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


import torch

import vllm._custom_ops as ops
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
    USE_FP32_REDUCE_DEFAULT,
    get_marlin_input_dtype,
    marlin_make_workspace_new,
    marlin_permute_bias,
    marlin_permute_scales,
    marlin_quant_input,
    should_use_atomic_add_reduce,
)
from vllm.platforms import current_platform
from vllm.scalar_type import scalar_types

FP4_MARLIN_SUPPORTED_GROUP_SIZES = [16]

logger = init_logger(__name__)


def is_fp4_marlin_supported():
    return current_platform.is_cuda() and current_platform.has_device_capability(75)


def _nvfp4_compute_scale_factor(
    marlin_scales: torch.Tensor,
    a_dtype: torch.dtype | None = None,
) -> float:
    """Compute the power-of-2 scale_factor needed so that all non-zero
    values in marlin_scales * 2^7 are >= 2 after rescaling.
    Returns a Python float (power of 2, >= 1.0)."""

    # Since half has a smaller dynamic range compared to bfloat16,
    # no rescaling is applied here if active dtype is half.
    if a_dtype is not None and a_dtype == torch.half:
        return 1.0

    ws_float = marlin_scales.float() * (2**7)
    nonzero_mask = ws_float > 0
    if nonzero_mask.any():
        max_val = ws_float[nonzero_mask].max()
        if max_val < 448 * (2**7):
            sf = (448 * (2**7) / max_val).log2().floor().exp2()
            return sf.item()
    return 1.0


def nvfp4_marlin_process_scales(
    marlin_scales: torch.Tensor,
    scale_factor: float | None = None,
    a_dtype: torch.dtype | None = None,
) -> tuple[torch.Tensor, float]:
    """Process NVFP4 weight scales into the special S0E5M3 format for Marlin.

    Args:
        marlin_scales: Weight scales tensor in half precision, already
            permuted for the Marlin kernel layout.
        scale_factor: Optional power-of-2 rescaling factor. If None, the
            factor is computed automatically so that every non-zero scale
            satisfies ``scale * 2^7 >= 2`` (i.e., the MSB of the S0E5M3
            representation is always 1). When provided (e.g., for MoE
            layers where all experts must share the same factor), the
            given value is used directly. The caller is responsible for
            dividing ``global_scale`` by the returned ``scale_factor`` to
            preserve numerical correctness.

    Returns:
        A tuple of (processed_scales, scale_factor).
    """
    if not (marlin_scales >= 0).all():
        logger.warning_once(
            "NVFP4 Marlin assumes the scales to be >=0, but has encountered "
            "negative scales. Accuracy will likely be degraded. This is "
            "because it changes the scales from FP8-S1E4M3 to a special "
            "FP8-S0E5M3 format to speedup the dequantization."
        )

    # convert to half first, we would convert to fp8 later
    marlin_scales = marlin_scales.to(torch.half)

    # fit the layout of fp8 dequantization
    marlin_scales = marlin_scales.view(-1, 4)[:, [0, 2, 1, 3]].view(
        marlin_scales.size(0), -1
    )

    # We assume that weight_scale (FP8-S1E4M3) is always greater
    # than or equal to 0. So we can convert
    # (weight_scale * (2 ** 7) to a special FP8-S0E5M3 format.
    # After multiplying by 2 ** 7, the top bit of FP8-S0E5M3 would always be 1
    # when weight_scale > 0. This allows us to have an exponent bias
    # closer to zero after dequantization.

    # Rescale weight_scale so that all non-zero values have MSB=1
    # after multiplying by 2^7 (i.e., weight_scale * 2^7 >= 2).
    # This is needed for models whose E4M3 scales were not normalized
    # to fully utilize the E4M3 dynamic range (e.g., global_scale=1).
    # The caller must compensate by dividing global_scale by scale_factor.
    if scale_factor is None:
        scale_factor = _nvfp4_compute_scale_factor(marlin_scales, a_dtype)
    if scale_factor > 1.0:
        marlin_scales = (marlin_scales.float() * scale_factor).to(torch.half)

    marlin_scales = marlin_scales * (2**7)
    marlin_scales[marlin_scales < 2] = 0
    marlin_scales = marlin_scales.view(torch.int16) << 1
    marlin_scales = marlin_scales.view(torch.float8_e4m3fn)
    marlin_scales = marlin_scales[:, 1::2].contiguous()

    return marlin_scales, scale_factor


def mxfp4_marlin_process_scales(marlin_scales, input_dtype=None):
    # fit the layout of fp8 dequantization
    if input_dtype is None or input_dtype.itemsize == 2:
        marlin_scales = marlin_scales.view(-1, 4)[:, [0, 2, 1, 3]].view(
            marlin_scales.size(0), -1
        )

    marlin_scales = marlin_scales.to(torch.float8_e8m0fnu)
    if input_dtype == torch.float8_e4m3fn:
        marlin_scales = marlin_scales.view(torch.uint8)
        assert marlin_scales.max() <= 249
        # exponent_bias (fp4->fp8) = 2 ** 3 - 2 ** 1 = 6
        marlin_scales = marlin_scales + 6
        marlin_scales = marlin_scales.view(torch.float8_e8m0fnu)
    return marlin_scales


def nvfp4_marlin_process_global_scale(global_scale, a_dtype: torch.dtype | None = None):
    if a_dtype is None:
        a_dtype = global_scale.dtype
    assert a_dtype in [torch.half, torch.bfloat16]
    fp4_exponent = 2
    if a_dtype == torch.half:
        target_exponent = 5
    elif a_dtype == torch.bfloat16:
        target_exponent = 8
    # exponent_bias_fp16 = 2 ** 4 - 2 ** 1 = 14
    # exponent_bias_bf16 = 2 ** 7 - 2 ** 1 = 126
    exponent_bias = 2 ** (target_exponent - 1) - 2 ** (fp4_exponent - 1)
    return global_scale * (2.0 ** (exponent_bias - 7))


def apply_fp4_marlin_linear(
    input: torch.Tensor,
    weight: torch.Tensor,
    weight_scale: torch.Tensor,
    weight_global_scale: torch.Tensor | None,
    workspace: torch.Tensor,
    size_n: int,
    size_k: int,
    bias: torch.Tensor | None = None,
    input_dtype: torch.dtype | None = None,
    use_fp32_reduce: bool = USE_FP32_REDUCE_DEFAULT,
) -> torch.Tensor:
    # For GPUs that lack FP4 hardware support, we can leverage the
    # Marlin kernel for fast weight-only FP4 quantization

    reshaped_x = input.reshape(-1, input.shape[-1])
    out_shape = input.shape[:-1] + (size_n,)

    use_atomic_add = should_use_atomic_add_reduce(
        m=reshaped_x.size(0), n=size_n, k=size_k, device=input.device, dtype=input.dtype
    )

    inputs = reshaped_x
    a_scales = None
    is_nvfp4 = weight_global_scale is not None
    if input_dtype is not None and input_dtype.itemsize == 1:
        if is_nvfp4:
            raise RuntimeError("NVFP4 weight + INT8/FP8 activation is not supported.")
        elif input_dtype != torch.float8_e4m3fn:
            raise RuntimeError("MXFP4 weight + INT8 activation is not supported.")

        inputs, a_scales = marlin_quant_input(inputs, torch.float8_e4m3fn)

    output = ops.marlin_gemm(
        a=inputs,
        c=None,
        b_q_weight=weight,
        b_bias=bias,
        b_scales=weight_scale,
        a_scales=a_scales,
        global_scale=weight_global_scale,
        b_zeros=None,
        g_idx=None,
        perm=None,
        workspace=workspace,
        b_q_type=scalar_types.float4_e2m1f,
        size_m=reshaped_x.size(0),
        size_n=size_n,
        size_k=size_k,
        use_atomic_add=use_atomic_add,
        use_fp32_reduce=use_fp32_reduce,
    )

    return output.reshape(out_shape)


def prepare_fp4_layer_for_marlin(
    layer: torch.nn.Module, input_dtype: torch.dtype | None = None
) -> None:
    is_nvfp4 = hasattr(layer, "weight_global_scale")
    if input_dtype is not None and input_dtype.itemsize == 1:
        if is_nvfp4:
            raise RuntimeError("NVFP4 weight + INT8/FP8 activation is not supported.")
        elif input_dtype != torch.float8_e4m3fn:
            raise RuntimeError("MXFP4 weight + INT8 activation is not supported.")

    group_size = 16 if is_nvfp4 else 32

    part_size_n = layer.output_size_per_partition
    part_size_k = layer.input_size_per_partition
    param_dtype = layer.params_dtype

    assert layer.weight.shape == (part_size_n, part_size_k // 2)

    device = layer.weight.device

    # WORKSPACE
    layer.workspace = marlin_make_workspace_new(device)

    # WEIGHT
    # Repack weights to marlin format
    perm = torch.empty(0, dtype=torch.int, device=device)
    qweight = layer.weight.view(torch.int32).T.contiguous()

    is_a_8bit = input_dtype is not None and input_dtype.itemsize == 1
    marlin_qweight = ops.gptq_marlin_repack(
        b_q_weight=qweight,
        perm=perm,
        size_k=part_size_k,
        size_n=part_size_n,
        num_bits=4,
        is_a_8bit=is_a_8bit,
    )
    layer.weight = torch.nn.Parameter(marlin_qweight, requires_grad=False)

    # WEIGHT SCALES
    # Permute scales
    weight_scale = layer.weight_scale.T.contiguous()

    if not is_nvfp4:
        weight_scale = weight_scale.view(torch.float8_e8m0fnu)

    weight_scale = weight_scale.to(param_dtype)
    weight_scale = marlin_permute_scales(
        s=weight_scale,
        size_k=part_size_k,
        size_n=part_size_n,
        group_size=group_size,
        is_a_8bit=is_a_8bit,
    )

    if is_nvfp4:
        weight_scale, scale_factor = nvfp4_marlin_process_scales(
            weight_scale, a_dtype=param_dtype
        )
        layer.weight_scale = torch.nn.Parameter(weight_scale, requires_grad=False)

        weight_global_scale = layer.weight_global_scale.to(torch.float32)
        weight_global_scale = nvfp4_marlin_process_global_scale(
            weight_global_scale, param_dtype
        )
        weight_global_scale = weight_global_scale / scale_factor
        layer.weight_global_scale = torch.nn.Parameter(
            weight_global_scale, requires_grad=False
        )
    else:
        weight_scale = mxfp4_marlin_process_scales(
            weight_scale, input_dtype=input_dtype
        )
        layer.weight_scale = torch.nn.Parameter(weight_scale, requires_grad=False)

    if hasattr(layer, "bias") and layer.bias is not None:
        assert layer.bias.shape == (part_size_n,)
        bias = marlin_permute_bias(layer.bias)
        layer.bias = torch.nn.Parameter(bias, requires_grad=False)

    return


def prepare_nvfp4_moe_layer_for_marlin(
    layer: torch.nn.Module,
    w13: torch.Tensor,
    w13_scale: torch.Tensor,
    w13_scale_2: torch.Tensor,
    w2: torch.Tensor,
    w2_scale: torch.Tensor,
    w2_scale_2: torch.Tensor,
    is_act_and_mul: bool,
) -> tuple[
    torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor
]:
    logger.warning_once(
        "Your GPU does not have native support for FP4 computation but "
        "FP4 quantization is being used. Weight-only FP4 compression will "
        "be used leveraging the Marlin kernel. This may degrade "
        "performance for compute-heavy workloads."
    )

    input_dtype = get_marlin_input_dtype(prefix="")
    if input_dtype is not None and input_dtype.itemsize == 1:
        raise RuntimeError("NVFP4 weight + INT8/FP8 activation is not supported.")

    GROUP_SIZE = 16
    E = layer.num_experts
    K = layer.hidden_size
    N = layer.intermediate_size_per_partition

    device = w13.device
    param_dtype = layer.params_dtype
    is_a_8bit = input_dtype is not None and input_dtype.itemsize == 1

    # WORKSPACE
    layer.workspace = marlin_make_workspace_new(device, 4)
    perm = torch.empty(0, dtype=torch.int, device=device)

    # WEIGHT
    # Repack weights to marlin format
    def repack_weight(weight: torch.Tensor, name: str) -> torch.Tensor:
        tensor_list = []
        num_shards = 2 if is_act_and_mul else 1
        if "w13" in name:
            size_n, size_k = N * num_shards, K
        else:
            size_n, size_k = K, N

        assert weight.shape == (E, size_n, size_k // 2)

        for i in range(E):
            qweight = weight[i].view(torch.int32).T.contiguous()

            marlin_qweight = ops.gptq_marlin_repack(
                b_q_weight=qweight,
                perm=perm,
                size_k=size_k,
                size_n=size_n,
                num_bits=4,
                is_a_8bit=is_a_8bit,
            )
            tensor_list.append(marlin_qweight)

        return torch.cat([x.unsqueeze(0) for x in tensor_list], 0)

    w13 = repack_weight(w13, "w13")
    w2 = repack_weight(w2, "w2")

    # WEIGHT SCALES
    # Permute scales
    def premute_scales(
        scales: torch.Tensor, g_scales: torch.Tensor, name: str
    ) -> tuple[torch.Tensor, torch.Tensor]:
        scales = scales.to(param_dtype)

        tensor_list = []
        num_shards = 2 if is_act_and_mul else 1
        if "w13" in name:
            size_n, size_k = N * num_shards, K
        else:
            size_n, size_k = K, N

        # All experts share one global_scale, so compute the max
        # scale_factor across all experts first, then apply uniformly.
        combined_scale_factor = _nvfp4_compute_scale_factor(scales, param_dtype)

        for i in range(E):
            scale = scales[i].T
            marlin_scales = marlin_permute_scales(
                s=scale,
                size_k=size_k,
                size_n=size_n,
                group_size=GROUP_SIZE,
                is_a_8bit=is_a_8bit,
            )
            marlin_scales, _ = nvfp4_marlin_process_scales(
                marlin_scales, scale_factor=combined_scale_factor, a_dtype=param_dtype
            )
            tensor_list.append(marlin_scales)

        scales = torch.cat([x.unsqueeze(0) for x in tensor_list], 0)
        g_scales = nvfp4_marlin_process_global_scale(g_scales, param_dtype)
        g_scales = g_scales / combined_scale_factor
        return scales, g_scales

    w13_scale, w13_scale_2 = premute_scales(w13_scale, w13_scale_2, "w13")
    w2_scale, w2_scale_2 = premute_scales(w2_scale, w2_scale_2, "w2")

    return w13, w13_scale, w13_scale_2, w2, w2_scale, w2_scale_2


def prepare_moe_fp4_layer_for_marlin(
    layer: torch.nn.Module, input_dtype: torch.dtype | None = None
) -> None:
    is_nvfp4 = hasattr(layer, "w13_weight_scale_2")
    if input_dtype is not None and input_dtype.itemsize == 1:
        if is_nvfp4:
            raise RuntimeError("NVFP4 weight + INT8/FP8 activation is not supported.")
        elif input_dtype != torch.float8_e4m3fn:
            raise RuntimeError("MXFP4 weight + INT8 activation is not supported.")

    group_size = 16 if is_nvfp4 else 32

    e = layer.moe_config.num_experts
    k = layer.moe_config.hidden_dim
    n = layer.moe_config.intermediate_size_per_partition

    # WORKSPACE
    device = layer.w13_weight.device
    param_dtype = layer.params_dtype
    layer.workspace = marlin_make_workspace_new(device, 4)
    perm = torch.empty(0, dtype=torch.int, device=device)
    is_a_8bit = input_dtype is not None and input_dtype.itemsize == 1

    # WEIGHT
    # Repack weights to marlin format
    for name in ["w13_weight", "w2_weight"]:
        weight = getattr(layer, name)
        tensor_list = []
        if "w13" in name:
            size_n, size_k = n * 2, k
        else:
            size_n, size_k = k, n

        assert weight.shape == (e, size_n, size_k // 2)

        for i in range(e):
            qweight = weight[i].view(torch.int32).T.contiguous()

            marlin_qweight = ops.gptq_marlin_repack(
                b_q_weight=qweight,
                perm=perm,
                size_k=size_k,
                size_n=size_n,
                num_bits=4,
                is_a_8bit=is_a_8bit,
            )
            tensor_list.append(marlin_qweight)

        weight = torch.cat([x.unsqueeze(0) for x in tensor_list], 0)
        weight = torch.nn.Parameter(weight, requires_grad=False)

        setattr(layer, name, weight)

    # WEIGHT SCALES
    # Permute scales
    for name in ["w13", "w2"]:
        scales = getattr(layer, name + "_weight_scale")
        if not is_nvfp4:
            scales = scales.view(torch.float8_e8m0fnu)
        scales = scales.to(param_dtype)
        if is_nvfp4:
            global_scale = getattr(layer, name + "_weight_scale_2")

        tensor_list = []
        if "w13" in name:
            size_n, size_k = n * 2, k
        else:
            size_n, size_k = k, n

        # For NVFP4: compute unified scale_factor across all experts
        combined_scale_factor = None
        if is_nvfp4:
            combined_scale_factor = _nvfp4_compute_scale_factor(scales, param_dtype)

        for i in range(e):
            scale = scales[i].T

            marlin_scales = marlin_permute_scales(
                s=scale,
                size_k=size_k,
                size_n=size_n,
                group_size=group_size,
                is_a_8bit=is_a_8bit,
            )
            if is_nvfp4:
                marlin_scales, _ = nvfp4_marlin_process_scales(
                    marlin_scales,
                    scale_factor=combined_scale_factor,
                    a_dtype=param_dtype,
                )
            else:
                marlin_scales = mxfp4_marlin_process_scales(
                    marlin_scales, input_dtype=input_dtype
                )
            tensor_list.append(marlin_scales)

        scales = torch.cat([x.unsqueeze(0) for x in tensor_list], 0)
        scales = torch.nn.Parameter(scales, requires_grad=False)
        setattr(layer, name + "_weight_scale", scales)

        if is_nvfp4:
            assert combined_scale_factor is not None
            global_scale = nvfp4_marlin_process_global_scale(global_scale, param_dtype)
            global_scale = global_scale / combined_scale_factor
            global_scale = torch.nn.Parameter(global_scale, requires_grad=False)
            setattr(layer, name + "_weight_scale_2", global_scale)

    # BIAS
    # Permute bias
    for name in ["w13_bias", "w2_bias"]:
        if not hasattr(layer, name):
            continue
        bias = getattr(layer, name).to(param_dtype)

        tensor_list = []
        for i in range(e):
            expert_bias = bias[i]

            tensor_list.append(marlin_permute_bias(expert_bias))

        bias = torch.cat([x.unsqueeze(0) for x in tensor_list], 0)
        bias = torch.nn.Parameter(bias, requires_grad=False)
        setattr(layer, name, bias)


def prepare_moe_mxfp4_layer_for_marlin(
    layer: torch.nn.Module,
    w13: torch.Tensor,
    w2: torch.Tensor,
    w13_scale: torch.Tensor,
    w2_scale: torch.Tensor,
    w13_bias: torch.Tensor | None,
    w2_bias: torch.Tensor | None,
) -> tuple[
    torch.Tensor,
    torch.Tensor,
    torch.Tensor,
    torch.Tensor,
    torch.Tensor | None,
    torch.Tensor | None,
]:
    """Pure-function version of prepare_moe_fp4_layer_for_marlin for MXFP4.

    Takes weight tensors as inputs and returns transformed tensors.
    Does NOT modify the layer in-place.
    """
    input_dtype = get_marlin_input_dtype()
    if (
        input_dtype is not None
        and input_dtype.itemsize == 1
        and input_dtype != torch.float8_e4m3fn
    ):
        raise RuntimeError("MXFP4 weight + INT8 activation is not supported.")

    group_size = 32  # MXFP4 block size

    # Derive dimensions from actual weight shapes to handle rounded/padded
    # sizes correctly (e.g., Mxfp4MoEMethod rounds up hidden_dim).
    # w13 shape: (E, 2*N, K//2)
    e = w13.shape[0]
    n = w13.shape[1] // 2  # intermediate_size_per_partition
    k = w13.shape[2] * 2  # hidden_size

    device = w13.device
    param_dtype = layer.params_dtype
    is_a_8bit = input_dtype is not None and input_dtype.itemsize == 1
    perm = torch.empty(0, dtype=torch.int, device=device)

    # WEIGHT: Repack weights to marlin format
    def repack_weight(weight: torch.Tensor, name: str) -> torch.Tensor:
        tensor_list = []
        if "w13" in name:
            size_n, size_k = n * 2, k
        else:
            size_n, size_k = k, n

        assert weight.shape == (e, size_n, size_k // 2)

        for i in range(e):
            qweight = weight[i].view(torch.int32).T.contiguous()
            marlin_qweight = ops.gptq_marlin_repack(
                b_q_weight=qweight,
                perm=perm,
                size_k=size_k,
                size_n=size_n,
                num_bits=4,
                is_a_8bit=is_a_8bit,
            )
            tensor_list.append(marlin_qweight)
        return torch.cat([x.unsqueeze(0) for x in tensor_list], 0)

    w13 = repack_weight(w13, "w13")
    w2 = repack_weight(w2, "w2")

    # WEIGHT SCALES: Permute scales
    def permute_scales(scales: torch.Tensor, name: str) -> torch.Tensor:
        scales = scales.view(torch.float8_e8m0fnu)
        scales = scales.to(param_dtype)

        tensor_list = []
        if "w13" in name:
            size_n, size_k = n * 2, k
        else:
            size_n, size_k = k, n

        for i in range(e):
            scale = scales[i].T
            marlin_scales = marlin_permute_scales(
                s=scale,
                size_k=size_k,
                size_n=size_n,
                group_size=group_size,
                is_a_8bit=is_a_8bit,
            )
            marlin_scales = mxfp4_marlin_process_scales(
                marlin_scales, input_dtype=input_dtype
            )
            tensor_list.append(marlin_scales)
        return torch.cat([x.unsqueeze(0) for x in tensor_list], 0)

    w13_scale = permute_scales(w13_scale, "w13")
    w2_scale = permute_scales(w2_scale, "w2")

    # BIAS: Permute bias
    def permute_bias(bias: torch.Tensor | None) -> torch.Tensor | None:
        if bias is None:
            return None
        bias = bias.to(param_dtype)
        tensor_list = []
        for i in range(e):
            tensor_list.append(marlin_permute_bias(bias[i]))
        return torch.cat([x.unsqueeze(0) for x in tensor_list], 0)

    w13_bias = permute_bias(w13_bias)
    w2_bias = permute_bias(w2_bias)

    return w13, w2, w13_scale, w2_scale, w13_bias, w2_bias


def rand_marlin_weight_nvfp4_like(weight, group_size, input_dtype=None):
    is_a_8bit = input_dtype is not None and input_dtype.itemsize == 1

    assert not is_a_8bit, "NVFP4 weight + INT8/FP8 activation is not supported."
    assert group_size > 0
    size_n, size_k = weight.shape
    device = weight.device

    scales = weight.view(size_n, -1, group_size).abs().max(-1)[0] / 6
    global_scale = scales.max() / 448
    scales = (scales / global_scale).to(torch.float8_e4m3fn)

    fp4_weight = torch.randint(
        0, 256, (size_n, size_k // 2), dtype=torch.uint8, device=weight.device
    )
    fp4_weight_part_1 = (fp4_weight & 0b10000000) | ((fp4_weight & 0b01110000) >> 2)
    fp4_weight_part_1 = fp4_weight_part_1.view(torch.float8_e4m3fn)
    fp4_weight_part_1 = fp4_weight_part_1.to(weight.dtype) * (2**6)

    fp4_weight2 = fp4_weight << 4
    fp4_weight_part_2 = (fp4_weight2 & 0b10000000) | ((fp4_weight2 & 0b01110000) >> 2)
    fp4_weight_part_2 = fp4_weight_part_2.view(torch.float8_e4m3fn)
    fp4_weight_part_2 = fp4_weight_part_2.to(weight.dtype) * (2**6)

    weight_ref = torch.cat(
        [fp4_weight_part_2.unsqueeze(2), fp4_weight_part_1.unsqueeze(2)], 2
    ).view(size_n, size_k)
    weight_ref = (
        weight_ref
        * global_scale.to(weight.dtype)
        * scales.repeat_interleave(group_size, 1).to(weight.dtype)
    )

    marlin_qweight = ops.gptq_marlin_repack(
        b_q_weight=fp4_weight.view(torch.int32).T.contiguous(),
        perm=torch.empty(0, dtype=torch.int, device=device),
        size_k=size_k,
        size_n=size_n,
        num_bits=4,
        is_a_8bit=is_a_8bit,
    )

    marlin_scales = marlin_permute_scales(
        s=scales.T.to(weight.dtype),
        size_k=size_k,
        size_n=size_n,
        group_size=group_size,
        is_a_8bit=is_a_8bit,
    )
    marlin_scales, scale_factor = nvfp4_marlin_process_scales(marlin_scales)

    global_scale = nvfp4_marlin_process_global_scale(global_scale).to(torch.float32)
    global_scale = global_scale / scale_factor

    return weight_ref.T, marlin_qweight, marlin_scales, global_scale


def rand_marlin_weight_mxfp4_like(weight, group_size, input_dtype=None):
    is_a_8bit = input_dtype is not None and input_dtype.itemsize == 1
    if is_a_8bit:
        assert input_dtype == torch.float8_e4m3fn, (
            "MXFP4 weight + INT8 activation is not supported."
        )

    assert group_size > 0
    size_n, size_k = weight.shape
    device = weight.device

    scales = torch.randint(
        110,
        120,
        (size_n, size_k // group_size),
        dtype=torch.uint8,
        device=weight.device,
    )
    scales = scales.view(torch.float8_e8m0fnu)

    fp4_weight = torch.randint(
        0, 256, (size_n, size_k // 2), dtype=torch.uint8, device=weight.device
    )
    fp4_weight_part_1 = (fp4_weight & 0b10000000) | ((fp4_weight & 0b01110000) >> 2)
    fp4_weight_part_1 = fp4_weight_part_1.view(torch.float8_e4m3fn)
    fp4_weight_part_1 = fp4_weight_part_1.to(weight.dtype) * (2**6)

    fp4_weight2 = fp4_weight << 4
    fp4_weight_part_2 = (fp4_weight2 & 0b10000000) | ((fp4_weight2 & 0b01110000) >> 2)
    fp4_weight_part_2 = fp4_weight_part_2.view(torch.float8_e4m3fn)
    fp4_weight_part_2 = fp4_weight_part_2.to(weight.dtype) * (2**6)

    weight_ref = torch.cat(
        [fp4_weight_part_2.unsqueeze(2), fp4_weight_part_1.unsqueeze(2)], 2
    ).view(size_n, size_k)
    weight_ref = weight_ref * scales.repeat_interleave(group_size, 1).to(weight.dtype)

    perm = torch.empty(0, dtype=torch.int, device=device)
    fp4_weight = fp4_weight.view(torch.int32).T.contiguous()
    marlin_qweight = ops.gptq_marlin_repack(
        b_q_weight=fp4_weight,
        perm=perm,
        size_k=size_k,
        size_n=size_n,
        num_bits=4,
        is_a_8bit=is_a_8bit,
    )

    marlin_scales = marlin_permute_scales(
        s=scales.T.to(weight.dtype),
        size_k=size_k,
        size_n=size_n,
        group_size=group_size,
        is_a_8bit=is_a_8bit,
    )

    marlin_scales = mxfp4_marlin_process_scales(marlin_scales, input_dtype=input_dtype)

    return weight_ref.T, marlin_qweight, marlin_scales.to(torch.float8_e8m0fnu)
