Note
Click here to download the full example code
Benchmark Random Forests, Tree Ensemble, (AoS and SoA)#
The script compares different implementations for the operator TreeEnsembleRegressor.
baseline: RandomForestRegressor from scikit-learn
ort: onnxruntime,
mlprodict: an implementation based on an array of structures, every structure describes a node,
mlprodict2 similar implementation but instead of having an array of structures, it relies on a structure of arrays, it parallelizes by blocks of 128 observations and inside every block, goes through trees then through observations (double loop),
mlprodict3: parallelizes by trees, this implementation is faster when the depth is higher than 10.
A structure of arrays has better performance: Case study: Comparing Arrays of Structures and Structures of Arrays Data Layouts for a Compute-Intensive Loop. See also AoS and SoA.
Profile the execution
py-spy can be used to profile the execution of a program. The profile is more informative if the code is compiled with debug information.
py-spy record --native -r 10 -o plot_random_forest_reg.svg -- python plot_random_forest_reg.py
Import#
import warnings
from time import perf_counter as time
from multiprocessing import cpu_count
import numpy
from numpy.random import rand
from numpy.testing import assert_almost_equal
import pandas
import matplotlib.pyplot as plt
from sklearn import config_context
from sklearn.ensemble import RandomForestRegressor
from sklearn.utils._testing import ignore_warnings
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
from onnxruntime import InferenceSession
from mlprodict.onnxrt import OnnxInference
Available optimisation on this machine.
from mlprodict.testing.experimental_c_impl.experimental_c import code_optimisation
print(code_optimisation())
AVX-omp=8
Versions#
def version():
from datetime import datetime
import sklearn
import numpy
import onnx
import onnxruntime
import skl2onnx
import mlprodict
df = pandas.DataFrame([
{"name": "date", "version": str(datetime.now())},
{"name": "numpy", "version": numpy.__version__},
{"name": "scikit-learn", "version": sklearn.__version__},
{"name": "onnx", "version": onnx.__version__},
{"name": "onnxruntime", "version": onnxruntime.__version__},
{"name": "skl2onnx", "version": skl2onnx.__version__},
{"name": "mlprodict", "version": mlprodict.__version__},
])
return df
version()
Implementations to benchmark#
def fcts_model(X, y, max_depth, n_estimators, n_jobs):
"RandomForestClassifier."
rf = RandomForestRegressor(max_depth=max_depth, n_estimators=n_estimators,
n_jobs=n_jobs)
rf.fit(X, y)
initial_types = [('X', FloatTensorType([None, X.shape[1]]))]
onx = convert_sklearn(rf, initial_types=initial_types)
sess = InferenceSession(onx.SerializeToString())
outputs = [o.name for o in sess.get_outputs()]
oinf = OnnxInference(onx, runtime="python")
oinf.sequence_[0].ops_._init(numpy.float32, 1)
name = outputs[0]
oinf2 = OnnxInference(onx, runtime="python")
oinf2.sequence_[0].ops_._init(numpy.float32, 2)
oinf3 = OnnxInference(onx, runtime="python")
oinf3.sequence_[0].ops_._init(numpy.float32, 3)
def predict_skl_predict(X, model=rf):
return rf.predict(X)
def predict_onnxrt_predict(X, sess=sess):
return sess.run(outputs[:1], {'X': X})[0]
def predict_onnx_inference(X, oinf=oinf):
return oinf.run({'X': X})[name]
def predict_onnx_inference2(X, oinf2=oinf2):
return oinf2.run({'X': X})[name]
def predict_onnx_inference3(X, oinf3=oinf3):
return oinf3.run({'X': X})[name]
return {'predict': (
predict_skl_predict, predict_onnxrt_predict,
predict_onnx_inference, predict_onnx_inference2,
predict_onnx_inference3)}
Benchmarks#
def allow_configuration(**kwargs):
return True
def bench(n_obs, n_features, max_depths, n_estimatorss, n_jobss,
methods, repeat=10, verbose=False):
res = []
for nfeat in n_features:
ntrain = 50000
X_train = numpy.empty((ntrain, nfeat)).astype(numpy.float32)
X_train[:, :] = rand(ntrain, nfeat)[:, :]
eps = rand(ntrain) - 0.5
y_train = X_train.sum(axis=1) + eps
for n_jobs in n_jobss:
for max_depth in max_depths:
for n_estimators in n_estimatorss:
fcts = fcts_model(X_train, y_train,
max_depth, n_estimators, n_jobs)
for n in n_obs:
for method in methods:
fct1, fct2, fct3, fct4, fct5 = fcts[method]
if not allow_configuration(
n=n, nfeat=nfeat, max_depth=max_depth,
n_estimator=n_estimators, n_jobs=n_jobs,
method=method):
continue
obs = dict(n_obs=n, nfeat=nfeat,
max_depth=max_depth,
n_estimators=n_estimators,
method=method,
n_jobs=n_jobs)
# creates different inputs to avoid caching
Xs = []
for r in range(repeat):
x = numpy.empty((n, nfeat))
x[:, :] = rand(n, nfeat)[:, :]
Xs.append(x.astype(numpy.float32))
# measures the baseline
with config_context(assume_finite=True):
st = time()
repeated = 0
for X in Xs:
p1 = fct1(X)
repeated += 1
if time() - st >= 1:
break # stops if longer than a second
end = time()
obs["time_skl"] = (end - st) / repeated
# measures the new implementation
st = time()
r2 = 0
for X in Xs:
p2 = fct2(X)
r2 += 1
if r2 >= repeated:
break
end = time()
obs["time_ort"] = (end - st) / r2
# measures the other new implementation
st = time()
r2 = 0
for X in Xs:
p2 = fct3(X)
r2 += 1
if r2 >= repeated:
break
end = time()
obs["time_mlprodict"] = (end - st) / r2
# measures the other new implementation 2
st = time()
r2 = 0
for X in Xs:
p2 = fct4(X)
r2 += 1
if r2 >= repeated:
break
end = time()
obs["time_mlprodict2"] = (end - st) / r2
# measures the other new implementation 3
st = time()
r2 = 0
for X in Xs:
p2 = fct5(X)
r2 += 1
if r2 >= repeated:
break
end = time()
obs["time_mlprodict3"] = (end - st) / r2
# final
res.append(obs)
if verbose and (len(res) % 1 == 0 or n >= 10000):
print("bench", len(res), ":", obs)
# checks that both produce the same outputs
if n <= 10000:
if len(p1.shape) == 1 and len(p2.shape) == 2:
p2 = p2.ravel()
try:
assert_almost_equal(
p1.ravel(), p2.ravel(), decimal=5)
except AssertionError as e:
warnings.warn(str(e))
return res
Graphs#
def plot_rf_models(dfr):
def autolabel(ax, rects):
for rect in rects:
height = rect.get_height()
ax.annotate(f'{height:1.1f}x',
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom',
fontsize=8)
engines = [_.split('_')[-1] for _ in dfr.columns if _.startswith("time_")]
engines = [_ for _ in engines if _ != 'skl']
for engine in engines:
dfr[f"speedup_{engine}"] = dfr["time_skl"] / dfr[f"time_{engine}"]
print(dfr.tail().T)
ncols = 4
fig, axs = plt.subplots(len(engines), ncols, figsize=(
14, 4 * len(engines)), sharey=True)
row = 0
for row, engine in enumerate(engines):
pos = 0
name = f"RandomForestRegressor - {engine}"
for max_depth in sorted(set(dfr.max_depth)):
for nf in sorted(set(dfr.nfeat)):
for est in sorted(set(dfr.n_estimators)):
for n_jobs in sorted(set(dfr.n_jobs)):
sub = dfr[(dfr.max_depth == max_depth) &
(dfr.nfeat == nf) &
(dfr.n_estimators == est) &
(dfr.n_jobs == n_jobs)]
ax = axs[row, pos]
labels = sub.n_obs
means = sub[f"speedup_{engine}"]
x = numpy.arange(len(labels))
width = 0.90
rects1 = ax.bar(x, means, width, label='Speedup')
if pos == 0:
ax.set_yscale('log')
ax.set_ylim([0.1, max(dfr[f"speedup_{engine}"])])
if pos == 0:
ax.set_ylabel('Speedup')
ax.set_title(
'%s\ndepth %d - %d features\n %d estimators %d '
'jobs' % (name, max_depth, nf, est, n_jobs))
if row == len(engines) - 1:
ax.set_xlabel('batch size')
ax.set_xticks(x)
ax.set_xticklabels(labels)
autolabel(ax, rects1)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(8)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(8)
pos += 1
fig.tight_layout()
return fig, ax
Run benchs#
@ignore_warnings(category=FutureWarning)
def run_bench(repeat=100, verbose=False):
n_obs = [1, 10, 100, 1000, 10000]
methods = ['predict']
n_features = [30]
max_depths = [6, 8, 10, 12]
n_estimatorss = [100]
n_jobss = [cpu_count()]
start = time()
results = bench(n_obs, n_features, max_depths, n_estimatorss, n_jobss,
methods, repeat=repeat, verbose=verbose)
end = time()
results_df = pandas.DataFrame(results)
print("Total time = %0.3f sec cpu=%d\n" % (end - start, cpu_count()))
# plot the results
return results_df
name = "plot_random_forest_reg"
df = run_bench(verbose=True)
df.to_csv(f"{name}.csv", index=False)
df.to_excel(f"{name}.xlsx", index=False)
fig, ax = plot_rf_models(df)
fig.savefig(f"{name}.png")
plt.show()
bench 1 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04274427462466216, 'time_ort': 6.223437473333131e-05, 'time_mlprodict': 0.006505213750642724, 'time_mlprodict2': 0.006133636333591615, 'time_mlprodict3': 0.006030462420312688}
bench 2 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04139167800080031, 'time_ort': 0.0003842888819053769, 'time_mlprodict': 0.012418290758505464, 'time_mlprodict2': 0.0001894340803846717, 'time_mlprodict3': 0.012340408395975827}
bench 3 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.045885168090039355, 'time_ort': 0.00047946963670917535, 'time_mlprodict': 0.012305136224974624, 'time_mlprodict2': 0.007038850136185912, 'time_mlprodict3': 0.012557338637469167}
bench 4 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06382088743703207, 'time_ort': 0.0029533385022659786, 'time_mlprodict': 0.041088564685196616, 'time_mlprodict2': 0.011018224620784167, 'time_mlprodict3': 0.03776891368761426}
bench 5 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.08683001716660026, 'time_ort': 0.015782713249791414, 'time_mlprodict': 0.736361120827496, 'time_mlprodict2': 0.02297877207941686, 'time_mlprodict3': 0.9753920404182281}
bench 6 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04192163804448986, 'time_ort': 6.702054330768685e-05, 'time_mlprodict': 0.006381189208089684, 'time_mlprodict2': 0.006371027251589112, 'time_mlprodict3': 0.0018173234566347674}
bench 7 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.042143816623138264, 'time_ort': 0.0006803655414842069, 'time_mlprodict': 0.012322351249167696, 'time_mlprodict2': 0.000588648957394374, 'time_mlprodict3': 0.012241991668512734}
bench 8 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04502972130380247, 'time_ort': 0.0008717101705058113, 'time_mlprodict': 0.012853026612783256, 'time_mlprodict2': 0.007448452828532975, 'time_mlprodict3': 0.012698329914280253}
bench 9 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0660240198703832, 'time_ort': 0.003177096186846029, 'time_mlprodict': 0.07283168956200825, 'time_mlprodict2': 0.016596586247032974, 'time_mlprodict3': 0.07363988437282387}
bench 10 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0887689823381758, 'time_ort': 0.02344617566753489, 'time_mlprodict': 0.6914812588268736, 'time_mlprodict2': 0.033903778491852186, 'time_mlprodict3': 0.7002630396649087}
bench 11 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04237027762671156, 'time_ort': 0.00010302728818108638, 'time_mlprodict': 0.00621231846162118, 'time_mlprodict2': 0.006183585418815103, 'time_mlprodict3': 0.0032372772499608495}
bench 12 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.043248251958478555, 'time_ort': 0.0009172522938267017, 'time_mlprodict': 0.012395918873759607, 'time_mlprodict2': 0.000969078416043582, 'time_mlprodict3': 0.01256808833568357}
bench 13 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.05404166847246846, 'time_ort': 0.001302548261408351, 'time_mlprodict': 0.013250650004728845, 'time_mlprodict2': 0.008324564207884433, 'time_mlprodict3': 0.013145678002681387}
bench 14 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06621509043907281, 'time_ort': 0.006222726879059337, 'time_mlprodict': 0.09657030319067417, 'time_mlprodict2': 0.02533866231533466, 'time_mlprodict3': 0.06031203343445668}
bench 15 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.09021130333227727, 'time_ort': 0.03384415574449425, 'time_mlprodict': 0.8070350550891211, 'time_mlprodict2': 0.0613647821592167, 'time_mlprodict3': 0.7027815164183266}
bench 16 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04291177958172435, 'time_ort': 8.826204187547167e-05, 'time_mlprodict': 0.006334640498001439, 'time_mlprodict2': 0.006213610497070476, 'time_mlprodict3': 0.006276590705965646}
bench 17 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.043733895477919796, 'time_ort': 0.0010634556048266265, 'time_mlprodict': 0.008363155212820224, 'time_mlprodict2': 0.0012041385255187101, 'time_mlprodict3': 0.012366141997399214}
bench 18 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.05578579688962135, 'time_ort': 0.0017745679425489572, 'time_mlprodict': 0.013762781221885234, 'time_mlprodict2': 0.009069721499044035, 'time_mlprodict3': 0.013756523555558588}
bench 19 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06995020406320691, 'time_ort': 0.009843397132741908, 'time_mlprodict': 0.08748375320186218, 'time_mlprodict2': 0.030305100868766508, 'time_mlprodict3': 0.10346798666287213}
bench 20 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.09470866154879332, 'time_ort': 0.07119774518915536, 'time_mlprodict': 1.0159253080968151, 'time_mlprodict2': 0.10117462536701086, 'time_mlprodict3': 1.08055495608344}
Total time = 389.856 sec cpu=8
15 16 17 18 19
n_obs 1 10 100 1000 10000
nfeat 30 30 30 30 30
max_depth 12 12 12 12 12
n_estimators 100 100 100 100 100
method predict predict predict predict predict
n_jobs 8 8 8 8 8
time_skl 0.042912 0.043734 0.055786 0.06995 0.094709
time_ort 0.000088 0.001063 0.001775 0.009843 0.071198
time_mlprodict 0.006335 0.008363 0.013763 0.087484 1.015925
time_mlprodict2 0.006214 0.001204 0.00907 0.030305 0.101175
time_mlprodict3 0.006277 0.012366 0.013757 0.103468 1.080555
speedup_ort 486.186119 41.124326 31.43627 7.106307 1.33022
speedup_mlprodict 6.774146 5.229354 4.053381 0.799579 0.093224
speedup_mlprodict2 6.906094 36.319655 6.150773 2.308199 0.936091
speedup_mlprodict3 6.836797 3.536584 4.055225 0.676056 0.087648
somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_opml_random_forest_reg.py:323: MatplotlibDeprecationWarning: The label function was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use Tick.label1 instead.
tick.label.set_fontsize(8)
somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_opml_random_forest_reg.py:325: MatplotlibDeprecationWarning: The label function was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use Tick.label1 instead.
tick.label.set_fontsize(8)
Total running time of the script: ( 6 minutes 41.805 seconds)