Compares implementations of ReduceSumSquare

This example compares the numpy for the operator ReduceSumSquare 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 OnnxReduceSumSquare
from cpyquickhelper.numbers import measure_time
from tqdm import tqdm
from mlprodict.testing.experimental_c_impl.experimental_c import code_optimisation
print(code_optimisation())

AVX-omp=8

ReduceSumSquare implementations

try:
    from tensorflow.math import reduce_sum as tf_reduce_sum
    from tensorflow import convert_to_tensor
except ImportError:
    tf_reduce_sum = None
try:
    from torch import sum as torch_sum, from_numpy
except ImportError:
    torch_sum = None


def build_ort_reducesumsquare(axes, op_version=14):  # opset=13, 14, ...
    node = OnnxReduceSumSquare('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="ReduceSumSquare", shape_fct=None):
    if shape_fct is None:
        def shape_fct(dim):
            return (3, dim, 1, 128, 64)
    ort_fct = build_ort_reducesumsquare(axes)
    res = []
    for dim in tqdm([8, 16, 32, 64, 100, 128, 200,
                     256, 400, 512, 1024]):
        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
        ctx = dict(
            xs=xs, ys=ys,
            fct=lambda x, y: numpy.sum(x ** 2, *y),
            loop_fct=loop_fct)
        obs = measure_time(
            "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
        ctx['fct'] = ort_fct
        obs = measure_time(
            "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_sum is not None:
            # tensorflow
            ctx['fct'] = lambda x, y: tf_reduce_sum(x ** 2, y)
            ctx['xs'] = [convert_to_tensor(x) for x in xs]
            ctx['ys'] = ys
            obs = measure_time(
                "loop_fct(fct, xs, 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_sum is not None:
            def torch_sum1(x, y):
                return torch_sum(x ** 2, y[0])

            def torch_sum2(x, y):
                return torch_sum(torch_sum(x ** 2, y[1]), y[0])

            # torch
            ctx['fct'] = torch_sum1 if len(axes) == 1 else torch_sum2
            ctx['xs'] = [from_numpy(x) for x in xs]
            ctx['ys'] = ys  # [from_numpy(y) for y in ys]
            obs = measure_time(
                "loop_fct(fct, xs, 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=f"{name} benchmark\n{shape_name!r} - {axes!r} lower better")
    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. KRK means kept axis - reduced axis - kept 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")

Traceback (most recent call last):
  File "somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_op_reducesumsquare.py", line 171, in <module>
    df, piv, ax = benchmark_op(axes, shape_fct=lambda dim: (8, 24, 48, dim))
  File "somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_op_reducesumsquare.py", line 64, in benchmark_op
    ort_fct = build_ort_reducesumsquare(axes)
  File "somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_op_reducesumsquare.py", line 49, in build_ort_reducesumsquare
    onx = node.to_onnx(inputs=[('x', FloatTensorType())],
  File "somewhere/workspace/mlprodict/mlprodict_UT_39_std/_venv/lib/python3.9/site-packages/skl2onnx/algebra/onnx_operator.py", line 930, in to_onnx
    self.add_to(scope, container, run_converters=True)
  File "somewhere/workspace/mlprodict/mlprodict_UT_39_std/_venv/lib/python3.9/site-packages/skl2onnx/algebra/onnx_operator.py", line 725, in add_to
    self.state.run()
  File "somewhere/workspace/mlprodict/mlprodict_UT_39_std/_venv/lib/python3.9/site-packages/skl2onnx/algebra/graph_state.py", line 631, in run
    self.container.add_node(
  File "somewhere/workspace/mlprodict/mlprodict_UT_39_std/_venv/lib/python3.9/site-packages/skl2onnx/common/_container.py", line 576, in add_node
    raise TypeError(
TypeError: axes must be a list or an array not <class 'tuple'>.

Reduction on a particular case RK

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

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")

(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")

(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")

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")

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")

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 = "reducesumsquare"
merged.to_csv(f"plot_{name}.csv", index=False)
merged.to_excel(f"plot_{name}.xlsx", index=False)
plt.savefig(f"plot_{name}.png")

plt.show()