submission 69349

import torch
from task import input_t, output_t
from utils import make_match_reference

# Scaling factor vector size
sf_vec_size = 16

# Helper function for ceiling division


def ceil_div(a, b):
    return (a + b - 1) // b


# Helper function to convert scale factor tensor to blocked format
def to_blocked(input_matrix):
    rows, cols = input_matrix.shape

    # Please ensure rows and cols are multiples of 128 and 4 respectively
    n_row_blocks = ceil_div(rows, 128)
    n_col_blocks = ceil_div(cols, 4)

    padded = input_matrix
    blocks = padded.view(n_row_blocks, 128, n_col_blocks,
                         4).permute(0, 2, 1, 3)
    rearranged = blocks.reshape(-1, 4, 32,
                                4).transpose(1, 2).reshape(-1, 32, 16)

    return rearranged.flatten()


def custom_kernel(
    data: input_t,
) -> output_t:
    """
    PyTorch reference implementation of NVFP4 block-scaled GEMV.
    """
    a_ref, b_ref, sfa_ref_cpu, sfb_ref_cpu, _, _, c_ref = data

    # Get dimensions from MxNxL layout
    _, _, l = c_ref.shape

    # Call torch._scaled_mm to compute the GEMV result
    for l_idx in range(l):
        # Convert the scale factor tensor to blocked format
        scale_a = to_blocked(sfa_ref_cpu[:, :, l_idx])
        scale_b = to_blocked(sfb_ref_cpu[:, :, l_idx])
        # (m, k) @ (n, k).T -> (m, n)
        res = torch._scaled_mm(
            a_ref[:, :, l_idx],
            b_ref[:, :, l_idx].transpose(0, 1),
            scale_a.cuda(),
            scale_b.cuda(),
            bias=None,
            out_dtype=torch.float16,
        )
        c_ref[:, 0, l_idx] = res[:, 0]
    return c_ref