sgl-project--sglang/python/sglang/srt/layers/quantization/petit_utils.py

from typing import Optional

import torch

try:
    from petit_kernel import mul_nvfp4_a16, process_nvfp4_scales, repack_nvfp4
except ImportError:

    def _check_petit_nvfp4_supported(
        quant_method: str, group_size: Optional[int]
    ) -> tuple[bool, Optional[str]]:
        return (
            False,
            "Petit is not installed. Please install it with `pip install petit-kernel`.",
        )

    def prepare_nvfp4_layer_for_petit(layer: torch.nn.Module) -> None:
        raise ValueError(
            "Petit is not installed. Please install it with `pip install petit-kernel`."
        )

    def apply_petit_nvfp4_linear(
        input: torch.Tensor,
        weight: torch.Tensor,
        weight_scale: torch.Tensor,
        weight_scale_2: torch.Tensor,
        size_n: int,
        size_k: int,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        raise ValueError(
            "Petit is not installed. Please install it with `pip install petit-kernel`."
        )


def _check_petit_nvfp4_supported(
    quant_method: str, group_size: Optional[int]
) -> tuple[bool, Optional[str]]:
    if quant_method != "NVFP4":
        return (
            False,
            "Petit currently only supports: NVFP4"
            " quantizations in sglang. Please check the "
            "`hf_quant_config.json` file for your model's "
            "quant configuration.",
        )
    if group_size is not None and group_size != 16:
        return (
            False,
            "Petit currently only supports: group_size=16" " quantizations.",
        )
    return (True, None)


def verify_petit_nvfp4_supported(quant_method: str, group_size: Optional[int]) -> None:
    supported, error_msg = _check_petit_nvfp4_supported(quant_method, group_size)
    if not supported:
        raise ValueError(error_msg)


def prepare_nvfp4_layer_for_petit(layer: torch.nn.Module) -> None:
    # Repack weights to petit format
    part_size_n = layer.output_size_per_partition
    part_size_k = layer.input_size_per_partition
    qweight = layer.weight.view(torch.int32).contiguous()
    petit_qweight = repack_nvfp4(qweight, size_n=part_size_n, size_k=part_size_k)
    layer.weight = torch.nn.Parameter(petit_qweight, requires_grad=False)

    # Permute scales
    weight_scale = process_nvfp4_scales(
        scales=layer.weight_scale, size_k=part_size_k, size_n=part_size_n
    )
    layer.weight_scale = torch.nn.Parameter(weight_scale, requires_grad=False)

    return


def apply_petit_nvfp4_linear(
    input: torch.Tensor,
    weight: torch.Tensor,
    weight_scale: torch.Tensor,
    weight_scale_2: torch.Tensor,
    size_n: int,
    size_k: int,
    bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
    reshaped_x = input.reshape(-1, input.shape[-1])
    out_shape = input.shape[:-1] + (size_n,)

    # TODO: Use auto-tuning to find the performant solution_id
    output = mul_nvfp4_a16(
        a=reshaped_x,
        b=weight,
        s=weight_scale,
        global_scale=weight_scale_2,
        size_m=reshaped_x.size(0),
        size_n=size_n,
        size_k=size_k,
        solution_id=-1,
    )
    if bias is not None:
        output.add_(bias)  # In-place add

    return output.reshape(out_shape)