vllm.model_executor.layers.quantization.modelopt ¶

class ModelOptFp8Config(ModelOptQuantConfigBase):
    """Config class for ModelOpt FP8."""

    def __init__(
        self,
        quant_method: str,
        is_checkpoint_fp8_serialized: bool,
        kv_cache_quant_method: str | None,
        exclude_modules: list[str],
    ) -> None:
        super().__init__(exclude_modules)
        self.quant_method = quant_method
        self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
        self.kv_cache_quant_method = kv_cache_quant_method
        if is_checkpoint_fp8_serialized:
            logger.warning(
                "Detected ModelOpt fp8 checkpoint (quant_algo=%s). Please note "
                "that the format is experimental and could change.",
                quant_method,
            )

        # Select LinearMethod implementation based on quant_algo.
        if self.quant_method == "FP8":
            self.LinearMethodCls = ModelOptFp8LinearMethod
        elif self.quant_method == "FP8_PER_CHANNEL_PER_TOKEN":
            self.LinearMethodCls = ModelOptFp8PcPtLinearMethod
        elif self.quant_method == "FP8_PB_WO":
            self.LinearMethodCls = ModelOptFp8PbWoLinearMethod
        else:
            raise ValueError(
                "Unsupported ModelOpt FP8 quant_algo for vLLM: "
                f"{self.quant_method}. Supported: FP8 / "
                "FP8_PER_CHANNEL_PER_TOKEN / FP8_PB_WO."
            )

    def get_name(self) -> QuantizationMethods:
        return "modelopt"

    def get_supported_act_dtypes(self) -> list[torch.dtype]:
        return [torch.bfloat16, torch.half]

    @classmethod
    def get_min_capability(cls) -> int:
        return 89

    @classmethod
    def override_quantization_method(
        cls, hf_quant_cfg, user_quant
    ) -> QuantizationMethods | None:
        algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
        if algo is not None and algo == "FP8":
            return "modelopt"
        return None

    @classmethod
    def _from_config(
        cls,
        *,
        quant_method: str,
        kv_cache_quant_method: str | None,
        exclude_modules: list[str],
        original_config: dict[str, Any],
        **kwargs: Any,
    ) -> "ModelOptFp8Config":
        is_checkpoint_fp8_serialized = "FP8" in quant_method

        return cls(
            quant_method,
            is_checkpoint_fp8_serialized,
            kv_cache_quant_method,
            exclude_modules,
        )

class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
    """
    Supports loading kv-cache scaling factors from FP8 checkpoints.
    """

    def __init__(self, quant_config: "ModelOptQuantConfigBase"):
        super().__init__(quant_config)

class ModelOptFp8LinearMethod(LinearMethodBase):
    """Linear method for Model Optimizer static quantization.
    Supports loading FP8 checkpoints with static weight scale and
    activation scale. Future support might be added for dynamic
    scales.

    Limitations:
    1. Only support per-tensor quantization due to torch._scaled_mm support.
    2. Only support float8_e4m3fn datatype
        Args: quant_config: The ModelOpt quantization config.
    """

    def __init__(self, quant_config: ModelOptFp8Config) -> None:
        self.quant_config = quant_config
        self.fp8_linear = init_fp8_linear_kernel(
            activation_quant_key=kFp8StaticTensorSym,
            weight_quant_key=kFp8StaticTensorSym,
            out_dtype=torch.get_default_dtype(),
            module_name=self.__class__.__name__,
        )

    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,
    ):
        del input_size, output_size
        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")
        layer.logical_widths = output_partition_sizes
        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition
        weight_dtype = (
            torch.float8_e4m3fn
            if self.quant_config.is_checkpoint_fp8_serialized
            else params_dtype
        )
        weight = ModelWeightParameter(
            data=torch.empty(
                output_size_per_partition, input_size_per_partition, dtype=weight_dtype
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight", weight)

        if self.quant_config.is_checkpoint_fp8_serialized:
            # WEIGHT SCALE
            weight_scale = PerTensorScaleParameter(
                data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
                weight_loader=weight_loader,
            )
            weight_scale[:] = torch.finfo(torch.float32).min
            layer.register_parameter("weight_scale", weight_scale)
            # INPUT SCALE
            scale = PerTensorScaleParameter(
                data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
                weight_loader=weight_loader,
            )

            scale[:] = torch.finfo(torch.float32).min
            layer.register_parameter("input_scale", scale)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        weight = layer.weight
        max_w_scale = layer.weight_scale.max()
        if not (layer.weight_scale == layer.weight_scale[0]).all():
            max_w_scale, weight = requantize_with_max_scale(
                layer.weight, layer.weight_scale, layer.logical_widths
            )
        layer.weight = Parameter(weight.t(), requires_grad=False)
        layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
        layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return self.fp8_linear.apply_weights(layer, x, bias)

class ModelOptFp8MoEMethod(FusedMoEMethodBase):
    """MoE method for ModelOpt FP8.
    Supports loading FP8 checkpoints with static weight scale and
    activation scale.
    Args:
        quant_config: The ModelOpt quantization config.
    """

    def __init__(
        self,
        quant_config: ModelOptFp8Config,
        moe_config: FusedMoEConfig,
    ) -> None:
        super().__init__(moe_config)
        self.quant_config = quant_config
        assert self.quant_config.is_checkpoint_fp8_serialized

        # Select Fp8 MoE backend
        self.fp8_backend, self.experts_cls = select_fp8_moe_backend(
            config=self.moe,
            weight_key=kFp8StaticTensorSym,
            activation_key=kFp8StaticTensorSym,
        )

    def maybe_make_prepare_finalize(
        self,
        routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
    ) -> mk.FusedMoEPrepareAndFinalizeModular | None:
        raise ValueError(
            f"{self.__class__.__name__} uses the new modular kernel initialization "
            "logic. This function should not be called."
        )

    def select_gemm_impl(
        self,
        prepare_finalize: mk.FusedMoEPrepareAndFinalizeModular,
        layer: torch.nn.Module,
    ) -> mk.FusedMoEExpertsModular:
        raise ValueError(
            f"{self.__class__.__name__} uses the new modular kernel initialization "
            "logic. This function should not be called."
        )

    def create_weights(
        self,
        layer: torch.nn.Module,
        num_experts: int,
        hidden_size: int,
        intermediate_size_per_partition: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        layer.orig_dtype = params_dtype
        layer.num_experts = num_experts

        # Use FP8 dtype if checkpoint is serialized
        weight_dtype = (
            torch.float8_e4m3fn
            if self.quant_config.is_checkpoint_fp8_serialized
            else params_dtype
        )
        weight_loader = extra_weight_attrs.get("weight_loader")

        w13_num_shards = 2 if self.moe.is_act_and_mul else 1

        w13_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                w13_num_shards * intermediate_size_per_partition,
                hidden_size,
                dtype=weight_dtype,
            ),
            input_dim=2,
            output_dim=1,
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_weight", w13_weight)

        w2_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                hidden_size,
                intermediate_size_per_partition,
                dtype=weight_dtype,
            ),
            input_dim=2,
            output_dim=1,
            weight_loader=weight_loader,
        )
        layer.register_parameter("w2_weight", w2_weight)

        # WEIGHT SCALES - Per-tensor scaling for ModelOpts
        # For gated MoE, allocate 2 scales for w1 and w3 respectively.
        # They will be combined to a single scale after weight loading.
        # For non-gated MoE, allocate 1 scale for w13.
        w13_weight_scale = PerTensorScaleParameter(
            data=torch.full(
                (num_experts, w13_num_shards),
                1.0,
                dtype=torch.float32,
            ),
            weight_loader=weight_loader,
        )
        w2_weight_scale = PerTensorScaleParameter(
            data=torch.full((num_experts,), 1.0, dtype=torch.float32),
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_weight_scale", w13_weight_scale)
        layer.register_parameter("w2_weight_scale", w2_weight_scale)

        # INPUT SCALES - Per-tensor scaling for ModelOpt
        w13_input_scale = PerTensorScaleParameter(
            data=torch.full((num_experts,), 1.0, dtype=torch.float32),
            weight_loader=weight_loader,
        )
        w2_input_scale = PerTensorScaleParameter(
            data=torch.full((num_experts,), 1.0, dtype=torch.float32),
            weight_loader=weight_loader,
        )
        layer.register_parameter("w13_input_scale", w13_input_scale)
        layer.register_parameter("w2_input_scale", w2_input_scale)

    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,
        w2_input_scale: torch.Tensor,
    ):
        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 parameters with updated versions. Note that this helper
        # function ensures the replacement is compatible with RL weight reloads.
        replace_parameter(layer, "w13_weight", w13)
        replace_parameter(layer, "w2_weight", w2)
        replace_parameter(layer, "w13_weight_scale", w13_scale)
        replace_parameter(layer, "w2_weight_scale", w2_scale)

        # Setup modular kernel.
        self.moe_quant_config = self.get_fused_moe_quant_config(layer)
        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 process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        w13 = layer.w13_weight
        w2 = layer.w2_weight
        w13_scale = layer.w13_weight_scale
        w2_scale = layer.w2_weight_scale
        w13_input_scale = layer.w13_input_scale
        w2_input_scale = layer.w2_input_scale

        # Per tensor kernels require single activation scale. Use the max.
        w13_input_scale, w2_input_scale = process_fp8_input_tensor_strategy_moe(
            w13_input_scale, w2_input_scale
        )
        replace_parameter(layer, "w13_input_scale", w13_input_scale)
        replace_parameter(layer, "w2_input_scale", w2_input_scale)

        # Per tensor kernels require single weight scale for w13 per expert, but
        # on disk there is a scale for w1 and w3. Use the max to requantize.
        shard_size = layer.intermediate_size_per_partition
        w13, w13_scale = process_fp8_weight_tensor_strategy_moe(
            w13,
            w13_scale,
            shard_size,
            num_experts=layer.w13_weight.shape[0],
            is_act_and_mul=self.moe.is_act_and_mul,
        )

        # Shuffle weights to runtime format and setup kernel.
        self._setup_kernel(
            layer, w13, w2, w13_scale, w2_scale, w13_input_scale, w2_input_scale
        )

    def get_fused_moe_quant_config(self, layer: torch.nn.Module) -> FusedMoEQuantConfig:
        w1_scale = layer.w13_weight_scale
        w2_scale = layer.w2_weight_scale
        a1_scale = layer.w13_input_scale
        a2_scale = layer.w2_input_scale

        return 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,
        )

    def apply_monolithic(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        router_logits: torch.Tensor,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        assert self.is_monolithic
        assert self.moe_kernel is not None
        return self.moe_kernel.apply_monolithic(
            x,
            layer.w13_weight,
            layer.w2_weight,
            router_logits,
            activation=layer.activation,
            global_num_experts=layer.global_num_experts,
            expert_map=layer.expert_map,
            apply_router_weight_on_input=layer.apply_router_weight_on_input,
            num_expert_group=layer.num_expert_group,
            topk_group=layer.topk_group,
            e_score_correction_bias=layer.e_score_correction_bias,
            routed_scaling_factor=layer.routed_scaling_factor,
        )

    def apply(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        shared_experts_input: torch.Tensor | None,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        assert not self.is_monolithic
        assert self.moe_kernel is not None
        return self.moe_kernel.apply(
            x,
            layer.w13_weight,
            layer.w2_weight,
            topk_weights,
            topk_ids,
            activation=layer.activation,
            global_num_experts=layer.global_num_experts,
            expert_map=layer.expert_map,
            apply_router_weight_on_input=layer.apply_router_weight_on_input,
            shared_experts_input=shared_experts_input,
        )

class ModelOptFp8PbWoLinearMethod(LinearMethodBase):
    """Linear method for ModelOpt FP8_PB_WO checkpoints.

    ModelOpt exports `weight_scale` as a 4D tensor:
      [out_blk, 1, in_blk, 1]
    where block size is typically 128 for both dims.

    vLLM executes it as FP8 GEMM with *dynamic per-token* activation quant.
    """

    _WEIGHT_BLOCK_SIZE: tuple[int, int] = (128, 128)

    def __init__(self, quant_config: ModelOptFp8Config) -> None:
        self.quant_config = quant_config
        block_n, block_k = self._WEIGHT_BLOCK_SIZE
        self.weight_block_size = list(self._WEIGHT_BLOCK_SIZE)
        self.w8a8_block_fp8_linear = W8A8BlockFp8LinearOp(
            weight_group_shape=GroupShape(block_n, block_k),
            act_quant_group_shape=GroupShape(1, block_k),
            cutlass_block_fp8_supported=cutlass_block_fp8_supported(),
            use_aiter_and_is_supported=False,
        )

    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,
    ):
        del input_size, output_size

        if not self.quant_config.is_checkpoint_fp8_serialized:
            raise ValueError(
                "FP8_PB_WO currently only supports FP8-serialized checkpoints."
            )

        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")
        layer.logical_widths = output_partition_sizes
        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition

        # Expose block size so the v2 weight loaders can translate offsets from
        # element-space -> block-space for BlockQuantScaleParameter.
        layer.weight_block_size = self.weight_block_size

        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)

        block_n, block_k = self._WEIGHT_BLOCK_SIZE
        if output_size_per_partition % block_n != 0:
            raise ValueError(
                "ModelOpt FP8_PB_WO requires out_features divisible by "
                f"{block_n}, got {output_size_per_partition}."
            )
        if input_size_per_partition % block_k != 0:
            raise ValueError(
                "ModelOpt FP8_PB_WO requires in_features divisible by "
                f"{block_k}, got {input_size_per_partition}."
            )

        out_blks = output_size_per_partition // block_n
        in_blks = input_size_per_partition // block_k

        # Match ModelOpt's exported shape so weight loading works without a
        # custom loader: [out_blk, 1, in_blk, 1]
        weight_scale = BlockQuantScaleParameter(
            data=torch.empty((out_blks, 1, in_blks, 1), dtype=torch.float32),
            input_dim=2,
            output_dim=0,
            weight_loader=weight_loader,
        )
        weight_scale[:] = torch.finfo(torch.float32).min
        layer.register_parameter("weight_scale", weight_scale)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        # Keep weight in [out, in] layout for W8A8BlockFp8LinearOp.
        layer.weight = Parameter(layer.weight.data, requires_grad=False)

        scale = layer.weight_scale
        if scale.dim() == 4:
            # [out_blk, 1, in_blk, 1] -> [out_blk, in_blk]
            scale = scale.squeeze(1).squeeze(-1)
        elif scale.dim() != 2:
            raise ValueError(
                "Unexpected ModelOpt FP8_PB_WO weight_scale shape: "
                f"{tuple(scale.shape)}."
            )

        layer.weight_scale = Parameter(scale.contiguous(), requires_grad=False)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return self.w8a8_block_fp8_linear.apply(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            input_scale=None,
            bias=bias,
        )

class ModelOptFp8PcPtLinearMethod(LinearMethodBase):
    """Linear method for ModelOpt FP8_PER_CHANNEL_PER_TOKEN checkpoints.

    Expected checkpoint structure (per Linear):
    - weight: fp8-e4m3fn, shape [out, in]
    - weight_scale: fp32, shape [out] (per-output-channel)
    - no input_scale (activations are dynamically quantized per-token)
    """

    def __init__(self, quant_config: ModelOptFp8Config) -> None:
        self.quant_config = quant_config
        self.fp8_linear = init_fp8_linear_kernel(
            activation_quant_key=kFp8DynamicTokenSym,
            weight_quant_key=kFp8StaticTokenSym,
            out_dtype=torch.get_default_dtype(),
            module_name=self.__class__.__name__,
        )

    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,
    ):
        del input_size, output_size

        if not self.quant_config.is_checkpoint_fp8_serialized:
            raise ValueError(
                "FP8_PER_CHANNEL_PER_TOKEN currently only supports "
                "FP8-serialized checkpoints."
            )

        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")
        layer.logical_widths = output_partition_sizes
        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition

        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 = ChannelQuantScaleParameter(
            data=torch.empty(output_size_per_partition, dtype=torch.float32),
            output_dim=0,
            weight_loader=weight_loader,
        )
        weight_scale[:] = torch.finfo(torch.float32).min
        layer.register_parameter("weight_scale", weight_scale)

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

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return self.fp8_linear.apply_weights(layer, x, bias)

class ModelOptMixedPrecisionConfig(ModelOptQuantConfigBase):
    """Config class for ModelOpt MIXED_PRECISION.

    Supports checkpoints where different layers use different quantization
    algorithms (e.g., FP8 for dense layers and NVFP4 for MoE experts).
    The per-layer algorithm is specified in the ``quantized_layers`` dict
    inside ``config.json``'s ``quantization_config`` (preferred) or the
    legacy ``hf_quant_config.json``.
    """

    def __init__(
        self,
        kv_cache_quant_method: str | None,
        exclude_modules: list[str],
        quantized_layers: dict[str, dict[str, Any]],
        fp8_config: ModelOptFp8Config,
        nvfp4_config: ModelOptNvFp4Config,
    ) -> None:
        super().__init__(exclude_modules)
        self.kv_cache_quant_method = kv_cache_quant_method
        self.quantized_layers = quantized_layers
        self.fp8_config = fp8_config
        self.nvfp4_config = nvfp4_config

    def get_name(self) -> QuantizationMethods:
        return "modelopt_mixed"

    def get_supported_act_dtypes(self) -> list[torch.dtype]:
        return [torch.bfloat16, torch.half]

    @classmethod
    def get_min_capability(cls) -> int:
        return 89

    @classmethod
    def override_quantization_method(
        cls, hf_quant_cfg, user_quant
    ) -> QuantizationMethods | None:
        algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
        if algo is not None and algo == "MIXED_PRECISION":
            return "modelopt_mixed"
        return None

    @classmethod
    def _from_config(
        cls,
        *,
        quant_method: str,
        kv_cache_quant_method: str | None,
        exclude_modules: list[str],
        original_config: dict[str, Any],
        group_size: int | None,
        **kwargs: Any,
    ) -> "ModelOptMixedPrecisionConfig":
        if "quantization" in original_config:
            quantized_layers = original_config["quantization"].get(
                "quantized_layers", {}
            )
        else:
            quantized_layers = original_config.get("quantized_layers", {})

        if not quantized_layers:
            raise ValueError(
                "MIXED_PRECISION quant_algo requires a non-empty "
                "'quantized_layers' mapping in the quantization config."
            )

        # Determine group_size from the first NVFP4 entry if not provided.
        if group_size is None:
            for layer_info in quantized_layers.values():
                if layer_info.get("quant_algo", "").upper() == "NVFP4":
                    group_size = layer_info.get("group_size", 16)
                    break
        if group_size is None:
            group_size = 16

        fp8_config = ModelOptFp8Config(
            quant_method="FP8",
            is_checkpoint_fp8_serialized=True,
            kv_cache_quant_method=kv_cache_quant_method,
            exclude_modules=[],
        )
        nvfp4_config = ModelOptNvFp4Config(
            is_checkpoint_nvfp4_serialized=True,
            kv_cache_quant_algo=kv_cache_quant_method,
            exclude_modules=[],
            group_size=group_size,
        )

        return cls(
            kv_cache_quant_method=kv_cache_quant_method,
            exclude_modules=exclude_modules,
            quantized_layers=quantized_layers,
            fp8_config=fp8_config,
            nvfp4_config=nvfp4_config,
        )

    def _resolve_quant_algo(self, prefix: str) -> str | None:
        """Look up the quant_algo for a vLLM-side layer prefix.

        Tries three strategies in order:
        1. Direct lookup in ``quantized_layers``.
        2. Packed/fused-layer lookup (unfuse via ``packed_modules_mapping``).
        3. Prefix-based lookup for FusedMoE (any child key starts with
           ``prefix + "."``).

        Returns the upper-cased quant_algo string, or *None* if the prefix
        is not found.
        """
        # 1. Direct lookup
        if prefix in self.quantized_layers:
            return self.quantized_layers[prefix]["quant_algo"].upper()

        # 2. Packed / fused layer lookup
        proj_name = prefix.rsplit(".", 1)[-1]
        if self.packed_modules_mapping and proj_name in self.packed_modules_mapping:
            algos: set[str] = set()
            base = prefix.rsplit(".", 1)[0]
            for shard_name in self.packed_modules_mapping[proj_name]:
                shard_prefix = f"{base}.{shard_name}"
                if shard_prefix in self.quantized_layers:
                    algos.add(self.quantized_layers[shard_prefix]["quant_algo"].upper())
            if len(algos) == 1:
                return algos.pop()
            if len(algos) > 1:
                raise ValueError(
                    f"Mixed quant_algo within fused layer {prefix}: "
                    f"{algos}. All shards must use the same quantization."
                )

        # 3. Prefix-based lookup (for FusedMoE / parent modules)
        prefix_dot = prefix + "."
        for key, info in self.quantized_layers.items():
            if key.startswith(prefix_dot):
                return info["quant_algo"].upper()

        return None

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> "QuantizeMethodBase | None":
        """Return quantize-method based on layer."""
        # KV-cache quantization
        if isinstance(layer, Attention):
            if self.kv_cache_quant_method:
                return ModelOptFp8KVCacheMethod(self)
            return None

        # Excluded layers
        if self.is_layer_excluded(prefix):
            if isinstance(layer, LinearBase):
                return UnquantizedLinearMethod()
            return None

        quant_algo = self._resolve_quant_algo(prefix)

        if isinstance(layer, LinearBase):
            if quant_algo == "FP8":
                return ModelOptFp8LinearMethod(self.fp8_config)
            if quant_algo == "NVFP4":
                return ModelOptNvFp4LinearMethod(self.nvfp4_config)
            # Layer not in quantized_layers — leave unquantized
            return UnquantizedLinearMethod()

        if isinstance(layer, FusedMoE):
            if quant_algo == "FP8":
                return ModelOptFp8MoEMethod(
                    quant_config=self.fp8_config,
                    moe_config=layer.moe_config,
                )
            if quant_algo == "NVFP4":
                return ModelOptNvFp4FusedMoE(
                    quant_config=self.nvfp4_config,
                    moe_config=layer.moe_config,
                )
            return None

        return None

    def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
        super().apply_vllm_mapper(hf_to_vllm_mapper)
        if self.quantized_layers:
            self.quantized_layers = hf_to_vllm_mapper.apply_dict(self.quantized_layers)

class ModelOptMxFp8Config(ModelOptQuantConfigBase):
    """Config class for ModelOpt MXFP8."""

    def __init__(
        self,
        is_checkpoint_mxfp8_serialized: bool,
        kv_cache_quant_algo: str | None,
        exclude_modules: list[str],
    ) -> None:
        super().__init__(exclude_modules)
        self.is_checkpoint_mxfp8_serialized = is_checkpoint_mxfp8_serialized

        if not is_checkpoint_mxfp8_serialized:
            raise ValueError(
                "MXFP8 quantization requires a serialized checkpoint. "
                "Dynamic quantization is not supported."
            )

        logger.warning(
            "Detected ModelOpt MXFP8 checkpoint. Please note that "
            "the format is experimental and could change in future."
        )

        self.kv_cache_quant_algo = kv_cache_quant_algo

    def get_name(self) -> QuantizationMethods:
        return "modelopt_mxfp8"

    def get_supported_act_dtypes(self) -> list[torch.dtype]:
        return [torch.bfloat16]

    @classmethod
    def get_min_capability(cls) -> int:
        # MXFP8 hardware acceleration requires Blackwell (SM100) or newer
        return 100

    @classmethod
    def override_quantization_method(
        cls, hf_quant_cfg, user_quant
    ) -> QuantizationMethods | None:
        algo = cls._extract_modelopt_quant_algo(hf_quant_cfg)
        if algo is not None and "MXFP8" in algo:
            return "modelopt_mxfp8"
        return None

    @classmethod
    def _from_config(
        cls,
        *,
        quant_method: str,
        kv_cache_quant_method: str | None,
        exclude_modules: list[str],
        original_config: dict[str, Any],
        **kwargs: Any,
    ) -> "ModelOptMxFp8Config":
        is_checkpoint_mxfp8_serialized = "MXFP8" in quant_method.upper()

        # For MXFP8, validate required fields in the config
        if is_checkpoint_mxfp8_serialized and "quantization" in original_config:
            quant_config = original_config["quantization"]
            required_fields = ["kv_cache_quant_algo", "exclude_modules"]
            missing_fields = [
                field for field in required_fields if field not in quant_config
            ]
            if missing_fields:
                raise ValueError(
                    f"MXFP8 quantization requires the following fields in "
                    f"hf_quant_config.json: {missing_fields}"
                )

        return cls(
            is_checkpoint_mxfp8_serialized,
            kv_cache_quant_method,
            exclude_modules,
        )