mirror of
https://github.com/zama-ai/concrete.git
synced 2026-02-17 16:11:26 -05:00
d1b62462f2
On Mac arm, the c api backing the python bindings does not propagate the exceptions properly to the concretelang python module. This makes all exceptions raised through `CompilerEngine.cpp` fall in the catch-all case of the pybind exceptions handler. Since there is no particular need for a public c api, we just remove it from the bindings, and move all the content of `CompilerEngine.cpp` directly in the `CompilerAPIModule.cpp` file.
460 lines
19 KiB
Python
460 lines
19 KiB
Python
import platform
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import pytest
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import os.path
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import shutil
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import numpy as np
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from concrete.compiler import (
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LibrarySupport,
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ClientSupport,
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CompilationOptions,
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CompilationFeedback,
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)
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def assert_result(result, expected_result):
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"""Assert that result and expected result are equal.
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result and expected_result can be integers on numpy arrays.
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"""
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assert type(expected_result) == type(result)
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if isinstance(expected_result, int):
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assert result == expected_result
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else:
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assert np.all(result == expected_result)
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def run(engine, args, compilation_result, keyset_cache):
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"""Execute engine on the given arguments.
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Perform required loading, encryption, execution, and decryption."""
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# Dev
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compilation_feedback = engine.load_compilation_feedback(compilation_result)
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assert isinstance(compilation_feedback, CompilationFeedback)
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assert isinstance(compilation_feedback.complexity, float)
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assert isinstance(compilation_feedback.p_error, float)
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assert isinstance(compilation_feedback.global_p_error, float)
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assert isinstance(compilation_feedback.total_secret_keys_size, int)
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assert isinstance(compilation_feedback.total_bootstrap_keys_size, int)
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assert isinstance(compilation_feedback.total_inputs_size, int)
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assert isinstance(compilation_feedback.total_output_size, int)
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# Client
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client_parameters = engine.load_client_parameters(compilation_result)
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key_set = ClientSupport.key_set(client_parameters, keyset_cache)
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public_arguments = ClientSupport.encrypt_arguments(client_parameters, key_set, args)
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# Server
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server_lambda = engine.load_server_lambda(compilation_result, False)
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evaluation_keys = key_set.get_evaluation_keys()
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public_result = engine.server_call(server_lambda, public_arguments, evaluation_keys)
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# Client
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result = ClientSupport.decrypt_result(client_parameters, key_set, public_result)
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return result
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def compile_run_assert(
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engine,
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mlir_input,
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args,
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expected_result,
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keyset_cache,
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options=CompilationOptions.new("main"),
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):
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"""Compile run and assert result."""
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compilation_result = engine.compile(mlir_input, options)
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result = run(engine, args, compilation_result, keyset_cache)
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assert_result(result, expected_result)
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end_to_end_fixture = [
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pytest.param(
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"""
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func.func @main(%arg0: !FHE.eint<7>, %arg1: i8) -> !FHE.eint<7> {
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%1 = "FHE.add_eint_int"(%arg0, %arg1): (!FHE.eint<7>, i8) -> (!FHE.eint<7>)
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return %1: !FHE.eint<7>
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}
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""",
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(5, 7),
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12,
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id="add_eint_int",
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),
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pytest.param(
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"""
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func.func @main(%arg0: !FHE.eint<7>, %arg1: i8) -> !FHE.eint<7> {
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%1 = "FHE.add_eint_int"(%arg0, %arg1): (!FHE.eint<7>, i8) -> (!FHE.eint<7>)
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return %1: !FHE.eint<7>
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}
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""",
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(np.array(4, dtype=np.int64), np.array(5, dtype=np.uint8)),
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9,
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id="add_eint_int_with_ndarray_as_scalar",
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),
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pytest.param(
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"""
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func.func @main(%arg0: !FHE.eint<7>) -> !FHE.eint<7> {
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%tlu = 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127]> : tensor<128xi64>
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%1 = "FHE.apply_lookup_table"(%arg0, %tlu): (!FHE.eint<7>, tensor<128xi64>) -> (!FHE.eint<7>)
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return %1: !FHE.eint<7>
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}
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""",
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(73,),
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73,
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id="apply_lookup_table",
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),
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pytest.param(
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"""
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func.func @main(%arg0: tensor<4x!FHE.eint<7>>, %arg1: tensor<4xi8>) -> !FHE.eint<7>
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{
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%ret = "FHELinalg.dot_eint_int"(%arg0, %arg1) :
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(tensor<4x!FHE.eint<7>>, tensor<4xi8>) -> !FHE.eint<7>
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return %ret : !FHE.eint<7>
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}
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""",
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(
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np.array([1, 2, 3, 4], dtype=np.uint8),
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np.array([4, 3, 2, 1], dtype=np.uint8),
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),
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20,
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id="dot_eint_int_uint8",
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),
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pytest.param(
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"""
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func.func @main(%a0: tensor<4x!FHE.eint<6>>, %a1: tensor<4xi7>) -> tensor<4x!FHE.eint<6>> {
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%res = "FHELinalg.add_eint_int"(%a0, %a1) : (tensor<4x!FHE.eint<6>>, tensor<4xi7>) -> tensor<4x!FHE.eint<6>>
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return %res : tensor<4x!FHE.eint<6>>
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}
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""",
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(
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np.array([31, 6, 12, 9], dtype=np.uint8),
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np.array([32, 9, 2, 3], dtype=np.uint8),
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),
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np.array([63, 15, 14, 12]),
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id="add_eint_int_1D",
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),
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pytest.param(
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"""
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func.func @main(%arg0: !FHE.esint<7>) -> !FHE.esint<7> {
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%0 = "FHE.neg_eint"(%arg0): (!FHE.esint<7>) -> !FHE.esint<7>
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return %0: !FHE.esint<7>
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}
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""",
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(5,),
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-5,
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id="neg_eint_signed",
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),
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pytest.param(
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"""
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func.func @main(%arg0: tensor<2x!FHE.esint<7>>) -> tensor<2x!FHE.esint<7>> {
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%0 = "FHELinalg.neg_eint"(%arg0): (tensor<2x!FHE.esint<7>>) -> tensor<2x!FHE.esint<7>>
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return %0: tensor<2x!FHE.esint<7>>
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}
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""",
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(np.array([-5, 3]),),
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np.array([5, -3]),
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id="neg_eint_signed_2",
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),
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]
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end_to_end_parallel_fixture = [
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pytest.param(
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"""
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func.func @main(%x: tensor<3x4x!FHE.eint<7>>, %y: tensor<3x4x!FHE.eint<7>>) -> tensor<3x2x!FHE.eint<7>> {
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%c = arith.constant dense<[[1, 2], [3, 4], [5, 0], [1, 2]]> : tensor<4x2xi8>
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%0 = "FHELinalg.matmul_eint_int"(%x, %c): (tensor<3x4x!FHE.eint<7>>, tensor<4x2xi8>) -> tensor<3x2x!FHE.eint<7>>
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%1 = "FHELinalg.matmul_eint_int"(%y, %c): (tensor<3x4x!FHE.eint<7>>, tensor<4x2xi8>) -> tensor<3x2x!FHE.eint<7>>
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%2 = "FHELinalg.add_eint"(%0, %1): (tensor<3x2x!FHE.eint<7>>, tensor<3x2x!FHE.eint<7>>) -> tensor<3x2x!FHE.eint<7>>
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return %2 : tensor<3x2x!FHE.eint<7>>
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}
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""",
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(
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np.array([[1, 2, 3, 4], [4, 2, 1, 0], [2, 3, 1, 5]], dtype=np.uint8),
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np.array([[1, 2, 3, 4], [4, 2, 1, 1], [2, 3, 1, 5]], dtype=np.uint8),
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),
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np.array([[52, 36], [31, 34], [42, 52]]),
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id="matmul_eint_int_uint8",
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),
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pytest.param(
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"""
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func.func @main(%a0: tensor<4x!FHE.eint<6>>, %a1: tensor<4xi7>, %a2: tensor<4x!FHE.eint<6>>, %a3: tensor<4xi7>) -> tensor<4x!FHE.eint<6>> {
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%1 = "FHELinalg.add_eint_int"(%a0, %a1) : (tensor<4x!FHE.eint<6>>, tensor<4xi7>) -> tensor<4x!FHE.eint<6>>
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%2 = "FHELinalg.add_eint_int"(%a2, %a3) : (tensor<4x!FHE.eint<6>>, tensor<4xi7>) -> tensor<4x!FHE.eint<6>>
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%res = "FHELinalg.add_eint"(%1, %2) : (tensor<4x!FHE.eint<6>>, tensor<4x!FHE.eint<6>>) -> tensor<4x!FHE.eint<6>>
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return %res : tensor<4x!FHE.eint<6>>
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}
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""",
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(
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np.array([1, 2, 3, 4], dtype=np.uint8),
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np.array([9, 8, 6, 5], dtype=np.uint8),
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np.array([3, 2, 7, 0], dtype=np.uint8),
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np.array([1, 4, 2, 11], dtype=np.uint8),
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),
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np.array([14, 16, 18, 20]),
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id="add_eint_int_1D",
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),
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]
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@pytest.mark.parametrize("mlir_input, args, expected_result", end_to_end_fixture)
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def test_lib_compile_and_run(mlir_input, args, expected_result, keyset_cache):
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artifact_dir = "./py_test_lib_compile_and_run"
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engine = LibrarySupport.new(artifact_dir)
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compile_run_assert(engine, mlir_input, args, expected_result, keyset_cache)
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shutil.rmtree(artifact_dir)
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@pytest.mark.parametrize("mlir_input, args, expected_result", end_to_end_fixture)
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def test_lib_compile_reload_and_run(mlir_input, args, expected_result, keyset_cache):
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artifact_dir = "./test_lib_compile_reload_and_run"
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engine = LibrarySupport.new(artifact_dir)
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# Here don't save compilation result, reload
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engine.compile(mlir_input)
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compilation_result = engine.reload()
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result = run(engine, args, compilation_result, keyset_cache)
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# Check result
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assert_result(result, expected_result)
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shutil.rmtree(artifact_dir)
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def test_lib_compilation_artifacts():
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mlir_str = """
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func.func @main(%a0: tensor<4x!FHE.eint<6>>, %a1: tensor<4xi7>) -> tensor<4x!FHE.eint<6>> {
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%res = "FHELinalg.add_eint_int"(%a0, %a1) : (tensor<4x!FHE.eint<6>>, tensor<4xi7>) -> tensor<4x!FHE.eint<6>>
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return %res : tensor<4x!FHE.eint<6>>
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}
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"""
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artifact_dir = "./test_artifacts"
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engine = LibrarySupport.new(artifact_dir)
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engine.compile(mlir_str)
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assert os.path.exists(engine.get_program_info_path())
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assert os.path.exists(engine.get_shared_lib_path())
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shutil.rmtree(artifact_dir)
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assert not os.path.exists(engine.get_program_info_path())
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assert not os.path.exists(engine.get_shared_lib_path())
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def _test_lib_compile_and_run_with_options(keyset_cache, options):
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mlir_input = """
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func.func @main(%arg0: !FHE.eint<7>) -> !FHE.eint<7> {
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%tlu = 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127]> : tensor<128xi64>
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%1 = "FHE.apply_lookup_table"(%arg0, %tlu): (!FHE.eint<7>, tensor<128xi64>) -> (!FHE.eint<7>)
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return %1: !FHE.eint<7>
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}
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"""
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args = (73,)
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expected_result = 73
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engine = LibrarySupport.new("./py_test_lib_compile_and_run_custom_perror")
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compile_run_assert(engine, mlir_input, args, expected_result, keyset_cache, options)
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def test_lib_compile_and_run_p_error(keyset_cache):
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options = CompilationOptions.new("main")
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options.set_p_error(0.00001)
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options.set_display_optimizer_choice(True)
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_test_lib_compile_and_run_with_options(keyset_cache, options)
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def test_lib_compile_and_run_global_p_error(keyset_cache):
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options = CompilationOptions.new("main")
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options.set_global_p_error(0.00001)
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options.set_display_optimizer_choice(True)
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_test_lib_compile_and_run_with_options(keyset_cache, options)
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def test_lib_compile_and_run_security_level(keyset_cache):
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options = CompilationOptions.new("main")
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options.set_security_level(80)
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options.set_display_optimizer_choice(True)
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_test_lib_compile_and_run_with_options(keyset_cache, options)
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@pytest.mark.parallel
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@pytest.mark.parametrize(
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"mlir_input, args, expected_result", end_to_end_parallel_fixture
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)
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def test_compile_and_run_auto_parallelize(
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mlir_input, args, expected_result, keyset_cache
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):
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artifact_dir = "./py_test_compile_and_run_auto_parallelize"
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engine = LibrarySupport.new(artifact_dir)
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options = CompilationOptions.new("main")
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options.set_auto_parallelize(True)
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compile_run_assert(
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engine, mlir_input, args, expected_result, keyset_cache, options=options
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)
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# This test was running in JIT mode at first. Problem is now, it does not work with the library
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# support. It is not clear to me why, but the dataflow runtime seems to have stuffs dedicated to
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# the dropped JIT support... I am cancelling it until further explored.
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#
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# # FIXME #51
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# @pytest.mark.xfail(
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# platform.system() == "Darwin",
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# reason="MacOS have issues with translating Cpp exceptions",
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# )
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# @pytest.mark.parametrize(
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# "mlir_input, args, expected_result", end_to_end_parallel_fixture
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# )
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# def test_compile_dataflow_and_fail_run(
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# mlir_input, args, expected_result, keyset_cache, no_parallel
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# ):
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# if no_parallel:
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# artifact_dir = "./py_test_compile_dataflow_and_fail_run"
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# engine = LibrarySupport.new(artifact_dir)
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# options = CompilationOptions.new("main")
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# options.set_auto_parallelize(True)
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# with pytest.raises(
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# RuntimeError,
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# match="call: current runtime doesn't support dataflow execution",
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# ):
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# compile_run_assert(
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# engine, mlir_input, args, expected_result, keyset_cache, options=options
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# )
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@pytest.mark.parametrize(
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"mlir_input, args, expected_result",
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[
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pytest.param(
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"""
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func.func @main(%x: tensor<3x4x!FHE.eint<7>>) -> tensor<3x2x!FHE.eint<7>> {
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%y = arith.constant dense<[[1, 2], [3, 4], [5, 0], [1, 2]]> : tensor<4x2xi8>
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%0 = "FHELinalg.matmul_eint_int"(%x, %y): (tensor<3x4x!FHE.eint<7>>, tensor<4x2xi8>) -> tensor<3x2x!FHE.eint<7>>
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return %0 : tensor<3x2x!FHE.eint<7>>
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}
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""",
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(np.array([[1, 2, 3, 4], [4, 2, 1, 0], [2, 3, 1, 5]], dtype=np.uint8),),
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np.array([[26, 18], [15, 16], [21, 26]]),
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id="matmul_eint_int_uint8",
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),
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],
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)
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def test_compile_and_run_loop_parallelize(
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mlir_input, args, expected_result, keyset_cache
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):
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artifact_dir = "./py_test_compile_and_run_loop_parallelize"
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engine = LibrarySupport.new(artifact_dir)
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options = CompilationOptions.new("main")
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options.set_loop_parallelize(True)
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compile_run_assert(
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engine, mlir_input, args, expected_result, keyset_cache, options=options
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)
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@pytest.mark.parametrize(
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"mlir_input, args",
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[
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pytest.param(
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"""
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func.func @main(%arg0: !FHE.eint<7>, %arg1: i8) -> !FHE.eint<7> {
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%1 = "FHE.add_eint_int"(%arg0, %arg1): (!FHE.eint<7>, i8) -> (!FHE.eint<7>)
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return %1: !FHE.eint<7>
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}
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""",
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(5, 7, 8),
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id="add_eint_int_invalid_arg_number",
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),
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],
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)
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def test_compile_and_run_invalid_arg_number(mlir_input, args, keyset_cache):
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artifact_dir = "./py_test_compile_and_run_invalid_arg_number"
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engine = LibrarySupport.new(artifact_dir)
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with pytest.raises(
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RuntimeError, match=r"function has arity 2 but is applied to too many arguments"
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):
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compile_run_assert(engine, mlir_input, args, None, keyset_cache)
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@pytest.mark.parametrize(
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"mlir_input",
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[
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pytest.param(
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"""
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func.func @test(%arg0: tensor<4x!FHE.eint<7>>, %arg1: tensor<4xi8>) -> !FHE.eint<7>
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{
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%ret = "FHELinalg.dot_eint_int"(%arg0, %arg1) :
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(tensor<4x!FHE.eint<7>>, tensor<4xi8>) -> !FHE.eint<7>
|
|
return %ret : !FHE.eint<7>
|
|
}
|
|
""",
|
|
id="not @main",
|
|
),
|
|
],
|
|
)
|
|
def test_compile_invalid(mlir_input):
|
|
artifact_dir = "./py_test_compile_invalid"
|
|
engine = LibrarySupport.new(artifact_dir)
|
|
with pytest.raises(RuntimeError, match=r"Function not found, name='main'"):
|
|
engine.compile(mlir_input)
|
|
|
|
|
|
def test_crt_decomposition_feedback():
|
|
mlir = """
|
|
|
|
func.func @main(%arg0: !FHE.eint<16>) -> !FHE.eint<16> {
|
|
%tlu = arith.constant dense<60000> : tensor<65536xi64>
|
|
%1 = "FHE.apply_lookup_table"(%arg0, %tlu): (!FHE.eint<16>, tensor<65536xi64>) -> (!FHE.eint<16>)
|
|
return %1: !FHE.eint<16>
|
|
}
|
|
|
|
"""
|
|
|
|
artifact_dir = "./py_test_crt_decomposition_feedback"
|
|
engine = LibrarySupport.new(artifact_dir)
|
|
compilation_result = engine.compile(mlir, options=CompilationOptions.new("main"))
|
|
compilation_feedback = engine.load_compilation_feedback(compilation_result)
|
|
|
|
assert isinstance(compilation_feedback, CompilationFeedback)
|
|
assert compilation_feedback.crt_decompositions_of_outputs == [[7, 8, 9, 11, 13]]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"mlir, expected_memory_usage_per_loc",
|
|
[
|
|
pytest.param(
|
|
"""
|
|
func.func @main(%arg0: tensor<4x4x!FHE.eint<6>>, %arg1: tensor<4x2xi7>) -> tensor<4x2x!FHE.eint<6>> {
|
|
%0 = "FHELinalg.matmul_eint_int"(%arg0, %arg1): (tensor<4x4x!FHE.eint<6>>, tensor<4x2xi7>) -> (tensor<4x2x!FHE.eint<6>>) loc("some/random/location.py":10:2)
|
|
%tlu = arith.constant dense<[40, 13, 20, 62, 47, 41, 46, 30, 59, 58, 17, 4, 34, 44, 49, 5, 10, 63, 18, 21, 33, 45, 7, 14, 24, 53, 56, 3, 22, 29, 1, 39, 48, 32, 38, 28, 15, 12, 52, 35, 42, 11, 6, 43, 0, 16, 27, 9, 31, 51, 36, 37, 55, 57, 54, 2, 8, 25, 50, 23, 61, 60, 26, 19]> : tensor<64xi64> loc("some/random/location.py":10:2)
|
|
%result = "FHELinalg.apply_lookup_table"(%0, %tlu): (tensor<4x2x!FHE.eint<6>>, tensor<64xi64>) -> (tensor<4x2x!FHE.eint<6>>) loc("some/random/location.py":10:2)
|
|
return %result: tensor<4x2x!FHE.eint<6>> loc("some/random/location.py":10:2)
|
|
}
|
|
""",
|
|
# 4*4*4097*8 (input1) + 4*2 (input2) + 4*2*4097*8 + 4097*3*8 + 4096*8 + 869*8 (temporary buffers) + 4*2*4097*8 (output buffer) + 64*8 (constant TLU)
|
|
{'loc("some/random/location.py":10:2)': 1187400},
|
|
id="single location",
|
|
),
|
|
pytest.param(
|
|
"""
|
|
func.func @main(%arg0: tensor<4x4x!FHE.eint<6>>, %arg1: tensor<4x2xi7>) -> tensor<4x2x!FHE.eint<6>> {
|
|
%0 = "FHELinalg.matmul_eint_int"(%arg0, %arg1): (tensor<4x4x!FHE.eint<6>>, tensor<4x2xi7>) -> (tensor<4x2x!FHE.eint<6>>) loc("@matmul some/random/location.py":10:2)
|
|
%tlu = arith.constant dense<[40, 13, 20, 62, 47, 41, 46, 30, 59, 58, 17, 4, 34, 44, 49, 5, 10, 63, 18, 21, 33, 45, 7, 14, 24, 53, 56, 3, 22, 29, 1, 39, 48, 32, 38, 28, 15, 12, 52, 35, 42, 11, 6, 43, 0, 16, 27, 9, 31, 51, 36, 37, 55, 57, 54, 2, 8, 25, 50, 23, 61, 60, 26, 19]> : tensor<64xi64> loc("@lut some/random/location.py":11:2)
|
|
%result = "FHELinalg.apply_lookup_table"(%0, %tlu): (tensor<4x2x!FHE.eint<6>>, tensor<64xi64>) -> (tensor<4x2x!FHE.eint<6>>) loc("@lut some/random/location.py":11:2)
|
|
return %result: tensor<4x2x!FHE.eint<6>> loc("@return some/random/location.py":12:2)
|
|
}
|
|
""",
|
|
{
|
|
# 4*4*4097*8 (input1) + 4*2 (input2) + 4*2*4097*8 (matmul result buffer) + 4097*2*8 (temporary buffers)
|
|
'loc("@matmul some/random/location.py":10:2)': 852184,
|
|
# 4*2*4097*8 (matmul result buffer) + 4*2*4097*8 (result buffer) + 4097*8 + 4096*8 + 869*8 (temporary buffers) + 64*8 (constant TLU)
|
|
'loc("@lut some/random/location.py":11:2)': 597424,
|
|
# 4*2*4097*8 (result buffer)
|
|
'loc("@return some/random/location.py":12:2)': 262208,
|
|
},
|
|
id="multiple location",
|
|
),
|
|
],
|
|
)
|
|
def test_memory_usage(mlir: str, expected_memory_usage_per_loc: dict):
|
|
artifact_dir = "./test_memory_usage"
|
|
engine = LibrarySupport.new(artifact_dir)
|
|
compilation_result = engine.compile(mlir)
|
|
compilation_feedback = engine.load_compilation_feedback(compilation_result)
|
|
assert isinstance(compilation_feedback, CompilationFeedback)
|
|
|
|
assert (
|
|
expected_memory_usage_per_loc == compilation_feedback.memory_usage_per_location
|
|
)
|
|
|
|
shutil.rmtree(artifact_dir)
|