mxfp4.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from enum import Enum

import torch
from torch.nn.parameter import Parameter

from vllm import envs
from vllm.config import get_current_vllm_config
from vllm.logger import init_logger
from vllm.model_executor.layers.attention import Attention
from vllm.model_executor.layers.fused_moe import (
    FusedMoE,
    FusedMoEConfig,
    FusedMoEMethodBase,
)
from vllm.model_executor.layers.fused_moe import modular_kernel as mk
from vllm.model_executor.layers.fused_moe.config import (
    FusedMoEQuantConfig,
    mxfp4_mxfp8_moe_quant_config,
    mxfp4_w4a16_moe_quant_config,
    ocp_mx_moe_quant_config,
)
from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
    BatchedMarlinExperts,
    MarlinExperts,
    fused_marlin_moe,
)
from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import (
    OAITritonExperts,
    UnfusedOAITritonExperts,
)
from vllm.model_executor.layers.fused_moe.trtllm_moe import TrtLlmGenExperts
from vllm.model_executor.layers.linear import LinearBase, 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.utils.marlin_utils import (
    get_marlin_input_dtype,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
    prepare_moe_fp4_layer_for_marlin,
)
from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
    _can_support_mxfp4,
    _swizzle_mxfp4,
    get_padding_alignment,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import is_layer_skipped
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.scalar_type import scalar_types
from vllm.utils.flashinfer import has_flashinfer
from vllm.utils.import_utils import has_triton_kernels
from vllm.utils.math_utils import round_up

logger = init_logger(__name__)


# enum for mxfp4 backend
class Mxfp4Backend(Enum):
    NONE = 0

    # FlashInfer Backend
    SM100_FI_MXFP4_MXFP8_TRTLLM = 1
    SM100_FI_MXFP4_MXFP8_CUTLASS = 2
    SM100_FI_MXFP4_BF16 = 3
    SM90_FI_MXFP4_BF16 = 4

    # Marlin Backend
    MARLIN = 5

    # Triton Backend
    TRITON = 6


def get_mxfp4_backend_with_lora() -> Mxfp4Backend:
    """
    Not all MXFP4 backends support LoRA. Select backends that are known to
    have LoRA support.
    """
    if not current_platform.is_cuda():
        return Mxfp4Backend.NONE

    # If FlashInfer is not available, try either Marlin or Triton
    triton_kernels_supported = (
        has_triton_kernels()
        # NOTE: triton_kernels are only confirmed to work on SM90 and SM100
        # SM110 fails with this error: https://github.com/vllm-project/vllm/issues/29317
        # SM120 needs this fix: https://github.com/triton-lang/triton/pull/8498
        and (9, 0) <= current_platform.get_device_capability() < (11, 0)
    )
    if envs.VLLM_MXFP4_USE_MARLIN is False and triton_kernels_supported:
        logger.info_once("[get_mxfp4_backend_with_lora] Using Triton backend")
        return Mxfp4Backend.TRITON

    logger.info_once("[get_mxfp4_backend_with_lora] Using Marlin backend")
    return Mxfp4Backend.MARLIN


def get_mxfp4_backend(with_lora_support: bool) -> Mxfp4Backend:
    # Backend Selection

    if with_lora_support:
        return get_mxfp4_backend_with_lora()

    if current_platform.is_cuda():
        if (
            current_platform.is_device_capability(90)
            and has_flashinfer()
            and envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16
        ):
            logger.info_once("Using FlashInfer MXFP4 BF16 backend for SM90")
            return Mxfp4Backend.SM90_FI_MXFP4_BF16
        elif (
            current_platform.is_device_capability_family(100)
            and has_flashinfer()
            and envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS
        ):
            logger.info_once("Using FlashInfer MXFP4 MXFP8 CUTLASS backend for SM100")
            return Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS
        elif (
            current_platform.is_device_capability_family(100)
            and has_flashinfer()
            and envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
        ):
            return Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM
        elif current_platform.is_device_capability_family(100) and has_flashinfer():
            logger.info_once(
                "Using FlashInfer MXFP4 BF16 backend for SM100, "
                "For faster performance on SM100, consider setting "
                "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8=1, though this may impact "
                "accuracy."
            )
            return Mxfp4Backend.SM100_FI_MXFP4_BF16
        elif (
            current_platform.is_device_capability_family(100)
            or current_platform.is_device_capability(90)
        ) and not has_flashinfer():
            logger.warning_once(
                "MXFP4 MoE is enabled on Hopper/Blackwell but FlashInfer "
                "is not available. This may result in degraded performance. "
                "Please `pip install vllm[flashinfer]` for best results."
            )

        # If FlashInfer is not available, try either Marlin or Triton
        triton_kernels_supported = (
            has_triton_kernels()
            # NOTE: triton_kernels are only confirmed to work on SM90 and SM100
            # SM110 fails with this error: https://github.com/vllm-project/vllm/issues/29317
            # SM120 needs this fix: https://github.com/triton-lang/triton/pull/8498
            and (9, 0) <= current_platform.get_device_capability() < (11, 0)
        )
        if envs.VLLM_MXFP4_USE_MARLIN or not triton_kernels_supported:
            logger.info_once("Using Marlin backend")
            return Mxfp4Backend.MARLIN
        else:
            logger.info_once("Using Triton backend")
            return Mxfp4Backend.TRITON
    elif current_platform.is_xpu():
        logger.info_once("Using xpu backend on XPU")
        return Mxfp4Backend.MARLIN
    elif current_platform.is_rocm() and has_triton_kernels():
        logger.info_once("Using Triton backend")
        return Mxfp4Backend.TRITON

    return Mxfp4Backend.NONE


class Mxfp4Config(QuantizationConfig):
    def __init__(self, ignored_layers: list[str] | None = None):
        super().__init__()
        self.ignored_layers = ignored_layers

    @classmethod
    def from_config(cls, config):
        return cls()

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

    @classmethod
    def get_name(cls) -> QuantizationMethods:
        return "mxfp4"

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

    @classmethod
    def get_config_filenames(cls) -> list[str]:
        return []

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> "QuantizeMethodBase | None":
        if isinstance(layer, LinearBase):
            if self.ignored_layers and is_layer_skipped(
                prefix=prefix,
                ignored_layers=self.ignored_layers,
                fused_mapping=self.packed_modules_mapping,
            ):
                return UnquantizedLinearMethod()
            # TODO: Add support for MXFP4 Linear Method.
            # MXFP4 LinearMethod is available in AMD-Quark, refer to that implementation
            # if you are interested in enabling MXFP4 here.
            logger.debug_once(
                "MXFP4 linear layer is not implemented - falling back to "
                "UnquantizedLinearMethod.",
                scope="local",
            )
            return UnquantizedLinearMethod()
        elif isinstance(layer, FusedMoE):
            if current_platform.is_xpu():
                return XpuMxfp4MoEMethod(layer.moe_config)
            else:
                quant_method = Mxfp4MoEMethod(layer.moe_config)
                quant_method.marlin_input_dtype = get_marlin_input_dtype(prefix)
                return quant_method
        elif isinstance(layer, Attention):
            # TODO: Add support for MXFP4 Attention.
            logger.debug_once(
                "MXFP4 attention layer is not implemented. "
                "Skipping quantization for this layer.",
                scope="local",
            )
        return None

    def is_mxfp4_quant(self, prefix: str, layer: torch.nn.Module) -> bool:
        """MXFP4 config always uses MXFP4 quantization."""
        return True


class Mxfp4MoEMethod(FusedMoEMethodBase):
    """MXFP4 MoE quantization method."""

    def __init__(self, moe: FusedMoEConfig):
        super().__init__(moe)
        self.weight_dtype = "mxfp4"
        self.mxfp4_backend = get_mxfp4_backend(moe.is_lora_enabled)

        self.marlin_input_dtype = None
        self.max_capture_size = (
            get_current_vllm_config().compilation_config.max_cudagraph_capture_size
        )

        assert self.mxfp4_backend != Mxfp4Backend.NONE, (
            f"get_mxfp4_backend(with_lora_support={moe.is_lora_enabled}) found"
            "no compatible MXFP4 MoE backend (FlashInfer/Marlin/Triton)."
            "Please check your environment and try again."
        )
        self._cache_permute_indices: dict[torch.Size, torch.Tensor] = {}

    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,
    ):
        self.num_experts = num_experts
        weight_dtype = torch.uint8
        scale_dtype = torch.uint8

        # FIXME (zyongye): ship after torch and safetensors support mxfp4
        # is_torch_mxfp4_available = (
        #     hasattr(torch, "float4_e2m1fn_x2") and
        #     hasattr(torch, "float8_e8m0fnu"))
        # if is_torch_mxfp4_available:
        #     weight_dtype = torch.float4_e2m1fn_x2
        #     scale_dtype = torch.float8_e8m0fnu

        mxfp4_block = 32

        intermediate_size_per_partition_after_pad = intermediate_size_per_partition
        if self.mxfp4_backend == Mxfp4Backend.MARLIN:
            # The moe marlin kernel requires that for each linear
            # n % 256 == 0 and k % 128 == 0.
            # In gate_up_proj:
            #    n = 2 * intermediate_size_per_partition_after_pad
            #    k = hidden_size
            # In down_proj
            #    n = hidden_size
            #    k = intermediate_size_per_partition_after_pad
            intermediate_size_per_partition_after_pad = round_up(
                intermediate_size_per_partition, 128
            )
            if current_platform.is_xpu():
                hidden_size = round_up(hidden_size, 128)
            else:
                hidden_size = round_up(hidden_size, 256)

            layer.params_dtype = params_dtype
            layer.num_experts = num_experts
            layer.hidden_size = hidden_size
            layer.intermediate_size_per_partition = (
                intermediate_size_per_partition_after_pad
            )
        elif (
            self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM
            or self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_BF16
        ):
            # pad the intermediate size to be a multiple of 2 * mxfp4_block
            # for to hold non-uniform sharded tensor as well as swizzling
            # other padding to increase performance
            intermediate_size_per_partition_after_pad = round_up(
                intermediate_size_per_partition, 256
            )
            hidden_size = round_up(hidden_size, 256)
        elif (
            self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS
            or self.mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16
        ):
            intermediate_size_per_partition_after_pad = round_up(
                intermediate_size_per_partition, 128
            )
            hidden_size = round_up(hidden_size, 128)
        elif current_platform.is_rocm():
            pad_align = get_padding_alignment()
            intermediate_size_per_partition_after_pad = round_up(
                intermediate_size_per_partition, pad_align
            )
            hidden_size = round_up(hidden_size, pad_align)
        else:
            intermediate_size_per_partition_after_pad = round_up(
                intermediate_size_per_partition, 64
            )

        self.intermediate_size = intermediate_size_per_partition_after_pad
        self.hidden_size = hidden_size
        # Fused gate_up_proj (column parallel)
        w13_weight = torch.nn.Parameter(
            torch.zeros(
                num_experts,
                2 * intermediate_size_per_partition_after_pad,
                hidden_size // 2,
                dtype=weight_dtype,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_weight", w13_weight)
        set_weight_attrs(w13_weight, extra_weight_attrs)

        w13_weight_scale = torch.nn.Parameter(
            torch.zeros(
                num_experts,
                2 * intermediate_size_per_partition_after_pad,
                hidden_size // mxfp4_block,
                dtype=scale_dtype,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_weight_scale", w13_weight_scale)
        set_weight_attrs(w13_weight_scale, extra_weight_attrs)

        w13_bias = torch.nn.Parameter(
            torch.zeros(
                num_experts,
                2 * intermediate_size_per_partition_after_pad,
                dtype=torch.bfloat16,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_bias", w13_bias)
        set_weight_attrs(w13_bias, extra_weight_attrs)

        # down_proj (row parallel)
        w2_weight = torch.nn.Parameter(
            torch.zeros(
                num_experts,
                hidden_size,
                intermediate_size_per_partition_after_pad // 2,
                dtype=weight_dtype,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_weight", w2_weight)
        set_weight_attrs(w2_weight, extra_weight_attrs)

        w2_weight_scale = torch.nn.Parameter(
            torch.zeros(
                num_experts,
                hidden_size,
                intermediate_size_per_partition_after_pad // mxfp4_block,
                dtype=scale_dtype,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_weight_scale", w2_weight_scale)
        set_weight_attrs(w2_weight_scale, extra_weight_attrs)

        w2_bias = torch.nn.Parameter(
            torch.zeros(
                num_experts,
                hidden_size,
                dtype=torch.bfloat16,
            ),
            requires_grad=False,
        )
        layer.register_parameter("w2_bias", w2_bias)
        set_weight_attrs(w2_bias, extra_weight_attrs)

    def process_weights_after_loading(self, layer):
        if self.mxfp4_backend == Mxfp4Backend.MARLIN:
            prepare_moe_fp4_layer_for_marlin(layer, input_dtype=self.marlin_input_dtype)
        elif (
            self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM
            or self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_BF16
        ):
            from flashinfer.fp4_quantization import nvfp4_block_scale_interleave
            from flashinfer.fused_moe.core import get_w2_permute_indices_with_cache

            layer.gemm1_alpha = Parameter(
                torch.tensor([1.702] * self.num_experts, dtype=torch.float32).cuda(),
                requires_grad=False,
            )
            layer.gemm1_beta = Parameter(
                torch.tensor([1.0] * self.num_experts, dtype=torch.float32).cuda(),
                requires_grad=False,
            )
            layer.gemm1_clamp_limit = Parameter(
                torch.tensor([7.0] * self.num_experts, dtype=torch.float32).cuda(),
                requires_grad=False,
            )
            sf_block_size = 32  # mxfp4 block size

            assert (
                layer.w13_weight.dim() == 3
                and layer.w13_weight.shape[0] == self.num_experts
                and layer.w13_weight.shape[1] == self.intermediate_size * 2
                and layer.w13_weight.shape[2] == self.hidden_size // 2
            )
            assert (
                layer.w13_weight_scale.dim() == 3
                and layer.w13_weight_scale.shape[0] == self.num_experts
                and layer.w13_weight_scale.shape[1] == self.intermediate_size * 2
                and layer.w13_weight_scale.shape[2] == self.hidden_size // sf_block_size
            )
            assert (
                layer.w2_weight.dim() == 3
                and layer.w2_weight.shape[0] == self.num_experts
                and layer.w2_weight.shape[1] == self.hidden_size
                and layer.w2_weight.shape[2] == self.intermediate_size // 2
            )
            assert (
                layer.w2_weight_scale.dim() == 3
                and layer.w2_weight_scale.shape[1] == self.hidden_size
                and layer.w2_weight_scale.shape[2]
                == self.intermediate_size // sf_block_size
            )
            assert (
                layer.w13_bias.dim() == 2
                and layer.w13_bias.shape[0] == self.num_experts
                and layer.w13_bias.shape[1] == self.intermediate_size * 2
            )
            assert (
                layer.w2_bias.dim() == 2
                and layer.w2_bias.shape[0] == self.num_experts
                and layer.w2_bias.shape[1] == self.hidden_size
            )

            w13_weight_scale = layer.w13_weight_scale.data
            w2_weight_scale = layer.w2_weight_scale.data
            w13_weight = layer.w13_weight.data
            w2_weight = layer.w2_weight.data
            w13_bias = layer.w13_bias.data.to(torch.float32)
            w2_bias = layer.w2_bias.data.to(torch.float32)

            # Swap w1 and w3 as the definition of
            # swiglu is different in the trtllm-gen
            def swap_every_two_rows(x, axis=-1):
                shape = x.shape
                if axis < 0:
                    axis = len(shape) + axis

                # Create a new shape with pairs swapped along specified axis
                new_shape = list(shape)
                new_shape[axis] = shape[axis] // 2
                new_shape.insert(axis + 1, 2)

                # Reshape to expose pairs, swap them, and reshape back
                x = x.reshape(*new_shape)
                x = x.flip(axis + 1)
                new_shape = list(shape)
                return x.reshape(*new_shape)

            w13_weight_scale = swap_every_two_rows(w13_weight_scale, -2)
            w13_weight = swap_every_two_rows(w13_weight, -2)
            w13_bias = swap_every_two_rows(w13_bias, -1)

            # Do not interleave as the checkpoint is already interleaved

            # Shuffle weights and scaling factors for transposed mma output
            gemm1_weights_mxfp4_shuffled = []
            gemm1_scales_mxfp4_shuffled = []
            gemm2_weights_mxfp4_shuffled = []
            gemm2_scales_mxfp4_shuffled = []
            gemm1_bias_shuffled = []
            gemm2_bias_shuffled = []
            epilogue_tile_m = 128  # FIXME: this depends on the kernel internals
            for i in range(self.num_experts):
                # w13 weight shuffling
                permute_indices = get_w2_permute_indices_with_cache(
                    self._cache_permute_indices,
                    w13_weight[i].view(torch.uint8),
                    epilogue_tile_m,
                )
                gemm1_weights_mxfp4_shuffled.append(
                    w13_weight[i]
                    .view(torch.uint8)[permute_indices.to(w13_weight.device)]
                    .contiguous()
                )
                # w13 scale shuffling
                permute_sf_indices = get_w2_permute_indices_with_cache(
                    self._cache_permute_indices,
                    w13_weight_scale[i].view(torch.uint8),
                    epilogue_tile_m,
                    num_elts_per_sf=16,
                )
                gemm1_scales_mxfp4_shuffled.append(
                    nvfp4_block_scale_interleave(
                        w13_weight_scale[i]
                        .view(torch.uint8)[
                            permute_sf_indices.to(w13_weight_scale.device)
                        ]
                        .contiguous()
                    )
                )
                # w13 bias shuffling
                permute_bias_indices = get_w2_permute_indices_with_cache(
                    self._cache_permute_indices,
                    w13_bias[i].clone().reshape(-1, 1),
                    epilogue_tile_m,
                )
                gemm1_bias_shuffled.append(
                    w13_bias[i]
                    .clone()
                    .reshape(-1, 1)[permute_bias_indices.to(w13_bias.device)]
                    .contiguous()
                )
                # w2 weight shuffling
                permute_indices = get_w2_permute_indices_with_cache(
                    self._cache_permute_indices,
                    w2_weight[i].view(torch.uint8),
                    epilogue_tile_m,
                )
                gemm2_weights_mxfp4_shuffled.append(
                    w2_weight[i]
                    .view(torch.uint8)[permute_indices.to(w2_weight.device)]
                    .contiguous()
                )
                # w2 scale shuffling
                permute_sf_indices = get_w2_permute_indices_with_cache(
                    self._cache_permute_indices,
                    w2_weight_scale[i].view(torch.uint8),
                    epilogue_tile_m,
                    num_elts_per_sf=16,
                )
                gemm2_scales_mxfp4_shuffled.append(
                    nvfp4_block_scale_interleave(
                        w2_weight_scale[i]
                        .view(torch.uint8)[
                            permute_sf_indices.to(w2_weight_scale.device)
                        ]
                        .contiguous()
                    )
                )
                # w2 bias shuffling
                permute_indices = get_w2_permute_indices_with_cache(
                    self._cache_permute_indices,
                    w2_bias[i].clone().reshape(-1, 1),
                    epilogue_tile_m,
                )
                gemm2_bias_shuffled.append(
                    w2_bias[i]
                    .clone()
                    .reshape(-1, 1)[permute_indices.to(w2_bias.device)]
                    .contiguous()
                )

            w13_weight = torch.stack(gemm1_weights_mxfp4_shuffled)
            w13_weight_scale = (
                torch.stack(gemm1_scales_mxfp4_shuffled)
                .reshape(
                    self.num_experts,
                    2 * self.intermediate_size,
                    self.hidden_size // sf_block_size,
                )
                .view(torch.float8_e4m3fn)
            )

            w2_weight = torch.stack(gemm2_weights_mxfp4_shuffled)
            w2_weight_scale = (
                torch.stack(gemm2_scales_mxfp4_shuffled)
                .reshape(
                    self.num_experts,
                    self.hidden_size,
                    self.intermediate_size // sf_block_size,
                )
                .view(torch.float8_e4m3fn)
            )

            layer.w13_weight = Parameter(w13_weight, requires_grad=False)
            layer.w13_weight_scale = Parameter(w13_weight_scale, requires_grad=False)
            layer.w2_weight = Parameter(w2_weight, requires_grad=False)
            layer.w2_weight_scale = Parameter(w2_weight_scale, requires_grad=False)
            layer.w13_bias = Parameter(
                torch.stack(gemm1_bias_shuffled).reshape(self.num_experts, -1),
                requires_grad=False,
            )
            layer.w2_bias = Parameter(
                torch.stack(gemm2_bias_shuffled).reshape(self.num_experts, -1),
                requires_grad=False,
            )
        elif (
            self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS
            or self.mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16
        ):
            layer.gemm1_alpha = Parameter(
                torch.tensor([1.702] * self.num_experts, dtype=torch.float32).cuda(),
                requires_grad=False,
            )
            layer.gemm1_beta = Parameter(
                torch.tensor([1.0] * self.num_experts, dtype=torch.float32).cuda(),
                requires_grad=False,
            )
            layer.gemm1_clamp_limit = Parameter(
                torch.tensor([7.0] * self.num_experts, dtype=torch.float32).cuda(),
                requires_grad=False,
            )

            sf_block_size = 32  # mxfp4 block size

            # Common shape assertions
            assert (
                layer.w13_weight.dim() == 3
                and layer.w13_weight.shape[0] == self.num_experts
                and layer.w13_weight.shape[1] == self.intermediate_size * 2
                and layer.w13_weight.shape[2] == self.hidden_size // 2
            )
            assert (
                layer.w13_weight_scale.dim() == 3
                and layer.w13_weight_scale.shape[0] == self.num_experts
                and layer.w13_weight_scale.shape[1] == self.intermediate_size * 2
                and layer.w13_weight_scale.shape[2] == self.hidden_size // sf_block_size
            )
            assert (
                layer.w2_weight.dim() == 3
                and layer.w2_weight.shape[0] == self.num_experts
                and layer.w2_weight.shape[1] == self.hidden_size
                and layer.w2_weight.shape[2] == self.intermediate_size // 2
            )
            assert (
                layer.w2_weight_scale.dim() == 3
                and layer.w2_weight_scale.shape[1] == self.hidden_size
                and layer.w2_weight_scale.shape[2]
                == self.intermediate_size // sf_block_size
            )
            assert (
                layer.w13_bias.dim() == 2
                and layer.w13_bias.shape[0] == self.num_experts
                and layer.w13_bias.shape[1] == self.intermediate_size * 2
            )
            assert (
                layer.w2_bias.dim() == 2
                and layer.w2_bias.shape[0] == self.num_experts
                and layer.w2_bias.shape[1] == self.hidden_size
            )

            # De-interleave and swap for w13 weight, bias, and scales
            w13_w = layer.w13_weight.data
            gate_w, up_w = w13_w[:, ::2, :], w13_w[:, 1::2, :]
            deinterleaved_w13_w = torch.cat([gate_w, up_w], dim=1)
            w1_w, w3_w = torch.chunk(deinterleaved_w13_w, 2, dim=1)
            w13_weight_swapped = torch.cat([w3_w, w1_w], dim=1)

            w13_b = layer.w13_bias.data.to(torch.float32)
            gate_b, up_b = w13_b[:, ::2], w13_b[:, 1::2]
            deinterleaved_w13_b = torch.cat([gate_b, up_b], dim=1)
            b1, b3 = torch.chunk(deinterleaved_w13_b, 2, dim=-1)
            w13_bias_swapped = torch.cat([b3, b1], dim=-1).to(torch.bfloat16)

            w13_s = layer.w13_weight_scale.data
            gate_s, up_s = w13_s[:, ::2, :], w13_s[:, 1::2, :]
            deinterleaved_w13_s = torch.cat([gate_s, up_s], dim=1)
            s1, s3 = torch.chunk(deinterleaved_w13_s, 2, dim=1)
            w13_scale_swapped = torch.cat([s3, s1], dim=1)

            if self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS:
                from flashinfer import block_scale_interleave

                orig_shape = w13_scale_swapped.shape
                w13_scale_interleaved = block_scale_interleave(
                    w13_scale_swapped.view(torch.uint8)
                ).reshape(orig_shape)

                w2_s = layer.w2_weight_scale.data
                orig_shape = w2_s.shape
                w2_scale_interleaved = block_scale_interleave(
                    w2_s.view(torch.uint8)
                ).reshape(orig_shape)

                layer.w13_weight = Parameter(w13_weight_swapped, requires_grad=False)
                layer.w13_weight_scale = Parameter(
                    w13_scale_interleaved, requires_grad=False
                )
                layer.w13_bias = Parameter(w13_bias_swapped, requires_grad=False)
                layer.w2_weight_scale = Parameter(
                    w2_scale_interleaved, requires_grad=False
                )
            elif self.mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16:

                def _interleave_mxfp4_cutlass_sm90(w):
                    w_shape = w.shape
                    w_interleaved = w.reshape(
                        w_shape[0], w_shape[1], (w_shape[2] // 4), 4
                    )
                    w_interleaved = w_interleaved.permute(0, 2, 1, 3)
                    w_interleaved = w_interleaved.reshape(
                        w_shape[0], w_shape[2] // 4, w_shape[1] * 4
                    )
                    return w_interleaved

                w31_scales = w13_scale_swapped.to(torch.uint8).view(torch.uint8)
                w31_scales_interleaved = _interleave_mxfp4_cutlass_sm90(w31_scales)

                w2_weight_scale = layer.w2_weight_scale.data
                w2_scales = w2_weight_scale.to(torch.uint8).view(torch.uint8)
                w2_scales_interleaved = _interleave_mxfp4_cutlass_sm90(w2_scales)

                layer.w13_weight = torch.nn.Parameter(
                    torch.cat([w3_w, w1_w], dim=1), requires_grad=False
                )
                layer.w13_bias = torch.nn.Parameter(
                    w13_bias_swapped, requires_grad=False
                )
                layer.w13_weight_scale = torch.nn.Parameter(
                    w31_scales_interleaved, requires_grad=False
                )
                layer.w2_weight_scale = torch.nn.Parameter(
                    w2_scales_interleaved, requires_grad=False
                )
        elif self.mxfp4_backend == Mxfp4Backend.TRITON:
            from triton_kernels.matmul_ogs import FlexCtx, PrecisionConfig

            w13_bias = layer.w13_bias.to(torch.float32)
            w2_bias = layer.w2_bias.to(torch.float32)

            layer.w13_bias = Parameter(w13_bias, requires_grad=False)
            layer.w2_bias = Parameter(w2_bias, requires_grad=False)

            # Ideally we'd use FusedMoEModularKernel.prepare_finalize object
            # (stored in self.fused_experts) to determine if the MoE has a
            # batched activation format. As self.fused_experts is not
            # initialized at this point, we resort to checking the MoE config
            # directly.
            is_batched_moe = self.moe.use_pplx_kernels or self.moe.use_deepep_ll_kernels
            if is_batched_moe:
                num_warps = 4 if envs.VLLM_MOE_DP_CHUNK_SIZE <= 512 else 8
            else:
                num_warps = 8

            w13_weight, w13_flex, w13_scale = _swizzle_mxfp4(
                layer.w13_weight, layer.w13_weight_scale, num_warps
            )
            w2_weight, w2_flex, w2_scale = _swizzle_mxfp4(
                layer.w2_weight, layer.w2_weight_scale, num_warps
            )

            self.w13_precision_config = PrecisionConfig(
                weight_scale=w13_scale, flex_ctx=FlexCtx(rhs_data=w13_flex)
            )
            self.w2_precision_config = PrecisionConfig(
                weight_scale=w2_scale, flex_ctx=FlexCtx(rhs_data=w2_flex)
            )

            self.w13_weight = w13_weight
            self.w2_weight = w2_weight
            del layer.w13_weight
            del layer.w2_weight
            layer.w13_weight = w13_weight
            layer.w2_weight = w2_weight
        else:
            raise ValueError(
                f"Unsupported mxfp4_backend: {self.mxfp4_backend}: "
                f"should be one of: {list(Mxfp4Backend)}."
            )

    def get_fused_moe_quant_config(
        self, layer: torch.nn.Module
    ) -> FusedMoEQuantConfig | None:
        if self.mxfp4_backend == Mxfp4Backend.MARLIN:
            return mxfp4_w4a16_moe_quant_config(
                w1_bias=layer.w13_bias,
                w2_bias=layer.w2_bias,
                w1_scale=layer.w13_weight_scale,
                w2_scale=layer.w2_weight_scale,
            )
        elif self.mxfp4_backend == Mxfp4Backend.TRITON:
            w1_scale = self.w13_precision_config
            w2_scale = self.w2_precision_config
            return mxfp4_w4a16_moe_quant_config(
                w1_bias=layer.w13_bias,
                w2_bias=layer.w2_bias,
                w1_scale=w1_scale,
                w2_scale=w2_scale,
            )
        elif self.mxfp4_backend in [
            Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM,
            Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS,
        ]:
            return mxfp4_mxfp8_moe_quant_config(
                w1_bias=layer.w13_bias,
                w2_bias=layer.w2_bias,
                w1_scale=layer.w13_weight_scale,
                w2_scale=layer.w2_weight_scale,
            )
        elif self.mxfp4_backend in [Mxfp4Backend.SM100_FI_MXFP4_BF16]:
            return mxfp4_w4a16_moe_quant_config(
                w1_bias=layer.w13_bias,
                w2_bias=layer.w2_bias,
                w1_scale=layer.w13_weight_scale,
                w2_scale=layer.w2_weight_scale,
            )
        else:
            w1_scale = layer.w13_weight_scale
            w2_scale = layer.w2_weight_scale
            return ocp_mx_moe_quant_config(
                quant_dtype="mxfp4",
                w1_bias=layer.w13_bias,
                w2_bias=layer.w2_bias,
                w1_scale=w1_scale,
                w2_scale=w2_scale,
            )

    def select_gemm_impl(
        self,
        prepare_finalize: mk.FusedMoEPrepareAndFinalize,
        layer: torch.nn.Module,
    ) -> mk.FusedMoEPermuteExpertsUnpermute:
        if (
            prepare_finalize.activation_format
            == mk.FusedMoEActivationFormat.BatchedExperts
        ):
            if self.mxfp4_backend == Mxfp4Backend.MARLIN:
                max_num_tokens_per_rank = prepare_finalize.max_num_tokens_per_rank()
                assert max_num_tokens_per_rank is not None
                assert self.moe_quant_config is not None
                return BatchedMarlinExperts(
                    max_num_tokens=max_num_tokens_per_rank,
                    num_dispatchers=prepare_finalize.num_dispatchers(),
                    quant_config=self.moe_quant_config,
                    moe_config=self.moe,
                )
            else:
                raise NotImplementedError(
                    f"Incompatible Mxfp4 backend ({self.mxfp4_backend}) for "
                    "EP batched experts format"
                )
        else:
            assert self.moe_quant_config is not None
            if (
                self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM
                or self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_BF16
            ):
                # B200 code-path
                kwargs = {
                    "gemm1_alpha": layer.gemm1_alpha,
                    "gemm1_beta": layer.gemm1_beta,
                    "gemm1_clamp_limit": layer.gemm1_clamp_limit,
                    # TODO(bnell): part of quant_config
                    "max_capture_size": self.max_capture_size,
                }
                return TrtLlmGenExperts(self.moe, self.moe_quant_config, **kwargs)
            elif self.mxfp4_backend == Mxfp4Backend.MARLIN:
                return MarlinExperts(self.moe, self.moe_quant_config)
            elif self.mxfp4_backend == Mxfp4Backend.TRITON:
                if self.moe.is_lora_enabled:
                    return UnfusedOAITritonExperts(self.moe, self.moe_quant_config)
                return OAITritonExperts(self.moe, self.moe_quant_config)
            else:
                raise NotImplementedError(
                    f"Incompatible Mxfp4 backend ({self.mxfp4_backend}) for EP"
                )

    @property
    def is_monolithic(self) -> bool:
        return (
            self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM
            or self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_BF16
            or self.mxfp4_backend == Mxfp4Backend.TRITON
        )

    def apply(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        shared_experts_input: torch.Tensor | None,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        assert not self.is_monolithic
        if layer.enable_eplb:
            raise NotImplementedError("EPLB is not supported for mxfp4")

        if self.mxfp4_backend == Mxfp4Backend.MARLIN:
            return fused_marlin_moe(
                x,
                layer.w13_weight,
                layer.w2_weight,
                layer.w13_bias,
                layer.w2_bias,
                layer.w13_weight_scale,
                layer.w2_weight_scale,
                topk_weights,
                topk_ids,
                global_scale1=None,
                global_scale2=None,
                quant_type_id=scalar_types.float4_e2m1f.id,
                apply_router_weight_on_input=layer.apply_router_weight_on_input,
                global_num_experts=layer.global_num_experts,
                activation=layer.activation,
                expert_map=layer.expert_map,
                input_dtype=self.marlin_input_dtype,
                inplace=not self.moe.disable_inplace,
            )

        assert _can_support_mxfp4(
            layer.use_grouped_topk,
            layer.topk_group,
            layer.num_expert_group,
            layer.expert_map,
            layer.custom_routing_function,
            layer.e_score_correction_bias,
            layer.apply_router_weight_on_input,
            layer.scoring_func,
            layer.activation,
            layer.eplb_state.expert_load_view,
            layer.eplb_state.logical_to_physical_map,
            layer.eplb_state.logical_replica_count,
        ), "MXFP4 are not supported with this configuration."

        assert (
            self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS
            or self.mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16
        )
        from vllm.utils.flashinfer import flashinfer_cutlass_fused_moe

        # Backend-specific preparation
        if self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_CUTLASS:
            from flashinfer import mxfp8_quantize

            x_quant, x_scale = mxfp8_quantize(x, True, 32)

            fake_input_scale = torch.ones(self.num_experts, device=x.device)
            quant_scales = [
                layer.w13_weight_scale.contiguous().view(torch.int32),
                fake_input_scale,
                layer.w2_weight_scale.contiguous().view(torch.int32),
                fake_input_scale,
            ]

            fi_input = x_quant
            extra_kwargs = dict(
                use_mxfp8_act_scaling=True,
                input_sf=x_scale,
                fc1_expert_weights=layer.w13_weight.contiguous().view(torch.long),
                fc2_expert_weights=layer.w2_weight.contiguous().view(torch.long),
            )
        elif self.mxfp4_backend == Mxfp4Backend.SM90_FI_MXFP4_BF16:
            assert x.dtype == torch.bfloat16

            quant_scales = [
                layer.w13_weight_scale,
                layer.w2_weight_scale,
            ]

            fi_input = x
            extra_kwargs = dict(
                use_w4_group_scaling=True,
                fc1_expert_weights=layer.w13_weight,
                fc2_expert_weights=layer.w2_weight,
            )

        output = torch.empty_like(x, dtype=torch.bfloat16)

        flashinfer_cutlass_fused_moe(
            input=fi_input,
            token_selected_experts=topk_ids.to(torch.int).contiguous(),
            token_final_scales=topk_weights,
            output_dtype=torch.bfloat16,
            output=output,
            quant_scales=quant_scales,
            fc1_expert_biases=layer.w13_bias,
            fc2_expert_biases=layer.w2_bias,
            swiglu_alpha=layer.gemm1_alpha,
            swiglu_beta=layer.gemm1_beta,
            swiglu_limit=layer.gemm1_clamp_limit,
            tp_size=self.moe.tp_size,
            tp_rank=self.moe.tp_rank,
            ep_size=self.moe.ep_size,
            ep_rank=self.moe.ep_rank,
            tune_max_num_tokens=max(self.max_capture_size, 1),
            **extra_kwargs,
        )

        return output

    def apply_monolithic(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        router_logits: torch.Tensor,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        assert self.is_monolithic

        if layer.enable_eplb:
            raise NotImplementedError("EPLB is not supported for mxfp4")

        assert _can_support_mxfp4(
            layer.use_grouped_topk,
            layer.topk_group,
            layer.num_expert_group,
            layer.expert_map,
            layer.custom_routing_function,
            layer.e_score_correction_bias,
            layer.apply_router_weight_on_input,
            layer.scoring_func,
            layer.activation,
            layer.eplb_state.expert_load_view,
            layer.eplb_state.logical_to_physical_map,
            layer.eplb_state.logical_replica_count,
        ), "MXFP4 are not supported with this configuration."

        if (
            self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM
            or self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_BF16
        ):
            from flashinfer import trtllm_fp4_block_scale_moe

            if self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_BF16:
                assert x.dtype == torch.bfloat16
                x_quant = x
                x_scale = None
            elif self.mxfp4_backend == Mxfp4Backend.SM100_FI_MXFP4_MXFP8_TRTLLM:
                from flashinfer import mxfp8_quantize

                x_quant, x_scale = mxfp8_quantize(x, False)  # to mxfp8
                x_scale = x_scale.view(torch.float8_e4m3fn).reshape(*x.shape[:-1], -1)

            trtllm_gen_output = trtllm_fp4_block_scale_moe(
                routing_logits=router_logits.to(torch.bfloat16),
                routing_bias=None,
                hidden_states=x_quant,
                hidden_states_scale=x_scale,
                gemm1_weights=layer.w13_weight,  # uint8 (e2m1 x 2)
                gemm1_weights_scale=layer.w13_weight_scale,  # uint8 (e4m3 x 2)
                gemm1_bias=layer.w13_bias,  # fp32 per expert per channel
                gemm1_alpha=layer.gemm1_alpha,  # fp32 per expert
                gemm1_beta=layer.gemm1_beta,  # fp32 per expert
                gemm1_clamp_limit=layer.gemm1_clamp_limit,  # fp32 per expert
                gemm2_weights=layer.w2_weight,  # uint8 (e2m1 x 2)
                gemm2_weights_scale=layer.w2_weight_scale,  # ue8m0
                gemm2_bias=layer.w2_bias,  # fp32 per expert per channel
                output1_scale_scalar=None,
                output1_scale_gate_scalar=None,
                output2_scale_scalar=None,
                num_experts=layer.global_num_experts,
                top_k=layer.top_k,
                n_group=None,
                topk_group=None,
                intermediate_size=self.intermediate_size,  # padded to multiple of 256
                local_expert_offset=layer.ep_rank * layer.local_num_experts,
                local_num_experts=self.num_experts,
                routed_scaling_factor=None,
                routing_method_type=1 if layer.renormalize else 0,
                do_finalize=True,
                tune_max_num_tokens=max(self.max_capture_size, 1),
            )[0]
            return trtllm_gen_output
        elif self.mxfp4_backend == Mxfp4Backend.TRITON:
            from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import (  # noqa: E501
                triton_kernel_moe_forward,
            )

            return triton_kernel_moe_forward(
                hidden_states=x,
                w1=layer.w13_weight,
                w2=layer.w2_weight,
                gating_output=router_logits,
                topk=layer.top_k,
                renormalize=layer.renormalize,
                global_num_experts=layer.global_num_experts,
                expert_map=layer.expert_map,
                quant_config=self.moe_quant_config,
                apply_router_weight_on_input=layer.apply_router_weight_on_input,
            )
        else:
            raise ValueError(f"Unsupported backend: {self.mxfp4_backend}")


class XpuMxfp4MoEMethod(Mxfp4MoEMethod):
    def __init__(self, moe_config: FusedMoEConfig):
        super().__init__(moe_config)
        self.moe_config = moe_config

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

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        pass

    @property
    def is_monolithic(self) -> bool:
        return True

    def apply_monolithic(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        router_logits: torch.Tensor,
    ) -> torch.Tensor:
        assert layer.activation == "swigluoai", (
            "Only swiglu_oai activation is supported for XPU MXFP4 MoE"
        )
        from vllm_xpu_kernels.fused_moe_interface import xpu_fused_moe

        M, _ = x.size()
        routing_weights = torch.empty(
            M, layer.top_k, dtype=torch.float32, device=x.device
        )
        selected_experts = torch.empty(
            M, layer.top_k, dtype=torch.int32, device=x.device
        )
        token_expert_indices = torch.empty(
            M, layer.top_k, dtype=torch.int32, device=x.device
        )

        if layer.use_grouped_topk:
            routing_weights, selected_experts = torch.ops._moe_C.fused_grouped_topk(
                x,
                router_logits,
                layer.top_k,
                layer.renormalize,
                n_expert_group=layer.num_expert_group,
                n_topk_group=layer.topk_group,
                scoring_func=layer.scoring_func,
                routed_scaling_factor=layer.routed_scaling_factor,
                bias=layer.e_score_correction_bias,
            )
        else:
            torch.ops._moe_C.topk_softmax(
                routing_weights,
                selected_experts,
                token_expert_indices,
                router_logits,
                layer.renormalize,
                layer.e_score_correction_bias,
            )

        return xpu_fused_moe(
            hidden_states=x,
            w13=layer.w13_weight,
            w13_bias=layer.w13_bias if self.moe.has_bias else None,
            w13_scales=layer.w13_weight_scale,
            w2=layer.w2_weight,
            w2_bias=layer.w2_bias if self.moe.has_bias else None,
            w2_scales=layer.w2_weight_scale,
            topk_weights=routing_weights,
            topk_ids=selected_experts,
            n_experts_per_token=layer.top_k,
            activation=layer.activation,
            num_experts=layer.local_num_experts,
            is_mxfp4=True,
        )