mirror of
https://github.com/MPCStats/zk-stats-lib.git
synced 2026-01-08 05:04:07 -05:00
327 lines
25 KiB
Plaintext
327 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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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 /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 1)) (7.0.0)\n",
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"Requirement already satisfied: torch in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 2)) (2.2.0)\n",
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"Requirement already satisfied: requests in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 3)) (2.31.0)\n",
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"Requirement already satisfied: scipy in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 4)) (1.12.0)\n",
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"Requirement already satisfied: numpy in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 5)) (1.26.3)\n",
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"Requirement already satisfied: matplotlib in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 6)) (3.8.2)\n",
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"Requirement already satisfied: statistics in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 7)) (1.0.3.5)\n",
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"Requirement already satisfied: onnx in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 8)) (1.15.0)\n",
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"Requirement already satisfied: filelock in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from torch->-r ../../requirements.txt (line 2)) (3.13.1)\n",
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"Requirement already satisfied: typing-extensions>=4.8.0 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from torch->-r ../../requirements.txt (line 2)) (4.9.0)\n",
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"Requirement already satisfied: sympy in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from torch->-r ../../requirements.txt (line 2)) (1.12)\n",
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"Requirement already satisfied: networkx in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from torch->-r ../../requirements.txt (line 2)) (3.2.1)\n",
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"Requirement already satisfied: jinja2 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from torch->-r ../../requirements.txt (line 2)) (3.1.3)\n",
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"Requirement already satisfied: fsspec in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from torch->-r ../../requirements.txt (line 2)) (2023.12.2)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from requests->-r ../../requirements.txt (line 3)) (3.3.2)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from requests->-r ../../requirements.txt (line 3)) (3.6)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from requests->-r ../../requirements.txt (line 3)) (2.2.0)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from requests->-r ../../requirements.txt (line 3)) (2024.2.2)\n",
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"Requirement already satisfied: contourpy>=1.0.1 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (1.2.0)\n",
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"Requirement already satisfied: cycler>=0.10 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (0.12.1)\n",
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"Requirement already satisfied: fonttools>=4.22.0 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (4.47.2)\n",
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"Requirement already satisfied: kiwisolver>=1.3.1 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (1.4.5)\n",
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"Requirement already satisfied: packaging>=20.0 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (23.2)\n",
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"Requirement already satisfied: pillow>=8 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (10.2.0)\n",
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"Requirement already satisfied: pyparsing>=2.3.1 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (3.1.1)\n",
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"Requirement already satisfied: python-dateutil>=2.7 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (2.8.2)\n",
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"Requirement already satisfied: docutils>=0.3 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/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 /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from onnx->-r ../../requirements.txt (line 8)) (4.25.2)\n",
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"Requirement already satisfied: six>=1.5 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/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 /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from jinja2->torch->-r ../../requirements.txt (line 2)) (2.1.4)\n",
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"Requirement already satisfied: mpmath>=0.19 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from sympy->torch->-r ../../requirements.txt (line 2)) (1.3.0)\n",
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\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": 2,
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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": 3,
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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": 4,
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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": 5,
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"metadata": {},
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"outputs": [],
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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())['col_name']\n",
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"data_tensor = torch.reshape(torch.tensor(data),(1,-1, 1))\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())['col_name']\n",
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"dummy_data_tensor = torch.reshape(torch.tensor(dummy_data), (1,-1,1))\n",
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"\n",
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"dummy_theory_output = torch.div(1.0,torch.mean(torch.div(1.0,dummy_data_tensor)))\n",
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"theory_output = torch.div(1.0,torch.mean(torch.div(1.0,data_tensor)))"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"scales = [6]\n",
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"selected_columns = ['col_name']\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": 7,
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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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"# since have division, scale must be very LARGE, cant use just 0\n",
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"# So, we allow error rate to be 10% instead of 1%\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_theory_output, requires_grad = False)\n",
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"\n",
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" def forward(self,X):\n",
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" return (torch.abs((self.w*torch.sum(torch.div(1.0,X)))-X.size()[1])<=torch.abs(0.1*X.size()[1]), self.w)\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": 8,
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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(45.2144)\n",
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"==== Generate & Calibrate Setting ====\n",
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"scale: [6]\n",
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"setting: {\"run_args\":{\"tolerance\":{\"val\":0.0,\"scale\":1.0},\"input_scale\":6,\"param_scale\":6,\"scale_rebase_multiplier\":10,\"lookup_range\":[-184392,11264],\"logrows\":18,\"num_inner_cols\":2,\"variables\":[[\"batch_size\",1]],\"input_visibility\":{\"Hashed\":{\"hash_is_public\":true,\"outlets\":[]}},\"output_visibility\":\"Public\",\"param_visibility\":\"Private\"},\"num_rows\":14432,\"total_assignments\":911,\"total_const_size\":4,\"model_instance_shapes\":[[1],[1]],\"model_output_scales\":[0,6],\"model_input_scales\":[6],\"module_sizes\":{\"kzg\":[],\"poseidon\":[14432,[1]],\"elgamal\":[0,[0]]},\"required_lookups\":[\"Abs\",{\"Recip\":{\"scale\":4096.0}},{\"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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"theory_output = torch.div(1.0,torch.mean(torch.div(1.0,data_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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" # w represents mean in this case\n",
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" self.w = nn.Parameter(data = theory_output, requires_grad = False)\n",
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"\n",
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" def forward(self,X):\n",
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" return (torch.abs((self.w*torch.sum(torch.div(1.0,X)))-X.size()[1])<=torch.abs(0.1*X.size()[1]), self.w)\n",
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"\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": 9,
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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: 24.54654574394226 seconds\n",
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"=======================================\n",
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"Theory output: tensor(45.2144)\n",
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"==== Generating Witness ====\n",
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"witness boolean: 1.0\n",
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"witness result 1 : 45.21875\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': [[[11712583131456230033, 7998639690917952521, 17628064685104548320, 3364894255067083796], [12436184717236109307, 3962172157175319849, 7381016538464732718, 1011752739694698287], [10409296726488761395, 15557203226960814343, 16692342436085548322, 2420882828764455196]]], 'proof': '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'transcript_type': 'EVM'}\n",
|
|
"Time gen prf: 34.77906394004822 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": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"45.21875"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Verifier verifies\n",
|
|
"verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"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": "3.11.4"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|