mirror of
https://github.com/zama-ai/concrete.git
synced 2026-02-09 12:15:09 -05:00
f6c9618b5a
* feat(mlir): conversion from HDKIR to MLIR * feat(mlir): support ir.Sub and ir.Mul - better type conversion from HDK to MLIR - Context management inside the converter class - better handling of input type in conversion functions * refactor(mlir): use input and output from OPGraph Co-authored-by: Arthur Meyre <arthur.meyre@zama.ai> * feat(mlir): eint-int subtractions * feat(mlir): adhere to spec for supported ops * feat(OPGraph): getters for ordered inputs/outputs + formatting * tests(mlir): test converion via compiler roundtrip * fix(mlir): flip operands on int_eint sym ops * feat(mlir): check that the outputs are unsigned * feat(mlir): set bit_width of all nodes to the max This is currently required as the compiler is already assuming this. Could be removed from HDK when the compiler can do it on its own * feat: value_is_integer + CRs disable some linting errors * tests: update compile tests + coverage * refactor: reorganize mlir package + better doc * doc: conformance with pydocstyle Co-authored-by: Arthur Meyre <arthur.meyre@zama.ai>
108 lines
3.3 KiB
Python
108 lines
3.3 KiB
Python
"""Test file for hnumpy compilation functions"""
|
|
import itertools
|
|
|
|
import pytest
|
|
|
|
from hdk.common.data_types.integers import Integer
|
|
from hdk.common.data_types.values import EncryptedValue
|
|
from hdk.common.debugging import draw_graph, get_printable_graph
|
|
from hdk.common.extensions.table import LookupTable
|
|
from hdk.hnumpy.compile import compile_numpy_function
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"function,input_ranges,list_of_arg_names",
|
|
[
|
|
pytest.param(lambda x: x + 42, ((-2, 2),), ["x"]),
|
|
pytest.param(lambda x, y: x + y + 8, ((2, 10), (4, 8)), ["x", "y"]),
|
|
pytest.param(lambda x, y: (x + 1, y + 10), ((-1, 1), (3, 4)), ["x", "y"]),
|
|
pytest.param(
|
|
lambda x, y, z: (x + y + 1 - z, x * y + 42, z, z + 99),
|
|
((4, 8), (3, 4), (0, 4)),
|
|
["x", "y", "z"],
|
|
),
|
|
],
|
|
)
|
|
def test_compile_function_multiple_outputs(function, input_ranges, list_of_arg_names):
|
|
"""Test function compile_numpy_function for a program with multiple outputs"""
|
|
|
|
def data_gen(args):
|
|
for prod in itertools.product(*args):
|
|
yield prod
|
|
|
|
function_parameters = {
|
|
arg_name: EncryptedValue(Integer(64, True)) for arg_name in list_of_arg_names
|
|
}
|
|
|
|
op_graph = compile_numpy_function(
|
|
function,
|
|
function_parameters,
|
|
data_gen(tuple(range(x[0], x[1] + 1) for x in input_ranges)),
|
|
)
|
|
|
|
# TODO: For the moment, we don't have really checks, but some printfs. Later,
|
|
# when we have the converter, we can check the MLIR
|
|
draw_graph(op_graph, block_until_user_closes_graph=False)
|
|
|
|
str_of_the_graph = get_printable_graph(op_graph, show_data_types=True)
|
|
print(f"\n{str_of_the_graph}\n")
|
|
|
|
|
|
def test_compile_function_with_direct_tlu():
|
|
"""Test compile_numpy_function for a program with direct table lookup"""
|
|
|
|
table = LookupTable([9, 2, 4, 11])
|
|
|
|
def function(x):
|
|
return x + table[x]
|
|
|
|
op_graph = compile_numpy_function(
|
|
function,
|
|
{"x": EncryptedValue(Integer(2, is_signed=False))},
|
|
iter([(0,), (1,), (2,), (3,)]),
|
|
)
|
|
|
|
str_of_the_graph = get_printable_graph(op_graph, show_data_types=True)
|
|
print(f"\n{str_of_the_graph}\n")
|
|
|
|
|
|
def test_compile_function_with_direct_tlu_overflow():
|
|
"""Test compile_numpy_function for a program with direct table lookup overflow"""
|
|
|
|
table = LookupTable([9, 2, 4, 11])
|
|
|
|
def function(x):
|
|
return table[x]
|
|
|
|
with pytest.raises(ValueError):
|
|
compile_numpy_function(
|
|
function,
|
|
{"x": EncryptedValue(Integer(3, is_signed=False))},
|
|
iter([(0,), (1,), (2,), (3,), (4,), (5,), (6,), (7,)]),
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"function,input_ranges,list_of_arg_names",
|
|
[
|
|
pytest.param(lambda x: x - 10, ((-2, 2),), ["x"]),
|
|
],
|
|
)
|
|
def test_fail_compile(function, input_ranges, list_of_arg_names):
|
|
"""Test function compile_numpy_function for a program with signed values"""
|
|
|
|
def data_gen(args):
|
|
for prod in itertools.product(*args):
|
|
yield prod
|
|
|
|
function_parameters = {
|
|
arg_name: EncryptedValue(Integer(64, True)) for arg_name in list_of_arg_names
|
|
}
|
|
|
|
with pytest.raises(TypeError, match=r"signed integers aren't supported for MLIR lowering"):
|
|
compile_numpy_function(
|
|
function,
|
|
function_parameters,
|
|
data_gen(tuple(range(x[0], x[1] + 1) for x in input_ranges)),
|
|
)
|