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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_getting-started_tutorials_07-libdevice-function.py>`
        to download the full example code

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

.. _sphx_glr_getting-started_tutorials_07-libdevice-function.py:


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 `trition/language/libdevice.py`, we try to aggregate functions with the same computation but different data types together.
For example, both `__nv_asin` and `__nvasinf` 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.libdevice.asinf`.
triton automatically selects the correct underlying device function to invoke based on input and output types.

.. GENERATED FROM PYTHON SOURCE LINES 15-17

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

.. GENERATED FROM PYTHON SOURCE LINES 17-39

.. code-block:: default


    import torch

    import triton
    import triton.language as tl


    @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 = tl.libdevice.asin(x)
        tl.store(y_ptr + offsets, x, mask=mask)








.. GENERATED FROM PYTHON SOURCE LINES 40-43

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

.. GENERATED FROM PYTHON SOURCE LINES 43-61

.. code-block:: default



    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

 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 62-65

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 65-75

.. code-block:: default


    output_triton = torch.empty_like(x)
    asin_kernel[grid](x, output_triton, n_elements, BLOCK_SIZE=1024,
                      extern_libs={'libdevice': '/usr/local/cuda/nvvm/libdevice/libdevice.10.bc'})
    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

 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  0.254 seconds)


.. _sphx_glr_download_getting-started_tutorials_07-libdevice-function.py:


.. only :: html

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



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

     :download:`Download Python source code: 07-libdevice-function.py <07-libdevice-function.py>`



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

     :download:`Download Jupyter notebook: 07-libdevice-function.ipynb <07-libdevice-function.ipynb>`


.. only:: html

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

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