Compares the conversions of the same model with different options

The script compares two onnx models obtained with the same trained scikit-learn models but converted with different options.

A model

from sklearn.mixture import GaussianMixture
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from skl2onnx import to_onnx
from onnx_array_api.reference import compare_onnx_execution
from onnx_array_api.plotting.text_plot import onnx_simple_text_plot


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

GaussianMixture()

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Conversion to onnx

onx = to_onnx(
    model, X_train[:1], options={id(model): {"score_samples": True}}, target_opset=12
)

print(onnx_simple_text_plot(onx))

opset: domain='' version=12
input: name='X' type=dtype('float64') shape=['', 4]
init: name='Ad_Addcst' type=dtype('float64') shape=(1,) -- array([7.35150827])
init: name='Ge_Gemmcst' type=dtype('float64') shape=(4, 4)
init: name='Ge_Gemmcst1' type=dtype('float64') shape=(4,) -- array([-7.05890183, -7.78812023,  5.41461403,  0.37703043])
init: name='Mu_Mulcst' type=dtype('float64') shape=(1,) -- array([-0.5])
init: name='Ad_Addcst1' type=dtype('float64') shape=(1,) -- array([3.20369698])
init: name='Ad_Addcst2' type=dtype('float64') shape=(1,) -- array([0.])
Gemm(X, Ge_Gemmcst, Ge_Gemmcst1, alpha=1.00, beta=1.00) -> Ge_Y0
  ReduceSumSquare(Ge_Y0, axes=[1], keepdims=1) -> Re_reduced0
    Concat(Re_reduced0, axis=1) -> Co_concat_result0
      Add(Ad_Addcst, Co_concat_result0) -> Ad_C02
        Mul(Ad_C02, Mu_Mulcst) -> Mu_C0
          Add(Mu_C0, Ad_Addcst1) -> Ad_C01
            Add(Ad_C01, Ad_Addcst2) -> Ad_C0
              ArgMax(Ad_C0, axis=1) -> label
              ReduceLogSumExp(Ad_C0, axes=[1], keepdims=1) -> score_samples
              Sub(Ad_C0, score_samples) -> Su_C0
                Exp(Su_C0) -> probabilities
output: name='label' type=dtype('int64') shape=['', 1]
output: name='probabilities' type=dtype('float64') shape=['', 1]
output: name='score_samples' type=dtype('float64') shape=['', 1]

Conversion to onnx without ReduceLogSumExp

onx2 = to_onnx(
    model,
    X_train[:1],
    options={id(model): {"score_samples": True}},
    black_op={"ReduceLogSumExp"},
    target_opset=12,
)

print(onnx_simple_text_plot(onx2))

opset: domain='' version=12
input: name='X' type=dtype('float64') shape=['', 4]
init: name='Ad_Addcst' type=dtype('float64') shape=(1,) -- array([7.35150827])
init: name='Ge_Gemmcst' type=dtype('float64') shape=(4, 4)
init: name='Ge_Gemmcst1' type=dtype('float64') shape=(4,) -- array([-7.05890183, -7.78812023,  5.41461403,  0.37703043])
init: name='Mu_Mulcst' type=dtype('float64') shape=(1,) -- array([-0.5])
init: name='Ad_Addcst1' type=dtype('float64') shape=(1,) -- array([3.20369698])
init: name='Ad_Addcst2' type=dtype('float64') shape=(1,) -- array([0.])
Gemm(X, Ge_Gemmcst, Ge_Gemmcst1, alpha=1.00, beta=1.00) -> Ge_Y0
  Mul(Ge_Y0, Ge_Y0) -> Mu_C01
    ReduceSum(Mu_C01, axes=[1], keepdims=1) -> Re_reduced0
      Concat(Re_reduced0, axis=1) -> Co_concat_result0
        Add(Ad_Addcst, Co_concat_result0) -> Ad_C02
          Mul(Ad_C02, Mu_Mulcst) -> Mu_C0
            Add(Mu_C0, Ad_Addcst1) -> Ad_C01
              Add(Ad_C01, Ad_Addcst2) -> Ad_C0
                ArgMax(Ad_C0, axis=1) -> label
                ReduceMax(Ad_C0, axes=[1], keepdims=1) -> Re_reduced03
                Sub(Ad_C0, Re_reduced03) -> Su_C01
                  Exp(Su_C01) -> Ex_output0
                    ReduceSum(Ex_output0, axes=[1], keepdims=1) -> Re_reduced02
                      Log(Re_reduced02) -> Lo_output0
                  Add(Lo_output0, Re_reduced03) -> score_samples
                Sub(Ad_C0, score_samples) -> Su_C0
                  Exp(Su_C0) -> probabilities
output: name='label' type=dtype('int64') shape=['', 1]
output: name='probabilities' type=dtype('float64') shape=['', 1]
output: name='score_samples' type=dtype('float64') shape=['', 1]

Differences

Function onnx_array_api.reference.compare_onnx_execution() compares the intermediate results of two onnx models. Then it finds the best alignmet between the two models using an edit distance.

res1, res2, align, dc = compare_onnx_execution(onx, onx2, verbose=1)
print("------------")
text = dc.to_str(res1, res2, align)
print(text)

[compare_onnx_execution] generate inputs
[compare_onnx_execution] execute with 1 inputs
[compare_onnx_execution] execute first model
[compare_onnx_execution] got 21 results
[compare_onnx_execution] execute second model
[compare_onnx_execution] got 27 results
[compare_onnx_execution] compute edit distance
[compare_onnx_execution] got 27 pairs
[compare_onnx_execution] done
------------
001 = | INITIA float64  1:1                  HAAA                 Ad | INITIA float64  1:1                  HAAA                 Ad
002 = | INITIA float64  2:4x4                ACZF                 Ge | INITIA float64  2:4x4                ACZF                 Ge
003 = | INITIA float64  1:4                  TTFA                 Ge | INITIA float64  1:4                  TTFA                 Ge
004 = | INITIA float64  1:1                  AAAA                 Mu | INITIA float64  1:1                  AAAA                 Mu
005 = | INITIA float64  1:1                  DAAA                 Ad | INITIA float64  1:1                  DAAA                 Ad
006 = | INITIA float64  1:1                  AAAA                 Ad | INITIA float64  1:1                  AAAA                 Ad
007 = | INPUT  float64  2:1x4                AAAA                 X  | INPUT  float64  2:1x4                AAAA                 X
008 = | RESULT float64  2:1x4                TTGC Gemm            Ge | RESULT float64  2:1x4                TTGC Gemm            Ge
009 + |                                                              | RESULT float64  2:1x4                XZSG Mul             Mu
010 ~ | RESULT float64  2:1x1                VAAA ReduceSumSquare Re | RESULT float64  2:1x1                VAAA ReduceSum       Re
011 = | RESULT float64  2:1x1                VAAA Concat          Co | RESULT float64  2:1x1                VAAA Concat          Co
012 = | RESULT float64  2:1x1                DAAA Add             Ad | RESULT float64  2:1x1                DAAA Add             Ad
013 = | RESULT float64  2:1x1                ZAAA Mul             Mu | RESULT float64  2:1x1                ZAAA Mul             Mu
014 = | RESULT float64  2:1x1                CAAA Add             Ad | RESULT float64  2:1x1                CAAA Add             Ad
015 = | RESULT float64  2:1x1                CAAA Add             Ad | RESULT float64  2:1x1                CAAA Add             Ad
016 = | RESULT int64    2:1x1                AAAA ArgMax          la | RESULT int64    2:1x1                AAAA ArgMax          la
017 + |                                                              | RESULT float64  2:1x1                CAAA ReduceMax       Re
018 + |                                                              | RESULT float64  2:1x1                AAAA Sub             Su
019 + |                                                              | RESULT float64  2:1x1                BAAA Exp             Ex
020 + |                                                              | RESULT float64  2:1x1                BAAA ReduceSum       Re
021 + |                                                              | RESULT float64  2:1x1                AAAA Log             Lo
022 ~ | RESULT float64  2:1x1                CAAA ReduceLogSumExp sc | RESULT float64  2:1x1                CAAA Add             sc
023 = | RESULT float64  2:1x1                AAAA Sub             Su | RESULT float64  2:1x1                AAAA Sub             Su
024 = | RESULT float64  2:1x1                BAAA Exp             pr | RESULT float64  2:1x1                BAAA Exp             pr
025 = | OUTPUT int64    2:1x1                AAAA                 la | OUTPUT int64    2:1x1                AAAA                 la
026 = | OUTPUT float64  2:1x1                BAAA                 pr | OUTPUT float64  2:1x1                BAAA                 pr
027 = | OUTPUT float64  2:1x1                CAAA                 sc | OUTPUT float64  2:1x1                CAAA                 sc

See onnx_array_api.reference.compare_onnx_execution for a better view. The display shows that ReduceSumSquare was replaced by Mul + ReduceSum, and ReduceLogSumExp by ReduceMax + Sub + Exp + Log + Add.