modelopt.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from typing import Any, Callable, Optional, Union

import torch
from torch.nn import Module
from torch.nn.parameter import Parameter

import vllm.envs as envs
import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from vllm._custom_ops import (cutlass_scaled_fp4_mm,
                              cutlass_scaled_mm_supports_fp4, scaled_fp4_quant)
from vllm.distributed import get_ep_group
from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe.config import FusedMoEParallelConfig
from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize import (  # noqa: E501
    FlashInferCutlassMoEPrepareAndFinalize)
from vllm.model_executor.layers.fused_moe.layer import (
    FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                               UnquantizedLinearMethod)
from vllm.model_executor.layers.quantization import QuantizationMethods
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig, QuantizeMethodBase)
from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
    apply_fp4_marlin_linear, is_fp4_marlin_supported,
    prepare_fp4_layer_for_marlin, prepare_moe_fp4_layer_for_marlin)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
    GroupShape, is_layer_skipped)
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
    Fp8LinearOp, requantize_with_max_scale)
from vllm.model_executor.parameter import (ModelWeightParameter,
                                           PerTensorScaleParameter)
from vllm.platforms import current_platform
from vllm.scalar_type import scalar_types

logger = init_logger(__name__)

QUANT_ALGOS = ["FP8", "NVFP4"]
KV_CACHE_QUANT_ALGOS = ["FP8"]


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

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

    @classmethod
    def get_name(cls) -> QuantizationMethods:
        return "modelopt"

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

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

    @classmethod
    def get_config_filenames(cls) -> list[str]:
        return ["hf_quant_config.json"]

    @classmethod
    def override_quantization_method(
            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
        """Detect if this ModelOpt config should be used based on
        quantization config."""

        if hf_quant_cfg is None:
            return None

        # Use the community standard 'quant_method'
        quant_method = hf_quant_cfg.get("quant_method", "").lower()

        # Only proceed if the method is explicitly "modelopt"
        if quant_method != "modelopt":
            return None

        # Look for ModelOpt-specific config structure
        if "quantization" in hf_quant_cfg:
            quant_config = hf_quant_cfg["quantization"]
            if isinstance(quant_config, dict):
                quant_algo = quant_config.get("quant_algo", "")
                if "FP8" in quant_algo:
                    return "modelopt"
        else:
            # Check for compressed-tensors style config with specific quant_algo
            quant_algo = hf_quant_cfg.get("quant_algo", "")
            if isinstance(quant_algo, str) and "FP8" in quant_algo:
                return "modelopt"

        return None

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> "ModelOptFp8Config":
        # Handle both ModelOpt format and compressed-tensors style format
        if "quantization" in config:
            # ModelOpt format: {"quantization": {"quant_algo": "..."}}
            quant_config = cls.get_from_keys(config, ["quantization"])
            if not isinstance(quant_config, dict):
                raise ValueError(
                    "Expected 'quantization' to be a dictionary in config")
            quant_method = quant_config.get("quant_algo", "")
            if not quant_method:
                raise ValueError("Missing 'quant_algo' in quantization config")
            kv_cache_quant_method = quant_config.get("kv_cache_quant_algo")
            exclude_modules = quant_config.get("exclude_modules")
        else:
            # Compressed-tensors style format:
            # {"quant_algo": "...", "quant_method": "modelopt"}
            quant_method = config.get("quant_algo", "")
            kv_cache_quant_method = config.get("kv_cache_quant_algo")
            exclude_modules = config.get("exclude_modules")

        if quant_method not in QUANT_ALGOS:
            raise ValueError(
                f"ModelOpt currently only supports: {QUANT_ALGOS} "
                "quantizations in vLLM. Please check the "
                "`hf_quant_config.json` file for your model's "
                "quant configuration.")
        is_checkpoint_fp8_serialized = ("FP8" in quant_method)

        return cls(is_checkpoint_fp8_serialized, kv_cache_quant_method,
                   exclude_modules)

    def is_layer_excluded(self, prefix: str) -> bool:
        """
        Check if a layer should be excluded from quantization.

        This method handles both regular models and multimodal models that use
        the language_model prefix. For multimodal models, it checks if the
        module name (without the language_model prefix) is in the exclude list.
        """
        if self.exclude_modules is None:
            return False

        # Check if any excluded module matches the prefix
        for module in self.exclude_modules:
            if (module in prefix
                    or (prefix.startswith("language_model.")
                        and module in prefix.removeprefix("language_model."))):
                return True
        return False

    def get_quant_method(self, layer: torch.nn.Module,
                         prefix: str) -> Optional["QuantizeMethodBase"]:
        from vllm.attention.layer import Attention  # Avoid circular import
        if isinstance(layer, LinearBase):
            if self.is_layer_excluded(prefix):
                return UnquantizedLinearMethod()
            return ModelOptFp8LinearMethod(self)
        elif isinstance(layer, Attention):
            return ModelOptFp8KVCacheMethod(self)
        elif isinstance(layer, FusedMoE):
            return ModelOptFp8MoEMethod(self)
        return None


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):
        self.quant_config = quant_config
        self.fp8_linear = Fp8LinearOp(
            act_quant_static=True, act_quant_group_shape=GroupShape.PER_TENSOR)

    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: 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: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        return self.fp8_linear.apply(input=x,
                                     weight=layer.weight,
                                     weight_scale=layer.weight_scale,
                                     input_scale=layer.input_scale,
                                     bias=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):
        self.quant_config = quant_config
        from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
            cutlass_fp8_supported)
        self.cutlass_fp8_supported = cutlass_fp8_supported()

    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,
    ):

        # 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_weight = ModelWeightParameter(
            data=torch.empty(num_experts,
                             2 * 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)

        if self.quant_config.is_checkpoint_fp8_serialized:
            # WEIGHT SCALES - Per-tensor scaling for ModelOpts
            # Allocate 2 scales for w1 and w3 respectively.
            # They will be combined to a single scale after weight loading.
            w13_weight_scale = PerTensorScaleParameter(
                data=torch.full(
                    (num_experts, 2),
                    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)

            # Set weight loader attributes for scales
            extra_weight_attrs.update(
                {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})

            # 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 process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        """Process FP8 MoE weights after loading from serialized checkpoint.
        Only supports pre-quantized checkpoints with FP8 weights and scales.
        """

        layer.w13_weight = Parameter(layer.w13_weight.data,
                                     requires_grad=False)
        layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)

        from vllm._custom_ops import scaled_fp8_quant
        from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
            per_tensor_dequantize)

        # Handle scale parameters
        if hasattr(layer,
                   "w13_weight_scale") and layer.w13_weight_scale is not None:
            # Fp8 moe kernel needs single weight scale for w13 per expert.
            # We take the max of the w1 and w3 scales
            # then dequant and requant each expert.
            if layer.w13_weight_scale.dim() == 2:

                # Get the maximum scale across w1 and w3 for each expert
                max_w13_scales = layer.w13_weight_scale.max(dim=1).values

                # Requantize each expert's weights using the combined scale
                # w13_weight (num_experts, 2 * intermediate_size, hidden_size)
                # where the first intermediate_size rows are w1, the next are w3
                intermediate_size = layer.w13_weight.shape[1] // 2
                for expert_id in range(layer.w13_weight.shape[0]):
                    start = 0
                    for shard_id in range(2):  # w1 and w3
                        # Dequantize using the original scale for this shard
                        dq_weight = per_tensor_dequantize(
                            layer.w13_weight[expert_id][start:start +
                                                        intermediate_size, :],
                            layer.w13_weight_scale[expert_id][shard_id],
                        )
                        # Requantize using the combined max scale

                        (
                            layer.w13_weight[expert_id][start:start +
                                                        intermediate_size, :],
                            _,
                        ) = scaled_fp8_quant(dq_weight,
                                             max_w13_scales[expert_id])

                        start += intermediate_size

                # Update the scale parameter to be per-expert
                layer.w13_weight_scale = Parameter(max_w13_scales,
                                                   requires_grad=False)
            else:
                layer.w13_weight_scale = Parameter(layer.w13_weight_scale.data,
                                                   requires_grad=False)

        if hasattr(layer,
                   "w2_weight_scale") and layer.w2_weight_scale is not None:
            layer.w2_weight_scale = Parameter(layer.w2_weight_scale.data,
                                              requires_grad=False)
        # Input scales must be equal for each expert in fp8 MoE layers.
        if hasattr(layer,
                   "w13_input_scale") and layer.w13_input_scale is not None:
            layer.w13_input_scale = Parameter(layer.w13_input_scale.max(),
                                              requires_grad=False)
        if hasattr(layer,
                   "w2_input_scale") and layer.w2_input_scale is not None:
            layer.w2_input_scale = Parameter(layer.w2_input_scale.max(),
                                             requires_grad=False)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        expert_load_view: Optional[torch.Tensor] = None,
        logical_to_physical_map: Optional[torch.Tensor] = None,
        logical_replica_count: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        if enable_eplb:
            raise NotImplementedError(
                "EPLB not supported for `ModelOptFp8MoEMethod` yet.")

        # Expert selection
        topk_weights, topk_ids = FusedMoE.select_experts(
            hidden_states=x,
            router_logits=router_logits,
            use_grouped_topk=use_grouped_topk,
            top_k=top_k,
            renormalize=renormalize,
            topk_group=topk_group,
            num_expert_group=num_expert_group,
            custom_routing_function=custom_routing_function,
            scoring_func=scoring_func,
            e_score_correction_bias=e_score_correction_bias,
        )
        from vllm.model_executor.layers.fused_moe.fused_moe import (
            fused_experts)
        return fused_experts(
            x,
            layer.w13_weight,
            layer.w2_weight,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            inplace=True,
            activation=activation,
            use_fp8_w8a8=True,
            per_channel_quant=False,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
            w1_scale=layer.w13_weight_scale,
            w2_scale=layer.w2_weight_scale,
            a1_scale=layer.w13_input_scale,
            a2_scale=layer.w2_input_scale,
            apply_router_weight_on_input=apply_router_weight_on_input,
        )


class ModelOptNvFp4Config(QuantizationConfig):
    """Config class for ModelOpt FP4."""

    def __init__(
        self,
        is_checkpoint_nvfp4_serialized: bool,
        kv_cache_quant_algo: Optional[str],
        exclude_modules: list[str],
        group_size: int = 16,
    ) -> None:
        super().__init__()
        self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
        if is_checkpoint_nvfp4_serialized:
            logger.warning(
                "Detected ModelOpt NVFP4 checkpoint. Please note that"
                " the format is experimental and could change in future.")

            self.group_size = group_size
            self.kv_cache_quant_algo = kv_cache_quant_algo
            self.exclude_modules = exclude_modules

    @classmethod
    def get_name(cls) -> QuantizationMethods:
        return "modelopt_fp4"

    @classmethod
    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
        return [torch.bfloat16, torch.half, torch.float8_e4m3fn]

    @classmethod
    def get_min_capability(cls) -> int:
        return 80

    @classmethod
    def get_config_filenames(cls) -> list[str]:
        return ["hf_quant_config.json"]

    @classmethod
    def override_quantization_method(
            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
        """Detect if this ModelOpt FP4 config should be used based on
        quantization config."""
        if hf_quant_cfg is None:
            return None

        # Use the community standard 'quant_method'
        quant_method = hf_quant_cfg.get("quant_method", "").lower()

        # Only proceed if the method is explicitly "modelopt"
        if quant_method != "modelopt":
            return None

        # Look for ModelOpt-specific config structure
        if "quantization" in hf_quant_cfg:
            quant_config = hf_quant_cfg["quantization"]
            if isinstance(quant_config, dict):
                quant_algo = quant_config.get("quant_algo", "")
                if "NVFP4" in quant_algo:
                    return "modelopt_fp4"
        else:
            # Check for compressed-tensors style config with specific
            # quant_algo field
            quant_algo = hf_quant_cfg.get("quant_algo", "")
            if isinstance(quant_algo, str) and "FP4" in quant_algo.upper():
                return "modelopt_fp4"

        return None

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> "ModelOptNvFp4Config":
        # Handle both traditional ModelOpt format and compressed-tensors
        # style format
        if "quantization" in config:
            # Traditional ModelOpt format:
            # {"quantization": {"quant_algo": "..."}}
            quant_config = cls.get_from_keys(config, ["quantization"])
            if not isinstance(quant_config, dict):
                raise ValueError(
                    "Expected 'quantization' to be a dictionary in config")

            quant_method = quant_config.get("quant_algo", "")
            if not quant_method:
                raise ValueError("Missing 'quant_algo' in quantization config")

            # Handle kv_cache_quant_algo with proper type validation
            kv_cache_quant_algo_raw = quant_config.get("kv_cache_quant_algo")
            if kv_cache_quant_algo_raw is None:
                # No KV cache quantization by default
                kv_cache_quant_algo = None
            elif isinstance(kv_cache_quant_algo_raw, str):
                kv_cache_quant_algo = kv_cache_quant_algo_raw
            else:
                raise ValueError(f"kv_cache_quant_algo must be a string, got "
                                 f"{type(kv_cache_quant_algo_raw)}")

            # Handle group_size with proper type validation
            group_size_raw = quant_config.get("group_size")
            if group_size_raw is None:
                group_size = 16  # Default value
            elif isinstance(group_size_raw, int):
                group_size = group_size_raw
            else:
                try:
                    group_size = int(group_size_raw)
                except (ValueError, TypeError):
                    raise ValueError(f"group_size must be an integer, got "
                                     f"{type(group_size_raw)}") from None

            exclude_modules = quant_config.get("exclude_modules", [])
            if not isinstance(exclude_modules, list):
                raise ValueError(f"exclude_modules must be a list, got "
                                 f"{type(exclude_modules)}")
        else:
            # Compressed-tensors style format:
            # {"quant_algo": "...", "quant_method": "modelopt"}
            quant_method = config.get("quant_algo", "")

            # Handle kv_cache_quant_algo with proper type validation
            kv_cache_quant_algo_raw = config.get("kv_cache_quant_algo")
            if kv_cache_quant_algo_raw is None:
                # No KV cache quantization by default
                kv_cache_quant_algo = None
            elif isinstance(kv_cache_quant_algo_raw, str):
                kv_cache_quant_algo = kv_cache_quant_algo_raw
            else:
                raise ValueError(f"kv_cache_quant_algo must be a string, got "
                                 f"{type(kv_cache_quant_algo_raw)}")

            # Handle group_size with proper type validation
            group_size_raw = config.get("group_size")
            if group_size_raw is None:
                group_size = 16  # Default value
            elif isinstance(group_size_raw, int):
                group_size = group_size_raw
            else:
                try:
                    group_size = int(group_size_raw)
                except (ValueError, TypeError):
                    raise ValueError(f"group_size must be an integer, got "
                                     f"{type(group_size_raw)}") from None

            exclude_modules = config.get("exclude_modules", [])
            if not isinstance(exclude_modules, list):
                raise ValueError(f"exclude_modules must be a list, got "
                                 f"{type(exclude_modules)}")

        if quant_method not in QUANT_ALGOS:
            raise ValueError(
                f"ModelOpt currently only supports: {QUANT_ALGOS} "
                "quantizations in vLLM. Please check the "
                "`hf_quant_config.json` file for your model's "
                "quant configuration.")
        is_checkpoint_nvfp4_serialized = ("NVFP4" in quant_method)

        # For FP4, these fields are required
        if is_checkpoint_nvfp4_serialized and "quantization" in config:
            # Check if required fields are present in the quantization config
            quant_config = config["quantization"]
            required_fields = [
                "group_size", "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"NVFP4 quantization requires the following fields in "
                    f"hf_quant_config.json: {missing_fields}")

        return cls(is_checkpoint_nvfp4_serialized, kv_cache_quant_algo,
                   exclude_modules, group_size)

    def is_layer_excluded(self, prefix: str, exclude_modules: list):
        import regex as re
        for pattern in exclude_modules:
            regex_str = pattern.replace('.', r'\.').replace('*', r'.*')
            if re.fullmatch(regex_str, prefix):
                return True
        return False

    def get_quant_method(self, layer: torch.nn.Module,
                         prefix: str) -> Optional["QuantizeMethodBase"]:
        from vllm.attention.layer import Attention  # Avoid circular import
        if isinstance(layer, LinearBase):
            if (is_layer_skipped(prefix, self.exclude_modules)
                    or self.is_layer_excluded(prefix, self.exclude_modules)):
                return UnquantizedLinearMethod()
            return ModelOptNvFp4LinearMethod(self)
        elif isinstance(layer, Attention):
            return ModelOptFp8KVCacheMethod(self)
        elif isinstance(layer, FusedMoE):
            return ModelOptNvFp4FusedMoE(self)
        return None


def cutlass_fp4_supported() -> bool:
    if not current_platform.is_cuda():
        return False
    capability_tuple = current_platform.get_device_capability()
    capability = -1 if capability_tuple is None else capability_tuple.to_int()
    return cutlass_scaled_mm_supports_fp4(capability)


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

    def __init__(self, quant_config: Union[ModelOptFp8Config,
                                           ModelOptNvFp4Config]):
        super().__init__(quant_config)


class ModelOptNvFp4LinearMethod(LinearMethodBase):
    """Linear method for Model Optimizer NVFP4.
    Supports loading NVFP4 checkpoints with the following structure:

    input_scale: torch.float32, scalar ,
    weight: NVFP4(represented as byte) Shape: [1, X, y/2]
    weight_scale: FP8-E4M3, Shape: [X, Y], aka per block scale,
    weight_scale_2: torch.float32, scalar,
    Args: quant_config: The ModelOpt quantization config.
    """

    def __init__(self, quant_config: ModelOptNvFp4Config):
        self.quant_config = quant_config
        self.cutlass_nvfp4_supported = cutlass_fp4_supported()
        self.use_marlin = False

        if not self.cutlass_nvfp4_supported:
            if is_fp4_marlin_supported():
                self.use_marlin = True
            else:
                raise ValueError("Current platform does not support NVFP4"
                                 " quantization. Please use Blackwell and"
                                 " above.")

    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_nvfp4_serialized:
            raise ValueError("NVFP4 quantization was selected, "
                             " dynamic quantization is not supported.")
        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

        if (input_size_per_partition % 16 != 0):
            raise ValueError("Unsupported model when in features size is "
                             "not multiple of 16")
        # The nvfp4 weight is still represented as
        weight_dtype = (torch.float8_e4m3fn
                        if self.quant_config.is_checkpoint_nvfp4_serialized
                        else params_dtype)
        # Weight
        weight = ModelWeightParameter(
            data=torch.empty(
                # 2 fp4 items are packed in the input dimension
                layer.output_size_per_partition,
                layer.input_size_per_partition // 2,
                dtype=torch.uint8),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader)
        layer.register_parameter("weight", weight)

        # Input Weight Scale
        input_scale = PerTensorScaleParameter(data=torch.empty(
            len(output_partition_sizes), dtype=torch.float32),
                                              weight_loader=weight_loader)
        layer.register_parameter("input_scale", input_scale)

        # Global Weight Scale
        weight_scale_2 = PerTensorScaleParameter(data=torch.empty(
            len(output_partition_sizes), dtype=torch.float32),
                                                 weight_loader=weight_loader)
        layer.register_parameter("weight_scale_2", weight_scale_2)

        # Per Block Weight Scale
        weight_scale = ModelWeightParameter(data=torch.empty(
            output_size_per_partition,
            input_size_per_partition // self.quant_config.group_size,
            dtype=weight_dtype,
        ),
                                            input_dim=1,
                                            output_dim=0,
                                            weight_loader=weight_loader)

        layer.register_parameter("weight_scale", weight_scale)

    def swizzle_blockscale(self, scale: torch.tensor):
        assert (scale.dtype == torch.float8_e4m3fn)
        # Pad and blockwise interleave weight_scale
        scale_ndim = scale.ndim
        if scale.ndim == 2:
            scale = scale.unsqueeze(0)
        assert scale.ndim == 3
        B, M, K = scale.shape
        round_up_multiple = lambda x, m: (x + m - 1) // m * m
        M_padded = round_up_multiple(M, 128)
        K_padded = round_up_multiple(K, 4)
        padded_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
        padded_scale[:B, :M, :K] = scale
        batches, rows, cols = padded_scale.shape
        assert rows % 128 == 0
        assert cols % 4 == 0
        padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32,
                                            cols // 4, 4)
        swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
        swizzled_scale = swizzled_scale.contiguous().cuda()
        return (swizzled_scale.reshape(M, K)
                if scale_ndim == 2 else swizzled_scale.reshape(B, M, K))

    def process_weights_after_loading(self, layer: Module) -> None:

        # global scales:
        input_scale_2 = layer.input_scale.max().to(torch.float32)
        layer.input_scale = Parameter(input_scale_2, requires_grad=False)

        weight_scale_2 = layer.weight_scale_2.max().to(torch.float32)
        layer.weight_scale_2 = Parameter(weight_scale_2, requires_grad=False)

        layer.alpha = Parameter(layer.input_scale * layer.weight_scale_2,
                                requires_grad=False)

        # Swizzle the weight blockscale.
        # contracting dimension is input dimension
        # block_size = 16;
        assert (layer.weight_scale.shape[1] % 16 == 0), (
            "Expected weight_scale.dim(1) to be divisible by 16")
        assert (layer.weight_scale.dtype == torch.float8_e4m3fn), (
            "Weight Block scale must be represented as FP8-E4M3")
        swizzled_weight_scale = self.swizzle_blockscale(layer.weight_scale)

        layer.weight_scale_swizzled = Parameter(swizzled_weight_scale,
                                                requires_grad=False)
        layer.weight = Parameter(layer.weight.data, requires_grad=False)

        if self.use_marlin:
            prepare_fp4_layer_for_marlin(layer)
            del layer.alpha
            del layer.input_scale
            del layer.weight_scale_swizzled

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        if self.use_marlin:
            return apply_fp4_marlin_linear(
                input=x,
                weight=layer.weight,
                weight_scale=layer.weight_scale,
                weight_scale_2=layer.weight_scale_2,
                workspace=layer.workspace,
                size_n=layer.output_size_per_partition,
                size_k=layer.input_size_per_partition,
                bias=bias)

        output_dtype = x.dtype
        output_shape = [x.shape[0], layer.weight.shape[0]]

        # quantize BF16 or FP16 to (FP4 and interleaved block scale)
        s_quant = 1 / layer.input_scale
        x_fp4, x_blockscale = scaled_fp4_quant(x, s_quant)

        # validate dtypes of quantized input, input block scale,
        # weight and weight_blockscale
        assert (x_fp4.dtype == torch.uint8)
        assert (layer.weight.dtype == torch.uint8)
        assert (x_blockscale.dtype == torch.float8_e4m3fn)
        assert (layer.weight_scale_swizzled.dtype == torch.float8_e4m3fn)
        assert (layer.alpha.dtype == torch.float32)

        out = cutlass_scaled_fp4_mm(x_fp4, layer.weight, x_blockscale,
                                    layer.weight_scale_swizzled, layer.alpha,
                                    output_dtype)
        if bias is not None:
            out = out + bias
        return out.view(*output_shape)


class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
    """
    MoE Method for FP4 Quantization.
    Args:
        quant_config: NVFP4 Quant Config
    """

    def __init__(self, quant_config: ModelOptNvFp4Config):
        self.quant_config = quant_config
        self.cutlass_nvfp4_supported = cutlass_fp4_supported()
        self.use_marlin = False
        self.allow_flashinfer_cutlass = False

        if envs.VLLM_USE_FLASHINFER_MOE_FP4:
            if self.cutlass_nvfp4_supported and current_platform.is_cuda() \
               and current_platform.is_device_capability(100):
                logger.info_once(
                    "Using FlashInfer kernels for ModelOptNvFp4FusedMoE.")
                self.allow_flashinfer_cutlass = True
            else:
                logger.warning_once(
                    "Flashinfer CUTLASS Fused MoE not supported "
                    "or found on the current platform.")

        if not self.cutlass_nvfp4_supported:
            if is_fp4_marlin_supported():
                self.use_marlin = True
            else:
                raise ValueError("Current platform does not support NVFP4"
                                 " quantization. Please use Blackwell and"
                                 " above.")

        self.fused_experts = None  # type: ignore

    def maybe_swap_experts_impl(
        self,
        moe_parallel_config: FusedMoEParallelConfig,
    ):
        if not self.allow_flashinfer_cutlass:
            return

        logger.debug_once("FlashInferExperts")
        # default to TP/EP case only

        experts_kwargs: dict[str, Any] = {
            "use_nvfp4_w4a4": True,
            "use_dp": moe_parallel_config.dp_size > 1,
            "ep_rank": moe_parallel_config.ep_rank,
            "ep_size": moe_parallel_config.ep_size,
            "tp_rank": moe_parallel_config.tp_rank,
            "tp_size": moe_parallel_config.tp_size,
        }

        from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
            FlashInferExperts)
        experts = FlashInferExperts(**experts_kwargs)
        self.fused_experts = mk.FusedMoEModularKernel(
            FlashInferCutlassMoEPrepareAndFinalize(
                quant_dtype=torch.uint8,
                #meaning 2x e2m1 packed in one, kernel requirement
            ),
            experts,
        )

    # This method update self.fused_experts
    # only prepare_finalize is not None call select_gemm_impl
    # so when native cutlass fp4, fused_expert is in fuse_moe.py fused_expert
    # when it's not called(TP case), we still have 2 kernels to use.
    def select_gemm_impl(self, prepare_finalize,
                         moe) -> mk.FusedMoEPermuteExpertsUnpermute:

        assert moe is not None
        assert prepare_finalize is not None
        experts = None
        all2all_manager = get_ep_group().device_communicator.all2all_manager
        assert all2all_manager is not None
        if self.allow_flashinfer_cutlass:
            from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
                FlashInferExperts)
            logger.debug_once("Using FlashInferExperts")
            experts = FlashInferExperts(
                use_nvfp4_w4a4=True,
                use_dp=moe.moe_parallel_config.dp_size > 1,
                ep_rank=moe.moe_parallel_config.ep_rank,
                ep_size=moe.moe_parallel_config.ep_size,
                tp_rank=moe.moe_parallel_config.tp_rank,
                tp_size=moe.moe_parallel_config.tp_size,
            )
        else:
            assert moe.dp_size > 1
            logger.debug_once("Using CutlassExpertsFp4")
            # Currently CutlassExpertsFp4 doesn't support DP
            raise ValueError("CutlassExpertsFp4 doesn't support DP. "
                             "Use flashinfer CUTLASS FusedMoE backend instead "
                             "(set VLLM_USE_FLASHINFER_MOE_FP4=1)")

        return experts

    def uses_weight_scale_2_pattern(self) -> bool:
        """
        FP4 variants use 'weight_scale_2' pattern for per-tensor weight scales.
        """
        return True

    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):
        if not self.quant_config.is_checkpoint_nvfp4_serialized:
            raise ValueError("NVFP4 quantization was selected, "
                             " dynamic quantization is not supported.")

        layer.num_experts = num_experts
        layer.params_dtype = params_dtype
        layer.quant_config = self.quant_config
        weight_dtype = torch.uint8
        weight_scale_dtype = torch.float8_e4m3fn
        weight_loader = extra_weight_attrs.get("weight_loader")
        # GEMM 1
        w13_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                2 * intermediate_size_per_partition,
                # 2 fp4 items are packed in the input dimension
                hidden_size // 2,
                dtype=weight_dtype),
            input_dim=1,
            output_dim=2,
            weight_loader=weight_loader)
        layer.register_parameter("w13_weight", w13_weight)

        # GEMM 2
        w2_weight = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                hidden_size,
                # 2 fp4 items are packed in the input dimension
                intermediate_size_per_partition // 2,
                dtype=weight_dtype),
            input_dim=1,
            output_dim=2,
            weight_loader=weight_loader)
        layer.register_parameter("w2_weight", w2_weight)

        w13_weight_scale = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                2 * intermediate_size_per_partition,
                # 2 fp4 items are packed in the input dimension
                hidden_size // self.quant_config.group_size,
                dtype=weight_scale_dtype),
            input_dim=1,
            output_dim=2,
            weight_loader=weight_loader)
        layer.register_parameter("w13_weight_scale", w13_weight_scale)

        w2_weight_scale = ModelWeightParameter(
            data=torch.empty(
                num_experts,
                hidden_size,
                # 2 fp4 items are packed in the input dimension
                intermediate_size_per_partition //
                self.quant_config.group_size,
                dtype=weight_scale_dtype),
            input_dim=1,
            output_dim=2,
            weight_loader=weight_loader)
        layer.register_parameter("w2_weight_scale", w2_weight_scale)

        extra_weight_attrs.update(
            {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value})

        w13_weight_scale_2 = PerTensorScaleParameter(
            data=torch.empty(num_experts, 2, dtype=torch.float32),
            weight_loader=weight_loader)
        layer.register_parameter("w13_weight_scale_2", w13_weight_scale_2)

        w2_weight_scale_2 = PerTensorScaleParameter(
            data=torch.empty(num_experts, dtype=torch.float32),
            weight_loader=weight_loader)
        layer.register_parameter("w2_weight_scale_2", w2_weight_scale_2)

        extra_weight_attrs.update(
            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})

        w13_input_scale = PerTensorScaleParameter(data=torch.empty(
            num_experts, 2, dtype=torch.float32),
                                                  weight_loader=weight_loader)
        layer.register_parameter("w13_input_scale", w13_input_scale)

        w2_input_scale = PerTensorScaleParameter(data=torch.empty(
            num_experts, dtype=torch.float32),
                                                 weight_loader=weight_loader)
        layer.register_parameter("w2_input_scale", w2_input_scale)

    def swizzle_blockscale(self, scale: torch.tensor):
        assert (scale.dtype == torch.float8_e4m3fn)
        # Pad and blockwise interleave weight_scale
        scale_ndim = scale.ndim
        if scale.ndim == 2:
            scale = scale.unsqueeze(0)
        assert scale.ndim == 3
        B, M, K = scale.shape
        round_up_multiple = lambda x, m: (x + m - 1) // m * m
        M_padded = round_up_multiple(M, 128)
        K_padded = round_up_multiple(K, 4)
        padded_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
        padded_scale[:B, :M, :K] = scale
        batches, rows, cols = padded_scale.shape
        assert rows % 128 == 0
        assert cols % 4 == 0
        padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32,
                                            cols // 4, 4)
        swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
        swizzled_scale = swizzled_scale.contiguous().cuda()
        return (swizzled_scale.reshape(M, K)
                if scale_ndim == 2 else swizzled_scale.reshape(B, M, K))

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        # GEMM 1
        # The FlashInfer Cutlass fused MoE kernel expects the combined weights
        # to be ordered as [w3, w1], unlike the standard [w1, w3] layout.
        gemm1_weight = layer.w13_weight.data
        gemm1_weight_scale = layer.w13_weight_scale.data

        if self.allow_flashinfer_cutlass:
            dim = -2
            size = gemm1_weight.size(dim)
            assert size % 2 == 0, f"Expected even size in dim {dim}, got {size}"
            half = size // 2

            # Reorder weight
            w1, w3 = gemm1_weight.split(half, dim=dim)
            gemm1_weight = torch.cat([w3, w1], dim=dim).contiguous()

            # Reorder scale
            s1, s3 = gemm1_weight_scale.split(half, dim=dim)
            gemm1_weight_scale = torch.cat([s3, s1], dim=dim).contiguous()

        layer.w13_weight = Parameter(gemm1_weight, requires_grad=False)
        layer.w13_weight_scale = Parameter(gemm1_weight_scale,
                                           requires_grad=False)

        if not torch.allclose(layer.w13_weight_scale_2[:, 0],
                              layer.w13_weight_scale_2[:, 1]):
            logger.warning_once(
                "w1_weight_scale_2 must match w3_weight_scale_2. "
                "Accuracy may be affected.")

        w13_weight_scale_2 = layer.w13_weight_scale_2[:, 0]
        layer.w13_weight_scale_2 = Parameter(w13_weight_scale_2,
                                             requires_grad=False)

        w13_input_scale = layer.w13_input_scale.max(dim=1).values.to(
            torch.float32)
        layer.g1_alphas = Parameter(
            (w13_input_scale * w13_weight_scale_2).to(torch.float32),
            requires_grad=False)

        assert (layer.w13_weight_scale.shape[2] % 16 == 0), (
            "Expected weight_scale.dim(1) to be divisible by 16")
        assert (layer.w13_weight_scale.dtype == torch.float8_e4m3fn), (
            "Weight Blockscale must be represented as FP8-E4M3")
        w13_blockscale_swizzled = self.swizzle_blockscale(
            layer.w13_weight_scale)

        layer.w13_blockscale_swizzled = Parameter(w13_blockscale_swizzled,
                                                  requires_grad=False)

        # This is for quantization, so we need to invert it.
        layer.w13_input_scale_quant = Parameter(
            (1 / w13_input_scale).to(torch.float32), requires_grad=False)

        # GEMM 2
        layer.g2_alphas = Parameter(
            (layer.w2_input_scale * layer.w2_weight_scale_2).to(torch.float32),
            requires_grad=False)

        # This is for quantization, so we need to invert it.
        layer.w2_input_scale_quant = Parameter(
            (1 / layer.w2_input_scale).to(torch.float32), requires_grad=False)

        assert (layer.w2_weight_scale.shape[2] % 16 == 0), (
            "Expected weight_scale.dim(1) to be divisible by 16")
        assert (layer.w2_weight_scale.dtype == torch.float8_e4m3fn), (
            "Weight Blockscale must be represented as FP8-E4M3")
        w2_blockscale_swizzled = self.swizzle_blockscale(layer.w2_weight_scale)

        layer.w2_blockscale_swizzled = Parameter(w2_blockscale_swizzled,
                                                 requires_grad=False)
        layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)

        if self.use_marlin:
            prepare_moe_fp4_layer_for_marlin(layer)
            del layer.g1_alphas
            del layer.g2_alphas
            del layer.w13_input_scale_quant
            del layer.w2_input_scale_quant
            del layer.w13_blockscale_swizzled
            del layer.w2_blockscale_swizzled

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        expert_load_view: Optional[torch.Tensor] = None,
        logical_to_physical_map: Optional[torch.Tensor] = None,
        logical_replica_count: Optional[torch.Tensor] = None,
    ):
        if enable_eplb:
            raise NotImplementedError(
                "EPLB not supported for `ModelOptNvFp4FusedMoE` yet.")
        assert activation == "silu", "Only SiLU activation is supported."

        topk_weights, topk_ids = FusedMoE.select_experts(
            hidden_states=x,
            router_logits=router_logits,
            use_grouped_topk=use_grouped_topk,
            top_k=top_k,
            renormalize=renormalize,
            topk_group=topk_group,
            num_expert_group=num_expert_group,
            custom_routing_function=custom_routing_function,
            scoring_func=scoring_func,
            e_score_correction_bias=e_score_correction_bias)

        if self.use_marlin:
            return torch.ops.vllm.fused_marlin_moe(
                x,
                layer.w13_weight,
                layer.w2_weight,
                layer.w13_weight_scale,
                layer.w2_weight_scale,
                router_logits,
                topk_weights,
                topk_ids,
                global_scale1=layer.w13_weight_scale_2,
                global_scale2=layer.w2_weight_scale_2,
                quant_type_id=scalar_types.float4_e2m1f.id,
                apply_router_weight_on_input=apply_router_weight_on_input,
                global_num_experts=global_num_experts,
                expert_map=expert_map)

        if self.fused_experts is None:
            # If no modular kernel is provided, use cutlass_moe_fp4 for TP case
            # only (no EP).
            from vllm.model_executor.layers.fused_moe.cutlass_moe import (
                cutlass_moe_fp4)
            out = cutlass_moe_fp4(
                a=x,
                w1_fp4=layer.w13_weight,
                w2_fp4=layer.w2_weight,
                w1_blockscale=layer.w13_blockscale_swizzled,
                w2_blockscale=layer.w2_blockscale_swizzled,
                g1_alphas=layer.g1_alphas,
                g2_alphas=layer.g2_alphas,
                a1_gscale=layer.w13_input_scale_quant,
                a2_gscale=layer.w2_input_scale_quant,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                m=x.shape[0],
                n=layer.w2_weight.shape[2] * 2,
                k=x.shape[1],
                e=layer.w13_weight.shape[0],
                device=x.device,
                expert_map=expert_map,
                apply_router_weight_on_input=apply_router_weight_on_input)
        else:
            # TP or DP case
            from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (  # noqa: E501
                is_valid_flashinfer_cutlass_fused_moe)
            assert is_valid_flashinfer_cutlass_fused_moe(
                x, layer.w13_weight, layer.w2_weight), (
                    "Flashinfer CUTLASS Fused MoE not applicable!")

            a1_gscale = torch.min(layer.w13_input_scale_quant)
            a2_gscale = torch.min(layer.w2_input_scale_quant)
            extra_expert_args = {
                'g1_alphas': layer.g1_alphas,
                'g2_alphas': layer.g2_alphas,
                'out_dtype': x.dtype,
                # Avoid confusion with a1_scale and a2_scale
                # where are batch size related.
                'a1_gscale': a1_gscale,
                'a2_gscale': a2_gscale,
            }
            extra_prepare_args = {
                'use_dp': layer.dp_size > 1,
                'local_tokens': x.shape[0],
                'a1_gscale': a1_gscale,
            }
            extra_finalize_args = {
                'use_dp': layer.dp_size > 1,
                'local_tokens': x.shape[0],
            }

            out = self.fused_experts(
                hidden_states=x,
                w1=layer.w13_weight,
                w2=layer.w2_weight,
                topk_weights=topk_weights,
                topk_ids=topk_ids,
                inplace=False,  # TODO(shuw): fix later, now output is high prec
                activation=activation,
                global_num_experts=global_num_experts,
                expert_map=expert_map,
                w1_scale=layer.w13_blockscale_swizzled,
                w2_scale=layer.w2_blockscale_swizzled,
                apply_router_weight_on_input=apply_router_weight_on_input,
                extra_expert_args=extra_expert_args,
                extra_prepare_args=extra_prepare_args,
                extra_finalize_args=extra_finalize_args,
            )
        return out