mirror of
https://github.com/zama-ai/concrete.git
synced 2026-02-09 03:55:04 -05:00
7bf2f09615
remove support for np.invert and propose to the user to use bitwise_xor instead, because of impossibilities with float fusing closes #658
937 lines
30 KiB
Python
937 lines
30 KiB
Python
"""Test file for numpy compilation functions"""
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import itertools
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import random
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import numpy
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import pytest
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from concrete.common.compilation import CompilationConfiguration
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from concrete.common.data_types.integers import Integer
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from concrete.common.debugging import draw_graph, get_printable_graph
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from concrete.common.extensions.table import LookupTable
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from concrete.common.values import ClearTensor, EncryptedScalar, EncryptedTensor
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from concrete.numpy import tracing
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from concrete.numpy.compile import compile_numpy_function, compile_numpy_function_into_op_graph
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def no_fuse_unhandled(x, y):
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"""No fuse unhandled"""
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x_intermediate = x + 2.8
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y_intermediate = y + 9.3
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intermediate = x_intermediate + y_intermediate
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return intermediate.astype(numpy.int32)
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def identity_lut_generator(n):
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"""Test lookup table"""
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return lambda x: LookupTable(list(range(2 ** n)))[x]
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def random_lut_1b(x):
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"""1-bit random table lookup"""
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# fmt: off
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table = LookupTable([10, 12])
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# fmt: on
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return table[x]
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def random_lut_2b(x):
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"""2-bit random table lookup"""
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# fmt: off
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table = LookupTable([3, 8, 22, 127])
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# fmt: on
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return table[x]
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def random_lut_3b(x):
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"""3-bit random table lookup"""
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# fmt: off
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table = LookupTable([30, 52, 125, 23, 17, 12, 90, 4])
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# fmt: on
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return table[x]
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def random_lut_4b(x):
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"""4-bit random table lookup"""
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# fmt: off
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table = LookupTable([30, 52, 125, 23, 17, 12, 90, 4, 21, 51, 22, 15, 53, 100, 75, 90])
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# fmt: on
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return table[x]
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def random_lut_5b(x):
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"""5-bit random table lookup"""
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# fmt: off
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table = LookupTable(
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[
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1, 5, 2, 3, 10, 2, 4, 8, 1, 12, 15, 12, 10, 1, 0, 2,
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4, 3, 8, 7, 10, 11, 6, 13, 9, 0, 2, 1, 15, 11, 12, 5
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]
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)
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# fmt: on
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return table[x]
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def random_lut_6b(x):
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"""6-bit random table lookup"""
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# fmt: off
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table = LookupTable(
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[
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95, 74, 11, 83, 24, 116, 28, 75, 26, 85, 114, 121, 91, 123, 78, 69,
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72, 115, 67, 5, 39, 11, 120, 88, 56, 43, 74, 16, 72, 85, 103, 92,
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44, 115, 50, 56, 107, 77, 25, 71, 52, 45, 80, 35, 69, 8, 40, 87,
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26, 85, 84, 53, 73, 95, 86, 22, 16, 45, 59, 112, 53, 113, 98, 116
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]
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)
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# fmt: on
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return table[x]
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def random_lut_7b(x):
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"""7-bit random table lookup"""
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# fmt: off
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table = LookupTable(
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[
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13, 58, 38, 58, 15, 15, 77, 86, 80, 94, 108, 27, 126, 60, 65, 95,
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50, 79, 22, 97, 38, 60, 25, 48, 73, 112, 27, 45, 88, 20, 67, 17,
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16, 6, 71, 60, 77, 43, 93, 40, 41, 31, 99, 122, 120, 40, 94, 13,
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111, 44, 96, 62, 108, 91, 34, 90, 103, 58, 3, 103, 19, 69, 55, 108,
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0, 111, 113, 0, 0, 73, 22, 52, 81, 2, 88, 76, 36, 121, 97, 121,
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123, 79, 82, 120, 12, 65, 54, 101, 90, 52, 84, 106, 23, 15, 110, 79,
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85, 101, 30, 61, 104, 35, 81, 30, 98, 44, 111, 32, 68, 18, 45, 123,
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84, 80, 68, 27, 31, 38, 126, 61, 51, 7, 49, 37, 63, 114, 22, 18,
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]
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)
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# fmt: on
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return table[x]
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def small_fused_table(x):
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"""Test with a small fused table"""
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return (10 * (numpy.cos(x + 1) + 1)).astype(numpy.uint32)
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def complicated_topology(x):
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"""Mix x in an intricated way."""
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intermediate = x
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x_p_1 = intermediate + 1
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x_p_2 = intermediate + 2
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x_p_3 = x_p_1 + x_p_2
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return (
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x_p_3.astype(numpy.int32),
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x_p_2.astype(numpy.int32),
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(x_p_2 + 3).astype(numpy.int32),
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x_p_3.astype(numpy.int32) + 67,
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)
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def mix_x_and_y_and_call_f(func, x, y):
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"""Create an upper function to test `func`"""
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z = numpy.abs(10 * func(x))
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z = z.astype(numpy.int32) + y
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return z
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def mix_x_and_y_and_call_f_with_float_inputs(func, x, y):
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"""Create an upper function to test `func`, with inputs which are forced to be floats"""
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z = numpy.abs(10 * func(x + 0.1))
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z = z.astype(numpy.int32) + y
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return z
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def mix_x_and_y_and_call_f_with_integer_inputs(func, x, y):
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"""Create an upper function to test `func`, with inputs which are forced to be integers but
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in a way which is fusable into a TLU"""
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a = x + 0.1
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a = numpy.rint(a).astype(numpy.int32)
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z = numpy.abs(10 * func(a))
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z = z.astype(numpy.int32) + y
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return z
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def mix_x_and_y_and_call_f_which_expects_small_inputs(func, x, y):
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"""Create an upper function to test `func`, which expects small values to not use too much
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precision"""
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a = numpy.abs(0.77 * numpy.sin(x))
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z = numpy.abs(3 * func(a))
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z = z.astype(numpy.int32) + y
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return z
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def mix_x_and_y_and_call_f_which_has_large_outputs(func, x, y):
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"""Create an upper function to test `func`, which outputs large values"""
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a = numpy.abs(2 * numpy.sin(x))
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z = numpy.abs(func(a) * 0.131)
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z = z.astype(numpy.int32) + y
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return z
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def mix_x_and_y_and_call_f_avoid_0_input(func, x, y):
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"""Create an upper function to test `func`, which makes that inputs are not 0"""
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a = numpy.abs(7 * numpy.sin(x)) + 1
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z = numpy.abs(5 * func(a))
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z = z.astype(numpy.int32) + y
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return z
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def mix_x_and_y_and_call_binary_f_one(func, c, x, y):
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"""Create an upper function to test `func`"""
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z = numpy.abs(func(x, c) + 1)
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z = z.astype(numpy.uint32) + y
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return z
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def mix_x_and_y_and_call_binary_f_two(func, c, x, y):
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"""Create an upper function to test `func`"""
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z = numpy.abs(func(c, x) + 1)
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z = z.astype(numpy.uint32) + y
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return z
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def check_is_good_execution(compiler_engine, function, args):
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"""Run several times the check compiler_engine.run(*args) == function(*args). If always wrong,
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return an error. One can set the expected probability of success of one execution and the
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number of tests, to finetune the probability of bad luck, ie that we run several times the
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check and always have a wrong result."""
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expected_probability_of_success = 0.95
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nb_tries = 5
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expected_bad_luck = (1 - expected_probability_of_success) ** nb_tries
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for i in range(1, nb_tries + 1):
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if compiler_engine.run(*args) == function(*args):
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# Good computation after i tries
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print(f"Good computation after {i} tries")
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return
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# Bad computation after nb_tries
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raise AssertionError(
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f"bad computation after {nb_tries} tries, which was supposed to happen with a "
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f"probability of {expected_bad_luck}"
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)
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def subtest_compile_and_run_unary_ufunc_correctness(
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ufunc,
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upper_function,
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input_ranges,
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default_compilation_configuration,
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):
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"""Test correctness of results when running a compiled function"""
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def get_function(ufunc, upper_function):
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return lambda x, y: upper_function(ufunc, x, y)
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function = get_function(ufunc, upper_function)
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def data_gen(args):
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for prod in itertools.product(*args):
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yield prod
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function_parameters = {arg_name: EncryptedScalar(Integer(64, False)) for arg_name in ["x", "y"]}
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compiler_engine = compile_numpy_function(
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function,
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function_parameters,
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data_gen(tuple(range(x[0], x[1] + 1) for x in input_ranges)),
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default_compilation_configuration,
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)
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args = [random.randint(low, high) for (low, high) in input_ranges]
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check_is_good_execution(compiler_engine, function, args)
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def subtest_compile_and_run_binary_ufunc_correctness(
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ufunc,
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upper_function,
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c,
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input_ranges,
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default_compilation_configuration,
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):
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"""Test correctness of results when running a compiled function"""
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def get_function(ufunc, upper_function):
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return lambda x, y: upper_function(ufunc, c, x, y)
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function = get_function(ufunc, upper_function)
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def data_gen(args):
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for prod in itertools.product(*args):
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yield prod
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function_parameters = {arg_name: EncryptedScalar(Integer(64, True)) for arg_name in ["x", "y"]}
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compiler_engine = compile_numpy_function(
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function,
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function_parameters,
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data_gen(tuple(range(x[0], x[1] + 1) for x in input_ranges)),
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default_compilation_configuration,
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)
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args = [random.randint(low, high) for (low, high) in input_ranges]
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check_is_good_execution(compiler_engine, function, args)
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@pytest.mark.parametrize(
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"ufunc",
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[f for f in tracing.NPTracer.LIST_OF_SUPPORTED_UFUNC if f.nin == 2],
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)
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def test_binary_ufunc_operations(ufunc, default_compilation_configuration):
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"""Test biary functions which are in tracing.NPTracer.LIST_OF_SUPPORTED_UFUNC."""
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if ufunc in [numpy.power, numpy.float_power]:
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# Need small constants to keep results really small
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subtest_compile_and_run_binary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_binary_f_one,
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3,
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((0, 4), (0, 5)),
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default_compilation_configuration,
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)
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elif ufunc in [numpy.lcm, numpy.left_shift]:
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# Need small constants to keep results sufficiently small
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subtest_compile_and_run_binary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_binary_f_one,
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3,
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((0, 5), (0, 5)),
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default_compilation_configuration,
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)
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else:
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# General case
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subtest_compile_and_run_binary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_binary_f_one,
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41,
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((0, 5), (0, 5)),
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default_compilation_configuration,
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)
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if ufunc in [numpy.power, numpy.float_power]:
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# Need small constants to keep results really small
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subtest_compile_and_run_binary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_binary_f_two,
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2,
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((0, 4), (0, 5)),
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default_compilation_configuration,
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)
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elif ufunc in [numpy.floor_divide, numpy.fmod, numpy.remainder, numpy.true_divide]:
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subtest_compile_and_run_binary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_binary_f_two,
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31,
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((1, 5), (1, 5)),
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default_compilation_configuration,
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)
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elif ufunc in [numpy.lcm, numpy.left_shift]:
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# Need small constants to keep results sufficiently small
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subtest_compile_and_run_binary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_binary_f_two,
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2,
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((0, 5), (0, 5)),
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default_compilation_configuration,
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)
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elif ufunc in [numpy.ldexp]:
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# Need small constants to keep results sufficiently small
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subtest_compile_and_run_binary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_binary_f_two,
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2,
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((0, 5), (0, 5)),
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default_compilation_configuration,
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)
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else:
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# General case
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subtest_compile_and_run_binary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_binary_f_two,
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42,
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((0, 5), (0, 5)),
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default_compilation_configuration,
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)
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@pytest.mark.parametrize(
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"ufunc", [f for f in tracing.NPTracer.LIST_OF_SUPPORTED_UFUNC if f.nin == 1]
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)
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def test_unary_ufunc_operations(ufunc, default_compilation_configuration):
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"""Test unary functions which are in tracing.NPTracer.LIST_OF_SUPPORTED_UFUNC."""
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if ufunc in [
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numpy.degrees,
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numpy.rad2deg,
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]:
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# Need to reduce the output value, to avoid to need too much precision
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subtest_compile_and_run_unary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_f_which_has_large_outputs,
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((0, 5), (0, 5)),
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default_compilation_configuration,
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)
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elif ufunc in [
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numpy.negative,
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]:
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# Need to turn the input into a float
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subtest_compile_and_run_unary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_f_with_float_inputs,
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((0, 5), (0, 5)),
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default_compilation_configuration,
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)
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elif ufunc in [
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numpy.arccosh,
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numpy.log,
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numpy.log2,
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numpy.log10,
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numpy.reciprocal,
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]:
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# No 0 in the domain of definition
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subtest_compile_and_run_unary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_f_avoid_0_input,
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((1, 5), (1, 5)),
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default_compilation_configuration,
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)
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elif ufunc in [
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numpy.cosh,
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numpy.exp,
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numpy.exp2,
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numpy.expm1,
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numpy.square,
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numpy.arccos,
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numpy.arcsin,
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numpy.arctanh,
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numpy.sinh,
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]:
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# Need a small range of inputs, to avoid to need too much precision
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subtest_compile_and_run_unary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_f_which_expects_small_inputs,
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((0, 5), (0, 5)),
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default_compilation_configuration,
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)
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else:
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# Regular case for univariate functions
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subtest_compile_and_run_unary_ufunc_correctness(
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ufunc,
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mix_x_and_y_and_call_f,
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((0, 5), (0, 5)),
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default_compilation_configuration,
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)
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@pytest.mark.parametrize(
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"function,input_ranges,list_of_arg_names",
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[
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pytest.param(lambda x: x + 42, ((-5, 5),), ["x"]),
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pytest.param(lambda x, y: x + y + 8, ((2, 10), (4, 8)), ["x", "y"]),
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pytest.param(lambda x, y: (x + 1, y + 10), ((-1, 1), (3, 8)), ["x", "y"]),
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pytest.param(
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lambda x, y, z: (x + y + 1 - z, x * y + 42, z, z + 99),
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((4, 8), (3, 4), (0, 4)),
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["x", "y", "z"],
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),
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pytest.param(
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no_fuse_unhandled,
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((-2, 2), (-2, 2)),
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["x", "y"],
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marks=pytest.mark.xfail(strict=True, raises=ValueError),
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),
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pytest.param(complicated_topology, ((0, 10),), ["x"]),
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],
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)
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def test_compile_function_multiple_outputs(
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function, input_ranges, list_of_arg_names, default_compilation_configuration
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):
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"""Test function compile_numpy_function_into_op_graph for a program with multiple outputs"""
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def data_gen(args):
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for prod in itertools.product(*args):
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yield prod
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function_parameters = {
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arg_name: EncryptedScalar(Integer(64, True)) for arg_name in list_of_arg_names
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}
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op_graph = compile_numpy_function_into_op_graph(
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function,
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function_parameters,
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data_gen(tuple(range(x[0], x[1] + 1) for x in input_ranges)),
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default_compilation_configuration,
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)
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# TODO: For the moment, we don't have really checks, but some printfs. Later,
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# when we have the converter, we can check the MLIR
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draw_graph(op_graph, show=False)
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str_of_the_graph = get_printable_graph(op_graph, show_data_types=True)
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print(f"\n{str_of_the_graph}\n")
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@pytest.mark.parametrize(
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"function,input_ranges,list_of_arg_names",
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[
|
|
pytest.param(lambda x: x + numpy.int32(42), ((0, 10),), ["x"]),
|
|
pytest.param(lambda x: x + 64, ((0, 10),), ["x"]),
|
|
pytest.param(lambda x: x * 3, ((0, 40),), ["x"]),
|
|
pytest.param(lambda x: 120 - x, ((40, 80),), ["x"]),
|
|
pytest.param(lambda x, y: x + y + 64, ((0, 20), (0, 20)), ["x", "y"]),
|
|
pytest.param(lambda x, y: 100 - y + x, ((0, 20), (0, 20)), ["x", "y"]),
|
|
pytest.param(lambda x, y: 50 - y * 2 + x, ((0, 20), (0, 20)), ["x", "y"]),
|
|
],
|
|
)
|
|
def test_compile_and_run_correctness(
|
|
function, input_ranges, list_of_arg_names, default_compilation_configuration
|
|
):
|
|
"""Test correctness of results when running a compiled function"""
|
|
|
|
def data_gen(args):
|
|
for prod in itertools.product(*args):
|
|
yield prod
|
|
|
|
function_parameters = {
|
|
arg_name: EncryptedScalar(Integer(64, False)) for arg_name in list_of_arg_names
|
|
}
|
|
|
|
compiler_engine = compile_numpy_function(
|
|
function,
|
|
function_parameters,
|
|
data_gen(tuple(range(x[0], x[1] + 1) for x in input_ranges)),
|
|
default_compilation_configuration,
|
|
)
|
|
|
|
args = [random.randint(low, high) for (low, high) in input_ranges]
|
|
assert compiler_engine.run(*args) == function(*args)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"size, input_range",
|
|
[
|
|
pytest.param(
|
|
1,
|
|
(0, 8),
|
|
),
|
|
pytest.param(
|
|
4,
|
|
(0, 5),
|
|
),
|
|
pytest.param(
|
|
6,
|
|
(0, 4),
|
|
),
|
|
pytest.param(
|
|
10,
|
|
(0, 3),
|
|
),
|
|
],
|
|
)
|
|
def test_compile_and_run_dot_correctness(size, input_range, default_compilation_configuration):
|
|
"""Test correctness of results when running a compiled function"""
|
|
|
|
low, high = input_range
|
|
shape = (size,)
|
|
|
|
inputset = [
|
|
(numpy.zeros(shape, dtype=numpy.uint32), numpy.zeros(shape, dtype=numpy.uint32)),
|
|
(
|
|
numpy.ones(shape, dtype=numpy.uint32) * high,
|
|
numpy.ones(shape, dtype=numpy.uint32) * high,
|
|
),
|
|
]
|
|
for _ in range(8):
|
|
inputset.append((numpy.random.randint(low, high + 1), numpy.random.randint(low, high + 1)))
|
|
|
|
function_parameters = {
|
|
"x": EncryptedTensor(Integer(64, False), shape),
|
|
"y": ClearTensor(Integer(64, False), shape),
|
|
}
|
|
|
|
def function(x, y):
|
|
return numpy.dot(x, y)
|
|
|
|
compiler_engine = compile_numpy_function(
|
|
function,
|
|
function_parameters,
|
|
inputset,
|
|
default_compilation_configuration,
|
|
)
|
|
|
|
args = [[random.randint(low, high) for _ in range(size)] for __ in range(2)]
|
|
assert compiler_engine.run(*args) == function(*args)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"size,input_range",
|
|
[
|
|
pytest.param(
|
|
1,
|
|
(0, 8),
|
|
),
|
|
pytest.param(
|
|
4,
|
|
(0, 5),
|
|
),
|
|
pytest.param(
|
|
6,
|
|
(0, 4),
|
|
),
|
|
pytest.param(
|
|
10,
|
|
(0, 3),
|
|
),
|
|
],
|
|
)
|
|
def test_compile_and_run_constant_dot_correctness(
|
|
size, input_range, default_compilation_configuration
|
|
):
|
|
"""Test correctness of results when running a compiled function"""
|
|
|
|
low, high = input_range
|
|
shape = (size,)
|
|
|
|
inputset = [
|
|
(numpy.zeros(shape, dtype=numpy.uint32),),
|
|
(numpy.ones(shape, dtype=numpy.uint32) * high,),
|
|
]
|
|
for _ in range(8):
|
|
inputset.append((numpy.random.randint(low, high + 1),))
|
|
|
|
constant = numpy.random.randint(low, high + 1, size=shape)
|
|
|
|
def left(x):
|
|
return numpy.dot(x, constant)
|
|
|
|
def right(x):
|
|
return numpy.dot(constant, x)
|
|
|
|
left_circuit = compile_numpy_function(
|
|
left,
|
|
{"x": EncryptedTensor(Integer(64, False), shape)},
|
|
inputset,
|
|
default_compilation_configuration,
|
|
)
|
|
right_circuit = compile_numpy_function(
|
|
left,
|
|
{"x": EncryptedTensor(Integer(64, False), shape)},
|
|
inputset,
|
|
default_compilation_configuration,
|
|
)
|
|
|
|
args = (numpy.random.randint(low, high + 1, size=shape).tolist(),)
|
|
assert left_circuit.run(*args) == left(*args)
|
|
assert right_circuit.run(*args) == right(*args)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"function,input_bits,list_of_arg_names",
|
|
[
|
|
pytest.param(identity_lut_generator(1), (1,), ["x"], id="identity function (1-bit)"),
|
|
pytest.param(identity_lut_generator(2), (2,), ["x"], id="identity function (2-bit)"),
|
|
pytest.param(identity_lut_generator(3), (3,), ["x"], id="identity function (3-bit)"),
|
|
pytest.param(identity_lut_generator(4), (4,), ["x"], id="identity function (4-bit)"),
|
|
pytest.param(identity_lut_generator(5), (5,), ["x"], id="identity function (5-bit)"),
|
|
pytest.param(identity_lut_generator(6), (6,), ["x"], id="identity function (6-bit)"),
|
|
pytest.param(identity_lut_generator(7), (7,), ["x"], id="identity function (7-bit)"),
|
|
pytest.param(random_lut_1b, (1,), ["x"], id="random function (1-bit)"),
|
|
pytest.param(random_lut_2b, (2,), ["x"], id="random function (2-bit)"),
|
|
pytest.param(random_lut_3b, (3,), ["x"], id="random function (3-bit)"),
|
|
pytest.param(random_lut_4b, (4,), ["x"], id="random function (4-bit)"),
|
|
pytest.param(random_lut_5b, (5,), ["x"], id="random function (5-bit)"),
|
|
pytest.param(random_lut_6b, (6,), ["x"], id="random function (6-bit)"),
|
|
pytest.param(random_lut_7b, (7,), ["x"], id="random function (7-bit)"),
|
|
pytest.param(small_fused_table, (5,), ["x"], id="small fused table (5-bits)"),
|
|
],
|
|
)
|
|
def test_compile_and_run_lut_correctness(
|
|
function,
|
|
input_bits,
|
|
list_of_arg_names,
|
|
default_compilation_configuration,
|
|
):
|
|
"""Test correctness of results when running a compiled function with LUT"""
|
|
|
|
input_ranges = tuple((0, 2 ** input_bit - 1) for input_bit in input_bits)
|
|
|
|
def data_gen(args):
|
|
for prod in itertools.product(*args):
|
|
yield prod
|
|
|
|
function_parameters = {
|
|
arg_name: EncryptedScalar(Integer(input_bit, False))
|
|
for input_bit, arg_name in zip(input_bits, list_of_arg_names)
|
|
}
|
|
|
|
compiler_engine = compile_numpy_function(
|
|
function,
|
|
function_parameters,
|
|
data_gen(tuple(range(x[0], x[1] + 1) for x in input_ranges)),
|
|
default_compilation_configuration,
|
|
)
|
|
|
|
# testing random values
|
|
for _ in range(10):
|
|
args = [random.randint(low, high) for (low, high) in input_ranges]
|
|
check_is_good_execution(compiler_engine, function, args)
|
|
|
|
# testing low values
|
|
args = [low for (low, _) in input_ranges]
|
|
check_is_good_execution(compiler_engine, function, args)
|
|
|
|
# testing high values
|
|
args = [high for (_, high) in input_ranges]
|
|
check_is_good_execution(compiler_engine, function, args)
|
|
|
|
|
|
def test_compile_function_with_direct_tlu(default_compilation_configuration):
|
|
"""Test compile_numpy_function_into_op_graph 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_into_op_graph(
|
|
function,
|
|
{"x": EncryptedScalar(Integer(2, is_signed=False))},
|
|
[(0,), (1,), (2,), (3,)],
|
|
default_compilation_configuration,
|
|
)
|
|
|
|
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(default_compilation_configuration):
|
|
"""Test compile_numpy_function_into_op_graph 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_into_op_graph(
|
|
function,
|
|
{"x": EncryptedScalar(Integer(3, is_signed=False))},
|
|
[(0,), (1,), (2,), (3,), (4,), (5,), (6,), (7,)],
|
|
default_compilation_configuration,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"function,parameters,inputset,match",
|
|
[
|
|
pytest.param(
|
|
lambda x: 1 - x,
|
|
{"x": EncryptedScalar(Integer(3, is_signed=False))},
|
|
[(i,) for i in range(8)],
|
|
(
|
|
"function you are trying to compile isn't supported for MLIR lowering\n"
|
|
"\n"
|
|
"%0 = Constant(1) # ClearScalar<Integer<unsigned, 1 bits>>\n" # noqa: E501 # pylint: disable=line-too-long
|
|
"%1 = x # EncryptedScalar<Integer<unsigned, 3 bits>>\n" # noqa: E501 # pylint: disable=line-too-long
|
|
"%2 = Sub(%0, %1) # EncryptedScalar<Integer<signed, 4 bits>>\n" # noqa: E501 # pylint: disable=line-too-long
|
|
"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ signed integer outputs aren't supported\n" # noqa: E501 # pylint: disable=line-too-long
|
|
"return(%2)\n"
|
|
),
|
|
),
|
|
pytest.param(
|
|
lambda x: x + 1,
|
|
{"x": EncryptedTensor(Integer(3, is_signed=False), shape=(2, 2))},
|
|
[(numpy.random.randint(0, 8, size=(2, 2)),) for i in range(10)],
|
|
(
|
|
"function you are trying to compile isn't supported for MLIR lowering\n"
|
|
"\n"
|
|
"%0 = x # EncryptedTensor<Integer<unsigned, 3 bits>, shape=(2, 2)>\n" # noqa: E501 # pylint: disable=line-too-long
|
|
"%1 = Constant(1) # ClearScalar<Integer<unsigned, 1 bits>>\n" # noqa: E501 # pylint: disable=line-too-long
|
|
"%2 = Add(%0, %1) # EncryptedTensor<Integer<unsigned, 4 bits>, shape=(2, 2)>\n" # noqa: E501 # pylint: disable=line-too-long
|
|
"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ non scalar outputs aren't supported\n" # noqa: E501 # pylint: disable=line-too-long
|
|
"return(%2)\n"
|
|
),
|
|
),
|
|
],
|
|
)
|
|
def test_fail_compile(function, parameters, inputset, match, default_compilation_configuration):
|
|
"""Test function compile_numpy_function_into_op_graph for a program with signed values"""
|
|
|
|
try:
|
|
compile_numpy_function(
|
|
function,
|
|
parameters,
|
|
inputset,
|
|
default_compilation_configuration,
|
|
)
|
|
except RuntimeError as error:
|
|
assert str(error) == match
|
|
|
|
|
|
def test_small_inputset_no_fail():
|
|
"""Test function compile_numpy_function_into_op_graph with an unacceptably small inputset"""
|
|
compile_numpy_function_into_op_graph(
|
|
lambda x: x + 42,
|
|
{"x": EncryptedScalar(Integer(5, is_signed=False))},
|
|
[(0,), (3,)],
|
|
CompilationConfiguration(dump_artifacts_on_unexpected_failures=False),
|
|
)
|
|
|
|
|
|
def test_small_inputset_treat_warnings_as_errors():
|
|
"""Test function compile_numpy_function_into_op_graph with an unacceptably small inputset"""
|
|
with pytest.raises(ValueError, match=".* inputset contains too few inputs .*"):
|
|
compile_numpy_function_into_op_graph(
|
|
lambda x: x + 42,
|
|
{"x": EncryptedScalar(Integer(5, is_signed=False))},
|
|
[(0,), (3,)],
|
|
CompilationConfiguration(
|
|
dump_artifacts_on_unexpected_failures=False,
|
|
treat_warnings_as_errors=True,
|
|
),
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"function,params,shape,ref_graph_str",
|
|
[
|
|
# pylint: disable=unnecessary-lambda
|
|
(
|
|
lambda x, y: numpy.dot(x, y),
|
|
{
|
|
"x": EncryptedTensor(Integer(2, is_signed=False), shape=(4,)),
|
|
"y": EncryptedTensor(Integer(2, is_signed=False), shape=(4,)),
|
|
},
|
|
(4,),
|
|
# Remark that, when you do the dot of tensors of 4 values between 0 and 3,
|
|
# you can get a maximal value of 4*3*3 = 36, ie something on 6 bits
|
|
"%0 = x "
|
|
"# EncryptedTensor<Integer<unsigned, 6 bits>, shape=(4,)>"
|
|
"\n%1 = y "
|
|
"# EncryptedTensor<Integer<unsigned, 6 bits>, shape=(4,)>"
|
|
"\n%2 = Dot(%0, %1) "
|
|
"# EncryptedScalar<Integer<unsigned, 6 bits>>"
|
|
"\nreturn(%2)\n",
|
|
),
|
|
# pylint: enable=unnecessary-lambda
|
|
],
|
|
)
|
|
def test_compile_function_with_dot(
|
|
function, params, shape, ref_graph_str, default_compilation_configuration
|
|
):
|
|
"""Test compile_numpy_function_into_op_graph for a program with np.dot"""
|
|
|
|
# This is the exhaust, but if ever we have too long inputs (ie, large 'repeat'),
|
|
# we'll have to take random values, not all values one by one
|
|
def data_gen(max_for_ij, repeat):
|
|
iter_i = itertools.product(range(0, max_for_ij + 1), repeat=repeat)
|
|
iter_j = itertools.product(range(0, max_for_ij + 1), repeat=repeat)
|
|
for prod_i, prod_j in itertools.product(iter_i, iter_j):
|
|
yield numpy.array(prod_i), numpy.array(prod_j)
|
|
|
|
max_for_ij = 3
|
|
assert len(shape) == 1
|
|
repeat = shape[0]
|
|
|
|
op_graph = compile_numpy_function_into_op_graph(
|
|
function,
|
|
params,
|
|
data_gen(max_for_ij, repeat),
|
|
default_compilation_configuration,
|
|
)
|
|
str_of_the_graph = get_printable_graph(op_graph, show_data_types=True)
|
|
assert str_of_the_graph == ref_graph_str, (
|
|
f"\n==================\nGot \n{str_of_the_graph}"
|
|
f"==================\nExpected \n{ref_graph_str}"
|
|
f"==================\n"
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"function,input_ranges,list_of_arg_names",
|
|
[
|
|
pytest.param(lambda x: x + 64, ((0, 10),), ["x"]),
|
|
pytest.param(lambda x: x * 3, ((0, 40),), ["x"]),
|
|
pytest.param(lambda x: 120 - x, ((40, 80),), ["x"]),
|
|
pytest.param(lambda x, y: x + y + 64, ((0, 20), (0, 20)), ["x", "y"]),
|
|
pytest.param(lambda x, y: 100 - y + x, ((0, 20), (0, 20)), ["x", "y"]),
|
|
pytest.param(lambda x, y: 50 - y * 2 + x, ((0, 20), (0, 20)), ["x", "y"]),
|
|
],
|
|
)
|
|
def test_compile_with_show_mlir(
|
|
function, input_ranges, list_of_arg_names, default_compilation_configuration
|
|
):
|
|
"""Test show_mlir option"""
|
|
|
|
def data_gen(args):
|
|
for prod in itertools.product(*args):
|
|
yield prod
|
|
|
|
function_parameters = {
|
|
arg_name: EncryptedScalar(Integer(64, False)) for arg_name in list_of_arg_names
|
|
}
|
|
|
|
compile_numpy_function(
|
|
function,
|
|
function_parameters,
|
|
data_gen(tuple(range(x[0], x[1] + 1) for x in input_ranges)),
|
|
default_compilation_configuration,
|
|
show_mlir=True,
|
|
)
|
|
|
|
|
|
def test_compile_too_high_bitwidth(default_compilation_configuration):
|
|
"""Check that the check of maximal bitwidth of intermediate data works fine."""
|
|
|
|
def function(x, y):
|
|
return x + y
|
|
|
|
def data_gen(args):
|
|
for prod in itertools.product(*args):
|
|
yield prod
|
|
|
|
function_parameters = {
|
|
"x": EncryptedScalar(Integer(64, False)),
|
|
"y": EncryptedScalar(Integer(64, False)),
|
|
}
|
|
|
|
# A bit too much
|
|
input_ranges = [(0, 100), (0, 28)]
|
|
|
|
with pytest.raises(RuntimeError) as excinfo:
|
|
compile_numpy_function(
|
|
function,
|
|
function_parameters,
|
|
data_gen(tuple(range(x[0], x[1] + 1) for x in input_ranges)),
|
|
default_compilation_configuration,
|
|
)
|
|
|
|
assert (
|
|
"max_bit_width of some nodes is too high for the current version of the "
|
|
"compiler (maximum must be 7 which is not compatible with" in str(excinfo.value)
|
|
)
|
|
|
|
assert str(excinfo.value).endswith(", 8)])")
|
|
|
|
# Just ok
|
|
input_ranges = [(0, 99), (0, 28)]
|
|
|
|
compile_numpy_function(
|
|
function,
|
|
function_parameters,
|
|
data_gen(tuple(range(x[0], x[1] + 1) for x in input_ranges)),
|
|
default_compilation_configuration,
|
|
)
|