vllm.model_executor.layers.quantization.mxfp4 ¶

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

class Mxfp4Config(QuantizationConfig):
    def __init__(self, ignored_layers: Optional[list[str]] = 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"]:
        from vllm.attention.layer import Attention  # Avoid circular import

        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()
            raise NotImplementedError("Mxfp4 linear layer is not implemented")
        elif isinstance(layer, FusedMoE):
            return Mxfp4MoEMethod(layer.moe_config)
        elif isinstance(layer, Attention):
            raise NotImplementedError("Mxfp4 attention layer is not implemented")
        return None

class Mxfp4MoEMethod(FusedMoEMethodBase):
    def __init__(self, moe: FusedMoEConfig):
        super().__init__(moe)
        self.topk_indices_dtype = None
        self.moe = moe
        self.mxfp4_backend = get_mxfp4_backend()
        self.max_capture_size = (
            get_current_vllm_config().compilation_config.max_capture_size
        )

        assert self.mxfp4_backend != Mxfp4Backend.NONE, (
            "No MXFP4 MoE backend (FlashInfer/Marlin/Triton) available."
            "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
            )
            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():
            intermediate_size_per_partition_after_pad = round_up(
                intermediate_size_per_partition, 256
            )
            hidden_size = round_up(hidden_size, 256)
        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 _maybe_get_cached_w2_permute_indices

            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 = _maybe_get_cached_w2_permute_indices(
                    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 = _maybe_get_cached_w2_permute_indices(
                    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 = _maybe_get_cached_w2_permute_indices(
                    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 = _maybe_get_cached_w2_permute_indices(
                    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 = _maybe_get_cached_w2_permute_indices(
                    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 = _maybe_get_cached_w2_permute_indices(
                    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_triton_tensor = w13_weight
            self.w2_weight_triton_tensor = w2_weight

            # need to delete the original weights to save memory on single GPU
            del layer.w13_weight
            del layer.w2_weight
            layer.w13_weight = None
            layer.w2_weight = None
            torch.cuda.empty_cache()
        else:
            raise ValueError(f"Unsupported backend: {self.mxfp4_backend}")

    def _get_tile_tokens_dim(self, x: torch.Tensor, top_k: int):
        # Number of tokens in the input tensor.
        num_tokens = x.shape[0]
        # Factor to account for the imbalance of the experts.
        # factor equals to the
        # max_real_num_tokens_per_expert / perfect_num_tokens_per_expert
        # - 1.0 means perfect expert distribution.
        # - > 1.0 means some experts have more
        #     tokens than the perfect distribution.
        # - < 1.0 does not make sense.
        imbalance_factor = 1.3
        # Calculate the number of tokens per expert
        # assuming perfect distribution.
        num_tokens_per_expert = (num_tokens * top_k) // self.num_experts
        # Apply the imbalance factor.
        num_tokens_per_expert = int(num_tokens_per_expert * imbalance_factor)
        # And pad the number to the next power of 2.
        tile_tokens_dim = next_power_of_2(num_tokens_per_expert)
        # Cap to 8-64 tokens per CTA tile
        # as it's the range supported by the kernel.
        tile_tokens_dim = min(max(tile_tokens_dim, 8), 64)

        return tile_tokens_dim

    def get_fused_moe_quant_config(
        self, layer: torch.nn.Module
    ) -> Optional[FusedMoEQuantConfig]:
        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,
            )
        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
        ):
            raise NotImplementedError(
                "Mxfp4 does not support batched experts format for EP"
            )
        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)
            else:
                return OAITritonExperts(self.moe_quant_config)

    def _route_and_experts(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        expert_load_view: Optional[torch.Tensor] = None,
        logical_to_physical_map: Optional[torch.Tensor] = None,
        logical_replica_count: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        assert isinstance(self.fused_experts, mk.FusedMoEModularKernel)

        topk_weights, topk_ids, _ = FusedMoE.select_experts(
            hidden_states=x,
            router_logits=router_logits,
            use_grouped_topk=use_grouped_topk,
            top_k=top_k,
            renormalize=renormalize,
            topk_group=topk_group,
            num_expert_group=num_expert_group,
            custom_routing_function=custom_routing_function,
            scoring_func=scoring_func,
            e_score_correction_bias=e_score_correction_bias,
            indices_type=self.topk_indices_dtype,
            enable_eplb=enable_eplb,
            expert_map=expert_map,
            expert_load_view=expert_load_view,
            logical_to_physical_map=logical_to_physical_map,
            logical_replica_count=logical_replica_count,
        )

        w13_weight = (
            self.w13_weight_triton_tensor
            if layer.w13_weight is None
            else layer.w13_weight
        )
        w2_weight = (
            self.w2_weight_triton_tensor if layer.w2_weight is None else layer.w2_weight
        )
        assert all([w is not None for w in [w13_weight, w2_weight]])

        return self.fused_experts(
            hidden_states=x,
            w1=w13_weight,
            w2=w2_weight,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            inplace=True,
            activation=activation,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
            apply_router_weight_on_input=apply_router_weight_on_input,
        )

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

        if self.fused_experts is not None:
            return self._route_and_experts(
                layer,
                x,
                router_logits,
                top_k,
                renormalize,
                use_grouped_topk,
                topk_group,
                num_expert_group,
                global_num_experts,
                expert_map,
                custom_routing_function,
                scoring_func,
                e_score_correction_bias,
                apply_router_weight_on_input,
                activation,
                enable_eplb,
                expert_load_view,
                logical_to_physical_map,
                logical_replica_count,
            )

        if self.mxfp4_backend == Mxfp4Backend.MARLIN:
            topk_weights, topk_ids, _ = FusedMoE.select_experts(
                hidden_states=x,
                router_logits=router_logits,
                use_grouped_topk=use_grouped_topk,
                top_k=top_k,
                renormalize=renormalize,
                topk_group=topk_group,
                num_expert_group=num_expert_group,
                custom_routing_function=custom_routing_function,
                scoring_func=scoring_func,
                routed_scaling_factor=routed_scaling_factor,
                e_score_correction_bias=e_score_correction_bias,
            )

            return torch.ops.vllm.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,
                self._get_tile_tokens_dim(x, top_k),
                1 if renormalize else 0,  # routing_method_type, renormalize
                True,  # do finalize
                tune_max_num_tokens=self.max_capture_size,
            )[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, _ = FusedMoE.select_experts(
                hidden_states=x,
                router_logits=router_logits,
                use_grouped_topk=use_grouped_topk,
                top_k=top_k,
                renormalize=renormalize,
                topk_group=topk_group,
                num_expert_group=num_expert_group,
                custom_routing_function=custom_routing_function,
                scoring_func=scoring_func,
                e_score_correction_bias=e_score_correction_bias,
            )

            # 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=self.max_capture_size,
                **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=self.w13_weight_triton_tensor,
                w2=self.w2_weight_triton_tensor,
                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}")