mirror of
https://github.com/MPCStats/zk-stats-lib.git
synced 2026-01-08 05:04:07 -05:00
365 lines
26 KiB
Plaintext
365 lines
26 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: ezkl==7.0.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from -r ../../requirements.txt (line 1)) (7.0.0)\n",
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"Requirement already satisfied: torch in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from -r ../../requirements.txt (line 2)) (2.1.1)\n",
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"Requirement already satisfied: requests in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from -r ../../requirements.txt (line 3)) (2.31.0)\n",
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"Requirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from -r ../../requirements.txt (line 4)) (1.11.4)\n",
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"Requirement already satisfied: numpy in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from -r ../../requirements.txt (line 5)) (1.26.2)\n",
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"Requirement already satisfied: matplotlib in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from -r ../../requirements.txt (line 6)) (3.8.2)\n",
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"Requirement already satisfied: statistics in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from -r ../../requirements.txt (line 7)) (1.0.3.5)\n",
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"Requirement already satisfied: onnx in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from -r ../../requirements.txt (line 8)) (1.15.0)\n",
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"Requirement already satisfied: fsspec in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from torch->-r ../../requirements.txt (line 2)) (2023.10.0)\n",
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"Requirement already satisfied: typing-extensions in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from torch->-r ../../requirements.txt (line 2)) (4.8.0)\n",
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"Requirement already satisfied: jinja2 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from torch->-r ../../requirements.txt (line 2)) (3.1.2)\n",
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"Requirement already satisfied: filelock in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from torch->-r ../../requirements.txt (line 2)) (3.13.1)\n",
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"Requirement already satisfied: sympy in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from torch->-r ../../requirements.txt (line 2)) (1.12)\n",
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"Requirement already satisfied: networkx in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from torch->-r ../../requirements.txt (line 2)) (3.2.1)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from requests->-r ../../requirements.txt (line 3)) (3.6)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from requests->-r ../../requirements.txt (line 3)) (3.3.2)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from requests->-r ../../requirements.txt (line 3)) (2023.11.17)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from requests->-r ../../requirements.txt (line 3)) (2.1.0)\n",
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"Requirement already satisfied: packaging>=20.0 in /Users/jernkun/Library/Python/3.10/lib/python/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (23.2)\n",
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"Requirement already satisfied: pyparsing>=2.3.1 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (3.1.1)\n",
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"Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (0.12.1)\n",
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"Requirement already satisfied: contourpy>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (1.2.0)\n",
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"Requirement already satisfied: python-dateutil>=2.7 in /Users/jernkun/Library/Python/3.10/lib/python/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (2.8.2)\n",
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"Requirement already satisfied: fonttools>=4.22.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (4.45.1)\n",
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"Requirement already satisfied: pillow>=8 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (10.1.0)\n",
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"Requirement already satisfied: kiwisolver>=1.3.1 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (1.4.5)\n",
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"Requirement already satisfied: docutils>=0.3 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from statistics->-r ../../requirements.txt (line 7)) (0.20.1)\n",
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"Requirement already satisfied: protobuf>=3.20.2 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from onnx->-r ../../requirements.txt (line 8)) (4.25.1)\n",
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"Requirement already satisfied: six>=1.5 in /Users/jernkun/Library/Python/3.10/lib/python/site-packages (from python-dateutil>=2.7->matplotlib->-r ../../requirements.txt (line 6)) (1.16.0)\n",
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"Requirement already satisfied: MarkupSafe>=2.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from jinja2->torch->-r ../../requirements.txt (line 2)) (2.1.3)\n",
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"Requirement already satisfied: mpmath>=0.19 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from sympy->torch->-r ../../requirements.txt (line 2)) (1.3.0)\n",
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"\u001b[33mWARNING: You are using pip version 21.2.3; however, version 23.3.2 is available.\n",
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"You should consider upgrading via the '/usr/local/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"pip install -r ../../requirements.txt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import ezkl\n",
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"import torch\n",
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"from torch import nn\n",
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"import json\n",
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"import os\n",
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"import time\n",
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"import scipy\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import statistics\n",
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"import math"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"%run -i ../../zkstats/core.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# init path\n",
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"os.makedirs(os.path.dirname('shared/'), exist_ok=True)\n",
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"os.makedirs(os.path.dirname('prover/'), exist_ok=True)\n",
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"verifier_model_path = os.path.join('shared/verifier.onnx')\n",
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"prover_model_path = os.path.join('prover/prover.onnx')\n",
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"verifier_compiled_model_path = os.path.join('shared/verifier.compiled')\n",
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"prover_compiled_model_path = os.path.join('prover/prover.compiled')\n",
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"pk_path = os.path.join('shared/test.pk')\n",
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"vk_path = os.path.join('shared/test.vk')\n",
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"proof_path = os.path.join('shared/test.pf')\n",
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"settings_path = os.path.join('shared/settings.json')\n",
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"srs_path = os.path.join('shared/kzg.srs')\n",
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"witness_path = os.path.join('prover/witness.json')\n",
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"# this is private to prover since it contains actual data\n",
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"sel_data_path = os.path.join('prover/sel_data.json')\n",
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"# this is just dummy random value\n",
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"sel_dummy_data_path = os.path.join('shared/sel_dummy_data.json')"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"======================= ZK-STATS FLOW ======================="
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"reg fit: [1.98835287 3.14238058 0.06413647]\n"
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]
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}
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],
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"source": [
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"data_path = os.path.join('data.json')\n",
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"dummy_data_path = os.path.join('shared/dummy_data.json')\n",
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"\n",
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"data = json.loads(open(data_path, \"r\").read())\n",
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"x1_vals = data['x1']\n",
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"x2_vals = data['x2']\n",
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"y_vals = data['y']\n",
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"\n",
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"create_dummy(data_path, dummy_data_path)\n",
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"dummy_data = json.loads(open(dummy_data_path, \"r\").read())\n",
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"dummy_x1_vals = dummy_data['x1']\n",
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"dummy_x2_vals = dummy_data['x2']\n",
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"dummy_y_vals = dummy_data['y']\n",
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"\n",
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"\n",
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"def stacked_x(*args):\n",
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" result = np.column_stack((*args, np.ones_like(args[0])))\n",
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" return result\n",
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"\n",
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"x_one = stacked_x(x1_vals, x2_vals)\n",
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"dummy_x_one = stacked_x(dummy_x1_vals, dummy_x2_vals)\n",
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"\n",
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"w_vals = np.matmul(np.matmul(np.linalg.inv(np.matmul(x_one.transpose(), x_one)), x_one.transpose()), y_vals)\n",
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"dummy_w_vals = np.matmul(np.matmul(np.linalg.inv(np.matmul(dummy_x_one.transpose(), dummy_x_one)), dummy_x_one.transpose()), dummy_y_vals)\n",
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"\n",
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"print(\"reg fit: \", w_vals)\n",
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"w_tensor = torch.tensor(w_vals).reshape(1,-1,1)\n",
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"dummy_w_tensor = torch.tensor(dummy_w_vals).reshape(1,-1,1)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"scales = [0]\n",
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"selected_columns = ['x1', 'x2', 'y']\n",
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"commitment_maps = get_data_commitment_maps(data_path, scales)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Verifier/ data consumer side:\n",
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"class verifier_model(nn.Module):\n",
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" def __init__(self):\n",
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" super(verifier_model, self).__init__()\n",
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" self.w = nn.Parameter(data = dummy_w_tensor, requires_grad = False)\n",
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"\n",
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" def forward(self, *args):\n",
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" # infer Y from the last parameter\n",
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" Y = args[-1]\n",
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" X_one = torch.cat((*args[:-1], torch.ones_like(args[0])), dim=2)\n",
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" X_T = torch.transpose(X_one, 1, 2)\n",
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" return (\n",
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" torch.sum(torch.abs(X_T @ X_one @ self.w - X_T @ Y)) <= 0.01 * torch.sum(torch.abs(X_T @ Y)),\n",
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" self.w\n",
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" )\n",
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"\n",
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"\n",
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"verifier_define_calculation(dummy_data_path, selected_columns, sel_dummy_data_path,verifier_model, verifier_model_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Theory output: tensor([[[1.9884],\n",
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" [3.1424],\n",
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" [0.0641]]], dtype=torch.float64)\n",
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"==== Generate & Calibrate Setting ====\n",
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"scale: [0]\n",
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"setting: {\"run_args\":{\"tolerance\":{\"val\":0.0,\"scale\":1.0},\"input_scale\":0,\"param_scale\":0,\"scale_rebase_multiplier\":10,\"lookup_range\":[-23296,18300],\"logrows\":16,\"num_inner_cols\":2,\"variables\":[[\"batch_size\",1]],\"input_visibility\":{\"Hashed\":{\"hash_is_public\":true,\"outlets\":[]}},\"output_visibility\":\"Public\",\"param_visibility\":\"Private\"},\"num_rows\":11808,\"total_assignments\":794,\"total_const_size\":13,\"model_instance_shapes\":[[1],[1,3,1]],\"model_output_scales\":[0,0],\"model_input_scales\":[0,0,0],\"module_sizes\":{\"kzg\":[],\"poseidon\":[11808,[3]],\"elgamal\":[0,[0]]},\"required_lookups\":[\"Abs\",{\"Div\":{\"denom\":100.0}},{\"GreaterThan\":{\"a\":0.0}}],\"check_mode\":\"UNSAFE\",\"version\":\"7.0.0\",\"num_blinding_factors\":null}\n"
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]
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}
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],
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"source": [
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"# prover calculates settings, send to verifier\n",
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"\n",
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"theory_output = w_tensor\n",
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"print(\"Theory output: \", theory_output)\n",
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"class prover_model(nn.Module):\n",
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" def __init__(self):\n",
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" super(prover_model, self).__init__()\n",
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" self.w = nn.Parameter(data = w_tensor, requires_grad = False)\n",
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"\n",
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" def forward(self, *args):\n",
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" # infer Y from the last parameter\n",
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" Y = args[-1]\n",
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" X_one = torch.cat((*args[:-1], torch.ones_like(args[0])), dim=2)\n",
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" X_T = torch.transpose(X_one, 1, 2)\n",
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" return (\n",
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" torch.sum(torch.abs(X_T @ X_one @ self.w - X_T @ Y)) <= 0.01 * torch.sum(torch.abs(X_T @ Y)),\n",
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" self.w\n",
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" )\n",
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"# try scale = [3] for more accuracy\n",
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"prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"spawning module 0\n",
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"spawning module 2\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"==== setting up ezkl ====\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"spawning module 0\n",
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"spawning module 2\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Time setup: 6.932987928390503 seconds\n",
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"=======================================\n",
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"Theory output: tensor([[[1.9884],\n",
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" [3.1424],\n",
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" [0.0641]]], dtype=torch.float64)\n",
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"!@# compiled_model exists? False\n",
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"!@# compiled_model exists? True\n",
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"==== Generating Witness ====\n",
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"witness boolean: 1.0\n",
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"witness result 1 : 2.0\n",
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"witness result 2 : 3.0\n",
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"witness result 3 : 0.0\n",
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"==== Generating Proof ====\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"spawning module 0\n",
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"spawning module 2\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"proof: {'instances': [[[6145674602038562713, 11796601694033167407, 3132644448460071153, 1431119980703310933], [17187590983289934876, 11857991285122296962, 971807162298867662, 379283799527326290], [3957842973089931008, 9845595232537184463, 786695466761881781, 2995319695946854765], [12436184717236109307, 3962172157175319849, 7381016538464732718, 1011752739694698287], [6425625360762666998, 7924344314350639699, 14762033076929465436, 2023505479389396574], [415066004289224689, 11886516471525959549, 3696305541684646538, 3035258219084094862], [0, 0, 0, 0]]], 'proof': '10363b8402c70e7b80d4f2acd6c3babe01485159a5bccda49d49459ca566e85e2d2c53f12010849171586e24e4d7162f7d183277453197b328311743f255dcb2162cacf200e8d9766319ccc0f01761dc05e8d247ab9390cd3207fd13399d1bb906e3ff51f966b2ed8bffc54e226603bd1c18835dae832c7fb6ed3d8af340a18f28aa38b6ac3145e9c0d93d21d2446d7ee7890d4a1e590322e9406b6e5d823c86258bbb8b7c7ea512419fa7fcd6d57e52285e6ef29905fb822bf0d1648e4842fd0362fd965f28966b3bd0404e74de92e4175bbee869f497d580ed3f6eeddb25ca038fdc208815b0cbda4359c68e1072676af1a6aa18c6b71ecf696f293eb844b60e875c268721ac53e6df3c3eb18f347c0b98b12c9fda5d5b83be995de763ee8924a8e8a77a97be449876959ef32f92967cd544e2717907eae28d6b6da5b5a5650362a14f328af3c701f4d424bf603723d90dc213930530283c7352b4aa17de531c9c5472035b0a1153b4ba444ce48068bed1999abc660a70361fc6d14679663a24496a0c995452b942dd6f300adcd4b6872e23b23f4897025edb7d9089114dc417c0ff75d0ea6db3f757461bcd50e66d09f99ca47b81c8f83eca57d8eaa872be08c62f216d4c543d953a62b03f0f6e309ffc7abae407ddc1f5a97344d28f4c2627fd04dc21df9d34323733bf27c92a94824e39591ac1e472f0a49bb159f1a0d01e709eaeb3dd2d2e517dc6dcd1c109bdac3a32d63b799fdce2d2e18cbcf05a1413c17fb45ddbce66922d6a65fe4340e8f806544b6409942669bc928a82d3bf0a050c74ca14b1cde0fb5d7cf415aaa5fd82ba54ac40bb133e56d600b160307ae02d2eb733a4e2e3921e5e8ad3118657cbc2609b8b4012b328c932ade76c46c9271b9fd15cef1c1bb15c118f4a4a5a4452d6236f82387de3b34b6c82225afb04381b758979d25643aa9366d3c2bf4f7d6cad2d87a4f05bee59ef9f2e24b9f91bd70ae2e4e785dacb57b21afd92807e236c297059545d36060ad5f03a1a41b0628c0246eb7a48a82f20b1f987852c1b2fdc73a79d16276458fadd94b17145eef33610923fc17f250f67ea7d803fbab9e04874f72097b6846c22a3c3f63e8991af6e29fc13f6d413264d51b687e311ca85a42d45c84aa11ae401a823f20ff9c6ec4110923fc17f250f67ea7d803fbab9e04874f72097b6846c22a3c3f63e8991af6e29fc13f6d413264d51b687e311ca85a42d45c84aa11ae401a823f20ff9c6ec41163b2f3a9dcaa385da2f6f53f99fb603971e3e501433d52466625bf7823457e12ceebda1cda7b745179ef24891b80d23bd9eeb20a1148cf7df2a9faf09917a7c1d6632e3ade0d5e7c98d10014adb9e8d0221f784884ecd055037d7eb39b81cf808507452c206b761a503b13ab4dec7b43ad138db5909d8e364af418cc2e11f040e2e67a122381fa19d96ae8012411b106efac18dc3f0a1ace12a06fcc4eaa6322e4d505f2d8914e5c373083e31253a2343d082b40f6b717b4a671f0da05c594c04c15a747429065de787205d953b13fe25a932b56c1314220f0bf2c84cfebad32d7c6be9cfd18ca78f35e60ed5eb3120d74f667ef5eee7e92816e7492c1ec6c022d614bac43448397254b5662ea4df7c41440ad2b697a63244fedf6726e75e1324e3bc6ef48e1af1c34637a196c9975aa4a1d7b05e946772f190b90c98cb95af16b8bad6bbcfd2b48704ae2c20665852ae1c5487422ebe3a7927265b2324c56b0cb02f6ab662d8db9cf46eddebc4257dc0936afd521389d72d7a5f141405ba972248791134cda356dd7a639f33fc4033fe840f0a497a3e4d2a9de194ab4eb2f62262385589734fed696e95b3794f37b53bc16080957575f8cc8b168a177ba61b0ef4ebcb1ef431f8d9d4382dbca7ef56b3c9152cb162801285c1453f9e1c6c9500809c5f0e726f3e3670a5051ed8036d021a5c3900cf5552ebb8634f12d8306b2c33e52fd5b01c00a5dab6179014aba31f81d89af6b6ace472e3b9d07042e4f8215f6f4cd4e46d4f6e5f7ee0c2606dc35f016fa1bffb328638aaaf0ff5a61bb422b7f9e94bda3da2fe6711c1970b7daa7207f8efacbb38749bc43c48297873772aa7df302d5fc5154768e838fac1b27b2f8508ebc970df1da4975db184e4bd15006be00aa35edae04ba1db60a819f23cde460c185752bd2aa4a73cdddb62ed652b9c8ae85ebde279e9a284039d474f34339f223771bb3c9fb6e671a051797b1113df76ba267d0139fe5d4c9165cb562bcc3e5994e7a74989e66691e75927fcce1e6898a2dadc3501c647224735f7466a9673a6f73e92f28734c903d0c37624ae2eca506b158068d94813f3e881c8b90f32c6805d1e705989cfbef142a212ba4a2d00e49b6e0add6f1b674fb67c94cb1fd0c14616cca3938c36e0ca026c3d29e81799c78123f83efeb9b249e27501349e03e0ea22bb39236b5d1bb1e3d297da4415589e7b5eef4e9839cbb0b3c6c2bd481bb1436cf5f502c48e05cbc0b8d2afde27e78c4a11ffed4f42f667634eb55d503ca8affd1799d46f8854d380558547de2f72d67215390d9f1573db08d3987362bbf585c4aeb823c543aaf10a5393658b26169cf7d0bf847f906ecc3ac8a83c1155001e894550c7cb58ea28c5c58ee4441c1ceff039685125b032212294a1cc25556ad81f6048ca83b9550de8d493fdca009559504041e20bba4f9223800d9ac34cdff6556b75aad4ef3b6730cb08230f25453c0c8f094193f9409b21e7dbf10fb0393191a387b67a74d5f4ac8f7788d821dc29a8816db1d9919f55bf4cbd70800dacc90f45ca028e14b65620de9ba1992b013de6beb457553c8c80e5214b66e031de993b603ca55eb3d103bca8278fc70d597edb9d73b9e708cebdc378723e32330ca4c577d2bdb04d627fd2e769d537258aab478cd5159127e71f58625dc3d4ef99985043b6e62716d17bc9d63de018239a52302147c8beae161429e768f2d47492a04a9527406a8fac8189f6a3a1fc07a4e0581c59fd60c2eb62588f40e3068f963ad427e9a42f29cb783ddb0c612a16a8277e4e6f4de5cd274303d2b1969263d9a649f2dd17f7f52f764951fa471d1ef4d0f5931abf036e078a941227ddd5c3944a24c9412825a3cc2cb3468205c1133412ad93d22222d12be4d2e0720db0420ce72c0b94586176964f4c27e827ae01bc7127685ead9f137d6b5fce8e5676620ab887db8f45be8e08e7640344d3b513642c2f74fe43c88b61099c7d5deb78fbe058cef88cfc7b23012f700dc494401bfe4061b2be48f1e52208b7df0bc29678f67882c00cee3965ed2d0600ba46941781e7c7e4a7888e18f1167bd942115f26aa4512391e5face0865c6ec0a3238e12cb191063169fde5a2d75a935e764474bd449b2b7c5ae425dfff3acb8df87fb006d1af4057d22a2d729c363a53e12dee1631cc2b60da3d0ec7d9a73e56adc5323004f05c5d0b5aaf9ac907bc980c84291d5e019d512de89fba79d702a27035806ccab0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'transcript_type': 'EVM'}\n",
|
|
"Time gen prf: 9.653532981872559 seconds\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
|
|
"# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
|
|
"setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
|
|
"\n",
|
|
"print(\"=======================================\")\n",
|
|
"# Prover generates proof\n",
|
|
"print(\"Theory output: \", theory_output)\n",
|
|
"prover_gen_proof(prover_model_path, sel_data_path, witness_path, prover_compiled_model_path, settings_path, proof_path, pk_path)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"num_inputs: 3\n",
|
|
"prf instances: [[[6145674602038562713, 11796601694033167407, 3132644448460071153, 1431119980703310933], [17187590983289934876, 11857991285122296962, 971807162298867662, 379283799527326290], [3957842973089931008, 9845595232537184463, 786695466761881781, 2995319695946854765], [12436184717236109307, 3962172157175319849, 7381016538464732718, 1011752739694698287], [6425625360762666998, 7924344314350639699, 14762033076929465436, 2023505479389396574], [415066004289224689, 11886516471525959549, 3696305541684646538, 3035258219084094862], [0, 0, 0, 0]]]\n",
|
|
"proof boolean: 1.0\n",
|
|
"proof result 1 : 2.0\n",
|
|
"proof result 2 : 3.0\n",
|
|
"proof result 3 : 0.0\n",
|
|
"verified\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.1"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|