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CUDA C++/PTX intrinsics
=======================

When no tile primitive covers what you need, two escape hatches reach the hardware
directly: **call a backend intrinsic** (the ``T.cuda.*`` / ``T.ptx.*`` namespaces
from ``tvm.backend.cuda``), or **inline raw CUDA** source.

Calling backend intrinsics
--------------------------

``T.cuda.*`` and ``T.ptx.*`` expose the CUDA backend's device intrinsics directly —
synchronization, mbarriers, reductions, and the PTX data-movement / MMA families:

.. code-block:: python

    T.cuda.cta_sync()                    # block barrier (__syncthreads)
    T.cuda.warp_sync()                   # __syncwarp
    T.cuda.warpgroup_sync(8)             # warpgroup barrier
    T.cuda.cta_sum(val, num_warps, scratch.ptr_to([0]))   # block-level reduction

    bar = T.alloc_shared((1,), "uint64")
    T.ptx.mbarrier.init(bar.data, 1)     # mbarrier for async completion
    T.ptx.mbarrier.try_wait(bar.data, phase)

A complete, runnable example — a warp all-reduce via ``T.tvm_warp_shuffle_xor``:

.. code-block:: python

    @T.prim_func
    def warp_reduce(A_ptr: T.handle):
        A = T.match_buffer(A_ptr, (32,), "float32", align=16)
        T.device_entry()
        cta_id = T.cta_id([1]); warp_id = T.warp_id([1]); lane_id = T.lane_id([32])
        v = T.alloc_local((1,), "float32"); i = T.alloc_local((1,), "int32")
        v[0] = T.float32(31 - lane_id)
        i[0] = 16
        while i[0] >= 1:
            v[0] += T.tvm_warp_shuffle_xor(0xFFFFFFFF, v[0], i[0], 32, 32)
            i[0] = i[0] // 2
        A[lane_id] = v[0]

The shuffle lowers straight to ``__shfl_xor_sync``:

.. code-block:: c++

    v_ptr[0] = v_ptr[0] + __shfl_xor_sync(0xFFFFFFFF, v_ptr[0], i_ptr[0], 32);

Other families under ``T.ptx.*`` / ``T.cuda.*``: ``cp_async`` (LDGSTS),
``cp_async.bulk.tensor`` (TMA), ``ldmatrix`` / ``stmatrix``, ``tcgen05.*``
(Blackwell MMA), ``atomic_add``, ``fence`` … See :doc:`../../api/backend` for the
full ``tvm.backend.cuda`` reference.

Inlining raw CUDA
-----------------

For something with no intrinsic at all, inject a ``__device__`` function from a
source string with ``T.cuda.func_call(name, *args, source_code=..., return_type=...)``:

.. code-block:: python

    SRC = r"""
    __device__ __forceinline__ float my_relu(float x) { return x > 0.f ? x : 0.f; }
    """

    @T.prim_func
    def k(A_ptr: T.handle, B_ptr: T.handle):
        A = T.match_buffer(A_ptr, (256,), "float32")
        B = T.match_buffer(B_ptr, (256,), "float32")
        T.device_entry(); bx = T.cta_id([1]); tx = T.thread_id([256])
        B[tx] = T.cuda.func_call("my_relu", A[tx], source_code=SRC, return_type="float32")

The source is emitted verbatim and the call is wired in:

.. code-block:: c++

    __device__ __forceinline__ float my_relu(float x) { return x > 0.f ? x : 0.f; }
    // ...
    B_ptr[tx] = my_relu(A_ptr[tx]);