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concrete/tests/common/representation/test_intermediate.py
Arthur Meyre 00916bcfdb refactor: rename ArbitraryFunction to UnivariateFunction 
- the naming has always been confusing and recent changes to the code make
this rename necessary for things to be clearer
2021-10-11 11:36:35 +02:00

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"""Test file for intermediate representation"""
import numpy
import pytest
from concrete.common.data_types.floats import Float
from concrete.common.data_types.integers import Integer
from concrete.common.representation import intermediate as ir
from concrete.common.values import ClearScalar, ClearTensor, EncryptedScalar, EncryptedTensor
@pytest.mark.parametrize(
"node,input_data,expected_result",
[
pytest.param(
ir.Add([EncryptedScalar(Integer(64, False)), EncryptedScalar(Integer(64, False))]),
[10, 4589],
4599,
id="Add",
),
pytest.param(
ir.Sub([EncryptedScalar(Integer(64, False)), EncryptedScalar(Integer(64, False))]),
[10, 4589],
-4579,
id="Sub",
),
pytest.param(
ir.Mul([EncryptedScalar(Integer(64, False)), EncryptedScalar(Integer(64, False))]),
[10, 4589],
45890,
id="Mul",
),
pytest.param(ir.Input(ClearScalar(Integer(32, True)), "in", 0), [42], 42, id="Input"),
pytest.param(ir.Constant(42), None, 42, id="Constant"),
pytest.param(ir.Constant(-42), None, -42, id="Constant"),
pytest.param(
ir.UnivariateFunction(
EncryptedScalar(Integer(7, False)), lambda x: x + 3, Integer(7, False)
),
[10],
13,
id="UnivariateFunction, x + 3",
),
pytest.param(
ir.UnivariateFunction(
EncryptedScalar(Integer(7, False)),
lambda x, y: x + y,
Integer(7, False),
op_kwargs={"y": 3},
),
[10],
13,
id="UnivariateFunction, (x, y) -> x + y, where y is constant == 3",
),
pytest.param(
ir.UnivariateFunction(
EncryptedScalar(Integer(7, False)),
lambda x, y: y[x],
Integer(7, False),
op_kwargs={"y": (1, 2, 3, 4)},
),
[2],
3,
id="UnivariateFunction, (x, y) -> y[x], where y is constant == (1, 2, 3, 4)",
),
pytest.param(
ir.UnivariateFunction(
EncryptedScalar(Integer(7, False)),
lambda x, y: y[3],
Integer(7, False),
op_kwargs={"y": (1, 2, 3, 4)},
),
[2],
4,
id="UnivariateFunction, x, y -> y[3], where y is constant == (1, 2, 3, 4)",
),
pytest.param(
ir.Dot(
[
EncryptedTensor(Integer(32, True), shape=(4,)),
ClearTensor(Integer(32, True), shape=(4,)),
],
Integer(32, True),
),
[[1, 2, 3, 4], [4, 3, 2, 1]],
20,
id="Dot, [1, 2, 3, 4], [4, 3, 2, 1]",
),
pytest.param(
ir.Dot(
[
EncryptedTensor(Float(32), shape=(4,)),
ClearTensor(Float(32), shape=(4,)),
],
Float(32),
),
[[1.0, 2.0, 3.0, 4.0], [4.0, 3.0, 2.0, 1.0]],
20,
id="Dot, [1.0, 2.0, 3.0, 4.0], [4.0, 3.0, 2.0, 1.0]",
),
pytest.param(
ir.Dot(
[
EncryptedTensor(Integer(32, True), shape=(4,)),
ClearTensor(Integer(32, True), shape=(4,)),
],
Integer(32, True),
delegate_evaluation_function=numpy.dot,
),
[
numpy.array([1, 2, 3, 4], dtype=numpy.int32),
numpy.array([4, 3, 2, 1], dtype=numpy.int32),
],
20,
id="Dot, np.array([1, 2, 3, 4]), np.array([4, 3, 2, 1])",
),
],
)
def test_evaluate(
node: ir.IntermediateNode,
input_data,
expected_result: int,
):
"""Test evaluate methods on IntermediateNodes"""
assert node.evaluate(input_data) == expected_result
@pytest.mark.parametrize(
"node1,node2,expected_result",
[
(
ir.Add([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(32, False))]),
ir.Add([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(32, False))]),
True,
),
(
ir.Add([EncryptedScalar(Integer(16, False)), EncryptedScalar(Integer(32, False))]),
ir.Add([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(16, False))]),
True,
),
(
ir.Add([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(32, False))]),
ir.Sub([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(32, False))]),
False,
),
(
ir.Sub([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(32, False))]),
ir.Sub([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(32, False))]),
True,
),
(
ir.Sub([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(16, False))]),
ir.Sub([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(16, False))]),
True,
),
(
ir.Sub([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(16, False))]),
ir.Sub([EncryptedScalar(Integer(16, False)), EncryptedScalar(Integer(32, False))]),
False,
),
(
ir.Mul([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(32, False))]),
ir.Mul([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(32, False))]),
True,
),
(
ir.Mul([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(32, False))]),
ir.Sub([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(32, False))]),
False,
),
(
ir.Input(EncryptedScalar(Integer(32, False)), "x", 0),
ir.Sub([EncryptedScalar(Integer(32, False)), EncryptedScalar(Integer(32, False))]),
False,
),
(
ir.Input(EncryptedScalar(Integer(32, False)), "x", 0),
ir.Input(EncryptedScalar(Integer(32, False)), "x", 0),
True,
),
(
ir.Input(EncryptedScalar(Integer(32, False)), "x", 0),
ir.Input(EncryptedScalar(Integer(32, False)), "y", 0),
False,
),
(
ir.Input(EncryptedScalar(Integer(32, False)), "x", 0),
ir.Input(EncryptedScalar(Integer(32, False)), "x", 1),
False,
),
(
ir.Input(EncryptedScalar(Integer(32, False)), "x", 0),
ir.Input(EncryptedScalar(Integer(8, False)), "x", 0),
False,
),
(
ir.Constant(10),
ir.Constant(10),
True,
),
(
ir.Constant(10),
ir.Input(EncryptedScalar(Integer(8, False)), "x", 0),
False,
),
(
ir.Constant(10),
ir.Constant(10.0),
False,
),
(
ir.UnivariateFunction(
EncryptedScalar(Integer(8, False)), lambda x: x, Integer(8, False)
),
ir.UnivariateFunction(
EncryptedScalar(Integer(8, False)), lambda x: x, Integer(8, False)
),
True,
),
(
ir.UnivariateFunction(
EncryptedScalar(Integer(8, False)),
lambda x: x,
Integer(8, False),
op_args=(1, 2, 3),
),
ir.UnivariateFunction(
EncryptedScalar(Integer(8, False)), lambda x: x, Integer(8, False)
),
False,
),
(
ir.UnivariateFunction(
EncryptedScalar(Integer(8, False)),
lambda x: x,
Integer(8, False),
op_kwargs={"tuple": (1, 2, 3)},
),
ir.UnivariateFunction(
EncryptedScalar(Integer(8, False)), lambda x: x, Integer(8, False)
),
False,
),
(
ir.Dot(
[
EncryptedTensor(Integer(32, True), shape=(4,)),
ClearTensor(Integer(32, True), shape=(4,)),
],
Integer(32, True),
delegate_evaluation_function=numpy.dot,
),
ir.Dot(
[
EncryptedTensor(Integer(32, True), shape=(4,)),
ClearTensor(Integer(32, True), shape=(4,)),
],
Integer(32, True),
delegate_evaluation_function=numpy.dot,
),
True,
),
(
ir.Dot(
[
EncryptedTensor(Integer(32, True), shape=(4,)),
ClearTensor(Integer(32, True), shape=(4,)),
],
Integer(32, True),
delegate_evaluation_function=numpy.dot,
),
ir.Dot(
[
EncryptedTensor(Integer(32, True), shape=(4,)),
ClearTensor(Integer(32, True), shape=(4,)),
],
Integer(32, True),
),
False,
),
],
)
def test_is_equivalent_to(
node1: ir.IntermediateNode,
node2: ir.IntermediateNode,
expected_result: bool,
test_helpers,
):
"""Test is_equivalent_to methods on IntermediateNodes"""
assert (
test_helpers.nodes_are_equivalent(node1, node2)
== test_helpers.nodes_are_equivalent(node2, node1)
== expected_result
)
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