submission 80998

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 ref_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


def generate_input(
    m: int,
    k: int,
    l: int,
    seed: int,
):
    """
    Generate input tensors for NVFP4 block-scaled GEMV.
    
    Args:
        m: Number of rows in matrix A
        k: Number of columns in A (and length of vector b)
        l: Batch size
        seed: Random seed for reproducibility
    
    Returns:
        Tuple of (a, b, scale_a, scale_b, c) where:
            a: [m, k, l] - Input matrix in torch.float4e2m1fn_x2 data type
            b: [1, k, l] - Input vector in torch.float4e2m1fn_x2 data type
            scale_a: [m, k, l] - Input scale factors in torch.float8e4m3fn data type
            scale_b: [1, k, l] - Input scale factors in torch.float8e4m3fn data type
            scale_a_permuted: [32, 4, rest_m, 4, rest_k, l] - Input scale factors in torch.float8e4m3fn data type
            scale_b_permuted: [32, 4, rest_n, 4, rest_k, l] - Input scale factors in torch.float8e4m3fn data type
            c: [m, 1, l] - Output vector in torch.float16 data type
    """
    torch.manual_seed(seed)

    # GEMV N dimension is always 1
    n = 1
    # Scaling factor needs to pad the N size to 128
    n_padded_128 = 128
    
    # Generate uint8 tensor, then convert to float4e2m1fn_x2 data type
    a_ref = torch.randint(
        0, 4, (l, m, k // 2), dtype=torch.uint8, device="cuda"
    ).permute(1, 2, 0)
    # Pad b tensor's N dimension to 128 to call torch._scaled_mm for nvfp4 dot product computation
    b_ref = torch.randint(
        0, 4, (l, n_padded_128, k // 2), dtype=torch.uint8, device="cuda"
    ).permute(1, 2, 0)
    a_ref = a_ref.view(torch.float4_e2m1fn_x2)
    b_ref = b_ref.view(torch.float4_e2m1fn_x2)

    # Create float16 output tensor
    c_ref = torch.randn((l, m, n), dtype=torch.float16, device="cuda").permute(
        1, 2, 0
    )
    
    # Helper function to prepare the scale factor tensors for both reference
    # kernel and customize kernel. The customized data layout can be found in:
    # https://docs.nvidia.com/cuda/cublas/index.html?highlight=fp4#d-block-scaling-factors-layout
    def create_scale_factor_tensors(l, mn, sf_k):
        # Create the reference scale factor tensor (mn, sf_k, l) on CPU.
        ref_shape = (l, mn, sf_k)
        ref_permute_order = (1, 2, 0)
        # Init with uint8 tensor, then convert to float8_e4m3fn
        ref_f8_random_int = torch.randint(0, 3, ref_shape, dtype=torch.int8, device='cuda')
        ref_f8_torch_tensor = ref_f8_random_int.to(dtype=torch.float8_e4m3fn)
        # permute to match ref_permute_order
        ref_f8_torch_tensor_permuted = ref_f8_torch_tensor.permute(*ref_permute_order)
        
        atom_m = (32, 4)
        atom_k = 4
        mma_shape = (
            l,  # batch size
            ceil_div(mn, atom_m[0] * atom_m[1]),
            ceil_div(sf_k, atom_k),
            atom_m[0],
            atom_m[1],
            atom_k,
        )

        # Reorder scale factor tensor to (32, 4, rest_m, 4, rest_k, l) layout
        # Which is needed by the CuTe customized kernel
        mma_permute_order = (3, 4, 1, 5, 2, 0)
        # Generate a random int8 tensor, then convert to float8_e4m3fn
        rand_int_tensor = torch.randint(0, 3, mma_shape, dtype=torch.int8, device='cuda')
        reordered_f8_torch_tensor = rand_int_tensor.to(dtype=torch.float8_e4m3fn)
        # Permute according to mma_permute_order
        reordered_f8_torch_tensor = reordered_f8_torch_tensor.permute(*mma_permute_order)

        # GPU-side vectorized reordering (replaces slow CPU nested loops)
        # Create index grids for all dimensions
        i_idx = torch.arange(mn, device='cuda')
        j_idx = torch.arange(sf_k, device='cuda')
        b_idx = torch.arange(l, device='cuda')
        
        # Create meshgrid for all combinations of (i, j, b)
        i_grid, j_grid, b_grid = torch.meshgrid(i_idx, j_idx, b_idx, indexing='ij')
        
        # Calculate target indices in vectorized manner
        mm = i_grid // (atom_m[0] * atom_m[1])
        mm32 = i_grid % atom_m[0]
        mm4 = (i_grid % 128) // atom_m[0]
        kk = j_grid // atom_k
        kk4 = j_grid % atom_k
        
        # Perform the reordering with advanced indexing (all on GPU)
        reordered_f8_torch_tensor[mm32, mm4, mm, kk4, kk, b_grid] = ref_f8_torch_tensor_permuted[i_grid, j_grid, b_grid]
        
        return ref_f8_torch_tensor_permuted.cpu(), reordered_f8_torch_tensor

    sf_k = ceil_div(k, sf_vec_size)
    sfa_ref_cpu, sfa_permuted = create_scale_factor_tensors(l, m, sf_k)
    sfb_ref_cpu, sfb_permuted = create_scale_factor_tensors(l, n_padded_128, sf_k)

    sfa_ref = sfa_ref_cpu.to("cuda")
    sfb_ref = sfb_ref_cpu.to("cuda")
    
    return (a_ref, b_ref, sfa_ref, sfb_ref, sfa_permuted, sfb_permuted, c_ref)


def custom_kernel(input: input_t) -> output_t:
    # Unpack the input tuple
    a_ref, b_ref, sfa_ref, sfb_ref, sfa_permuted, sfb_permuted, c_ref = input
    
    # TODO: Implement your custom CUDA kernel here
    # For now, you can call the reference implementation or your custom CUDA code
    
    # Return only the output tensor, not the entire input
    return ref_kernel(input)


check_implementation = make_match_reference(ref_kernel, rtol=1e-03, atol=1e-03)