Convert a model with a reduced list of operators

Some runtime dedicated to onnx do not implement all the operators and a converted model may not run if one of them is missing from the list of available operators. Some converters may convert a model in different ways if the users wants to blacklist some operators.

GaussianMixture

The first converter to change its behaviour depending on a black list of operators is for model GaussianMixture.

import onnxruntime
import onnx
import numpy
import os
from timeit import timeit
import numpy as np
import matplotlib.pyplot as plt
from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
from onnxruntime import InferenceSession
from sklearn.mixture import GaussianMixture
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from skl2onnx import to_onnx

data = load_iris()
X_train, X_test = train_test_split(data.data)
model = GaussianMixture()
model.fit(X_train)

GaussianMixture()

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Default conversion

model_onnx = to_onnx(
    model, X_train[:1].astype(np.float32),
    options={id(model): {'score_samples': True}},
    target_opset=12)
sess = InferenceSession(model_onnx.SerializeToString(),
                        providers=["CPUExecutionProvider"])

xt = X_test[:5].astype(np.float32)
print(model.score_samples(xt))
print(sess.run(None, {'X': xt})[2])

[-2.35262849 -1.77470989 -1.85001598 -3.50263433 -2.44689391]
[[-2.3526287]
 [-1.7747092]
 [-1.8500156]
 [-3.5026336]
 [-2.4468932]]

Display the ONNX graph.

pydot_graph = GetPydotGraph(
    model_onnx.graph, name=model_onnx.graph.name, rankdir="TB",
    node_producer=GetOpNodeProducer("docstring", color="yellow",
                                    fillcolor="yellow", style="filled"))
pydot_graph.write_dot("mixture.dot")

os.system('dot -O -Gdpi=300 -Tpng mixture.dot')

image = plt.imread("mixture.dot.png")
fig, ax = plt.subplots(figsize=(40, 20))
ax.imshow(image)
ax.axis('off')

(-0.5, 5287.5, 8425.5, -0.5)

Conversion without ReduceLogSumExp

Parameter black_op is used to tell the converter not to use this operator. Let’s see what the converter produces in that case.

model_onnx2 = to_onnx(
    model, X_train[:1].astype(np.float32),
    options={id(model): {'score_samples': True}},
    black_op={'ReduceLogSumExp'},
    target_opset=12)
sess2 = InferenceSession(model_onnx2.SerializeToString(),
                         providers=["CPUExecutionProvider"])

xt = X_test[:5].astype(np.float32)
print(model.score_samples(xt))
print(sess2.run(None, {'X': xt})[2])

[-2.35262849 -1.77470989 -1.85001598 -3.50263433 -2.44689391]
[[-2.3526287]
 [-1.7747092]
 [-1.8500156]
 [-3.5026336]
 [-2.4468932]]

Display the ONNX graph.

pydot_graph = GetPydotGraph(
    model_onnx2.graph, name=model_onnx2.graph.name, rankdir="TB",
    node_producer=GetOpNodeProducer("docstring", color="yellow",
                                    fillcolor="yellow", style="filled"))
pydot_graph.write_dot("mixture2.dot")

os.system('dot -O -Gdpi=300 -Tpng mixture2.dot')

image = plt.imread("mixture2.dot.png")
fig, ax = plt.subplots(figsize=(40, 20))
ax.imshow(image)
ax.axis('off')

(-0.5, 4921.5, 13264.5, -0.5)

Processing time

print(timeit(stmt="sess.run(None, {'X': xt})",
             number=10000, globals={'sess': sess, 'xt': xt}))

print(timeit(stmt="sess2.run(None, {'X': xt})",
             number=10000, globals={'sess2': sess2, 'xt': xt}))

0.4812870999999177
0.8075209660000837

The model using ReduceLogSumExp is much faster.

If the converter cannot convert without…

Many converters do not consider the white and black lists of operators. If a converter fails to convert without using a blacklisted operator (or only whitelisted operators), skl2onnx raises an error.

try:
    to_onnx(
        model, X_train[:1].astype(np.float32),
        options={id(model): {'score_samples': True}},
        black_op={'ReduceLogSumExp', 'Add'},
        target_opset=12)
except RuntimeError as e:
    print('Error:', e)

Error: Operator 'Add' is black listed.

Versions used for this example

import sklearn  # noqa
print("numpy:", numpy.__version__)
print("scikit-learn:", sklearn.__version__)
import skl2onnx  # noqa
print("onnx: ", onnx.__version__)
print("onnxruntime: ", onnxruntime.__version__)
print("skl2onnx: ", skl2onnx.__version__)

numpy: 1.23.5
scikit-learn: 1.3.dev0
onnx:  1.14.0
onnxruntime:  1.15.0+cpu
skl2onnx:  1.14.0