Compares implementations of ReduceMean

This example compares the numpy for the operator ReduceMean to onnxruntime implementation. If available, tensorflow and pytorch are included as well.

Available optimisation

The code shows which parallelisation optimisation could be used, AVX or SSE and the number of available processors.

import numpy
import pandas
import matplotlib.pyplot as plt
from onnxruntime import InferenceSession
from skl2onnx.common.data_types import FloatTensorType
from skl2onnx.algebra.onnx_ops import OnnxReduceMean
from cpyquickhelper.numbers import measure_time
from tqdm import tqdm
from mlprodict.testing.experimental_c_impl.experimental_c import code_optimisation
print(code_optimisation())

Out:

AVX-omp=8

ReduceMean implementations

try:
    from tensorflow.math import reduce_mean as tf_reduce_mean
    from tensorflow import convert_to_tensor
except ImportError:
    tf_reduce_mean = None
try:
    from torch import mean as torch_mean, from_numpy
except ImportError:
    torch_mean = None


def build_ort_reducemean(axes, op_version=14):  # opset=13, 14, ...
    node = OnnxReduceMean('x', axes=axes, op_version=op_version,
                          output_names=['z'])
    onx = node.to_onnx(inputs=[('x', FloatTensorType())],
                       target_opset=op_version)
    sess = InferenceSession(onx.SerializeToString())
    return lambda x, y: sess.run(None, {'x': x})


def loop_fct(fct, xs, ys):
    for x, y in zip(xs, ys):
        fct(x, y)


def benchmark_op(axes, repeat=2, number=5, name="ReduceMean",
                 shape_fct=None, max_dim=None):
    if shape_fct is None:
        def shape_fct(dim):
            return (3, dim, 1, 128, 64)
    ort_fct = build_ort_reducemean(axes)
    res = []
    for dim in tqdm([4, 8, 16, 32, 64, 100, 128, 200,
                     256, 400, 512, 1024]):
        if max_dim is not None and dim > max_dim:
            continue
        shape = shape_fct(dim)
        n_arrays = 10 if dim < 512 else 4
        xs = [numpy.random.rand(*shape).astype(numpy.float32)
              for _ in range(n_arrays)]
        ys = [numpy.array(axes, dtype=numpy.int64)
              for _ in range(n_arrays)]
        info = dict(axes=axes, shape=shape)

        # numpy
        fct = lambda x, y: numpy.mean(x, axis=tuple(y))
        ctx = dict(
            xs=xs, ys=ys,
            loop_fct=loop_fct)
        obs = measure_time(
            lambda: loop_fct(fct, xs, ys),
            div_by_number=True, context=ctx, repeat=repeat, number=number)
        obs['dim'] = dim
        obs['fct'] = 'numpy'
        obs.update(info)
        res.append(obs)

        # onnxruntime
        fct = ort_fct
        obs = measure_time(
            lambda: loop_fct(fct, xs, ys),
            div_by_number=True, context=ctx, repeat=repeat, number=number)
        obs['dim'] = dim
        obs['fct'] = 'ort'
        obs.update(info)
        res.append(obs)

        if tf_reduce_mean is not None:
            # tensorflow
            fct = tf_reduce_mean
            ctx['xs'] = [convert_to_tensor(x) for x in xs]
            ctx['ys'] = ys
            obs = measure_time(
                lambda: loop_fct(fct, ctx['xs'], ctx['ys']),
                div_by_number=True, context=ctx, repeat=repeat, number=number)
            obs['dim'] = dim
            obs['fct'] = 'tf'
            obs.update(info)
            res.append(obs)

        if torch_mean is not None:
            def torch_mean1(x, y):
                return torch_mean(x, y[0])

            def torch_mean2(x, y):
                return torch_mean(torch_mean(x, y[1]), y[0])

            # torch
            fct = torch_mean1 if len(axes) == 1 else torch_mean2
            ctx['xs'] = [from_numpy(x) for x in xs]
            ctx['ys'] = ys  # [from_numpy(y) for y in ys]
            obs = measure_time(
                lambda: loop_fct(fct, ctx['xs'], ctx['ys']),
                div_by_number=True, context=ctx, repeat=repeat, number=number)
            obs['dim'] = dim
            obs['fct'] = 'torch'
            obs.update(info)
            res.append(obs)

    # Dataframes
    shape_name = str(shape).replace(str(dim), "N")
    df = pandas.DataFrame(res)
    df.columns = [_.replace('dim', 'N') for _ in df.columns]
    piv = df.pivot('N', 'fct', 'average')

    rs = piv.copy()
    for c in ['ort', 'torch', 'tf', 'tf_copy']:
        if c in rs.columns:
            rs[c] = rs['numpy'] / rs[c]
    rs['numpy'] = 1.

    # Graphs.
    fig, ax = plt.subplots(1, 2, figsize=(12, 4))
    piv.plot(logx=True, logy=True, ax=ax[0],
             title="%s benchmark\n%r - %r"
                   " lower better" % (name, shape_name, axes))
    ax[0].legend(prop={"size": 9})
    rs.plot(logx=True, logy=True, ax=ax[1],
            title="%s Speedup, baseline=numpy\n%r - %r"
                  " higher better" % (name, shape_name, axes))
    ax[1].plot([min(rs.index), max(rs.index)], [0.5, 0.5], 'g--')
    ax[1].plot([min(rs.index), max(rs.index)], [2., 2.], 'g--')
    ax[1].legend(prop={"size": 9})
    return df, rs, ax


dfs = []

Reduction on a particular case KR

Consecutive axis not reduced and consecutive reduced axis are merged. KR means kept axis - reduced axis

(8, 24, 48, N), axis=(3, )

axes = (3, )
df, piv, ax = benchmark_op(axes, shape_fct=lambda dim: (8, 24, 48, dim))
dfs.append(df)
df.pivot("fct", "N", "average")

Out:

  0%|          | 0/12 [00:00<?, ?it/s]
  8%|8         | 1/12 [00:01<00:11,  1.05s/it]
 17%|#6        | 2/12 [00:02<00:10,  1.08s/it]
 25%|##5       | 3/12 [00:03<00:09,  1.10s/it]
 33%|###3      | 4/12 [00:04<00:09,  1.14s/it]
 42%|####1     | 5/12 [00:05<00:08,  1.20s/it]
 50%|#####     | 6/12 [00:07<00:07,  1.31s/it]
 58%|#####8    | 7/12 [00:08<00:07,  1.41s/it]
 67%|######6   | 8/12 [00:10<00:06,  1.58s/it]
 75%|#######5  | 9/12 [00:13<00:05,  1.77s/it]
 83%|########3 | 10/12 [00:15<00:04,  2.09s/it]
 92%|#########1| 11/12 [00:17<00:01,  1.87s/it]
100%|##########| 12/12 [00:19<00:00,  1.98s/it]
100%|##########| 12/12 [00:19<00:00,  1.62s/it]

N	4	8	16	32	64	100	128	200	256	400	512	1024
fct
numpy	0.005250	0.005936	0.007138	0.008883	0.010977	0.013939	0.016843	0.020975	0.025859	0.039186	0.020295	0.039581
ort	0.001735	0.002064	0.002815	0.002725	0.004195	0.005668	0.006194	0.008099	0.009913	0.015901	0.006448	0.011842
torch	0.096752	0.098519	0.097425	0.098018	0.095185	0.101653	0.100556	0.103768	0.104353	0.107269	0.046290	0.050245

Reduction on a particular case RK

Consecutive axis not reduced and consecutive reduced axis are merged. RK means reduced axis - kept axis

(8, 24, 48, N), axis=(0, )

axes = (0, )
df, piv, ax = benchmark_op(axes, shape_fct=lambda dim: (8, 24, 48, dim))
dfs.append(df)
df.pivot("fct", "N", "average")

Out:

  0%|          | 0/12 [00:00<?, ?it/s]
  8%|8         | 1/12 [00:00<00:09,  1.11it/s]
 17%|#6        | 2/12 [00:01<00:09,  1.06it/s]
 25%|##5       | 3/12 [00:02<00:08,  1.02it/s]
 33%|###3      | 4/12 [00:05<00:11,  1.42s/it]
 42%|####1     | 5/12 [00:08<00:14,  2.05s/it]
 50%|#####     | 6/12 [00:11<00:14,  2.37s/it]
 58%|#####8    | 7/12 [00:14<00:13,  2.68s/it]
 67%|######6   | 8/12 [00:18<00:12,  3.09s/it]
 75%|#######5  | 9/12 [00:22<00:09,  3.27s/it]
 83%|########3 | 10/12 [00:27<00:08,  4.01s/it]
 92%|#########1| 11/12 [00:30<00:03,  3.57s/it]
100%|##########| 12/12 [00:34<00:00,  3.71s/it]
100%|##########| 12/12 [00:34<00:00,  2.86s/it]

N	4	8	16	32	64	100	128	200	256	400	512	1024
fct
numpy	0.001822	0.002499	0.004362	0.007497	0.013902	0.020801	0.028685	0.044102	0.058816	0.087648	0.044360	0.094852
ort	0.001220	0.002980	0.003107	0.005234	0.011832	0.014126	0.018739	0.027379	0.034409	0.051938	0.026700	0.052839
torch	0.085861	0.088730	0.090265	0.186813	0.270201	0.233663	0.244172	0.264116	0.198005	0.305209	0.124955	0.131681

Reduction on a particular case KRK

Consecutive axis not reduced and consecutive reduced axis are merged. KRK means kept axis - reduced axis - kept axis,

(8, 24, 48, N), axis=(1, 2)

axes = (1, 2)
df, piv, ax = benchmark_op(axes, shape_fct=lambda dim: (8, 24, 48, dim))
dfs.append(df)
df.pivot("fct", "N", "average")

Out:

  0%|          | 0/12 [00:00<?, ?it/s]
  8%|8         | 1/12 [00:00<00:10,  1.06it/s]
 17%|#6        | 2/12 [00:01<00:10,  1.00s/it]
 25%|##5       | 3/12 [00:02<00:07,  1.26it/s]
 33%|###3      | 4/12 [00:03<00:07,  1.08it/s]
 42%|####1     | 5/12 [00:04<00:07,  1.07s/it]
 50%|#####     | 6/12 [00:06<00:07,  1.23s/it]
 58%|#####8    | 7/12 [00:08<00:06,  1.37s/it]
 67%|######6   | 8/12 [00:12<00:09,  2.33s/it]
 75%|#######5  | 9/12 [00:16<00:08,  2.89s/it]
 83%|########3 | 10/12 [00:22<00:07,  3.67s/it]
 92%|#########1| 11/12 [00:24<00:03,  3.32s/it]
100%|##########| 12/12 [00:28<00:00,  3.40s/it]
100%|##########| 12/12 [00:28<00:00,  2.35s/it]

N	4	8	16	32	64	100	128	200	256	400	512	1024
fct
numpy	0.005359	0.005713	0.006710	0.008671	0.012293	0.014961	0.017101	0.022878	0.027222	0.038827	0.018957	0.035353
ort	0.009541	0.008113	0.001715	0.002966	0.005379	0.007951	0.010492	0.015985	0.019872	0.027983	0.014768	0.032070
torch	0.078247	0.087355	0.041193	0.091218	0.093541	0.100807	0.100111	0.337551	0.286967	0.356549	0.154748	0.166918

(8, 24 * 48, N), axis=1

axes = (1, )
df, piv, ax = benchmark_op(axes, shape_fct=lambda dim: (8, 24 * 48, dim))
dfs.append(df)
df.pivot("fct", "N", "average")

Out:

  0%|          | 0/12 [00:00<?, ?it/s]
  8%|8         | 1/12 [00:01<00:11,  1.02s/it]
 17%|#6        | 2/12 [00:02<00:10,  1.05s/it]
 25%|##5       | 3/12 [00:03<00:09,  1.04s/it]
 33%|###3      | 4/12 [00:04<00:08,  1.06s/it]
 42%|####1     | 5/12 [00:05<00:07,  1.10s/it]
 50%|#####     | 6/12 [00:06<00:07,  1.17s/it]
 58%|#####8    | 7/12 [00:08<00:06,  1.30s/it]
 67%|######6   | 8/12 [00:10<00:06,  1.52s/it]
 75%|#######5  | 9/12 [00:12<00:05,  1.75s/it]
 83%|########3 | 10/12 [00:15<00:04,  2.16s/it]
 92%|#########1| 11/12 [00:17<00:01,  1.96s/it]
100%|##########| 12/12 [00:19<00:00,  2.23s/it]
100%|##########| 12/12 [00:19<00:00,  1.66s/it]

N	4	8	16	32	64	100	128	200	256	400	512	1024
fct
numpy	0.005330	0.005643	0.006662	0.008672	0.012390	0.014904	0.017044	0.022853	0.027157	0.038807	0.018985	0.035367
ort	0.007697	0.007809	0.001867	0.003561	0.006697	0.007964	0.010090	0.015878	0.018874	0.027757	0.014524	0.032014
torch	0.087321	0.090323	0.089668	0.087096	0.076718	0.078709	0.090962	0.098146	0.103550	0.119886	0.056559	0.093756

(2, 8, 12, 24, 2, N), axis=(2, 3)

axes = (2, 3)
df, piv, ax = benchmark_op(axes, shape_fct=lambda dim: (2, 8, 12, 24, 2, dim))
dfs.append(df)
df.pivot("fct", "N", "average")

Out:

  0%|          | 0/12 [00:00<?, ?it/s]
  8%|8         | 1/12 [00:00<00:10,  1.05it/s]
 17%|#6        | 2/12 [00:01<00:08,  1.17it/s]
 25%|##5       | 3/12 [00:02<00:06,  1.29it/s]
 33%|###3      | 4/12 [00:03<00:05,  1.39it/s]
 42%|####1     | 5/12 [00:04<00:06,  1.09it/s]
 50%|#####     | 6/12 [00:07<00:10,  1.79s/it]
 58%|#####8    | 7/12 [00:10<00:10,  2.03s/it]
 67%|######6   | 8/12 [00:15<00:11,  2.89s/it]
 75%|#######5  | 9/12 [00:20<00:10,  3.57s/it]
 83%|########3 | 10/12 [00:25<00:08,  4.04s/it]
 92%|#########1| 11/12 [00:27<00:03,  3.57s/it]
100%|##########| 12/12 [00:31<00:00,  3.57s/it]
100%|##########| 12/12 [00:31<00:00,  2.61s/it]

N	4	8	16	32	64	100	128	200	256	400	512	1024
fct
numpy	0.003709	0.003746	0.004778	0.007519	0.009932	0.012931	0.015051	0.021055	0.026913	0.037367	0.018578	0.034983
ort	0.004433	0.001429	0.002857	0.004706	0.005341	0.009290	0.011103	0.014446	0.018934	0.026832	0.015756	0.035943
torch	0.085483	0.070374	0.054785	0.041941	0.090934	0.294361	0.188108	0.377195	0.383099	0.324247	0.153697	0.161906

Reduction on a particular case RKR

(N, 64, 16, 16), axis=(0, 2, 3)

axes = (0, 2, 3)
df, piv, ax = benchmark_op(
    axes, shape_fct=lambda dim: (dim, 64, 16, 16))
dfs.append(df)
df.pivot("fct", "N", "average")

Out:

  0%|          | 0/12 [00:00<?, ?it/s]
  8%|8         | 1/12 [00:00<00:07,  1.38it/s]
 17%|#6        | 2/12 [00:01<00:08,  1.16it/s]
 25%|##5       | 3/12 [00:02<00:07,  1.22it/s]
 33%|###3      | 4/12 [00:04<00:10,  1.33s/it]
 42%|####1     | 5/12 [00:08<00:15,  2.24s/it]
 50%|#####     | 6/12 [00:12<00:18,  3.03s/it]
 58%|#####8    | 7/12 [00:17<00:18,  3.62s/it]
 67%|######6   | 8/12 [00:23<00:16,  4.23s/it]
 75%|#######5  | 9/12 [00:29<00:14,  4.81s/it]
 83%|########3 | 10/12 [00:36<00:11,  5.58s/it]
 92%|#########1| 11/12 [00:40<00:04,  4.91s/it]
100%|##########| 12/12 [00:45<00:00,  5.02s/it]
100%|##########| 12/12 [00:45<00:00,  3.78s/it]

N	4	8	16	32	64	100	128	200	256	400	512	1024
fct
numpy	0.001802	0.002750	0.004987	0.007666	0.012809	0.018650	0.022998	0.034023	0.043471	0.065507	0.033721	0.066487
ort	0.000935	0.001526	0.001861	0.004468	0.006951	0.010838	0.012678	0.020324	0.025832	0.039905	0.020763	0.041462
torch	0.067245	0.086429	0.061145	0.182533	0.330078	0.373787	0.379160	0.390240	0.402757	0.412018	0.173057	0.199808

Reduction on a particular case RKRK

(8, 24, 48, N), axis=(0, 2)

axes = (0, 2)
df, piv, ax = benchmark_op(axes, shape_fct=lambda dim: (8, 24, 48, dim))
dfs.append(df)
df.pivot("fct", "N", "average")

Out:

  0%|          | 0/12 [00:00<?, ?it/s]
  8%|8         | 1/12 [00:00<00:04,  2.58it/s]
 17%|#6        | 2/12 [00:01<00:06,  1.64it/s]
 25%|##5       | 3/12 [00:01<00:04,  1.84it/s]
 33%|###3      | 4/12 [00:02<00:06,  1.30it/s]
 42%|####1     | 5/12 [00:03<00:06,  1.09it/s]
 50%|#####     | 6/12 [00:05<00:06,  1.09s/it]
 58%|#####8    | 7/12 [00:06<00:06,  1.25s/it]
 67%|######6   | 8/12 [00:10<00:08,  2.05s/it]
 75%|#######5  | 9/12 [00:15<00:08,  2.89s/it]
 83%|########3 | 10/12 [00:21<00:07,  3.81s/it]
 92%|#########1| 11/12 [00:24<00:03,  3.54s/it]
100%|##########| 12/12 [00:30<00:00,  4.31s/it]
100%|##########| 12/12 [00:30<00:00,  2.52s/it]

N	4	8	16	32	64	100	128	200	256	400	512	1024
fct
numpy	0.006172	0.006453	0.007489	0.009412	0.012839	0.015435	0.017713	0.024007	0.028091	0.040313	0.019482	0.036825
ort	0.000983	0.001413	0.002412	0.004430	0.008724	0.011734	0.017137	0.027030	0.040898	0.056082	0.080110	0.265781
torch	0.030096	0.065485	0.031435	0.088182	0.075088	0.084769	0.085916	0.262728	0.328832	0.369703	0.130138	0.180364

Conclusion

Some of the configurations should be investigated. l-reducesum-problem1. The reduction on tensorflow in one dimension seems to be lazy.

merged = pandas.concat(dfs)
name = "reducemean"
merged.to_csv("plot_%s.csv" % name, index=False)
merged.to_excel("plot_%s.xlsx" % name, index=False)
plt.savefig("plot_%s.png" % name)

plt.show()

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