CReferenceEvaluator

Class CReferenceEvaluator extends onnx.reference.ReferenceEvaluator with custom operators implemented in C++ in order to speed up the evaluation of this python runtime. This class inherits from onnx.reference.ReferenceEvaluator to automatically add the C++ implementation of this operators. It rewrites the following kernels and can be used as follows.

<<<

import numpy as np
from onnx import TensorProto
from onnx.helper import (
    make_graph,
    make_model,
    make_node,
    make_opsetid,
    make_tensor_value_info,
)
from onnx.reference import ReferenceEvaluator
from onnxruntime import InferenceSession
from onnx_extended.ext_test_case import measure_time
from onnx_extended.reference import CReferenceEvaluator

X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
B = make_tensor_value_info("B", TensorProto.FLOAT, [None, None, None, None])
W = make_tensor_value_info("W", TensorProto.FLOAT, [None, None, None, None])
node = make_node(
    "Conv",
    ["X", "W", "B"],
    ["Y"],
    pads=[1, 1, 1, 1],
    dilations=[1, 1],
    strides=[2, 2],
)
graph = make_graph([node], "g", [X, W, B], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 16)])

sH, sW = 64, 64
X = np.arange(sW * sH).reshape((1, 1, sH, sW)).astype(np.float32)
W = np.ones((1, 1, 3, 3), dtype=np.float32)
B = np.array([[[[0]]]], dtype=np.float32)

sess1 = ReferenceEvaluator(onnx_model)
sess2 = CReferenceEvaluator(onnx_model)  # 10 to 100 times faster

expected = sess1.run(None, {"X": X, "W": W, "B": B})[0]
got = sess2.run(None, {"X": X, "W": W, "B": B})[0]
diff = np.abs(expected - got).max()
print(f"difference: {diff}")

>>>

    difference: 0.0

It rewrites the following examples.

<<<

import pprint
from onnx_extended.reference import CReferenceEvaluator

pprint.pprint([cl.__name__ for cl in CReferenceEvaluator.default_ops()])

>>>

    ['Conv',
     'SVMClassifier',
     'SVMRegressor',
     'TfIdfVectorizer',
     'Tokenizer',
     'TreeEnsembleClassifier_1',
     'TreeEnsembleClassifier_3',
     'TreeEnsembleRegressor_1',
     'TreeEnsembleRegressor_3',
     'TreeEnsembleRegressor_1',
     'TreeEnsembleRegressor_3']