Function require_contiguous_format

backends/mlx/ops.py:266–301

Validate that layout/memory_format/dim_order specify contiguous format. MLX only supports contiguous (strided) tensors. Raises ValueError if sparse layouts or non-contiguous memory formats are requested. Args: layout: The torch layout (e.g., torch.strided, torch.sparse_coo

(
    *,
    layout=None,
    memory_format=None,
    dim_order=None,
    op_name: str,
)

def require_contiguous_format(
    *,
    layout=None,
    memory_format=None,
    dim_order=None,
    op_name: str,
) -> None:
    """
    Validate that layout/memory_format/dim_order specify contiguous format.

    MLX only supports contiguous (strided) tensors. Raises ValueError if
    sparse layouts or non-contiguous memory formats are requested.

    Args:
        layout: The torch layout (e.g., torch.strided, torch.sparse_coo)
        memory_format: The torch memory format (e.g., torch.contiguous_format,
            torch.channels_last)
        dim_order: The dimension order (list of ints, identity = contiguous)
        op_name: Name of the operation (for error message)
    """
    if layout is not None and layout != torch.strided:
        raise ValueError(f"{op_name}: only strided layout supported, got {layout}")

    if memory_format is not None and memory_format not in (
        torch.contiguous_format,
        torch.preserve_format,
    ):
        raise ValueError(
            f"{op_name}: only contiguous memory format supported, got {memory_format}"
        )

    if dim_order is not None:
        if list(dim_order) != list(range(len(dim_order))):
            raise ValueError(
                f"{op_name}: only contiguous dim_order supported, got {dim_order}"
            )


def is_static_value(value: Any) -> bool: