mxfp4.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Callable
from enum import Enum
from typing import Optional

import torch
from torch.nn.parameter import Parameter

from vllm import envs
from vllm.attention.layer import Attention
from vllm.config import get_current_vllm_config
from vllm.logger import init_logger
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_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
from vllm.utils.torch_utils import is_torch_equal_or_newer

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()
        and is_torch_equal_or_newer("2.8.0")
        # 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("[get_mxfp4_backend_with_lora] Using Marlin backend")
        return Mxfp4Backend.MARLIN

    logger.info_once("[get_mxfp4_backend_with_lora] Using Triton backend")
    return Mxfp4Backend.TRITON


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(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(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(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(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()
            and is_torch_equal_or_newer("2.8.0")
            # 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 ipex marlin 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
    ) -> Optional["QuantizeMethodBase"]:
        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 IpexMxfp4MoEMethod(layer.moe_config)
            else:
                return Mxfp4MoEMethod(layer.moe_config)
        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


class Mxfp4MoEMethod(FusedMoEMethodBase):
    def __init__(self, moe: FusedMoEConfig):
        super().__init__(moe)
        self.mxfp4_backend = get_mxfp4_backend(moe.is_lora_enabled)
        self.use_marlin = self.mxfp4_backend == Mxfp4Backend.MARLIN
        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)
        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 backend: {self.mxfp4_backend}")

    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,
                )
            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_quant_config)
            elif self.mxfp4_backend == Mxfp4Backend.TRITON:
                if self.moe.is_lora_enabled:
                    return UnfusedOAITritonExperts(self.moe_quant_config)
                return OAITritonExperts(self.moe_quant_config)
            else:
                raise NotImplementedError(
                    f"Incompatible Mxfp4 backend ({self.mxfp4_backend}) for EP"
                )

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

    def apply(
        self,
        layer: FusedMoE,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: int | None = None,
        num_expert_group: int | None = None,
        global_num_experts: int = -1,
        expert_map: torch.Tensor | None = None,
        custom_routing_function: Callable | None = None,
        scoring_func: str = "softmax",
        routed_scaling_factor: float = 1.0,
        e_score_correction_bias: torch.Tensor | None = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        expert_load_view: torch.Tensor | None = None,
        logical_to_physical_map: torch.Tensor | None = None,
        logical_replica_count: torch.Tensor | None = None,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        if enable_eplb:
            raise NotImplementedError("EPLB is not supported for mxfp4")

        if self.mxfp4_backend == Mxfp4Backend.MARLIN:
            topk_weights, topk_ids, _ = layer.select_experts(
                hidden_states=x,
                router_logits=router_logits,
            )

            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,
                router_logits,
                topk_weights,
                topk_ids,
                global_scale1=None,
                global_scale2=None,
                quant_type_id=scalar_types.float4_e2m1f.id,
                apply_router_weight_on_input=apply_router_weight_on_input,
                global_num_experts=global_num_experts,
                activation=activation,
                expert_map=expert_map,
            )

        assert _can_support_mxfp4(
            use_grouped_topk,
            topk_group,
            num_expert_group,
            expert_map,
            custom_routing_function,
            e_score_correction_bias,
            apply_router_weight_on_input,
            scoring_func,
            activation,
            expert_load_view,
            logical_to_physical_map,
            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(
                router_logits.to(torch.bfloat16),
                None,  # routing_bias
                x_quant,
                x_scale,
                layer.w13_weight,  # uint8 (e2m1 x 2)
                layer.w13_weight_scale,  # uint8 (e4m3 x 2)
                layer.w13_bias,  # fp32 per expert per channel
                layer.gemm1_alpha,  # fp32 per expert
                layer.gemm1_beta,  # fp32 per expert
                layer.gemm1_clamp_limit,  # fp32 per expert
                layer.w2_weight,  # uint8 (e2m1 x 2)
                layer.w2_weight_scale,  # ue8m0
                layer.w2_bias,  # fp32 per expert per channel
                None,  # output1_scale_scalar
                None,  # output1_scale_gate_scalar
                None,  # output2_scale_scalar
                global_num_experts,
                top_k,
                None,  # n_group
                None,  # topk_group
                self.intermediate_size,  # padded to multiple of 256
                layer.ep_rank * layer.local_num_experts,  # local_expert_offset
                self.num_experts,  # local num experts
                None,
                None,
                1 if renormalize else 0,  # routing_method_type, renormalize
                True,  # do finalize
                tune_max_num_tokens=max(self.max_capture_size, 1),
            )[0]
            return trtllm_gen_output
        elif (
            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

            topk_weights, topk_ids, _ = layer.select_experts(
                hidden_states=x,
                router_logits=router_logits,
            )

            # 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
        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=top_k,
                renormalize=renormalize,
                global_num_experts=global_num_experts,
                expert_map=expert_map,
                quant_config=self.moe_quant_config,
                apply_router_weight_on_input=apply_router_weight_on_input,
            )
        else:
            raise ValueError(f"Unsupported backend: {self.mxfp4_backend}")


class IpexMxfp4MoEMethod(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:
        import intel_extension_for_pytorch as ipex

        layer.w13_weight.data = layer.w13_weight.data.view(torch.int32)
        layer.w2_weight.data = layer.w2_weight.data.view(torch.int32)
        ep_rank_start = self.moe_config.ep_rank * self.moe_config.num_local_experts
        layer.ipex_fusion = ipex.llm.modules.GatedMLPMOE(
            layer.w13_weight,
            layer.w2_weight,
            w1_scale_inv=layer.w13_weight_scale,
            w2_scale_inv=layer.w2_weight_scale,
            w13_bias=layer.w13_bias,
            w2_bias=layer.w2_bias,
            is_mxfp4=True,
            experts_start_id=ep_rank_start,
        )

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: int | None = None,
        num_expert_group: int | None = None,
        global_num_experts: int = -1,
        expert_map: torch.Tensor | None = None,
        custom_routing_function: Callable | None = None,
        scoring_func: str = "softmax",
        routed_scaling_factor: float = 1.0,
        e_score_correction_bias: torch.Tensor | None = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        expert_load_view: torch.Tensor | None = None,
        logical_to_physical_map: torch.Tensor | None = None,
        logical_replica_count: torch.Tensor | None = None,
    ) -> torch.Tensor:
        assert activation == "swigluoai", (
            "Only swiglu_oai activation is supported for IPEX MXFP4 MoE"
        )
        hidden_size_pad = round_up(self.original_hidden_size, 128)
        x_pad = torch.nn.functional.pad(x, (0, hidden_size_pad - x.size(-1)))
        hidden_states = layer.ipex_fusion(
            x_pad,
            use_grouped_topk,
            top_k,
            router_logits,
            renormalize,
            topk_group,
            num_expert_group,
            activation="swiglu_oai",
        )
        hidden_states = hidden_states[..., : self.original_hidden_size].contiguous()
        return hidden_states