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])
[-1.55507181 -1.88300778 -3.57222331 -1.67405519 -1.85984688]
[[-1.5550716]
 [-1.8830082]
 [-3.572221 ]
 [-1.6740558]
 [-1.8598464]]

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")
plot black op
(-0.5, 4796.5, 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])
[-1.55507181 -1.88300778 -3.57222331 -1.67405519 -1.85984688]
[[-1.5550716]
 [-1.8830082]
 [-3.5722215]
 [-1.6740558]
 [-1.8598464]]

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")
plot black op
(-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},
    )
)
0.4618227000000843
0.5505055000000993

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: 1.4.dev0
onnx:  1.15.0
onnxruntime:  1.16.0+cu118
skl2onnx:  1.15.0

Total running time of the script: (0 minutes 31.602 seconds)

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