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concrete/tests/numpy/test_tracing.py
Umut 5aad8c50ac test: create check_array_equality fixture
2021-12-06 16:44:32 +03:00

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"""Test file for numpy tracing"""
import inspect
import networkx as nx
import numpy
import pytest
from concrete.common.data_types.dtypes_helpers import broadcast_shapes
from concrete.common.data_types.floats import Float
from concrete.common.data_types.integers import Integer
from concrete.common.debugging import format_operation_graph
from concrete.common.representation import intermediate as ir
from concrete.common.values import ClearScalar, ClearTensor, EncryptedScalar, EncryptedTensor
from concrete.numpy import tracing
OPERATIONS_TO_TEST = [ir.Add, ir.Sub, ir.Mul]
@pytest.mark.parametrize(
"operation",
OPERATIONS_TO_TEST,
)
@pytest.mark.parametrize(
"x",
[
pytest.param(EncryptedScalar(Integer(64, is_signed=False)), id="x: Encrypted uint"),
pytest.param(
EncryptedScalar(Integer(64, is_signed=True)),
id="x: Encrypted int",
),
pytest.param(
ClearScalar(Integer(64, is_signed=False)),
id="x: Clear uint",
),
pytest.param(
ClearScalar(Integer(64, is_signed=True)),
id="x: Clear int",
),
],
)
@pytest.mark.parametrize(
"y",
[
pytest.param(EncryptedScalar(Integer(64, is_signed=False)), id="y: Encrypted uint"),
pytest.param(
EncryptedScalar(Integer(64, is_signed=True)),
id="y: Encrypted int",
),
pytest.param(
ClearScalar(Integer(64, is_signed=False)),
id="y: Clear uint",
),
pytest.param(
ClearScalar(Integer(64, is_signed=True)),
id="y: Clear int",
),
],
)
def test_numpy_tracing_binary_op(operation, x, y, test_helpers):
"Test numpy tracing a binary operation (in the supported ops)"
# Remark that the functions here have a common structure (which is
# 2x op y), such that creating further the ref_graph is easy, by
# hand
def simple_add_function(x, y):
z = x + x
return z + y
def simple_sub_function(x, y):
z = x + x
return z - y
def simple_mul_function(x, y):
z = x + x
return z * y
assert operation in OPERATIONS_TO_TEST, f"unknown operation {operation}"
if operation == ir.Add:
function_to_compile = simple_add_function
elif operation == ir.Sub:
function_to_compile = simple_sub_function
elif operation == ir.Mul:
function_to_compile = simple_mul_function
op_graph = tracing.trace_numpy_function(function_to_compile, {"x": x, "y": y})
ref_graph = nx.MultiDiGraph()
input_x = ir.Input(x, input_name="x", program_input_idx=0)
input_y = ir.Input(y, input_name="y", program_input_idx=1)
add_node_z = ir.Add(
(
input_x.outputs[0],
input_x.outputs[0],
)
)
returned_final_node = operation(
(
add_node_z.outputs[0],
input_y.outputs[0],
)
)
ref_graph.add_node(input_x)
ref_graph.add_node(input_y)
ref_graph.add_node(add_node_z)
ref_graph.add_node(returned_final_node)
ref_graph.add_edge(input_x, add_node_z, input_idx=0, output_idx=0)
ref_graph.add_edge(input_x, add_node_z, input_idx=1, output_idx=0)
ref_graph.add_edge(add_node_z, returned_final_node, input_idx=0, output_idx=0)
ref_graph.add_edge(input_y, returned_final_node, input_idx=1, output_idx=0)
assert test_helpers.digraphs_are_equivalent(ref_graph, op_graph.graph)
def test_numpy_tracing_tensors():
"Test numpy tracing tensors"
def all_operations(x):
intermediate = x + numpy.array([[1, 2], [3, 4]])
intermediate = numpy.array([[5, 6], [7, 8]]) + intermediate
intermediate = numpy.array([[100, 200], [300, 400]]) - intermediate
intermediate = intermediate - numpy.array([[10, 20], [30, 40]])
intermediate = intermediate * numpy.array([[1, 2], [2, 1]])
intermediate = numpy.array([[2, 1], [1, 2]]) * intermediate
return intermediate
op_graph = tracing.trace_numpy_function(
all_operations, {"x": EncryptedTensor(Integer(32, True), shape=(2, 2))}
)
expected = """ %0 = [[2 1] [1 2]] # ClearTensor<uint2, shape=(2, 2)>
%1 = [[1 2] [2 1]] # ClearTensor<uint2, shape=(2, 2)>
%2 = [[10 20] [30 40]] # ClearTensor<uint6, shape=(2, 2)>
%3 = [[100 200] [300 400]] # ClearTensor<uint9, shape=(2, 2)>
%4 = [[5 6] [7 8]] # ClearTensor<uint4, shape=(2, 2)>
%5 = x # EncryptedTensor<int32, shape=(2, 2)>
%6 = [[1 2] [3 4]] # ClearTensor<uint3, shape=(2, 2)>
%7 = add(%5, %6) # EncryptedTensor<int32, shape=(2, 2)>
%8 = add(%4, %7) # EncryptedTensor<int32, shape=(2, 2)>
%9 = sub(%3, %8) # EncryptedTensor<int32, shape=(2, 2)>
%10 = sub(%9, %2) # EncryptedTensor<int32, shape=(2, 2)>
%11 = mul(%10, %1) # EncryptedTensor<int32, shape=(2, 2)>
%12 = mul(%0, %11) # EncryptedTensor<int32, shape=(2, 2)>
return %12""" # noqa: E501
assert format_operation_graph(op_graph) == expected, format_operation_graph(op_graph)
def test_numpy_explicit_tracing_tensors():
"Test numpy tracing tensors using explicit operations"
def all_explicit_operations(x):
intermediate = numpy.add(x, numpy.array([[1, 2], [3, 4]]))
intermediate = numpy.add(numpy.array([[5, 6], [7, 8]]), intermediate)
intermediate = numpy.subtract(numpy.array([[100, 200], [300, 400]]), intermediate)
intermediate = numpy.subtract(intermediate, numpy.array([[10, 20], [30, 40]]))
intermediate = numpy.multiply(intermediate, numpy.array([[1, 2], [2, 1]]))
intermediate = numpy.multiply(numpy.array([[2, 1], [1, 2]]), intermediate)
return intermediate
op_graph = tracing.trace_numpy_function(
all_explicit_operations, {"x": EncryptedTensor(Integer(32, True), shape=(2, 2))}
)
expected = """ %0 = [[2 1] [1 2]] # ClearTensor<uint2, shape=(2, 2)>
%1 = [[1 2] [2 1]] # ClearTensor<uint2, shape=(2, 2)>
%2 = [[10 20] [30 40]] # ClearTensor<uint6, shape=(2, 2)>
%3 = [[100 200] [300 400]] # ClearTensor<uint9, shape=(2, 2)>
%4 = [[5 6] [7 8]] # ClearTensor<uint4, shape=(2, 2)>
%5 = x # EncryptedTensor<int32, shape=(2, 2)>
%6 = [[1 2] [3 4]] # ClearTensor<uint3, shape=(2, 2)>
%7 = add(%5, %6) # EncryptedTensor<int32, shape=(2, 2)>
%8 = add(%4, %7) # EncryptedTensor<int32, shape=(2, 2)>
%9 = sub(%3, %8) # EncryptedTensor<int32, shape=(2, 2)>
%10 = sub(%9, %2) # EncryptedTensor<int32, shape=(2, 2)>
%11 = mul(%10, %1) # EncryptedTensor<int32, shape=(2, 2)>
%12 = mul(%0, %11) # EncryptedTensor<int32, shape=(2, 2)>
return %12""" # noqa: E501
assert format_operation_graph(op_graph) == expected
@pytest.mark.parametrize(
"x_shape,y_shape",
[
pytest.param((), ()),
pytest.param((3,), ()),
pytest.param((3,), (1,)),
pytest.param((3,), (2,), marks=pytest.mark.xfail(raises=AssertionError, strict=True)),
pytest.param((3,), (3,)),
pytest.param((2, 3), ()),
pytest.param((2, 3), (1,)),
pytest.param((2, 3), (2,), marks=pytest.mark.xfail(raises=AssertionError, strict=True)),
pytest.param((2, 3), (3,)),
pytest.param((2, 3), (1, 1)),
pytest.param((2, 3), (2, 1)),
pytest.param((2, 3), (3, 1), marks=pytest.mark.xfail(raises=AssertionError, strict=True)),
pytest.param((2, 3), (1, 2), marks=pytest.mark.xfail(raises=AssertionError, strict=True)),
pytest.param((2, 3), (2, 2), marks=pytest.mark.xfail(raises=AssertionError, strict=True)),
pytest.param((2, 3), (3, 2), marks=pytest.mark.xfail(raises=AssertionError, strict=True)),
pytest.param((2, 3), (1, 3)),
pytest.param((2, 3), (2, 3)),
pytest.param((2, 3), (3, 3), marks=pytest.mark.xfail(raises=AssertionError, strict=True)),
pytest.param((2, 1, 3), (1, 1, 1)),
pytest.param((2, 1, 3), (1, 4, 1)),
pytest.param((2, 1, 3), (2, 4, 3)),
],
)
def test_numpy_tracing_broadcasted_tensors(x_shape, y_shape):
"""Test numpy tracing broadcasted tensors"""
def f(x, y):
return x + y
op_graph = tracing.trace_numpy_function(
f,
{
"x": EncryptedTensor(Integer(3, True), shape=x_shape),
"y": EncryptedTensor(Integer(3, True), shape=y_shape),
},
)
assert op_graph.input_nodes[0].outputs[0].shape == x_shape
assert op_graph.input_nodes[1].outputs[0].shape == y_shape
assert op_graph.output_nodes[0].outputs[0].shape == broadcast_shapes(x_shape, y_shape)
@pytest.mark.parametrize(
"function_to_trace,op_graph_expected_output_type,input_and_expected_output_tuples",
[
(
lambda x: x.astype(numpy.int32),
Integer(32, is_signed=True),
[
(14, numpy.int32(14)),
(1.5, numpy.int32(1)),
(2.0, numpy.int32(2)),
(-1.5, numpy.int32(-1)),
(2 ** 31 - 1, numpy.int32(2 ** 31 - 1)),
(-(2 ** 31), numpy.int32(-(2 ** 31))),
],
),
(
lambda x: x.astype(numpy.uint32),
Integer(32, is_signed=False),
[
(14, numpy.uint32(14)),
(1.5, numpy.uint32(1)),
(2.0, numpy.uint32(2)),
(2 ** 32 - 1, numpy.uint32(2 ** 32 - 1)),
],
),
(
lambda x: x.astype(numpy.int64),
Integer(64, is_signed=True),
[
(14, numpy.int64(14)),
(1.5, numpy.int64(1)),
(2.0, numpy.int64(2)),
(-1.5, numpy.int64(-1)),
(2 ** 63 - 1, numpy.int64(2 ** 63 - 1)),
(-(2 ** 63), numpy.int64(-(2 ** 63))),
],
),
(
lambda x: x.astype(numpy.uint64),
Integer(64, is_signed=False),
[
(14, numpy.uint64(14)),
(1.5, numpy.uint64(1)),
(2.0, numpy.uint64(2)),
(2 ** 64 - 1, numpy.uint64(2 ** 64 - 1)),
],
),
(
lambda x: x.astype(numpy.float64),
Float(64),
[
(14, numpy.float64(14.0)),
(1.5, numpy.float64(1.5)),
(2.0, numpy.float64(2.0)),
(-1.5, numpy.float64(-1.5)),
],
),
(
lambda x: x.astype(numpy.float32),
Float(32),
[
(14, numpy.float32(14.0)),
(1.5, numpy.float32(1.5)),
(2.0, numpy.float32(2.0)),
(-1.5, numpy.float32(-1.5)),
],
),
],
)
def test_tracing_astype(
function_to_trace, op_graph_expected_output_type, input_and_expected_output_tuples
):
"""Test function for NPTracer.astype"""
for input_, expected_output in input_and_expected_output_tuples:
input_value = (
EncryptedScalar(Integer(64, is_signed=True))
if isinstance(input_, int)
else EncryptedScalar(Float(64))
)
op_graph = tracing.trace_numpy_function(function_to_trace, {"x": input_value})
output_node = op_graph.output_nodes[0]
assert op_graph_expected_output_type == output_node.outputs[0].dtype
node_results = op_graph.evaluate({0: numpy.array(input_)})
evaluated_output = node_results[output_node]
assert evaluated_output.dtype == expected_output.dtype
assert expected_output == evaluated_output
def test_tracing_astype_single_element_array_corner_case(check_array_equality):
"""Test corner case where an array could be transformed to its scalar element"""
a = numpy.array([1], dtype=numpy.float64)
op_graph = tracing.trace_numpy_function(
lambda x: x.astype(numpy.int32), {"x": EncryptedTensor(Float(64), (1,))}
)
eval_result = op_graph(a)
check_array_equality(eval_result, numpy.array([1], dtype=numpy.int32))
@pytest.mark.parametrize(
"function_to_trace,inputs,expected_output_node,expected_output_value",
[
pytest.param(
lambda x, y: numpy.dot(x, y),
{
"x": EncryptedTensor(Integer(7, is_signed=False), shape=(10,)),
"y": EncryptedTensor(Integer(7, is_signed=False), shape=(10,)),
},
ir.Dot,
EncryptedScalar(Integer(32, False)),
),
pytest.param(
lambda x, y: numpy.dot(x, y),
{
"x": EncryptedTensor(Float(64), shape=(10,)),
"y": EncryptedTensor(Float(64), shape=(10,)),
},
ir.Dot,
EncryptedScalar(Float(64)),
),
pytest.param(
lambda x, y: numpy.dot(x, y),
{
"x": ClearTensor(Integer(64, is_signed=True), shape=(6,)),
"y": ClearTensor(Integer(64, is_signed=True), shape=(6,)),
},
ir.Dot,
ClearScalar(Integer(64, is_signed=True)),
),
pytest.param(
lambda x: numpy.dot(x, numpy.array([1, 2, 3, 4, 5], dtype=numpy.int64)),
{
"x": EncryptedTensor(Integer(64, is_signed=True), shape=(5,)),
},
ir.Dot,
EncryptedScalar(Integer(64, True)),
),
pytest.param(
lambda x: x.dot(numpy.array([1, 2, 3, 4, 5], dtype=numpy.int64)),
{
"x": EncryptedTensor(Integer(64, is_signed=True), shape=(5,)),
},
ir.Dot,
EncryptedScalar(Integer(64, True)),
),
],
)
def test_trace_numpy_dot(function_to_trace, inputs, expected_output_node, expected_output_value):
"""Function to test dot tracing"""
op_graph = tracing.trace_numpy_function(function_to_trace, inputs)
assert len(op_graph.output_nodes) == 1
assert isinstance(op_graph.output_nodes[0], expected_output_node)
assert len(op_graph.output_nodes[0].outputs) == 1
assert op_graph.output_nodes[0].outputs[0] == expected_output_value
@pytest.mark.parametrize("np_function", tracing.NPTracer.LIST_OF_SUPPORTED_UFUNC)
def test_nptracer_get_tracing_func_for_np_functions(np_function):
"""Test NPTracer get_tracing_func_for_np_function"""
expected_tracing_func = tracing.NPTracer.UFUNC_ROUTING[np_function]
assert tracing.NPTracer.get_tracing_func_for_np_function(np_function) == expected_tracing_func
def test_nptracer_get_tracing_func_for_np_functions_not_implemented():
"""Check NPTracer in case of not-implemented function"""
with pytest.raises(NotImplementedError) as excinfo:
tracing.NPTracer.get_tracing_func_for_np_function(numpy.conjugate)
assert "NPTracer does not yet manage the following func: conjugate" in str(excinfo.value)
@pytest.mark.parametrize(
"operation,exception_type,match",
[
pytest.param(
lambda x: x + "fail",
TypeError,
"unsupported operand type(s) for +: 'NPTracer' and 'str'",
),
pytest.param(
lambda x: "fail" + x,
TypeError,
'can only concatenate str (not "NPTracer") to str',
),
pytest.param(
lambda x: x - "fail",
TypeError,
"unsupported operand type(s) for -: 'NPTracer' and 'str'",
),
pytest.param(
lambda x: "fail" - x,
TypeError,
"unsupported operand type(s) for -: 'str' and 'NPTracer'",
),
pytest.param(
lambda x: x * "fail",
TypeError,
"can't multiply sequence by non-int of type 'NPTracer'",
),
pytest.param(
lambda x: "fail" * x,
TypeError,
"can't multiply sequence by non-int of type 'NPTracer'",
),
pytest.param(
lambda x: x / "fail",
TypeError,
"unsupported operand type(s) for /: 'NPTracer' and 'str'",
),
pytest.param(
lambda x: "fail" / x,
TypeError,
"unsupported operand type(s) for /: 'str' and 'NPTracer'",
),
pytest.param(
lambda x: x // "fail",
TypeError,
"unsupported operand type(s) for //: 'NPTracer' and 'str'",
),
pytest.param(
lambda x: "fail" // x,
TypeError,
"unsupported operand type(s) for //: 'str' and 'NPTracer'",
),
pytest.param(
lambda x, y: x / y, NotImplementedError, "Can't manage binary operator truediv"
),
pytest.param(
lambda x, y: x // y, NotImplementedError, "Can't manage binary operator floordiv"
),
],
)
def test_nptracer_unsupported_operands(operation, exception_type, match):
"""Test cases where NPTracer cannot be used with other operands."""
tracers = [
tracing.NPTracer([], ir.Input(ClearScalar(Integer(32, True)), param_name, idx), 0)
for idx, param_name in enumerate(inspect.signature(operation).parameters.keys())
]
with pytest.raises(exception_type) as exc_info:
_ = operation(*tracers)
assert match in str(exc_info)
def subtest_tracing_calls(
function_to_trace,
input_value_input_and_expected_output_tuples,
check_array_equality,
):
"""Test memory function managed by GenericFunction node of the form numpy.something"""
for input_value, input_, expected_output in input_value_input_and_expected_output_tuples:
op_graph = tracing.trace_numpy_function(function_to_trace, {"x": input_value})
output_node = op_graph.output_nodes[0]
node_results = op_graph.evaluate({0: input_})
evaluated_output = node_results[output_node]
assert isinstance(evaluated_output, type(expected_output)), type(evaluated_output)
check_array_equality(evaluated_output, expected_output)
@pytest.mark.parametrize(
"function_to_trace,input_value_input_and_expected_output_tuples",
[
(
lambda x: numpy.transpose(x),
[
(
EncryptedTensor(Integer(4, is_signed=False), shape=(2, 2)),
numpy.arange(4).reshape(2, 2),
numpy.array([[0, 2], [1, 3]]),
),
(
EncryptedTensor(Integer(4, is_signed=False), shape=(2, 2)),
numpy.arange(4, 8).reshape(2, 2),
numpy.array([[4, 6], [5, 7]]),
),
(
EncryptedTensor(Integer(6, is_signed=False), shape=()),
numpy.int64(42),
numpy.int64(42),
),
],
),
(
lambda x: numpy.transpose(x) + 42,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(42, 57).reshape(3, 5).transpose(),
),
(
EncryptedTensor(Integer(6, is_signed=False), shape=()),
numpy.int64(42),
numpy.int64(84),
),
],
),
(
lambda x: numpy.ravel(x),
[
(
EncryptedTensor(Integer(4, is_signed=False), shape=(2, 2)),
numpy.arange(4),
numpy.array([0, 1, 2, 3]),
),
(
EncryptedTensor(Integer(4, is_signed=False), shape=(2, 2)),
numpy.arange(4).reshape(2, 2),
numpy.array([0, 1, 2, 3]),
),
(
EncryptedTensor(Integer(6, is_signed=False), shape=()),
numpy.int64(42),
numpy.array([42], dtype=numpy.int64),
),
],
),
(
lambda x: numpy.reshape(x, (5, 3)) + 42,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(42, 57).reshape(5, 3),
),
],
),
],
)
def test_tracing_numpy_calls(
function_to_trace,
input_value_input_and_expected_output_tuples,
check_array_equality,
):
"""Test memory function managed by GenericFunction node of the form numpy.something"""
subtest_tracing_calls(
function_to_trace, input_value_input_and_expected_output_tuples, check_array_equality
)
@pytest.mark.parametrize(
"function_to_trace,input_value_input_and_expected_output_tuples",
[
(
lambda x: x.transpose() + 42,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(42, 57).reshape(3, 5).transpose(),
),
(
EncryptedTensor(Integer(6, is_signed=False), shape=()),
numpy.int64(42),
numpy.int64(84),
),
],
),
(
lambda x: x.ravel(),
[
(
EncryptedTensor(Integer(4, is_signed=False), shape=(2, 2)),
numpy.arange(4),
numpy.array([0, 1, 2, 3]),
),
(
EncryptedTensor(Integer(4, is_signed=False), shape=(2, 2)),
numpy.arange(4).reshape(2, 2),
numpy.array([0, 1, 2, 3]),
),
(
EncryptedTensor(Integer(6, is_signed=False), shape=()),
numpy.int64(42),
numpy.array([42], dtype=numpy.int64),
),
],
),
(
lambda x: x.reshape((5, 3)) + 42,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(42, 57).reshape(5, 3),
),
],
),
pytest.param(
lambda x: x.reshape((5, 3)),
[
(
EncryptedTensor(Integer(6, is_signed=False), shape=()),
numpy.int64(42),
None,
)
],
marks=pytest.mark.xfail(strict=True, raises=AssertionError),
),
pytest.param(
lambda x: x.flatten(),
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(15),
)
],
),
pytest.param(
lambda x: abs(x),
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(15).reshape(3, 5),
)
],
),
pytest.param(
lambda x: +x,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(15).reshape(3, 5),
)
],
),
pytest.param(
lambda x: -x,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
(numpy.arange(15).reshape(3, 5)) * (-1),
)
],
),
pytest.param(
lambda x: ~x,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(15).reshape(3, 5).__invert__(),
)
],
),
pytest.param(
lambda x: x << 3,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(15).reshape(3, 5) * 8,
)
],
),
pytest.param(
lambda x: x >> 1,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(15).reshape(3, 5) // 2,
)
],
),
pytest.param(
lambda x: 2 << x,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5) % 8,
2 << (numpy.arange(15).reshape(3, 5) % 8),
)
],
),
pytest.param(
lambda x: 256 >> x,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5) % 8,
256 >> (numpy.arange(15).reshape(3, 5) % 8),
)
],
),
pytest.param(
lambda x: x > 4,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(15).reshape(3, 5) > 4,
)
],
),
pytest.param(
lambda x: x < 5,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(15).reshape(3, 5) < 5,
)
],
),
pytest.param(
lambda x: x <= 7,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(15).reshape(3, 5) <= 7,
)
],
),
pytest.param(
lambda x: x >= 9,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(15).reshape(3, 5) >= 9,
)
],
),
pytest.param(
lambda x: x == 11,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(15).reshape(3, 5) == 11,
)
],
),
pytest.param(
lambda x: x != 12,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.arange(15).reshape(3, 5) != 12,
)
],
),
# Remove misplaced-comparison-constant because precisely, we want to be sure it works fine
# pylint: disable=misplaced-comparison-constant
pytest.param(
lambda x: 4 > x,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
4 > numpy.arange(15).reshape(3, 5),
)
],
),
pytest.param(
lambda x: 5 < x,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
5 < numpy.arange(15).reshape(3, 5),
)
],
),
pytest.param(
lambda x: 7 <= x,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
7 <= numpy.arange(15).reshape(3, 5),
)
],
),
pytest.param(
lambda x: 9 >= x,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
9 >= numpy.arange(15).reshape(3, 5),
)
],
),
pytest.param(
lambda x: 11 == x,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
11 == numpy.arange(15).reshape(3, 5),
)
],
),
pytest.param(
lambda x: 12 != x,
[
(
EncryptedTensor(Integer(32, is_signed=True), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
12 != numpy.arange(15).reshape(3, 5),
)
],
),
# pylint: enable=misplaced-comparison-constant
(
lambda x: x & 11,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.array([i & 11 for i in range(15)]).reshape(3, 5),
),
],
),
(
lambda x: 13 & x,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.array([i & 13 for i in range(15)]).reshape(3, 5),
),
],
),
(
lambda x: x | 6,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.array([i | 6 for i in range(15)]).reshape(3, 5),
),
],
),
(
lambda x: 30 | x,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.array([i | 30 for i in range(15)]).reshape(3, 5),
),
],
),
(
lambda x: x ^ 91,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.array([i ^ 91 for i in range(15)]).reshape(3, 5),
),
],
),
(
lambda x: 115 ^ x,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.array([i ^ 115 for i in range(15)]).reshape(3, 5),
),
],
),
(
lambda x: x % 11,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.array([i % 11 for i in range(15)]).reshape(3, 5),
),
],
),
(
lambda x: 150 % (x + 1),
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.array([150 % (i + 1) for i in range(15)]).reshape(3, 5),
),
],
),
(
lambda x: x ** 2,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5),
numpy.array([i ** 2 for i in range(15)]).reshape(3, 5),
),
],
),
(
lambda x: 2 ** x,
[
(
EncryptedTensor(Integer(32, is_signed=False), shape=(3, 5)),
numpy.arange(15).reshape(3, 5) % 7,
numpy.array([2 ** (i % 7) for i in range(15)]).reshape(3, 5),
),
],
),
],
)
def test_tracing_ndarray_calls(
function_to_trace,
input_value_input_and_expected_output_tuples,
check_array_equality,
):
"""Test memory function managed by GenericFunction node of the form ndarray.something"""
subtest_tracing_calls(
function_to_trace, input_value_input_and_expected_output_tuples, check_array_equality
)
@pytest.mark.parametrize(
"lambda_f,params",
[
(
lambda x: numpy.reshape(x, (5, 3)),
{
"x": EncryptedTensor(Integer(2, is_signed=False), shape=(7, 5)),
},
),
],
)
def test_errors_with_generic_function(lambda_f, params):
"Test some errors with generic function"
with pytest.raises(AssertionError) as excinfo:
tracing.trace_numpy_function(lambda_f, params)
assert "shapes are not compatible (old shape (7, 5), new shape (5, 3))" in str(excinfo.value)
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