vllm.model_executor.layers.quantization.online.mxfp8 ¶

class Mxfp8OnlineLinearMethod(_Fp8OnlineLinearBase):
    """Online MXFP8 linear method.
    Loads bf16/fp16 checkpoints and quantizes weights to MXFP8 (microscaling
    FP8 with block-32 scales) during weight loading.
    """

    def __init__(self):
        super().__init__()
        self.kernel = init_mxfp8_linear_kernel()

    def create_weights(
        self,
        layer: torch.nn.Module,
        input_size_per_partition: int,
        output_partition_sizes: list[int],
        input_size: int,
        output_size: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        if input_size_per_partition % MXFP8_BLOCK_SIZE != 0:
            raise ValueError(
                f"MXFP8 requires input_size_per_partition "
                f"({input_size_per_partition}) to be divisible by "
                f"{MXFP8_BLOCK_SIZE}."
            )

        super().create_weights(
            layer,
            input_size_per_partition,
            output_partition_sizes,
            input_size,
            output_size,
            params_dtype,
            **extra_weight_attrs,
        )

    def process_weights_after_loading(self, layer: Module) -> None:
        if getattr(layer, "_already_called_process_weights_after_loading", False):
            return

        weight_fp8, weight_scale = mxfp8_e4m3_quantize(layer.weight.contiguous())

        layer.input_scale = None
        replace_parameter(layer, "weight", weight_fp8.data)
        replace_parameter(layer, "weight_scale", weight_scale.data)

        self.kernel.process_weights_after_loading(layer)

        layer._already_called_process_weights_after_loading = True

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return self.kernel.apply_weights(layer, x, bias)

class Mxfp8OnlineMoEMethod(OnlineMoEMethodBase):
    """MoE method for online MXFP8 (block) quantization."""

    fp8_backend: "Fp8MoeBackend"
    experts_cls: "type[mk.FusedMoEExperts] | None"

    def __init__(self, *, layer: torch.nn.Module):
        super().__init__(layer.moe_config)
        self.weight_block_size: list[int] = [1, MXFP8_BLOCK_SIZE]
        self.weight_scale_name = "weight_scale"

        self.fp8_backend, self.experts_cls = select_mxfp8_moe_backend(config=self.moe)

    def create_weights(
        self,
        layer: Module,
        num_experts: int,
        hidden_size: int,
        intermediate_size_per_partition: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        if (
            hidden_size % MXFP8_BLOCK_SIZE != 0
            or intermediate_size_per_partition % MXFP8_BLOCK_SIZE != 0
        ):
            raise ValueError(
                "Online MXFP8 MoE requires hidden/intermediate sizes divisible "
                f"by {MXFP8_BLOCK_SIZE}."
            )

        super().create_weights(
            layer=layer,
            num_experts=num_experts,
            hidden_size=hidden_size,
            intermediate_size_per_partition=intermediate_size_per_partition,
            params_dtype=params_dtype,
            **extra_weight_attrs,
        )

        layer.weight_block_size = [1, MXFP8_BLOCK_SIZE]

    def _quantize_mxfp8_moe_weight(
        self, weight: torch.Tensor
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """Batch quantization: bf16/fp16 weights -> MXFP8 (fp8 + uint8 scales)."""
        E = weight.size(0)
        first_q, first_s = mxfp8_e4m3_quantize(weight[0], is_sf_swizzled_layout=False)
        # Pre-allocate the output tensors rather than stacking.
        # This is important for consistent memory layout.
        w_quant = torch.empty(
            (E, *first_q.shape), dtype=first_q.dtype, device=weight.device
        )
        w_scales = torch.empty(
            (E, *first_s.shape), dtype=first_s.dtype, device=weight.device
        )
        w_quant[0] = first_q
        w_scales[0] = first_s
        for i in range(1, E):
            w_quant[i], w_scales[i] = mxfp8_e4m3_quantize(
                weight[i], is_sf_swizzled_layout=False
            )

        return w_quant, w_scales

    def _setup_kernel(
        self,
        layer: "FusedMoE",
        w13: torch.Tensor,
        w2: torch.Tensor,
        w13_scale: torch.Tensor,
        w2_scale: torch.Tensor,
        w13_input_scale: torch.Tensor | None,
        w2_input_scale: torch.Tensor | None,
    ) -> None:
        from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
            convert_to_fp8_moe_kernel_format,
            make_fp8_moe_kernel,
        )

        # Shuffle weights to runtime format.
        w13, w2, w13_scale, w2_scale = convert_to_fp8_moe_kernel_format(
            fp8_backend=self.fp8_backend,
            layer=layer,
            w13=w13,
            w2=w2,
            w13_scale=w13_scale,
            w2_scale=w2_scale,
            w13_input_scale=w13_input_scale,
            w2_input_scale=w2_input_scale,
        )

        replace_parameter(layer, "w13_weight", w13)
        replace_parameter(layer, "w2_weight", w2)
        replace_parameter(layer, f"w13_{self.weight_scale_name}", w13_scale)
        replace_parameter(layer, f"w2_{self.weight_scale_name}", w2_scale)

        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
        if self.moe_quant_config:
            assert self.experts_cls is not None
            self.moe_kernel = make_fp8_moe_kernel(
                moe_quant_config=self.moe_quant_config,
                moe_config=self.moe,
                fp8_backend=self.fp8_backend,
                experts_cls=self.experts_cls,
                routing_tables=layer._maybe_init_expert_routing_tables(),
                shared_experts=layer.shared_experts,
            )

    def get_fused_moe_quant_config(
        self, layer: torch.nn.Module
    ) -> "FusedMoEQuantConfig":
        from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
            make_fp8_moe_quant_config,
        )

        w1_scale = getattr(layer, f"w13_{self.weight_scale_name}")
        w2_scale = getattr(layer, f"w2_{self.weight_scale_name}")
        a1_scale = layer.w13_input_scale
        a2_scale = layer.w2_input_scale

        quant_config = make_fp8_moe_quant_config(
            fp8_backend=self.fp8_backend,
            w1_scale=w1_scale,
            w2_scale=w2_scale,
            a1_scale=a1_scale,
            a2_scale=a2_scale,
            block_shape=self.weight_block_size,
        )

        self._maybe_inject_biases(quant_config, layer)
        return quant_config

    def process_weights_after_loading(self, layer: Module) -> None:
        if getattr(layer, "_already_called_process_weights_after_loading", False):
            return

        fp8_dtype = current_platform.fp8_dtype()
        w13 = torch.empty_like(layer.w13_weight, dtype=fp8_dtype)
        w2 = torch.empty_like(layer.w2_weight, dtype=fp8_dtype)
        layer.w13_input_scale = None
        layer.w2_input_scale = None

        w13, w13_scale = self._quantize_mxfp8_moe_weight(layer.w13_weight)
        w2, w2_scale = self._quantize_mxfp8_moe_weight(layer.w2_weight)

        self._setup_kernel(
            layer,
            w13,
            w2,
            w13_scale,
            w2_scale,
            layer.w13_input_scale,
            layer.w2_input_scale,
        )

        layer._already_called_process_weights_after_loading = True