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Control flow
============

Control flow is ``if``, the loop family, and ``while`` — each maps to the obvious
CUDA.

if
--

A Python ``if`` / ``else`` becomes a CUDA ``if`` / ``else``. Guard work by a
thread/lane comparison, or elect a single issuing thread with
``T.ptx.elect_sync()``:

.. code-block:: python

    if tx < 128:
        A[tx] = A[tx] * T.float32(2.0)
    else:
        A[tx] = A[tx] + T.float32(1.0)

    if T.ptx.elect_sync():
        ...                              # one elected lane (e.g. to issue TMA/MMA)

.. code-block:: c++

    if (((int)threadIdx.x) < 128) {
      A_ptr[tx] = A_ptr[tx] * 2.0f;
    } else {
      A_ptr[tx] = A_ptr[tx] + 1.0f;
    }

For an expression-level choice (no branch), use ``T.if_then_else(cond, a, b)``.

loop
----

Loops come in four flavors; a plain Python ``range`` becomes ``T.serial``:

- ``T.serial(n)`` — a sequential loop (ptxas may still unroll it).
- ``T.unroll(n)`` — fully unrolled (expanded to straight-line statements).
- ``T.vectorized(n)`` — a vectorized loop.
- ``T.grid(*extents)`` — a nested loop nest.

``break`` / ``continue`` work inside loops.

.. code-block:: python

    for i, j in T.grid(8, 8):
        B[i, j] = T.max(A[i, j], T.float32(0.0))

.. code-block:: c++

    for (int i = 0; i < 8; ++i)
      for (int j = 0; j < 8; ++j)
        B_ptr[i * 8 + j] = max(A_ptr[i * 8 + j], 0.0f);

``T.unroll(4)`` instead expands to four straight-line statements with no loop.

while
-----

A ``while`` loop runs until its condition is false. Use a mutable scalar counter
(see :doc:`buffers`):

.. code-block:: python

    i: T.int32 = 0
    while i < 64:
        A[i] = A[i] + T.float32(1.0)
        i += 1

It lowers to a ``while (1)`` with an early-exit ``break`` (the counter is a
one-element register buffer):

.. code-block:: c++

    int i_ptr[1];
    i_ptr[0] = 0;
    while (1) {
      if (!(i_ptr[0] < 64)) { break; }
      A_ptr[i_ptr[0]] = A_ptr[i_ptr[0]] + 1.0f;
      i_ptr[0] = i_ptr[0] + 1;
    }