Using ONNX
In addition to Concrete-ML models and to custom models in torch, it is also possible to directly compile ONNX models. This can be particularly appealing, notably to import models trained with Keras (see in this subsection. It can also be interesting in the context of QAT (see in this subsection), since lot of ONNX are available on the web.
ONNX models can be compiled by performing post-training quantization (PTQ) or by directly importing models that are already quantized with quantization aware learning (QAT).

Post training quantization

The following example shows how to compile an ONNX model using post-training quantization. The model was initially tained using Keras, before being exported to ONNX. The training code is not shown here.
import numpy
import onnx
import tensorflow
import tf2onnx
from import compile_onnx_model
from concrete.numpy.compilation import Configuration
class FC(tensorflow.keras.Model):
"""A fully-connected model."""
def __init__(self):
hidden_layer_size = 10
output_size = 5
self.dense1 = tensorflow.keras.layers.Dense(
self.dense2 = tensorflow.keras.layers.Dense(output_size, activation=tensorflow.nn.relu6)
self.flatten = tensorflow.keras.layers.Flatten()
def call(self, inputs):
"""Forward function."""
x = self.flatten(inputs)
x = self.dense1(x)
x = self.dense2(x)
return self.flatten(x)
n_bits = 6
input_output_feature = 2
input_shape = (input_output_feature,)
num_inputs = 1
n_examples = 5000
# Define the Keras model
keras_model = FC(),) + input_shape)
keras_model.compute_output_shape(input_shape=(None, input_output_feature))
# Create random input
inputset = numpy.random.uniform(-100, 100, size=(n_examples, *input_shape))
# Convert to ONNX
tf2onnx.convert.from_keras(keras_model, opset=14, output_path="tmp.model.onnx")
onnx_model = onnx.load("tmp.model.onnx")
# Compile
quantized_numpy_module = compile_onnx_model(
onnx_model, inputset, n_bits=2
# Create test data from the same distribution and quantize using
# learned quantization parameters during compilation
x_test = tuple(numpy.random.uniform(-100, 100, size=(1, *input_shape)) for _ in range(num_inputs))
qtest = quantized_numpy_module.quantize_input(x_test)
y_clear = quantized_numpy_module(*qtest)
y_fhe = quantized_numpy_module.forward_fhe.encrypt_run_decrypt(*qtest)
print("Execution in clear: ", y_clear)
print("Execution in FHE: ", y_fhe)
print("Equality: ", numpy.sum(y_clear == y_fhe), "over", numpy.size(y_fhe), "values")
While Keras was used in this example, it is not officially supported, as additional work is needed to test all of Keras' types of layer and models.

Importing an existing model trained with QAT

QAT models contain quantizers in the ONNX graph. These quantizers ensure that the inputs to the Linear/Dense and Conv layers are quantized. Since these QAT models have quantizers that are configured during training to a specific number of bits, the ONNX graph will need to be imported using the same settings:
n_bits_qat = 3 # number of bits for weights and activations during training
quantized_numpy_module = compile_onnx_model(

Supported operators

The following operators are supported for evaluation and conversion to an equivalent FHE circuit. Other operators were not implemented either due to FHE constraints, or because they are rarely used in PyTorch activations or scikit-learn models.
  • Abs
  • Acos
  • Acosh
  • Add
  • Asin
  • Asinh
  • Atan
  • Atanh
  • AveragePool
  • BatchNormalization
  • Cast
  • Celu
  • Clip
  • Constant
  • Conv
  • Cos
  • Cosh
  • Div
  • Elu
  • Equal
  • Erf
  • Exp
  • Flatten
  • Gemm
  • Greater
  • GreaterOrEqual
  • HardSigmoid
  • HardSwish
  • Identity
  • LeakyRelu
  • Less
  • LessOrEqual
  • Log
  • MatMul
  • Mul
  • Not
  • Or
  • PRelu
  • Pad
  • Pow
  • ReduceSum
  • Relu
  • Reshape
  • Round
  • Selu
  • Sigmoid
  • Sin
  • Sinh
  • Softplus
  • Sub
  • Tan
  • Tanh
  • ThresholdedRelu
  • Transpose
  • Where
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Post training quantization
Importing an existing model trained with QAT
Supported operators