float32),
                output_dim=0,
                weight_loader=weight_loader,
            )
        else:
            assert self.weight_qscheme == "per_tensor"
            weight_scale = PerTensorScaleParameter(
                data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
                weight_loader=weight_loader,
            )

        # min requirement for fp8 kernels
        weight_scale[:] = torch.finfo(torch.float32).min
        layer.register_parameter("weight_scale", weight_scale)

        # INPUT SCALE
        if self.is_static_input_scheme:
            input_scale = PerTensorScaleParameter(
                data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
                weight_loader=weight_loader,
            )
            input_scale[:] = torch.finfo(torch.float32).min
            layer.register_parameter("input_scale", input_scale)

    def apply_weights(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        return self.fp8_linear.apply(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            out_dtype=self.out_dtype,
            input_scale=layer.input_scale,
            bias=bias,
        )