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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_gyexamples_plot_numba.py>`
        to download the full example code

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

.. _sphx_glr_gyexamples_plot_numba.py:


.. _l-b-numpy-numba-ort:

Compares numba, numpy, onnxruntime for simple functions
=======================================================

The following benchmark is inspired from `bench_arrayexprs.py
<https://github.com/numba/numba-benchmark/blob/master/benchmarks/bench_arrayexprs.py>`_.
It compares :epkg:`numba`, :epkg:`numpy` and :epkg:`onnxruntime`
for simple functions. As expected, :epkg:`numba` is better than the other options.

.. contents::
    :local:

The functions
+++++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 18-61

.. code-block:: default


    import numpy
    import pandas
    import matplotlib.pyplot as plt
    from numba import jit
    from typing import Any
    import numpy as np
    from tqdm import tqdm
    from cpyquickhelper.numbers.speed_measure import measure_time
    from mlprodict.npy import NDArray, onnxnumpy_np
    from mlprodict.npy.onnx_numpy_annotation import NDArrayType
    import mlprodict.npy.numpy_onnx_impl as npnx


    # @jit(nopython=True)
    def sum(a, b):
        return a + b

    # @jit(nopython=True)


    def sq_diff(a, b):
        return (a - b) * (a + b)

    # @jit(nopython=True)


    def rel_diff(a, b):
        return (a - b) / (a + b)

    # @jit(nopython=True)


    def square(a):
        # Note this is currently slower than `a ** 2 + b`, due to how LLVM
        # seems to lower the power intrinsic.  It's still faster than the naive
        # lowering as `exp(2 * log(a))`, though
        return a ** 2


    def cube(a):
        return a ** 3








.. GENERATED FROM PYTHON SOURCE LINES 62-66

ONNX version
++++++++++

The implementation uses the numpy API for ONNX to keep the same code.

.. GENERATED FROM PYTHON SOURCE LINES 66-98

.. code-block:: default



    @onnxnumpy_np(signature=NDArrayType(("T:all", "T"), dtypes_out=('T',)),
                  runtime="onnxruntime")
    def onnx_sum_32(a, b):
        return a + b


    @onnxnumpy_np(signature=NDArrayType(("T:all", "T"), dtypes_out=('T',)),
                  runtime="onnxruntime")
    def onnx_sq_diff_32(a, b):
        return (a - b) * (a + b)


    @onnxnumpy_np(signature=NDArrayType(("T:all", "T"), dtypes_out=('T',)),
                  runtime="onnxruntime")
    def onnx_rel_diff_32(a, b):
        return (a - b) / (a + b)


    @onnxnumpy_np(signature=NDArrayType(("T:all", ), dtypes_out=('T',)),
                  runtime="onnxruntime")
    def onnx_square_32(a):
        return a ** 2


    @onnxnumpy_np(signature=NDArrayType(("T:all", ), dtypes_out=('T',)),
                  runtime="onnxruntime")
    def onnx_cube_32(a):
        return a ** 3









.. GENERATED FROM PYTHON SOURCE LINES 99-101

numba optimized
++++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 101-109

.. code-block:: default


    jitter = jit(nopython=True)
    nu_sum = jitter(sum)
    nu_sq_diff = jitter(sq_diff)
    nu_rel_diff = jitter(rel_diff)
    nu_square = jitter(square)
    nu_cube = jitter(cube)








.. GENERATED FROM PYTHON SOURCE LINES 110-112

Benchmark
++++++++

.. GENERATED FROM PYTHON SOURCE LINES 112-177

.. code-block:: default


    obs = []

    for n in tqdm([10, 100, 1000, 10000, 100000, 1000000]):
        number = 100 if n < 1000000 else 10
        for dtype in [numpy.float32, numpy.float64]:
            samples = [
                [numpy.random.uniform(1.0, 2.0, size=n).astype(dtype)],
                [numpy.random.uniform(1.0, 2.0, size=n).astype(dtype)
                 for i in range(2)]]

            for fct1, fct2, fct3, n_inputs in [
                    (sum, nu_sum, onnx_sum_32, 2),
                    (sq_diff, nu_sq_diff, onnx_sq_diff_32, 2),
                    (rel_diff, nu_rel_diff, onnx_rel_diff_32, 2),
                    (square, nu_square, onnx_square_32, 1),
                    (cube, nu_cube, onnx_cube_32, 1)]:
                sample = samples[n_inputs - 1]
                if n_inputs == 2:
                    fct1(*sample)
                    fct1(*sample)
                    r = measure_time('fct1(a,b)', number=number, div_by_number=True,
                                     context={'fct1': fct1, 'a': sample[0], 'b': sample[1]})
                    r.update(dict(dtype=dtype, name='numpy', n=n, fct=fct1.__name__))
                    obs.append(r)

                    fct2(*sample)
                    fct2(*sample)
                    r = measure_time('fct2(a,b)', number=number, div_by_number=True,
                                     context={'fct2': fct2, 'a': sample[0], 'b': sample[1]})
                    r.update(dict(dtype=dtype, name='numba', n=n, fct=fct1.__name__))
                    obs.append(r)

                    fct3(*sample)
                    fct3(*sample)
                    r = measure_time('fct3(a,b)', number=number, div_by_number=True,
                                     context={'fct3': fct3, 'a': sample[0], 'b': sample[1]})
                    r.update(dict(dtype=dtype, name='onnx', n=n, fct=fct1.__name__))
                    obs.append(r)
                else:
                    fct1(*sample)
                    fct1(*sample)
                    r = measure_time('fct1(a)', number=number, div_by_number=True,
                                     context={'fct1': fct1, 'a': sample[0]})
                    r.update(dict(dtype=dtype, name='numpy', n=n, fct=fct1.__name__))
                    obs.append(r)

                    fct2(*sample)
                    fct2(*sample)
                    r = measure_time('fct2(a)', number=number, div_by_number=True,
                                     context={'fct2': fct2, 'a': sample[0]})
                    r.update(dict(dtype=dtype, name='numba', n=n, fct=fct1.__name__))
                    obs.append(r)

                    fct3(*sample)
                    fct3(*sample)
                    r = measure_time('fct3(a)', number=number, div_by_number=True,
                                     context={'fct3': fct3, 'a': sample[0]})
                    r.update(dict(dtype=dtype, name='onnx', n=n, fct=fct1.__name__))
                    obs.append(r)

    df = pandas.DataFrame(obs)
    print(df)






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

 .. code-block:: none


      0%|          | 0/6 [00:00<?, ?it/s]
     17%|#6        | 1/6 [00:06<00:31,  6.33s/it]
     33%|###3      | 2/6 [00:07<00:13,  3.36s/it]
     50%|#####     | 3/6 [00:09<00:07,  2.56s/it]
     67%|######6   | 4/6 [00:13<00:06,  3.21s/it]
     83%|########3 | 5/6 [00:47<00:14, 14.33s/it]
    100%|##########| 6/6 [01:19<00:00, 20.43s/it]
    100%|##########| 6/6 [01:19<00:00, 13.29s/it]
          average     deviation  min_exec  ...   name        n      fct
    0    0.000005  8.465340e-08  0.000005  ...  numpy       10      sum
    1    0.000008  8.500073e-08  0.000008  ...  numba       10      sum
    2    0.000104  6.628099e-07  0.000103  ...   onnx       10      sum
    3    0.000012  1.195233e-07  0.000012  ...  numpy       10  sq_diff
    4    0.000008  1.009042e-07  0.000008  ...  numba       10  sq_diff
    ..        ...           ...       ...  ...    ...      ...      ...
    175  0.002890  3.165271e-06  0.002886  ...  numba  1000000   square
    176  0.003891  6.762068e-06  0.003881  ...   onnx  1000000   square
    177  0.126445  2.506645e-05  0.126394  ...  numpy  1000000     cube
    178  0.002877  2.478375e-06  0.002873  ...  numba  1000000     cube
    179  0.003916  3.435483e-06  0.003910  ...   onnx  1000000     cube

    [180 rows x 12 columns]




.. GENERATED FROM PYTHON SOURCE LINES 178-180

Graphs
+++++

.. GENERATED FROM PYTHON SOURCE LINES 180-194

.. code-block:: default


    fcts = list(sorted(set(df.fct)))
    fig, ax = plt.subplots(len(fcts), 2, figsize=(14, len(fcts) * 3))

    for i, fn in enumerate(fcts):
        piv = pandas.pivot(data=df[(df.fct == fn) & (df.dtype == numpy.float32)],
                           index="n", columns="name", values="average")
        piv.plot(title=f"fct={fn} - float32",
                 logx=True, logy=True, ax=ax[i, 0])
        piv = pandas.pivot(data=df[(df.fct == fn) & (df.dtype == numpy.float64)],
                           index="n", columns="name", values="average")
        piv.plot(title=f"fct={fn} - float64",
                 logx=True, logy=True, ax=ax[i, 1])
    plt.show()



.. image-sg:: /gyexamples/images/sphx_glr_plot_numba_001.png
   :alt: fct=cube - float32, fct=cube - float64, fct=rel_diff - float32, fct=rel_diff - float64, fct=sq_diff - float32, fct=sq_diff - float64, fct=square - float32, fct=square - float64, fct=sum - float32, fct=sum - float64
   :srcset: /gyexamples/images/sphx_glr_plot_numba_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 1 minutes  32.162 seconds)


.. _sphx_glr_download_gyexamples_plot_numba.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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