mirror of
https://github.com/MPCStats/zk-stats-lib.git
synced 2026-01-08 05:04:07 -05:00
350 lines
26 KiB
Plaintext
350 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": 11,
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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": 12,
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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": 13,
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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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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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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": 15,
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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.exp(torch.mean(torch.log(dummy_data_tensor)))\n",
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"theory_output = torch.exp(torch.mean(torch.log(data_tensor)))\n",
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"\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": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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"scales = [8]\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": 33,
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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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"/var/folders/89/y9dw12v976ngdmqz4l7wbsnr0000gn/T/ipykernel_86541/1155542345.py:8: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
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" return (torch.abs((torch.log(self.w)*X.size()[1])-torch.sum(torch.log(X)))<=X.size()[1]*torch.log(torch.tensor(1.01)), self.w)\n"
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]
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}
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],
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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_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((torch.log(self.w)*X.size()[1])-torch.sum(torch.log(X)))<=X.size()[1]*torch.log(torch.tensor(1.01)), 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": 34,
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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(47.6981)\n",
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"==== Generate & Calibrate Setting ====\n",
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"scale: [8]\n",
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"setting: {\"run_args\":{\"tolerance\":{\"val\":0.0,\"scale\":1.0},\"input_scale\":8,\"param_scale\":8,\"scale_rebase_multiplier\":10,\"lookup_range\":[-1522,45056],\"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\":14432,\"total_assignments\":910,\"total_const_size\":4,\"model_instance_shapes\":[[1],[1]],\"model_output_scales\":[0,8],\"model_input_scales\":[8],\"module_sizes\":{\"kzg\":[],\"poseidon\":[14432,[1]],\"elgamal\":[0,[0]]},\"required_lookups\":[\"Abs\",{\"Ln\":{\"scale\":256.0}},{\"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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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/89/y9dw12v976ngdmqz4l7wbsnr0000gn/T/ipykernel_86541/669315669.py:10: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
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" return (torch.abs((torch.log(self.w)*X.size()[1])-torch.sum(torch.log(X)))<=X.size()[1]*torch.log(torch.tensor(1.01)), self.w)\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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"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((torch.log(self.w)*X.size()[1])-torch.sum(torch.log(X)))<=X.size()[1]*torch.log(torch.tensor(1.01)), self.w)\n",
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"\n",
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"\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": 35,
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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: 6.883466958999634 seconds\n",
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"=======================================\n",
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"Theory output: tensor(47.6981)\n",
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"==== Generating Witness ====\n",
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"witness boolean: 1.0\n",
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"witness result 1 : 47.69921875\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': [[[12773529019820117503, 7223165032241168505, 6388811546753741567, 1325363978837834469], [12436184717236109307, 3962172157175319849, 7381016538464732718, 1011752739694698287], [14148586052410868650, 7343294927740416096, 1182455714804702497, 77845129035718797]]], 'proof': '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'transcript_type': 'EVM'}\n",
|
|
"Time gen prf: 10.143173694610596 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": 36,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"47.69921875"
|
|
]
|
|
},
|
|
"execution_count": 36,
|
|
"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
|
|
}
|