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102 lines
2.8 KiB
Python
102 lines
2.8 KiB
Python
"""
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Tests execution of simulation.
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"""
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import numpy as np
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import pytest
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from concrete import fhe
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@pytest.mark.parametrize("p_error", [0.5, 0.01, 0.005])
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def test_tlu_error_rate(p_error):
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"""
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Test that error rates are realistic.
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"""
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repetition = max(1000, int(10 / p_error))
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assert repetition < 10_0000, "Test will be too long"
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conf = fhe.Configuration(p_error=p_error)
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precision = 3
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@fhe.compiler({"x": "encrypted"})
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def tlu(x):
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return fhe.univariate(lambda x: x)(x)
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inputset = [np.array(list(range(0, 2**precision)))]
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circuit = tlu.compile(inputset, conf)
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nok_fhe = 0
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nok_sim = 0
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size = 0
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for t in inputset * repetition:
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fhe_value = circuit.encrypt_run_decrypt(t)
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sim_value = circuit.simulate(t)
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for v, fhe_v, sim_v in zip(t, fhe_value, sim_value):
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size += 1
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nok_fhe += v != fhe_v
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nok_sim += v != sim_v
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fhe_p_error = nok_fhe / size
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sim_p_error = nok_sim / size
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assert p_error * size > 10 # test invalid if no more than 10 expected errors
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assert fhe_p_error
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assert sim_p_error
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# the sample size is not big => we accept significant releative error
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assert fhe_p_error == pytest.approx(p_error, rel=0.75)
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assert sim_p_error == pytest.approx(p_error, rel=0.75)
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assert fhe_p_error == pytest.approx(sim_p_error, rel=0.75)
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@pytest.mark.parametrize("p_error", [0.01, 1e-120])
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def test_approx_tlu_error_rate(p_error):
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"""
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Test that error rates are realistic in approximate mode.
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"""
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conf = fhe.Configuration(p_error=p_error)
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precision = 8
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lsbs_to_remove = 5
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def rnd(x):
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return (x + 2 ** (lsbs_to_remove - 1)) // 2**lsbs_to_remove
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@fhe.compiler({"x": "encrypted"})
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def tlu(x):
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rounded = fhe.round_bit_pattern(
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x,
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lsbs_to_remove=lsbs_to_remove,
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overflow_protection=False,
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exactness=fhe.Exactness.APPROXIMATE,
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)
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return fhe.univariate(rnd)(rounded)
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inputset = [np.array(list(range(0, 2**precision - 2**lsbs_to_remove)))]
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circuit = tlu.compile(inputset, conf)
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nok_fhe = 0
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nok_sim = 0
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size = 0
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for t in inputset * 30:
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fhe_values = circuit.encrypt_run_decrypt(t)
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sim_values = circuit.simulate(t)
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for x, fhe_v, sim_v in zip(t, fhe_values, sim_values):
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r = rnd(x)
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size += 1
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nok_fhe += r != fhe_v
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nok_sim += r != sim_v
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print(r, fhe_v, sim_v)
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fhe_p_error = nok_fhe / size
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sim_p_error = nok_sim / size
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# error rate is always significant (>3%) in approximate mode
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assert p_error + 0.005 < fhe_p_error
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assert p_error + 0.005 < sim_p_error
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assert fhe_p_error < 0.2 + p_error
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assert sim_p_error < 0.2 + p_error
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assert fhe_p_error == pytest.approx(sim_p_error, rel=0.75)
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