.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_abs_simd.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_abs_simd.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_plot_abs_simd.py:


Elementwise Abs with runtime SIMD dispatch
==========================================

This example exercises the SIMD-accelerated ``Abs`` kernels provided by
``onnx-light-cpu`` from Python. The extension detects the best available
instruction set (AVX-512, AVX2, AVX, SSE2, or a scalar fallback) once at
runtime and dispatches every call to that implementation.

Each kernel computes the elementwise absolute value of a contiguous 1-D array.
The example runs every supported data type (``float32``, ``float64``,
``int32`` and ``int64``), checks the result against :func:`numpy.abs`, and then
plots the input/output of the ``float32`` kernel to illustrate the operation.

.. GENERATED FROM PYTHON SOURCE LINES 17-23

Setup
-----

Import the compiled extension and report which SIMD level the current CPU
provides. The mapping is ``0=None``, ``1=SSE2``, ``2=AVX``, ``3=AVX2`` and
``4=AVX512``.

.. GENERATED FROM PYTHON SOURCE LINES 23-38

.. code-block:: Python


    import numpy as np

    from onnx_light_cpu.onnx_py._cpukernels import (
        abs,
        detect_simd_level,
        has_cpu_kernels,
    )

    _SIMD_NAMES = {0: "scalar", 1: "SSE2", 2: "AVX", 3: "AVX2", 4: "AVX-512"}

    assert has_cpu_kernels()
    level = detect_simd_level()
    print(f"CPU kernels available, SIMD level: {level} ({_SIMD_NAMES.get(level, level)})")





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

 .. code-block:: none

    CPU kernels available, SIMD level: 4 (AVX-512)




.. GENERATED FROM PYTHON SOURCE LINES 39-44

Run every supported data type
-----------------------------

A single ``abs`` entry point dispatches on the array dtype (just like
:func:`numpy.abs`) and returns a new array of the same dtype.

.. GENERATED FROM PYTHON SOURCE LINES 44-58

.. code-block:: Python


    dtypes = [np.float32, np.float64, np.int32, np.int64]

    rng = np.random.default_rng(0)

    for dtype in dtypes:
        if np.issubdtype(dtype, np.floating):
            inp = rng.uniform(-100.0, 100.0, size=1000).astype(dtype)
        else:
            inp = rng.integers(-100, 100, size=1000).astype(dtype)
        out = abs(inp)
        assert np.array_equal(out, np.abs(inp)), dtype
        print(f"{np.dtype(dtype).name:<8} {inp.size} elements -> matches numpy.abs")





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

 .. code-block:: none

    float32  1000 elements -> matches numpy.abs
    float64  1000 elements -> matches numpy.abs
    int32    1000 elements -> matches numpy.abs
    int64    1000 elements -> matches numpy.abs




.. GENERATED FROM PYTHON SOURCE LINES 59-64

Visualize the float32 kernel
----------------------------

Feed a smooth ramp through the ``float32`` kernel and plot the input against
the computed absolute value.

.. GENERATED FROM PYTHON SOURCE LINES 64-81

.. code-block:: Python


    import matplotlib.pyplot as plt

    x = np.linspace(-5.0, 5.0, 201).astype(np.float32)
    y = abs(x)

    fig, ax = plt.subplots(figsize=(6, 4))
    ax.plot(x, x, label="input", linestyle="--", color="#9b7ec8")
    ax.plot(x, y, label="abs(input)", color="#4a9eff")
    ax.axhline(0.0, color="black", linewidth=0.8)
    ax.axvline(0.0, color="black", linewidth=0.8)
    ax.set_title(f"onnx-light-cpu Abs (SIMD level: {_SIMD_NAMES.get(level, level)})")
    ax.set_xlabel("input")
    ax.set_ylabel("output")
    ax.legend()
    fig.tight_layout()
    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_plot_abs_simd_001.png
   :alt: onnx-light-cpu Abs (SIMD level: AVX-512)
   :srcset: /auto_examples/images/sphx_glr_plot_abs_simd_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.093 seconds)


.. _sphx_glr_download_auto_examples_plot_abs_simd.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_abs_simd.ipynb <plot_abs_simd.ipynb>`

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

      :download:`Download Python source code: plot_abs_simd.py <plot_abs_simd.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_abs_simd.zip <plot_abs_simd.zip>`


.. only:: html

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

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