# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/modelopt.py
from __future__ import annotations

import logging
from typing import TYPE_CHECKING, Any, Dict, List, Optional

import torch
from torch.nn.parameter import Parameter

from sglang.srt.distributed import get_tp_group
from sglang.srt.layers.dp_attention import get_dp_global_num_tokens, get_local_dp_buffer
from sglang.srt.layers.moe import (
    MoeRunner,
    MoeRunnerBackend,
    MoeRunnerConfig,
    should_use_flashinfer_cutlass_moe_fp4_allgather,
    should_use_flashinfer_trtllm_moe,
)
from sglang.srt.layers.moe.cutlass_moe_params import CutlassMoEParams, CutlassMoEType
from sglang.srt.layers.moe.moe_runner.triton import TritonMoeQuantInfo
from sglang.srt.layers.parameter import ModelWeightParameter, PerTensorScaleParameter
from sglang.srt.layers.quantization.base_config import (
    FusedMoEMethodBase,
    LinearMethodBase,
    QuantizationConfig,
    QuantizeMethodBase,
)
from sglang.srt.layers.quantization.fp8_utils import (
    apply_fp8_linear,
    cutlass_fp8_supported,
    is_blackwell_supported,
)
from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.layers.quantization.utils import (
    convert_to_channelwise,
    is_layer_skipped,
    per_tensor_dequantize,
    requantize_with_max_scale,
)
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.utils import get_bool_env_var, is_cuda, next_power_of_2

if TYPE_CHECKING:
    from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
    from sglang.srt.layers.moe.token_dispatcher import (
        CombineInput,
        StandardDispatchOutput,
    )
    from sglang.srt.single_batch_overlap import DownGemmOverlapArgs

try:
    from flashinfer import fp4_quantize
except ImportError:
    fp4_quantize = None

try:
    from flashinfer import mm_fp4 as fp4_gemm
    from flashinfer import reorder_rows_for_gated_act_gemm, shuffle_matrix_sf_a

    enable_flashinfer_fp4_gemm = True
except ImportError:
    if is_cuda():
        from sgl_kernel import cutlass_scaled_fp4_mm as fp4_gemm
    else:
        fp4_gemm = None
    enable_flashinfer_fp4_gemm = False
    reorder_rows_for_gated_act_gemm = None
    shuffle_matrix_a = None
    shuffle_matrix_sf_a = None

try:
    from flashinfer.fused_moe import cutlass_fused_moe as flashinfer_cutlass_fused_moe
except ImportError:
    flashinfer_cutlass_fused_moe = None

# Initialize logger for the module
logger = logging.getLogger(__name__)

CUTEDSL_MOE_SCALAR_INPUT_SCALE = get_bool_env_var(
    "SGLANG_CUTEDSL_MOE_SCALAR_INPUT_SCALE", "true"
)
USE_CUTLASS_BACKEND_FOR_FP4_GEMM = get_bool_env_var(
    "SGLANG_USE_CUTLASS_BACKEND_FOR_FP4_GEMM", "true"
)
# TODO make it true by default when the DeepEP PR is merged
CUTEDSL_MOE_NVFP4_DISPATCH = get_bool_env_var(
    "SGLANG_CUTEDSL_MOE_NVFP4_DISPATCH", "false"
)

# Supported activation schemes for the current configuration
ACTIVATION_SCHEMES = ["static"]


class ModelOptQuantConfig(QuantizationConfig):
    def __init__(
        self,
        kv_cache_quant_algo: Optional[str],
        exclude_modules: Optional[List[str]],
        packed_modules_mapping: Optional[Dict[str, List[str]]],
    ):
        super().__init__()
        self.packed_modules_mapping = packed_modules_mapping
        self.exclude_modules = exclude_modules or []
        self.kv_cache_quant_algo = kv_cache_quant_algo

    def _get_quant_method(
        self,
        layer: torch.nn.Module,
        prefix: str,
        *,
        Linear: type[LinearMethodBase],
        Moe: type[FusedMoEMethodBase],
    ) -> Optional[QuantizeMethodBase]:
        from sglang.srt.layers.linear import LinearBase
        from sglang.srt.layers.moe.fused_moe_triton import FusedMoE

        if isinstance(layer, LinearBase):
            if is_layer_skipped(
                prefix, self.exclude_modules, self.packed_modules_mapping
            ) or self.is_layer_excluded(prefix):
                return UnquantizedLinearMethod()
            return Linear(self)
        elif self.kv_cache_quant_algo and isinstance(layer, RadixAttention):
            return ModelOptFp8KVCacheMethod(self)
        elif isinstance(layer, FusedMoE):
            return Moe(self)
        return None

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

    def get_scaled_act_names(self) -> List[str]:
        return []


class ModelOptFp8Config(ModelOptQuantConfig):
    """Configuration for ModelOpt FP8 quantization, including serialization and compatibility checks."""

    def __init__(
        self,
        is_checkpoint_fp8_serialized: bool = False,
        kv_cache_quant_method: Optional[str] = None,
        exclude_modules: Optional[List[str]] = None,
        packed_modules_mapping: Optional[Dict[str, List[str]]] = None,
    ) -> None:
        """
        Args:
            is_checkpoint_fp8_serialized (bool): Indicates if the checkpoint uses serialized FP8 format.
        """
        super().__init__(kv_cache_quant_method, exclude_modules, packed_modules_mapping)
        self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
        if is_checkpoint_fp8_serialized:
            logger.warning(
                "Detected ModelOpt FP8 checkpoint. The format is experimental and subject to change."
            )

    @classmethod
    def override_quantization_method(cls, hf_quant_config, user_quant):
        """Override quantization method based on the model's config."""
        return cls._modelopt_override_quantization_method(hf_quant_config, user_quant)

    @classmethod
    def get_name(cls) -> str:
        return "modelopt_fp8"

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

    @classmethod
    def get_min_capability(cls) -> int:
        return 89  # Minimum hardware capability (e.g., Hopper GPUs).

    @classmethod
    def from_config(cls, config: Dict[str, Any]) -> ModelOptFp8Config:
        # Handle two different config formats:
        # 1. hf_quant_config.json format: {"quantization": {"quant_algo": "FP8", ...}}
        # 2. config.json quantization_config format: {"quant_algo": "FP8", ...}
        # In future modelopt will deprecate hf_quant_config.json, and only keep config.json.
        # For legacy reasons, we keep hf_quant_config.json for now.

        # Initialize variables
        kv_cache_quant_method = None
        exclude_modules = None

        # Try flat format first (config.json quantization_config - preferred format)
        quant_method = config.get("quant_algo")
        if quant_method is not None:
            # Flat format (config.json quantization_config)
            # For kv_cache, check if kv_cache_scheme exists and extract algo
            kv_cache_scheme = config.get("kv_cache_scheme")
            if (
                kv_cache_scheme
                and kv_cache_scheme.get("type") == "float"
                and kv_cache_scheme.get("num_bits") == 8
            ):
                kv_cache_quant_method = "FP8"

            # Map 'ignore' field to 'exclude_modules'
            exclude_modules = config.get("ignore")
        else:
            # Fall back to nested format (hf_quant_config.json - legacy format)
            try:
                quantization_section = cls.get_from_keys(config, ["quantization"])
                quant_method = quantization_section.get("quant_algo")
                kv_cache_quant_method = quantization_section.get("kv_cache_quant_algo")
                exclude_modules = quantization_section.get("exclude_modules")
            except ValueError:
                raise ValueError(
                    "Cannot find 'quant_algo' in the model's quantization config. "
                    "Expected either flat format (config.json) or nested format (hf_quant_config.json)."
                )
        if quant_method is None:
            raise ValueError(
                "Cannot find 'quant_algo' in the model's quantization config. "
            )
        if "FP8" not in quant_method:
            raise ValueError(
                "ModelOptFp8Config only supports static FP8 quantization in SGLang. "
                "For FP4 quantization, use ModelOptFp4Config. "
                "Check the quantization config for your model's configuration."
            )

        return cls(
            is_checkpoint_fp8_serialized=True,
            kv_cache_quant_method=kv_cache_quant_method,
            exclude_modules=exclude_modules,
            packed_modules_mapping=config.get("packed_modules_mapping"),
        )

    def is_layer_excluded(self, prefix: str) -> bool:
        if len(self.exclude_modules) == 0:
            return False
        return any(
            module in prefix
            or (
                prefix.startswith("language_model.")
                and module in prefix.removeprefix("language_model.")
            )
            for module in self.exclude_modules
        )

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional[QuantizeMethodBase]:
        return self._get_quant_method(
            layer, prefix, Linear=ModelOptFp8LinearMethod, Moe=ModelOptFp8MoEMethod
        )


class ModelOptFp8LinearMethod(LinearMethodBase):
    """Linear method for ModelOpt static FP8 quantization.

    Supports loading FP8 checkpoints with static weight and activation scales.
    Future support may include dynamic scales.

    **Limitations**:
    1. Only supports per-tensor quantization due to `torch._scaled_mm` limitations.
    2. Only supports the `float8_e4m3fn` data type.

    Args:
        quant_config (ModelOptFp8Config): The ModelOpt quantization configuration.
    """

    def __init__(self, quant_config: ModelOptFp8Config):
        super().__init__()
        self.quant_config = quant_config
        self.cutlass_fp8_supported = cutlass_fp8_supported()

    def create_weights(
        self,
        layer: torch.nn.Module,
        input_size_per_partition: int,
        output_partition_sizes: List[int],
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ) -> None:
        """Creates and registers weights, weight scales, and input scales for FP8 quantization."""
        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")
        weight_dtype = (
            torch.float8_e4m3fn
            if self.quant_config.is_checkpoint_fp8_serialized
            else params_dtype
        )

        # Set layer attributes
        layer.logical_widths = output_partition_sizes
        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition

        # Register weight
        layer.register_parameter(
            "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,
            ),
        )

        if self.quant_config.is_checkpoint_fp8_serialized:
            # Register weight and input scales
            for scale_name in ["weight_scale", "input_scale"]:
                layer.register_parameter(
                    scale_name,
                    PerTensorScaleParameter(
                        data=torch.full(
                            (len(output_partition_sizes),),
                            torch.finfo(torch.float32).min,
                            dtype=torch.float32,
                        ),
                        weight_loader=weight_loader,
                    ),
                )

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        """Requantizes weights after loading using the maximum scale."""
        max_w_scale, quantized_weight = requantize_with_max_scale(
            layer.weight, layer.weight_scale, layer.logical_widths
        )
        layer.weight = Parameter(quantized_weight.t(), requires_grad=False)
        # cutlass sgl-kernel only supports per-channel scale
        if self.cutlass_fp8_supported:
            max_w_scale = convert_to_channelwise(max_w_scale, layer.logical_widths)
        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:
        """Applies FP8 linear transformation."""
        return apply_fp8_linear(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            input_scale=layer.input_scale,
            bias=bias,
            cutlass_fp8_supported=self.cutlass_fp8_supported,
        )


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

    def __init__(self, quant_config: ModelOptFp8Config):
        super().__init__(quant_config)


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
        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,
    ):
        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported

        # Use FP8 dtype if checkpoint is serialized, otherwise use the default dtype
        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),
                    torch.finfo(torch.float32).min,
                    dtype=torch.float32,
                ),
                weight_loader=weight_loader,
            )
            w2_weight_scale = PerTensorScaleParameter(
                data=torch.full(
                    (num_experts,), torch.finfo(torch.float32).min, 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)

        # 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:  # Shape: (num_experts, 2)
                from sglang.srt.layers.quantization.fp8_kernel import scaled_fp8_quant

                # 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 has shape (num_experts, 2 * intermediate_size_per_partition, hidden_size)
                # where the first intermediate_size_per_partition rows are w1, the next are w3
                intermediate_size_per_partition = 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_per_partition, :
                            ],
                            layer.w13_weight_scale[expert_id][shard_id],
                        )
                        # Requantize using the combined max scale
                        (
                            layer.w13_weight[expert_id][
                                start : start + intermediate_size_per_partition, :
                            ],
                            _,
                        ) = scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])

                        start += intermediate_size_per_partition

                # Update the scale parameter to be per-expert instead of per-shard
                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
            )
        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 create_moe_runner(
        self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig
    ):
        self.moe_runner_config = moe_runner_config
        self.runner = MoeRunner(MoeRunnerBackend.TRITON, moe_runner_config)

    def apply(
        self,
        layer: torch.nn.Module,
        dispatch_output: StandardDispatchOutput,
    ) -> CombineInput:

        quant_info = TritonMoeQuantInfo(
            w13_weight=layer.w13_weight,
            w2_weight=layer.w2_weight,
            use_fp8_w8a8=True,
            per_channel_quant=False,
            w13_scale=layer.w13_weight_scale,
            w2_scale=layer.w2_weight_scale,
            a13_scale=layer.w13_input_scale,
            a2_scale=layer.w2_input_scale,
        )

        return self.runner.run(dispatch_output, quant_info)


class ModelOptFp4Config(ModelOptQuantConfig):
    """Config class for FP4."""

    def __init__(
        self,
        is_checkpoint_nvfp4_serialized: bool = False,
        kv_cache_quant_algo: str = None,
        group_size: int = None,
        exclude_modules: List[str] = None,
        packed_modules_mapping: Optional[Dict[str, List[str]]] = None,
    ) -> None:
        super().__init__(kv_cache_quant_algo, exclude_modules, packed_modules_mapping)
        self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
        if is_checkpoint_nvfp4_serialized:
            logger.warning(
                "Detected nvfp4 checkpoint. Please note that the "
                "format is experimental and subject to change."
            )
        self.group_size = group_size

    @classmethod
    def override_quantization_method(cls, hf_quant_config, user_quant):
        """Override quantization method based on the model's config."""
        return cls._modelopt_override_quantization_method(hf_quant_config, user_quant)

    @classmethod
    def get_name(cls) -> str:
        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 100

    @staticmethod
    def common_group_size(cfg: dict) -> int:
        """Return the unique group_size across the config; raise if missing/mismatched."""
        sizes = set()

        # Top-level and 'quantization' block
        v = cfg.get("group_size")
        if isinstance(v, int):
            sizes.add(v)
        q = cfg.get("quantization")
        if isinstance(q, dict):
            v = q.get("group_size")
            if isinstance(v, int):
                sizes.add(v)

        # config_groups: accept group-level or nested dicts (e.g., weights/input_activations)
        for g in (cfg.get("config_groups") or {}).values():
            if isinstance(g, dict):
                v = g.get("group_size")
                if isinstance(v, int):
                    sizes.add(v)
                for sub in g.values():
                    if isinstance(sub, dict):
                        v = sub.get("group_size")
                        if isinstance(v, int):
                            sizes.add(v)

        if not sizes:
            raise ValueError("No group_size found in config.")
        if len(sizes) > 1:
            raise ValueError(f"Inconsistent group_size values: {sorted(sizes)}")
        return next(iter(sizes))

    @classmethod
    def from_config(cls, config: Dict[str, Any]) -> ModelOptFp4Config:
        # Handle two different config formats:
        # 1. hf_quant_config.json format: {"quantization": {"quant_algo": "NVFP4", ...}}
        # 2. config.json quantization_config format: {"quant_algo": "NVFP4", ...}
        # In future modelopt will deprecate hf_quant_config.json, and only keep config.json.
        # For legacy reasons, we keep hf_quant_config.json for now.

        # Initialize variables
        kv_cache_quant_algo = None
        group_size = None
        exclude_modules = []

        # Try flat format first (config.json quantization_config - preferred format)
        quant_method = config.get("quant_algo")
        if quant_method is not None:
            # Flat format (config.json quantization_config)
            # Note: FP4 models in config.json format may not have all the detailed fields
            # that are present in hf_quant_config.json, so we need to handle defaults
            kv_cache_quant_algo = config.get("kv_cache_quant_algo")
            if not kv_cache_quant_algo:
                # For config.json format, derive from kv_cache_scheme if available
                kv_cache_scheme = config.get("kv_cache_scheme")
                if (
                    kv_cache_scheme
                    and kv_cache_scheme.get("type") == "float"
                    and kv_cache_scheme.get("num_bits") == 8
                ):
                    kv_cache_quant_algo = "FP8"
                else:
                    kv_cache_quant_algo = "auto"

            group_size = config.get("group_size")
            # If group_size is not at top level, try to extract from config_groups
            if group_size is None:
                config_groups = config.get("config_groups", {})
                if config_groups:
                    # Get group_size from the first group's weights config
                    first_group = next(iter(config_groups.values()), {})
                    weights_config = first_group.get("weights", {})
                    group_size = weights_config.get("group_size")

            exclude_modules = config.get("ignore", [])
        else:
            # Fall back to nested format (hf_quant_config.json - legacy format)
            try:
                quant_config = cls.get_from_keys(config, ["quantization"])
                quant_method = quant_config["quant_algo"]
                kv_cache_quant_algo = quant_config.get("kv_cache_quant_algo")
                if not kv_cache_quant_algo:
                    kv_cache_quant_algo = "auto"
                group_size = ModelOptFp4Config.common_group_size(config)
                exclude_modules = quant_config.get("exclude_modules", [])
            except (ValueError, KeyError):
                raise ValueError(
                    "Cannot find 'quant_algo' in the model's quantization config. "
                    "Expected either flat format (config.json) or nested format (hf_quant_config.json)."
                )

        if not quant_method in ["FP8", "NVFP4"]:
            raise ValueError(
                f"ModelOpt currently only supports: FP8, NVFP4"
                " quantizations in sglang. Please check the "
                "quantization config for your model's configuration."
            )
        is_checkpoint_nvfp4_serialized = "NVFP4" in quant_method

        if group_size is None or exclude_modules is None:
            logger.warning(
                f"group_size: {group_size},"
                f"kv_cache_quant_algo: {kv_cache_quant_algo},"
                f"exclude_modules: {exclude_modules}"
            )
            raise ValueError(
                "NVFP4 quantization requires group_size and exclude_modules "
                "specified in the quantization config"
            )
        return cls(
            is_checkpoint_nvfp4_serialized,
            kv_cache_quant_algo,
            group_size,
            exclude_modules,
            config.get("packed_modules_mapping"),
        )

    def is_layer_excluded(self, prefix: str):
        import regex as re

        fused_patterns = ["q_a_proj", "q_b_proj", "kv_a_proj_with_mqa", "kv_b_proj"]
        prefix_split = prefix.split(".")
        for pattern in self.exclude_modules:
            regex_str = pattern.replace(".", r"\.").replace("*", r".*")
            pattern_split = pattern.split(".")
            if re.fullmatch(regex_str, prefix):
                return True
            elif (
                pattern_split[-1] in fused_patterns
                and pattern_split[-1] in prefix_split[-1]
            ):
                # Check if the last part of the excluded pattern is contained in the last part of the prefix
                # This handles fused modules like fused_qkv_a_proj_with_mqa that contain q_a_proj and kv_a_proj_with_mqa
                # e.g., model.layers.{i}.self_attn.{fused_weight_name}
                assert len(prefix_split) == 5 and len(pattern_split) == 5
                return True
        return False

    def get_quant_method(self, layer: torch.nn.Module, prefix: str):
        return self._get_quant_method(
            layer,
            prefix,
            Linear=ModelOptFp4LinearMethod,
            Moe=ModelOptNvFp4FusedMoEMethod,  # FlashInferFP4MoE needs the same quantization method but with compatible attribute handling
        )


class ModelOptFp4LinearMethod(LinearMethodBase):
    """Linear method for NVFP4.
    Supports loading NVFP4 checkpoints with the following structure:

    |Tensor Name           | datatype      |  shape      |
    |----------------------------------------------------|
    |input_scale           | torch.float32 | scalar      |
    |weight                | NVFP4(SE2M1)  | [1, X, y/2] |
    |weight_scale          | FP8-E4M3      | [X, Y]      |
    |weight_scale_2        | torch.float32 | scalar      |

    The weights are quantized per block of 16 elements.
    Args: quant_config: The ModelOpt quantization config.
    """

    def __init__(self, quant_config: ModelOptFp4Config):
        self.quant_config = quant_config

    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"
            )

        weight_dtype = (
            torch.float8_e4m3fn
            if self.quant_config.is_checkpoint_nvfp4_serialized
            else params_dtype
        )

        weight = ModelWeightParameter(
            data=torch.empty(
                # 2 fp4 data is packed in one uint8 in the input dimension
                output_size_per_partition,
                input_size_per_partition // 2,
                dtype=torch.uint8,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight", weight)

        input_scale = PerTensorScaleParameter(
            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
            weight_loader=weight_loader,
        )

        layer.register_parameter("input_scale", input_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)

        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 process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        input_scale_2 = layer.input_scale.max().to(torch.float32)
        weight_scale_2 = layer.weight_scale_2.max().to(torch.float32)
        layer.input_scale = Parameter(input_scale_2, requires_grad=False)
        layer.weight_scale_2 = Parameter(weight_scale_2, requires_grad=False)
        layer.alpha = Parameter(
            layer.input_scale * layer.weight_scale_2, requires_grad=False
        )
        layer.input_scale_inv = Parameter(
            (1 / input_scale_2).to(torch.float32), requires_grad=False
        )

        # Pad and blockwise interleave weight_scale
        scales = layer.weight_scale
        scale_ndim = scales.ndim
        if scale_ndim == 2:
            scales = scales.unsqueeze(0)
        assert scales.ndim == 3
        B, M, K = scales.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_scales = torch.zeros((B, M_padded, K_padded), dtype=scales.dtype)
        padded_scales[:B, :M, :K] = scales
        batches, rows, cols = padded_scales.shape
        assert rows % 128 == 0
        assert cols % 4 == 0
        padded_scales = padded_scales.reshape(batches, rows // 128, 4, 32, cols // 4, 4)
        padded_scales = padded_scales.permute((0, 1, 4, 3, 2, 5))
        padded_scales = padded_scales.contiguous().cuda()
        padded_scales = (
            padded_scales.reshape(M_padded, K_padded)
            if scale_ndim == 2
            else padded_scales.reshape(B, M_padded, K_padded)
        )
        layer.weight_scale_interleaved = Parameter(padded_scales, requires_grad=False)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        output_dtype = x.dtype
        x_m, _ = x.shape
        w_n, _ = layer.weight.shape
        output_shape = [x_m, w_n]

        # Quantize BF16 or FP16 to (FP4 and interleaved block scale)
        x_fp4, x_scale_interleaved = fp4_quantize(x, layer.input_scale_inv)

        assert x_fp4.dtype == torch.uint8
        assert layer.weight.dtype == torch.uint8
        assert layer.weight_scale_interleaved.dtype == torch.float8_e4m3fn
        assert layer.alpha.dtype == torch.float32

        w = layer.weight
        w_scale_interleaved = layer.weight_scale_interleaved
        if enable_flashinfer_fp4_gemm:
            w = layer.weight.T
            w_scale_interleaved = layer.weight_scale_interleaved.T
        out = fp4_gemm(
            x_fp4,
            w,
            x_scale_interleaved,
            w_scale_interleaved,
            layer.alpha,
            output_dtype,
            **(dict(backend="cutlass") if USE_CUTLASS_BACKEND_FOR_FP4_GEMM else dict()),
        )
        if bias is not None:
            out = out + bias
        return out.view(*output_shape)


class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
    """
       MoE Method for FP4 Quantization with Blockscales and PerTensorScales
    Args:
        quant_config: NVFP4 Quant Config
    """

    def __init__(self, quant_config: ModelOptFp4Config):
        self.quant_config = quant_config
        if not is_blackwell_supported():
            raise ValueError(
                "Current platform does not support NVFP4"
                " quantization. Please use Blackwell and"
                " above."
            )
        self.enable_flashinfer_trtllm_moe = should_use_flashinfer_trtllm_moe()
        self._cache_permute_indices = {}

    @property
    def enable_flashinfer_cutlass_moe(self) -> bool:
        from sglang.srt.layers.moe import get_moe_runner_backend

        """Access the global enable_flashinfer_cutlass_moe setting."""
        return get_moe_runner_backend().is_flashinfer_cutlass()

    @property
    def enable_flashinfer_cutedsl_moe(self) -> bool:
        from sglang.srt.layers.moe import get_moe_runner_backend

        """Access the global enable_flashinfer_cutedsl_moe setting."""
        return get_moe_runner_backend().is_flashinfer_cutedsl()

    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."
            )

        # TODO(ch-wan): check if this is needed
        layer.intermediate_size_per_partition = intermediate_size_per_partition
        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(
                layer.num_local_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(
                layer.num_local_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(
                layer.num_local_experts,
                2 * intermediate_size_per_partition,
                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)

        # Only use `swizzle_blockscale` for shapes, not for real content
        layer.w13_blockscale_swizzled = Parameter(
            self.swizzle_blockscale(layer.w13_weight_scale), requires_grad=False
        )

        w2_weight_scale = ModelWeightParameter(
            data=torch.empty(
                layer.num_local_experts,
                hidden_size,
                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)

        layer.w2_blockscale_swizzled = Parameter(
            self.swizzle_blockscale(layer.w2_weight_scale), requires_grad=False
        )

        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported

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

        w13_weight_scale_2 = PerTensorScaleParameter(
            data=torch.empty(layer.num_local_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(layer.num_local_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(layer.num_experts, 2, dtype=torch.float32),
            weight_loader=weight_loader,
        )
        w13_input_scale._sglang_require_global_experts = True
        layer.register_parameter("w13_input_scale", w13_input_scale)

        w2_input_scale = PerTensorScaleParameter(
            data=torch.empty(layer.num_experts, dtype=torch.float32),
            weight_loader=weight_loader,
        )
        w2_input_scale._sglang_require_global_experts = True
        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_padded, K_padded)
            if scale_ndim == 2
            else swizzled_scale.reshape(B, M_padded, K_padded)
        )

    def prepare_static_weights_for_kernel(
        self,
        # args_dequant,
        # args,
        gemm1_weights,
        gemm2_weights,
        gemm1_scales_linear_fp4_bytes,
        gemm2_scales_linear_fp4_bytes,
        hidden_size,
        intermediate_size,
        num_experts,
    ):
        from flashinfer import nvfp4_block_scale_interleave
        from flashinfer.fused_moe.core import (
            _maybe_get_cached_w3_w1_permute_indices,
            get_w2_permute_indices_with_cache,
        )

        """Prepare quantized weights for kernel (done offline with weights)."""
        epilogue_tile_m = 128  # FIXME: this depends on the kernel internals

        # Convert quantized weights to proper formats
        gemm1_weights_fp4 = gemm1_weights.view(torch.float8_e4m3fn).reshape(
            num_experts, 2 * intermediate_size, hidden_size // 2
        )  # packed fp4
        gemm1_scales_linear_fp4 = gemm1_scales_linear_fp4_bytes.view(
            torch.float8_e4m3fn
        ).reshape(
            num_experts, 2 * intermediate_size, hidden_size // 16
        )  # fp8 scaling factors

        gemm2_weights_fp4 = gemm2_weights.view(torch.float8_e4m3fn).reshape(
            num_experts, hidden_size, intermediate_size // 2
        )  # packed fp4
        gemm2_scales_linear_fp4 = gemm2_scales_linear_fp4_bytes.view(
            torch.float8_e4m3fn
        ).reshape(
            num_experts, hidden_size, intermediate_size // 16
        )  # fp8 scaling factors

        gemm1_weights_fp4_shuffled = []
        gemm1_scales_fp4_shuffled = []
        gemm2_weights_fp4_shuffled = []
        gemm2_scales_fp4_shuffled = []
        for i in range(num_experts):
            # Calculate the permute indices for the following:
            # 1. Reorder rows of W1 and scales for fused gated activation
            # 2. Shuffle weights and scaling factors for transposed mma output
            # for both w3_w1 and w2 weights and scale factors
            permute_indices = _maybe_get_cached_w3_w1_permute_indices(
                self._cache_permute_indices,
                gemm1_weights_fp4[i].view(torch.uint8),
                epilogue_tile_m,
            )
            gemm1_weights_fp4_shuffled.append(
                gemm1_weights_fp4[i]
                .view(torch.uint8)[permute_indices.to(gemm1_weights_fp4.device)]
                .contiguous()
            )

            permute_sf_indices = _maybe_get_cached_w3_w1_permute_indices(
                self._cache_permute_indices,
                gemm1_scales_linear_fp4[i].view(torch.uint8),
                epilogue_tile_m,
                num_elts_per_sf=16,
            )
            gemm1_scales_fp4_shuffled.append(
                nvfp4_block_scale_interleave(
                    gemm1_scales_linear_fp4[i]
                    .view(torch.uint8)[
                        permute_sf_indices.to(gemm1_scales_linear_fp4.device)
                    ]
                    .contiguous()
                )
            )

            permute_indices = get_w2_permute_indices_with_cache(
                self._cache_permute_indices,
                gemm2_weights_fp4[i].view(torch.uint8),
                epilogue_tile_m,
            )
            gemm2_weights_fp4_shuffled.append(
                gemm2_weights_fp4[i]
                .view(torch.uint8)[permute_indices.to(gemm2_weights_fp4.device)]
                .contiguous()
            )

            permute_sf_indices = get_w2_permute_indices_with_cache(
                self._cache_permute_indices,
                gemm2_scales_linear_fp4[i].view(torch.uint8),
                epilogue_tile_m,
                num_elts_per_sf=16,
            )
            gemm2_scales_fp4_shuffled.append(
                nvfp4_block_scale_interleave(
                    gemm2_scales_linear_fp4[i]
                    .view(torch.uint8)[
                        permute_sf_indices.to(gemm2_scales_linear_fp4.device)
                    ]
                    .contiguous()
                )
            )

        # Stack weights for all experts
        gemm1_weights_fp4_shuffled = torch.stack(gemm1_weights_fp4_shuffled)
        gemm1_scales_fp4_shuffled = (
            torch.stack(gemm1_scales_fp4_shuffled)
            .view(torch.float8_e4m3fn)
            .reshape(num_experts, 2 * intermediate_size, hidden_size // 16)
        )

        gemm2_weights_fp4_shuffled = torch.stack(gemm2_weights_fp4_shuffled)
        gemm2_scales_fp4_shuffled = (
            torch.stack(gemm2_scales_fp4_shuffled)
            .view(torch.float8_e4m3fn)
            .reshape(num_experts, hidden_size, intermediate_size // 16)
        )
        return (
            gemm1_weights_fp4_shuffled,
            gemm1_scales_fp4_shuffled,
            gemm2_weights_fp4_shuffled,
            gemm2_scales_fp4_shuffled,
        )

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        """Process FP4 MoE weights after loading from serialized checkpoint.

        Only supports pre-quantized checkpoints with FP8 weights and scales.
        """

        # GEMM 1 scale processing
        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)

        # Calculate input scales based on strategy
        if self.enable_flashinfer_cutlass_moe or self.enable_flashinfer_trtllm_moe:
            w13_input_scale = layer.w13_input_scale.max().to(torch.float32)
            w2_input_scale = layer.w2_input_scale.max().to(torch.float32)
        elif self.enable_flashinfer_cutedsl_moe:
            # All-expert-one-input-scale is mathematically different from default per-expert-input-scale
            # Thus we allow users to switch the flag to do thorough testing
            if CUTEDSL_MOE_SCALAR_INPUT_SCALE:
                w13_input_scale = (
                    layer.w13_input_scale.max()
                    .to(torch.float32)
                    .repeat(layer.w13_input_scale.shape[0])
                )
            else:
                w13_input_scale = layer.w13_input_scale.max(dim=1).values.to(
                    torch.float32
                )

            w2_input_scale = layer.w2_input_scale

            def _slice_scale(w):
                assert w.shape == (layer.num_experts,)
                assert layer.moe_ep_size * layer.num_local_experts == layer.num_experts
                return w[
                    layer.moe_ep_rank
                    * layer.num_local_experts : (layer.moe_ep_rank + 1)
                    * layer.num_local_experts
                ]

            w13_input_scale = _slice_scale(w13_input_scale)
            w2_input_scale = _slice_scale(w2_input_scale)

            if CUTEDSL_MOE_NVFP4_DISPATCH:
                assert torch.all(w13_input_scale == w13_input_scale[0])
                w13_input_scale = w13_input_scale[0]
        else:
            w13_input_scale = layer.w13_input_scale.max(dim=1).values.to(torch.float32)
            w2_input_scale = layer.w2_input_scale

        # Create shared parameters
        layer.g1_alphas = Parameter(
            (w13_input_scale * w13_weight_scale_2).to(torch.float32),
            requires_grad=False,
        )
        layer.g2_alphas = Parameter(
            (w2_input_scale * layer.w2_weight_scale_2).to(torch.float32),
            requires_grad=False,
        )
        layer.w13_input_scale_quant = Parameter(
            (1 / w13_input_scale).to(torch.float32), requires_grad=False
        )
        layer.w2_input_scale_quant = Parameter(
            (1 / w2_input_scale).to(torch.float32), requires_grad=False
        )

        layer.dispatcher.set_quant_config(
            {"input_global_scale": layer.w13_input_scale_quant}
        )

        # Validate weight scales
        for name, weight_scale in [
            ("w13", layer.w13_weight_scale),
            ("w2", layer.w2_weight_scale),
        ]:
            assert (
                weight_scale.shape[2] % 16 == 0
            ), f"Expected {name}_weight_scale.dim(2) to be divisible by 16"
            assert (
                weight_scale.dtype == torch.float8_e4m3fn
            ), f"{name} Weight Blockscale must be represented as FP8-E4M3"

        # Weight processing based on strategy
        if (
            self.enable_flashinfer_trtllm_moe
            and reorder_rows_for_gated_act_gemm is not None
            and shuffle_matrix_sf_a is not None
        ):
            # FlashInfer TRTLLM processing - handles both w13 and w2
            (
                gemm1_weights_fp4_shuffled,
                gemm1_scales_fp4_shuffled,
                gemm2_weights_fp4_shuffled,
                gemm2_scales_fp4_shuffled,
            ) = self.prepare_static_weights_for_kernel(
                layer.w13_weight,
                layer.w2_weight,
                layer.w13_weight_scale,
                layer.w2_weight_scale,
                layer.w2_weight.size(-2),  # hidden_size
                layer.w13_weight.size(-2) // 2,  # intermediate_size
                layer.w13_weight.size(0),  # num_experts
            )

            # Set flashinfer parameters
            layer.gemm1_weights_fp4_shuffled = Parameter(
                gemm1_weights_fp4_shuffled, requires_grad=False
            )
            layer.gemm2_weights_fp4_shuffled = Parameter(
                gemm2_weights_fp4_shuffled, requires_grad=False
            )
            layer.gemm1_scales_fp4_shuffled = Parameter(
                gemm1_scales_fp4_shuffled, requires_grad=False
            )
            layer.gemm2_scales_fp4_shuffled = Parameter(
                gemm2_scales_fp4_shuffled, requires_grad=False
            )

            # Additional parameter needed for TRT-LLM
            layer.g1_scale_c = Parameter(
                (layer.w2_input_scale_quant * layer.g1_alphas).to(torch.float32),
                requires_grad=False,
            )

            # Clean up weights that won't be used by TRT-LLM
            del (
                layer.w2_weight,
                layer.w2_weight_scale,
                layer.w13_weight,
                layer.w13_weight_scale,
            )

        else:
            # CUTLASS processing - handle w13 and w2 separately

            # Process w13 weights
            w13_blockscale_swizzled = self.swizzle_blockscale(layer.w13_weight_scale)
            del layer.w13_weight_scale
            layer.w13_blockscale_swizzled.data.copy_(w13_blockscale_swizzled)
            layer.w13_weight = Parameter(layer.w13_weight.data, requires_grad=False)

            # Process w2 weights
            w2_blockscale_swizzled = self.swizzle_blockscale(layer.w2_weight_scale)
            del layer.w2_weight_scale
            layer.w2_blockscale_swizzled.data.copy_(w2_blockscale_swizzled)
            layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)

            # Both flashinfer cutlass and regular cutlass use same processing for w2

            # Set up CUTLASS MoE parameters
            device = layer.w13_weight.device
            layer.cutlass_moe_params = CutlassMoEParams(
                CutlassMoEType.BlockscaledFP4,
                device,
                num_experts=layer.num_experts,  # global num experts
                intermediate_size_per_partition=layer.w2_weight.shape[2] * 2,  # n
                hidden_size=layer.w13_weight.shape[2] * 2,
            )  # k

    @property
    def load_up_proj_weight_first(self) -> bool:
        # FlashInfer CUTLASS kernel assumes [Up, Gate] Proj as W13
        return self.enable_flashinfer_cutlass_moe

    def create_moe_runner(
        self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig
    ):
        self.moe_runner_config = moe_runner_config

    def apply(
        self,
        layer: FusedMoE,
        dispatch_output: StandardDispatchOutput,
        forward_shared_experts=None,
        alt_stream=None,
    ) -> CombineInput:

        from sglang.srt.layers.moe.token_dispatcher import StandardCombineInput

        x = dispatch_output.hidden_states
        topk_output = dispatch_output.topk_output

        assert (
            self.moe_runner_config.activation == "silu"
        ), "Only SiLU activation is supported."

        moe_runner_config = self.moe_runner_config

        # Check if this is a FlashInferFP4MoE layer that should handle its own forward
        if hasattr(layer, "gemm1_weights_fp4_shuffled"):
            # This layer was processed with flashinfer TRTLLM - delegate to its own forward
            return StandardCombineInput(hidden_states=layer.forward(x, topk_output))

        if self.enable_flashinfer_cutlass_moe:
            assert (
                not moe_runner_config.apply_router_weight_on_input
            ), "apply_router_weight_on_input is not supported for Flashinfer"
            # TRTLLM Cutlass moe takes in activations in BF16/Half/nvfp4 precision
            # and fp4 quantized weights loaded from the checkpoint
            topk_weights, topk_ids = topk_output.topk_weights, topk_output.topk_ids

            output_dtype = x.dtype
            x_sf = None
            if should_use_flashinfer_cutlass_moe_fp4_allgather():
                from flashinfer import nvfp4_block_scale_interleave

                # Quantize before comm, swizzle after.
                if x.shape[0] > 0:
                    x, x_sf = fp4_quantize(
                        x, layer.w13_input_scale_quant, is_sf_swizzled_layout=False
                    )
                else:
                    x_col = x.shape[1]
                    x = torch.zeros(0, x_col // 2, dtype=torch.uint8, device=x.device)
                    x_sf = torch.zeros(
                        0, x_col // 16, dtype=torch.uint8, device=x.device
                    )
                topk_weights, topk_ids, x, x_sf = get_tp_group().all_gatherv(
                    [topk_weights, topk_ids, x, x_sf], sizes=get_dp_global_num_tokens()
                )
                x_sf = nvfp4_block_scale_interleave(x_sf)

            output = flashinfer_cutlass_fused_moe(
                input=x,
                token_selected_experts=topk_ids.to(torch.int),
                token_final_scales=topk_weights,
                fc1_expert_weights=layer.w13_weight.view(torch.long),
                fc2_expert_weights=layer.w2_weight.view(torch.long),
                output_dtype=output_dtype,
                input_sf=x_sf,
                quant_scales=[
                    layer.w13_input_scale_quant,
                    layer.w13_blockscale_swizzled.view(torch.int32),
                    layer.g1_alphas,
                    layer.w2_input_scale_quant,
                    layer.w2_blockscale_swizzled.view(torch.int32),
                    layer.g2_alphas,
                ],
                ep_size=layer.moe_ep_size,
                ep_rank=layer.moe_ep_rank,
                tp_size=layer.moe_tp_size,
                tp_rank=layer.moe_tp_rank,
                tune_max_num_tokens=next_power_of_2(x.shape[0]),
            )[0]
            if should_use_flashinfer_cutlass_moe_fp4_allgather():
                output, global_output = get_local_dp_buffer(), output

                if forward_shared_experts is not None:
                    alt_stream.wait_stream(torch.cuda.current_stream())
                    with torch.cuda.stream(alt_stream):
                        forward_shared_experts()

                get_tp_group().reduce_scatterv(
                    global_output, output=output, sizes=get_dp_global_num_tokens()
                )

                if forward_shared_experts is not None:
                    torch.cuda.current_stream().wait_stream(alt_stream)

            return StandardCombineInput(hidden_states=output)

        from sglang.srt.layers.moe.cutlass_moe import cutlass_moe_fp4

        topk_weights, topk_ids = topk_output.topk_weights, topk_output.topk_ids
        output = cutlass_moe_fp4(
            a=x,
            a1_gscale=layer.w13_input_scale_quant,
            w1_fp4=layer.w13_weight,
            w1_blockscale=layer.w13_blockscale_swizzled,
            w1_alphas=layer.g1_alphas,
            a2_gscale=layer.w2_input_scale_quant,
            w2_fp4=layer.w2_weight,
            w2_blockscale=layer.w2_blockscale_swizzled,
            w2_alphas=layer.g2_alphas,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            params=layer.cutlass_moe_params,
            apply_router_weight_on_input=moe_runner_config.apply_router_weight_on_input,
        ).to(x.dtype)
        # Scale by routed_scaling_factor is fused into select_experts.
        return StandardCombineInput(hidden_states=output)

    def apply_without_routing_weights(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        masked_m: torch.Tensor,
        moe_runner_config: MoeRunnerConfig,
        down_gemm_overlap_args: Optional["DownGemmOverlapArgs"],
    ) -> torch.Tensor:
        assert (
            moe_runner_config.activation == "silu"
        ), "Only SiLU activation is supported."

        assert self.enable_flashinfer_cutedsl_moe, "only support flashinfer cutedsl moe"
        assert (
            not moe_runner_config.apply_router_weight_on_input
        ), "apply_router_weight_on_input is not supported for Flashinfer"

        from sglang.srt.layers.moe.flashinfer_cutedsl_moe import (
            flashinfer_cutedsl_moe_masked,
        )

        out = flashinfer_cutedsl_moe_masked(
            hidden_states=x,
            input_global_scale=(
                None if CUTEDSL_MOE_NVFP4_DISPATCH else layer.w13_input_scale_quant
            ),
            w1=layer.w13_weight,
            w1_blockscale=layer.w13_blockscale_swizzled,
            w1_alpha=layer.g1_alphas,
            w2=layer.w2_weight,
            a2_global_scale=layer.w2_input_scale_quant,
            w2_blockscale=layer.w2_blockscale_swizzled,
            w2_alpha=layer.g2_alphas,
            masked_m=masked_m,
            **(
                dict(
                    down_sm_count=down_gemm_overlap_args.num_sms,
                    down_signals=down_gemm_overlap_args.signal,
                    down_start_event=down_gemm_overlap_args.start_event,
                )
                if down_gemm_overlap_args is not None
                else {}
            ),
        )
        return out
