warp.tile
=========

.. function:: warp._src.lang.tile(x: Any, preserve_type: bool) -> Tile[Any,tuple]

   .. hlist::
      :columns: 8

      * Kernel
      * Differentiable

   Construct a new tile from per-thread kernel values.
   
   This function converts values computed using scalar kernel code to a tile representation for input into collective operations.
   
   * If the input value is a scalar, then the resulting tile has ``shape=(block_dim,)``
   * If the input value is a vector, then the resulting tile has ``shape=(length(vector), block_dim)``
   * If the input value is a vector, and ``preserve_type=True``, then the resulting tile has ``dtype=vector`` and ``shape=(block_dim,)``
   * If the input value is a matrix, then the resulting tile has ``shape=(rows, cols, block_dim)``
   * If the input value is a matrix, and ``preserve_type=True``, then the resulting tile has ``dtype=matrix`` and ``shape=(block_dim,)``
   
   :param x: A per-thread local value, e.g. scalar, vector, or matrix.
   :param preserve_type: If true, the tile will have the same data type as the input value.
   
   :returns: If ``preserve_type=True``, a tile of type ``x.type`` of length ``block_dim``. Otherwise, an N-dimensional tile such that the first N-1 dimensions match the shape of ``x`` and the final dimension is of size ``block_dim``.
   
   .. rubric:: Example
   
   This example shows how to create a linear sequence from thread variables.
   
   .. code-block:: python
   
       @wp.kernel
       def compute():
           i = wp.tid()
           t = wp.tile(i*2)
           print(t)
   
       wp.launch(compute, dim=16, inputs=[], block_dim=16)
   
   .. code-block:: text
   
       [0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30] = tile(shape=(16), storage=register)