mirror of
https://github.com/zama-ai/concrete.git
synced 2026-02-09 03:55:04 -05:00
2e831e4415
Also introduce new compilation options for parallel execution bump concrete-compiler to 0.6.0 which support loop parallelization
1559 lines
51 KiB
Python
1559 lines
51 KiB
Python
import random
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import numpy as np
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import py_progress_tracker as progress
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from common import BENCHMARK_CONFIGURATION
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import concrete.numpy as hnp
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@progress.track(
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[
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# Addition
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{
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"id": "x-plus-42-scalar",
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"name": "x + 42 {Scalar}",
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"parameters": {
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"function": lambda x: x + 42,
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"inputs": {
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"x": {
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"type": "encrypted",
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"minimum": 0,
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"maximum": 85,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-plus-42-tensor-2x3",
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"name": "x + 42 {Tensor of Shape 2x3}",
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"parameters": {
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"function": lambda x: x + 42,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 85,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-plus-10-20-30-tensor-3",
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"name": "x + [10, 20, 30] {Vector of Size 3}",
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"parameters": {
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"function": lambda x: x + np.array([10, 20, 30], dtype=np.uint8),
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (3,),
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"minimum": 0,
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"maximum": 97,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-plus-10-20-30-tensor-2x3",
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"name": "x + [10, 20, 30] {Tensor of Shape 2x3}",
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"parameters": {
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"function": lambda x: x + np.array([10, 20, 30], dtype=np.uint8),
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 97,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-plus-y-scalars",
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"name": "x + y {Scalars}",
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"parameters": {
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"function": lambda x, y: x + y,
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"inputs": {
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"x": {
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"type": "encrypted",
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"minimum": 0,
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"maximum": 27,
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},
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"y": {
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"type": "encrypted",
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"minimum": 0,
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"maximum": 100,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-plus-y-tensor-2x3-and-scalar",
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"name": "x + y {Tensor of Shape 2x3 and Scalar}",
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"parameters": {
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"function": lambda x, y: x + y,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 27,
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},
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"y": {
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"type": "encrypted",
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"minimum": 0,
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"maximum": 100,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-plus-y-tensors-2x3",
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"name": "x + y {Tensors of Shape 2x3}",
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"parameters": {
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"function": lambda x, y: x + y,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 27,
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},
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"y": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 100,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-plus-y-tensor-2x3-and-tensor-3",
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"name": "x + y {Tensor of Shape 2x3 and Vector of Size 3}",
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"parameters": {
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"function": lambda x, y: x + y,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 27,
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},
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"y": {
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"type": "encrypted",
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"shape": (3,),
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"minimum": 0,
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"maximum": 100,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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# Subtraction
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{
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"id": "x-minus-24-scalar",
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"name": "x - 24 {Scalar}",
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"parameters": {
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"function": lambda x: x - 24,
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"inputs": {
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"x": {
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"type": "encrypted",
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"minimum": 24,
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"maximum": 127,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "124-minus-x-scalar",
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"name": "124 - x {Scalar}",
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"parameters": {
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"function": lambda x: 124 - x,
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"inputs": {
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"x": {
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"type": "encrypted",
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"minimum": 0,
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"maximum": 124,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-minus-24-tensor-2x3",
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"name": "x - 24 {Tensor of Shape 2x3}",
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"parameters": {
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"function": lambda x: x - 24,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 24,
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"maximum": 127,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "124-minus-x-tensor-2x3",
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"name": "124 - x {Tensor of Shape 2x3}",
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"parameters": {
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"function": lambda x: 124 - x,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 124,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-minus-10-20-30-tensor-3",
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"name": "x - [10, 20, 30] {Vector of Size 3}",
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"parameters": {
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"function": lambda x: x - np.array([10, 20, 30], dtype=np.uint8),
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (3,),
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"minimum": 30,
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"maximum": 127,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "100-90-80-minus-x-tensor-3",
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"name": "[100, 90, 80] - x {Vector of Size 3}",
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"parameters": {
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"function": lambda x: np.array([100, 90, 80], dtype=np.uint8) - x,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (3,),
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"minimum": 0,
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"maximum": 80,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-minus-10-20-30-tensor-2x3",
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"name": "x - [10, 20, 30] {Tensor of Shape 2x3}",
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"parameters": {
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"function": lambda x: x - np.array([10, 20, 30], dtype=np.uint8),
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 30,
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"maximum": 127,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "100-90-80-minus-x-tensor-2x3",
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"name": "[100, 90, 80] - x {Tensor of Shape 2x3}",
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"parameters": {
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"function": lambda x: np.array([100, 90, 80], dtype=np.uint8) - x,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 80,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-minus-y-scalars",
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"name": "x - y {Scalars}",
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"parameters": {
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"function": lambda x, y: x - y,
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"inputs": {
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"x": {
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"type": "encrypted",
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"minimum": 35,
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"maximum": 127,
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},
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"y": {
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"type": "encrypted",
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"minimum": 0,
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"maximum": 35,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-minus-y-tensor-2x3-and-scalar",
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"name": "x - y {Tensor of Shape 2x3 and Scalar}",
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"parameters": {
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"function": lambda x, y: x - y,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 35,
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"maximum": 127,
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},
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"y": {
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"type": "encrypted",
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"minimum": 0,
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"maximum": 35,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-minus-y-tensors-2x3",
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"name": "x - y {Tensors of Shape 2x3}",
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"parameters": {
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"function": lambda x, y: x - y,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 35,
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"maximum": 127,
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},
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"y": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 35,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-minus-y-tensor-2x3-and-tensor-3",
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"name": "x - y {Tensor of Shape 2x3 and Vector of Size 3}",
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"parameters": {
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"function": lambda x, y: x - y,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 35,
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"maximum": 127,
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},
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"y": {
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"type": "encrypted",
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"shape": (3,),
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"minimum": 0,
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"maximum": 35,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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# Multiplication
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{
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"id": "x-times-7-scalar",
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"name": "x * 7 {Scalar}",
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"parameters": {
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"function": lambda x: x * 7,
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"inputs": {
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"x": {
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"type": "encrypted",
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"minimum": 0,
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"maximum": 18,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-times-7-tensor-2x3",
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"name": "x * 7 {Tensor of Shape 2x3}",
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"parameters": {
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"function": lambda x: x * 7,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 18,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-times-1-2-3-tensor-3",
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"name": "x * [1, 2, 3] {Vector of Size 3}",
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"parameters": {
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"function": lambda x: x * np.array([1, 2, 3], dtype=np.uint8),
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (3,),
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"minimum": 0,
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"maximum": 42,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-times-1-2-3-tensor-2x3",
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"name": "x * [1, 2, 3] {Tensor of Shape 2x3}",
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"parameters": {
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"function": lambda x: x * np.array([1, 2, 3], dtype=np.uint8),
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 42,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-times-y-scalars",
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"name": "x * y {Scalars}",
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"parameters": {
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"function": lambda x, y: x * y,
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"inputs": {
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"x": {
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"type": "encrypted",
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"minimum": 0,
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"maximum": 5,
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},
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"y": {
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"type": "encrypted",
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"minimum": 0,
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"maximum": 25,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-times-y-tensor-and-scalar",
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"name": "x * y {Tensor of Shape 2x3 and Scalar}",
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"parameters": {
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"function": lambda x, y: x * y,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 5,
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},
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"y": {
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"type": "encrypted",
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"minimum": 0,
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"maximum": 25,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-times-y-tensor-and-scalar",
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"name": "x * y {Tensors of Shape 2x3}",
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"parameters": {
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"function": lambda x, y: x * y,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 5,
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},
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"y": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 25,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-times-y-tensor-and-scalar",
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"name": "x * y {Tensor of Shape 2x3 and Vector of Size 3}",
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"parameters": {
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"function": lambda x, y: x * y,
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"inputs": {
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"x": {
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 5,
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},
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"y": {
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"type": "encrypted",
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"shape": (3,),
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"minimum": 0,
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"maximum": 25,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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# True Division
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{
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"id": "x-truediv-10-scalar",
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"name": "x // 10 {Scalar}",
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"parameters": {
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"function": lambda x: x // 10,
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"inputs": {
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"x": {
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"type": "encrypted",
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"minimum": 0,
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"maximum": 127,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "126-truediv-x-scalar",
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"name": "126 // x {Scalar}",
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"parameters": {
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"function": lambda x: 126 // x,
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"inputs": {
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"x": {
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"type": "encrypted",
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"minimum": 1,
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"maximum": 126,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "x-truediv-10-tensor-2x3",
|
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"name": "x // 10 {Tensor of Shape 2x3}",
|
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"parameters": {
|
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"function": lambda x: x // 10,
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"inputs": {
|
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"x": {
|
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"type": "encrypted",
|
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"shape": (2, 3),
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"minimum": 0,
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"maximum": 127,
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},
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},
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"accuracy_alert_threshold": 100,
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},
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},
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{
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"id": "126-truediv-x-tensor-2x3",
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"name": "126 // x {Tensor of Shape 2x3}",
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"parameters": {
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"function": lambda x: 126 // x,
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"inputs": {
|
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"x": {
|
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"type": "encrypted",
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"shape": (2, 3),
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"minimum": 1,
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"maximum": 127,
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},
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},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-truediv-5-10-15-tensor-3",
|
|
"name": "x // [5, 10, 15] {Vector of Size 3}",
|
|
"parameters": {
|
|
"function": lambda x: x // np.array([5, 10, 15], dtype=np.uint8),
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "120-60-30-truediv-x-tensor-3",
|
|
"name": "[120, 60, 30] // x {Vector of Size 3}",
|
|
"parameters": {
|
|
"function": lambda x: np.array([120, 60, 30], dtype=np.uint8) // x,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 1,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-truediv-5-10-15-tensor-2x3",
|
|
"name": "x // [5, 10, 15] {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: x // np.array([5, 10, 15], dtype=np.uint8),
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "120-60-30-truediv-x-tensor-2x3",
|
|
"name": "[120, 60, 30] // x {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: np.array([120, 60, 30], dtype=np.uint8) // x,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 1,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-truediv-y-scalars",
|
|
"name": "x // y {Scalars}",
|
|
"parameters": {
|
|
"function": lambda x, y: x // y,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
"y": {
|
|
"type": "encrypted",
|
|
"minimum": 1,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-truediv-y-tensor-2x3-and-scalar",
|
|
"name": "x // y {Tensor of Shape 2x3 and Scalar}",
|
|
"parameters": {
|
|
"function": lambda x, y: x // y,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
"y": {
|
|
"type": "encrypted",
|
|
"minimum": 1,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-truediv-y-tensors-2x3",
|
|
"name": "x // y {Tensors of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x, y: x // y,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
"y": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 1,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-truediv-y-tensor-2x3-and-tensor-3",
|
|
"name": "x // y {Tensor of Shape 2x3 and Vector of Size 3}",
|
|
"parameters": {
|
|
"function": lambda x, y: x // y,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
"y": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 1,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
# Dot Product
|
|
{
|
|
"id": "x-dot-2-3-1-tensor-3",
|
|
"name": "np.dot(x, [2, 3, 1]) {Vector of Size 3}",
|
|
"parameters": {
|
|
"function": lambda x: np.dot(x, np.array([2, 3, 1], dtype=np.uint8)),
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 0,
|
|
"maximum": 20,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "2-3-1-dot-x-tensor-3",
|
|
"name": "np.dot([2, 3, 1], x) {Vector of Size 3}",
|
|
"parameters": {
|
|
"function": lambda x: np.dot(np.array([2, 3, 1], dtype=np.uint8), x),
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 0,
|
|
"maximum": 20,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-dot-y-tensors-3",
|
|
"name": "np.dot(x, y) {Vectors of Size 3}",
|
|
"parameters": {
|
|
"function": lambda x, y: np.dot(x, y),
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 0,
|
|
"maximum": 14,
|
|
},
|
|
"y": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 0,
|
|
"maximum": 3,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
# Matrix Multiplication
|
|
{
|
|
"id": "x-matmul-c-tensor-2x3",
|
|
"name": "x @ [[1, 3], [3, 2], [2, 1]] {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: x @ np.array([[1, 3], [3, 2], [2, 1]], dtype=np.uint8),
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 20,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "c-matmul-x-tensor-2x3",
|
|
"name": "[[1, 3], [3, 2], [2, 1]] @ x {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: np.array([[1, 3], [3, 2], [2, 1]], dtype=np.uint8) @ x,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 25,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-matmul-y-tensor-2x3-and-tensor-3x2",
|
|
"name": "x @ y {Tensor of Shape 2x3 and Tensor of Shape 3x2}",
|
|
"parameters": {
|
|
"function": lambda x, y: x @ y,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 15,
|
|
},
|
|
"y": {
|
|
"type": "encrypted",
|
|
"shape": (3, 2),
|
|
"minimum": 0,
|
|
"maximum": 4,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
# Negation
|
|
{
|
|
"id": "negative-x-plus-127-scalar",
|
|
"name": "-x + 127 {Scalar}",
|
|
"parameters": {
|
|
"function": lambda x: -x + 127,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "negative-x-plus-127-tensor-2x3",
|
|
"name": "-x + 127 {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: -x + 127,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
# Power
|
|
{
|
|
"id": "x-to-the-power-of-2-scalar",
|
|
"name": "x ** 2 {Scalar}",
|
|
"parameters": {
|
|
"function": lambda x: x ** 2,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"minimum": 0,
|
|
"maximum": 11,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "2-to-the-power-of-x-scalar",
|
|
"name": "2 ** x {Scalar}",
|
|
"parameters": {
|
|
"function": lambda x: 2 ** x,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"minimum": 0,
|
|
"maximum": 6,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-to-the-power-of-2-tensor-2x3",
|
|
"name": "x ** 2 {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: x ** 2,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 11,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "2-to-the-power-of-x-tensor-2x3",
|
|
"name": "2 ** x {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: 2 ** x,
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 6,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
# Direct Table Lookup
|
|
{
|
|
"id": "single-table-lookup-5-bit-scalar",
|
|
"name": "Single Table Lookup (5-Bit) {Scalar}",
|
|
"parameters": {
|
|
"function": lambda x: hnp.LookupTable([(i ** 5) % 32 for i in range(32)])[x],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"minimum": 0,
|
|
"maximum": 31,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "single-table-lookup-5-bit-tensor-2x3",
|
|
"name": "Single Table Lookup (5-Bit) {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: hnp.LookupTable([(i ** 5) % 32 for i in range(32)])[x],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 31,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "multi-table-lookup-5-bit-tensor-2x3",
|
|
"name": "Multi Table Lookup (5-Bit) {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: hnp.MultiLookupTable(
|
|
[
|
|
[
|
|
hnp.LookupTable([((i ** 5) + 2) % 32 for i in range(32)]),
|
|
hnp.LookupTable([((i ** 5) * 3) % 32 for i in range(32)]),
|
|
hnp.LookupTable([((i ** 5) // 6) % 32 for i in range(32)]),
|
|
],
|
|
[
|
|
hnp.LookupTable([((i ** 5) // 2) % 32 for i in range(32)]),
|
|
hnp.LookupTable([((i ** 5) + 5) % 32 for i in range(32)]),
|
|
hnp.LookupTable([((i ** 5) * 4) % 32 for i in range(32)]),
|
|
],
|
|
]
|
|
)[x],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 31,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "single-table-lookup-6-bit-scalar",
|
|
"name": "Single Table Lookup (6-Bit) {Scalar}",
|
|
"parameters": {
|
|
"function": lambda x: hnp.LookupTable([(i ** 6) % 64 for i in range(64)])[x],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"minimum": 0,
|
|
"maximum": 63,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 99,
|
|
},
|
|
},
|
|
{
|
|
"id": "single-table-lookup-6-bit-tensor-2x3",
|
|
"name": "Single Table Lookup (6-Bit) {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: hnp.LookupTable([(i ** 6) % 64 for i in range(64)])[x],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 63,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 99,
|
|
},
|
|
},
|
|
{
|
|
"id": "multi-table-lookup-6-bit-tensor-2x3",
|
|
"name": "Multi Table Lookup (6-Bit) {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: hnp.MultiLookupTable(
|
|
[
|
|
[
|
|
hnp.LookupTable([((i ** 6) + 2) % 64 for i in range(64)]),
|
|
hnp.LookupTable([((i ** 6) * 3) % 64 for i in range(64)]),
|
|
hnp.LookupTable([((i ** 6) // 6) % 64 for i in range(64)]),
|
|
],
|
|
[
|
|
hnp.LookupTable([((i ** 6) // 2) % 64 for i in range(64)]),
|
|
hnp.LookupTable([((i ** 6) + 5) % 64 for i in range(64)]),
|
|
hnp.LookupTable([((i ** 6) * 4) % 64 for i in range(64)]),
|
|
],
|
|
]
|
|
)[x],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 63,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 99,
|
|
},
|
|
},
|
|
{
|
|
"id": "single-table-lookup-7-bit-scalar",
|
|
"name": "Single Table Lookup (7-Bit) {Scalar}",
|
|
"parameters": {
|
|
"function": lambda x: hnp.LookupTable([(i ** 7) % 128 for i in range(128)])[x],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 95,
|
|
},
|
|
},
|
|
{
|
|
"id": "single-table-lookup-7-bit-tensor-2x3",
|
|
"name": "Single Table Lookup (7-Bit) {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: hnp.LookupTable([(i ** 7) % 128 for i in range(128)])[x],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 95,
|
|
},
|
|
},
|
|
{
|
|
"id": "multi-table-lookup-7-bit-tensor-2x3",
|
|
"name": "Multi Table Lookup (7-Bit) {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: hnp.MultiLookupTable(
|
|
[
|
|
[
|
|
hnp.LookupTable([((i ** 7) + 2) % 128 for i in range(128)]),
|
|
hnp.LookupTable([((i ** 7) * 3) % 128 for i in range(128)]),
|
|
hnp.LookupTable([((i ** 7) // 6) % 128 for i in range(128)]),
|
|
],
|
|
[
|
|
hnp.LookupTable([((i ** 7) // 2) % 128 for i in range(128)]),
|
|
hnp.LookupTable([((i ** 7) + 5) % 128 for i in range(128)]),
|
|
hnp.LookupTable([((i ** 7) * 4) % 128 for i in range(128)]),
|
|
],
|
|
]
|
|
)[x],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 95,
|
|
},
|
|
},
|
|
# Manipulation
|
|
{
|
|
"id": "transpose-tensor-2x3",
|
|
"name": "np.transpose(x) {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: np.transpose(x),
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "reshape-to-1-3-1-2-1-tensor-2x3",
|
|
"name": "np.reshape(x, (1, 3, 1, 2, 1)) {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: np.reshape(x, (1, 3, 1, 2, 1)),
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (1, 3, 1, 2, 1),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "flatten-tensor-2x3",
|
|
"name": "x.flatten() {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: x.flatten(),
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
# Indexing
|
|
{
|
|
"id": "x-index-0-tensor-3",
|
|
"name": "x[0] {Vector of Size 3}",
|
|
"parameters": {
|
|
"function": lambda x: x[0],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-1-tensor-3",
|
|
"name": "x[1] {Vector of Size 3}",
|
|
"parameters": {
|
|
"function": lambda x: x[1],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-2-tensor-3",
|
|
"name": "x[2] {Vector of Size 3}",
|
|
"parameters": {
|
|
"function": lambda x: x[2],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-minus-1-tensor-3",
|
|
"name": "x[-1] {Vector of Size 3}",
|
|
"parameters": {
|
|
"function": lambda x: x[-1],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-minus-2-tensor-3",
|
|
"name": "x[-2] {Vector of Size 3}",
|
|
"parameters": {
|
|
"function": lambda x: x[-2],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-minus-3-tensor-3",
|
|
"name": "x[-3] {Vector of Size 3}",
|
|
"parameters": {
|
|
"function": lambda x: x[-3],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (3,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-0-and-0-tensor-2x3",
|
|
"name": "x[0, 0] {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: x[0, 0],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-minus-1-and-minus-1-tensor-2x3",
|
|
"name": "x[-1, -1] {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: x[-1, -1],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-0-tensor-2x3",
|
|
"name": "x[0] {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: x[0],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-minus-1-tensor-2x3",
|
|
"name": "x[-1] {Tensor of Shape 2x3}",
|
|
"parameters": {
|
|
"function": lambda x: x[-1],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (2, 3),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-y-tensor-5-and-scalar",
|
|
"name": "x[y] {Vector of Size 5 and Scalar}",
|
|
"parameters": {
|
|
"function": lambda x, y: x[y],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (5,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
"y": {
|
|
"type": "encrypted",
|
|
"minimum": 0,
|
|
"maximum": 4,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-y-and-z-tensor-5-and-scalars",
|
|
"name": "x[y] {Tensor of Shape 5x3 and Scalars}",
|
|
"parameters": {
|
|
"function": lambda x, y, z: x[y, z],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (5,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
"y": {
|
|
"type": "encrypted",
|
|
"minimum": 0,
|
|
"maximum": 4,
|
|
},
|
|
"z": {
|
|
"type": "encrypted",
|
|
"minimum": 0,
|
|
"maximum": 2,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
# Slicing
|
|
{
|
|
"id": "x-reversed-tensor-5",
|
|
"name": "x[::-1] {Vector of Size 5}",
|
|
"parameters": {
|
|
"function": lambda x: x[::-1],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (5,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-colon-tensor-5",
|
|
"name": "x[:] {Vector of Size 5}",
|
|
"parameters": {
|
|
"function": lambda x: x[:],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (5,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-2-colon-tensor-5",
|
|
"name": "x[2:] {Vector of Size 5}",
|
|
"parameters": {
|
|
"function": lambda x: x[2:],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (5,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-colon-3-tensor-5",
|
|
"name": "x[:3] {Vector of Size 5}",
|
|
"parameters": {
|
|
"function": lambda x: x[:3],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (5,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-1-colon-3-tensor-5",
|
|
"name": "x[1:3] {Vector of Size 5}",
|
|
"parameters": {
|
|
"function": lambda x: x[1:3],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (5,),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-colon-and-1-tensor-3x2",
|
|
"name": "x[:, 1] {Tensor of Shape 3x2}",
|
|
"parameters": {
|
|
"function": lambda x: x[:, 1],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (3, 2),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
{
|
|
"id": "x-index-1-colon-3-and-1-colon-3-tensor-4x4",
|
|
"name": "x[1:3, 1:3] {Tensor of Shape 4x4}",
|
|
"parameters": {
|
|
"function": lambda x: x[1:3, 1:3],
|
|
"inputs": {
|
|
"x": {
|
|
"type": "encrypted",
|
|
"shape": (4, 4),
|
|
"minimum": 0,
|
|
"maximum": 127,
|
|
},
|
|
},
|
|
"accuracy_alert_threshold": 100,
|
|
},
|
|
},
|
|
]
|
|
)
|
|
def main(function, inputs, accuracy_alert_threshold):
|
|
inputset = []
|
|
for _ in range(128):
|
|
input_ = []
|
|
|
|
for description in inputs.values():
|
|
minimum = description["minimum"]
|
|
maximum = description["maximum"]
|
|
|
|
assert minimum >= 0
|
|
assert maximum <= 127
|
|
|
|
if "shape" in description:
|
|
shape = description["shape"]
|
|
input_.append(np.random.randint(minimum, maximum + 1, size=shape, dtype=np.uint8))
|
|
else:
|
|
input_.append(random.randint(minimum, maximum))
|
|
|
|
inputset.append(tuple(input_) if len(input_) > 1 else input_[0])
|
|
|
|
compiler = hnp.NPFHECompiler(
|
|
function,
|
|
{name: description["type"] for name, description in inputs.items()},
|
|
compilation_configuration=BENCHMARK_CONFIGURATION,
|
|
)
|
|
|
|
circuit = compiler.compile_on_inputset(inputset)
|
|
|
|
samples = []
|
|
expectations = []
|
|
for _ in range(128):
|
|
sample = []
|
|
for description in inputs.values():
|
|
minimum = description["minimum"]
|
|
maximum = description["maximum"]
|
|
|
|
assert minimum >= 0
|
|
assert maximum <= 127
|
|
|
|
if "shape" in description:
|
|
shape = description["shape"]
|
|
sample.append(np.random.randint(minimum, maximum + 1, size=shape, dtype=np.uint8))
|
|
else:
|
|
sample.append(random.randint(minimum, maximum))
|
|
|
|
samples.append(sample)
|
|
expectations.append(function(*sample))
|
|
|
|
correct = 0
|
|
for sample_i, expectation_i in zip(samples, expectations):
|
|
with progress.measure(id="evaluation-time-ms", label="Evaluation Time (ms)"):
|
|
result_i = circuit.encrypt_run_decrypt(*sample_i)
|
|
|
|
np_result_i = np.array(result_i, dtype=np.uint8)
|
|
np_expectation_i = np.array(expectation_i, dtype=np.uint8)
|
|
|
|
if np_result_i.shape == np_expectation_i.shape:
|
|
correct += np.sum(np_result_i == np_expectation_i) / np_result_i.size
|
|
accuracy = (correct / len(samples)) * 100
|
|
|
|
print(f"Accuracy (%): {accuracy:.4f}")
|
|
progress.measure(
|
|
id="accuracy-percent",
|
|
label="Accuracy (%)",
|
|
value=accuracy,
|
|
alert=("<", accuracy_alert_threshold),
|
|
)
|