Simulation

During development, the speed of homomorphic execution can be a blocker for fast prototyping. You could call the function you're trying to compile directly, of course, but it won't be exactly the same as FHE execution, which has a certain probability of error (see Exactness).

To overcome this issue, simulation is introduced:

from concrete import fhe
import numpy as np

@fhe.compiler({"x": "encrypted"})
def f(x):
    return (x + 1) ** 2

inputset = [np.random.randint(0, 10, size=(10,)) for _ in range(10)]
circuit = f.compile(inputset, p_error=0.1, fhe_simulation=True)

sample = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

actual = f(sample)
simulation = circuit.simulate(sample)

print(actual.tolist())
print(simulation.tolist())

After the simulation runs, it prints the following:

[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
[1, 4, 9, 16, 16, 36, 49, 64, 81, 100]

There are some operations which are not supported in simulation yet. They will result in compilation failures. You can revert to simulation using graph execution using circuit.graph(...) instead of circuit.simulate(...), which won't simulate FHE, but it will evaluate the computation graph, which is like simulating the operations without any errors due to FHE.