.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "getting-started/tutorials/07-extern-functions.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_getting-started_tutorials_07-extern-functions.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_getting-started_tutorials_07-extern-functions.py:


Libdevice (`tl.extra.cuda.libdevice`) function
==============================
Triton can invoke a custom function from an external library.
In this example, we will use the `libdevice` library to apply `asin` on a tensor.
Please refer to https://docs.nvidia.com/cuda/libdevice-users-guide/index.html regarding the semantics of all available libdevice functions.
In `libdevice.py`, we try to aggregate functions with the same computation but different data types together.
For example, both `__nv_asin` and `__nv_asinf` calculate the principal value of the arc sine of the input, but `__nv_asin` operates on `double` and `__nv_asinf` operates on `float`.
Using triton, you can simply call `tl.math.asin`.
Triton automatically selects the correct underlying device function to invoke based on input and output types.

.. GENERATED FROM PYTHON SOURCE LINES 14-16

asin Kernel
------------

.. GENERATED FROM PYTHON SOURCE LINES 16-40

.. code-block:: Python


    import torch

    import triton
    import triton.language as tl
    from triton.language.extra.cuda import libdevice


    @triton.jit
    def asin_kernel(
        x_ptr,
        y_ptr,
        n_elements,
        BLOCK_SIZE: tl.constexpr,
    ):
        pid = tl.program_id(axis=0)
        block_start = pid * BLOCK_SIZE
        offsets = block_start + tl.arange(0, BLOCK_SIZE)
        mask = offsets < n_elements
        x = tl.load(x_ptr + offsets, mask=mask)
        x = libdevice.asin(x)
        tl.store(y_ptr + offsets, x, mask=mask)









.. GENERATED FROM PYTHON SOURCE LINES 41-44

Using the default libdevice library path
-----------------------------------------
We can use the default libdevice library path encoded in `triton/language/math.py`

.. GENERATED FROM PYTHON SOURCE LINES 44-59

.. code-block:: Python


    torch.manual_seed(0)
    size = 98432
    x = torch.rand(size, device='cuda')
    output_triton = torch.zeros(size, device='cuda')
    output_torch = torch.asin(x)
    assert x.is_cuda and output_triton.is_cuda
    n_elements = output_torch.numel()
    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']), )
    asin_kernel[grid](x, output_triton, n_elements, BLOCK_SIZE=1024)
    print(output_torch)
    print(output_triton)
    print(f'The maximum difference between torch and triton is '
          f'{torch.max(torch.abs(output_torch - output_triton))}')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    tensor([0.4105, 0.5430, 0.0249,  ..., 0.0424, 0.5351, 0.8149], device='cuda:0')
    tensor([0.4105, 0.5430, 0.0249,  ..., 0.0424, 0.5351, 0.8149], device='cuda:0')
    The maximum difference between torch and triton is 2.384185791015625e-07




.. GENERATED FROM PYTHON SOURCE LINES 60-63

Customize the libdevice library path
-------------------------------------
We can also customize the libdevice library path by passing the path to the `libdevice` library to the `asin` kernel.

.. GENERATED FROM PYTHON SOURCE LINES 63-70

.. code-block:: Python


    output_triton = torch.empty_like(x)
    asin_kernel[grid](x, output_triton, n_elements, BLOCK_SIZE=1024)
    print(output_torch)
    print(output_triton)
    print(f'The maximum difference between torch and triton is '
          f'{torch.max(torch.abs(output_torch - output_triton))}')




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    tensor([0.4105, 0.5430, 0.0249,  ..., 0.0424, 0.5351, 0.8149], device='cuda:0')
    tensor([0.4105, 0.5430, 0.0249,  ..., 0.0424, 0.5351, 0.8149], device='cuda:0')
    The maximum difference between torch and triton is 2.384185791015625e-07





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.209 seconds)


.. _sphx_glr_download_getting-started_tutorials_07-extern-functions.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 07-extern-functions.ipynb <07-extern-functions.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: 07-extern-functions.py <07-extern-functions.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_