import numpy as np import pytest import shutil import tempfile from concrete.compiler import ( ClientSupport, EvaluationKeys, LibrarySupport, PublicArguments, PublicResult, ) @pytest.mark.parametrize( "mlir, args, expected_result", [ pytest.param( """ func.func @main(%arg0: !FHE.eint<5>, %arg1: i6) -> !FHE.eint<5> { %1 = "FHE.add_eint_int"(%arg0, %arg1): (!FHE.eint<5>, i6) -> (!FHE.eint<5>) return %1: !FHE.eint<5> } """, (5, 7), 12, id="enc_plain_int_args", marks=pytest.mark.xfail, ), pytest.param( """ func.func @main(%arg0: !FHE.eint<5>, %arg1: !FHE.eint<5>) -> !FHE.eint<5> { %1 = "FHE.add_eint"(%arg0, %arg1): (!FHE.eint<5>, !FHE.eint<5>) -> (!FHE.eint<5>) return %1: !FHE.eint<5> } """, (5, 7), 12, id="enc_enc_int_args", ), pytest.param( """ func.func @main(%arg0: tensor<4x!FHE.eint<5>>, %arg1: tensor<4xi6>) -> !FHE.eint<5> { %ret = "FHELinalg.dot_eint_int"(%arg0, %arg1) : (tensor<4x!FHE.eint<5>>, tensor<4xi6>) -> !FHE.eint<5> return %ret : !FHE.eint<5> } """, ( np.array([1, 2, 3, 4], dtype=np.uint64), np.array([4, 3, 2, 1], dtype=np.uint8), ), 20, id="enc_plain_ndarray_args", marks=pytest.mark.xfail, ), pytest.param( """ func.func @main(%a0: tensor<4x!FHE.eint<5>>, %a1: tensor<4x!FHE.eint<5>>) -> tensor<4x!FHE.eint<5>> { %res = "FHELinalg.add_eint"(%a0, %a1) : (tensor<4x!FHE.eint<5>>, tensor<4x!FHE.eint<5>>) -> tensor<4x!FHE.eint<5>> return %res : tensor<4x!FHE.eint<5>> } """, ( np.array([1, 2, 3, 4], dtype=np.uint64), np.array([7, 0, 1, 5], dtype=np.uint64), ), np.array([8, 2, 4, 9]), id="enc_enc_ndarray_args", ), pytest.param( """ func.func @main(%arg0: !FHE.eint<3>, %arg1: !FHE.eint<4>, %arg2: !FHE.eint<5>, %arg3: !FHE.eint<6>) -> (!FHE.eint<3>, !FHE.eint<4>, !FHE.eint<5>, !FHE.eint<6>) { %lut0 = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7]> : tensor<8xi64> %bs0 = "FHE.apply_lookup_table"(%arg0, %lut0): (!FHE.eint<3>, tensor<8xi64>) -> (!FHE.eint<3>) %lut1 = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]> : tensor<16xi64> %bs1 = "FHE.apply_lookup_table"(%arg1, %lut1): (!FHE.eint<4>, tensor<16xi64>) -> (!FHE.eint<4>) %lut3 = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]> : tensor<32xi64> %bs3 = "FHE.apply_lookup_table"(%arg2, %lut3): (!FHE.eint<5>, tensor<32xi64>) -> (!FHE.eint<5>) %lut4 = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]> : tensor<64xi64> %bs4 = "FHE.apply_lookup_table"(%arg3, %lut4): (!FHE.eint<6>, tensor<64xi64>) -> (!FHE.eint<6>) return %bs0, %bs1, %bs3, %bs4 : !FHE.eint<3>, !FHE.eint<4>, !FHE.eint<5>, !FHE.eint<6> } """, (7, 15, 31, 63), (7, 15, 31, 63), id="apply_lookup_table_multi_ouput", ), ], ) def test_client_server_end_to_end(mlir, args, expected_result, keyset_cache): with tempfile.TemporaryDirectory() as tmpdirname: support = LibrarySupport.new(str(tmpdirname)) compilation_result = support.compile(mlir) server_lambda = support.load_server_lambda(compilation_result, False) client_parameters = support.load_client_parameters(compilation_result) keyset = ClientSupport.key_set(client_parameters, keyset_cache) evaluation_keys = keyset.get_evaluation_keys() evaluation_keys_serialized = evaluation_keys.serialize() evaluation_keys_deserialized = EvaluationKeys.deserialize( evaluation_keys_serialized ) args = ClientSupport.encrypt_arguments(client_parameters, keyset, args) args_serialized = args.serialize() args_deserialized = PublicArguments.deserialize( client_parameters, args_serialized ) result = support.server_call( server_lambda, args_deserialized, evaluation_keys_deserialized, ) result_serialized = result.serialize() result_deserialized = PublicResult.deserialize( client_parameters, result_serialized ) output = ClientSupport.decrypt_result( client_parameters, keyset, result_deserialized ) assert np.array_equal(output, expected_result)