mirror of
https://github.com/MPCStats/zk-stats-lib.git
synced 2026-01-10 05:57:55 -05:00
242 lines
20 KiB
Plaintext
242 lines
20 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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"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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from zkstats.core import create_dummy, verifier_define_calculation, prover_gen_settings, setup, prover_gen_proof, verifier_verify, get_data_commitment_maps"
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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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"# 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": 4,
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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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"create_dummy(data_path, dummy_data_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": 5,
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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": 6,
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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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"/Users/jernkun/Desktop/zk-stats-lib/zkstats/computation.py:172: 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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" is_precise_aggregated = torch.tensor(1.0)\n",
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"/Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages/torch/onnx/symbolic_opset9.py:2174: FutureWarning: 'torch.onnx.symbolic_opset9._cast_Bool' is deprecated in version 2.0 and will be removed in the future. Please Avoid using this function and create a Cast node instead.\n",
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" return fn(g, to_cast_func(g, input, False), to_cast_func(g, other, False))\n",
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"/Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages/torch/onnx/utils.py:1703: UserWarning: The exported ONNX model failed ONNX shape inference. The model will not be executable by the ONNX Runtime. If this is unintended and you believe there is a bug, please report an issue at https://github.com/pytorch/pytorch/issues. Error reported by strict ONNX shape inference: [ShapeInferenceError] (op_type:Where, node name: /Where): Y has inconsistent type tensor(float) (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/jit/serialization/export.cpp:1490.)\n",
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" _C._check_onnx_proto(proto)\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: send desired calculation\n",
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"from zkstats.computation import computation_to_model, State\n",
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"\n",
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"\n",
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"def computation(s: State, data: list[torch.Tensor]) -> torch.Tensor:\n",
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" x = data[0]\n",
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" return s.stdev(x)\n",
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"\n",
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"error = 0.01\n",
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"_, verifier_model = computation_to_model(computation, error)\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": 7,
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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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"Using 8 columns for non-linearity table.\n",
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"Using 8 columns for non-linearity table.\n",
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"Using 8 columns for non-linearity table.\n",
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"\n",
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"\n",
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" <------------- Numerical Fidelity Report (input_scale: 6, param_scale: 6, scale_input_multiplier: 1) ------------->\n",
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"\n",
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"+--------------+--------------+--------------+-----------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
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"| mean_error | median_error | max_error | min_error | mean_abs_error | median_abs_error | max_abs_error | min_abs_error | mean_squared_error | mean_percent_error | mean_abs_percent_error |\n",
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"+--------------+--------------+--------------+-----------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
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"| 0.0009598732 | 0.0019197464 | 0.0019197464 | 0 | 0.0009598732 | 0.0019197464 | 0.0019197464 | 0 | 0.0000018427131 | 0.00006583472 | 0.00006583472 |\n",
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"+--------------+--------------+--------------+-----------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
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"\n",
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"\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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"==== 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\":1,\"lookup_range\":[-162602,162656],\"logrows\":19,\"num_inner_cols\":2,\"variables\":[[\"batch_size\",1]],\"input_visibility\":{\"Hashed\":{\"hash_is_public\":true,\"outlets\":[]}},\"output_visibility\":\"Public\",\"param_visibility\":\"Private\",\"div_rebasing\":false,\"rebase_frac_zero_constants\":false,\"check_mode\":\"UNSAFE\"},\"num_rows\":14432,\"total_assignments\":9950,\"total_const_size\":2120,\"model_instance_shapes\":[[1],[1]],\"model_output_scales\":[0,6],\"model_input_scales\":[6],\"module_sizes\":{\"kzg\":[],\"poseidon\":[14432,[1]]},\"required_lookups\":[\"Abs\",{\"GreaterThan\":{\"a\":0.0}}],\"required_range_checks\":[[-32,32]],\"check_mode\":\"UNSAFE\",\"version\":\"9.1.0\",\"num_blinding_factors\":null,\"timestamp\":1709714355149}\n"
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]
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}
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],
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"source": [
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"# Prover/ data owner side\n",
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"_, prover_model = computation_to_model(computation, error)\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": 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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"==== setting up ezkl ====\n",
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"Time setup: 59.26989006996155 seconds\n",
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"=======================================\n",
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"==== Generating Witness ====\n",
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"witness boolean: 1.0\n",
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"witness result 1 : 14.578125\n",
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"==== Generating Proof ====\n",
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"proof: {'instances': [['c371c58876874ace4444c2f2683e0d7bb7ce264f49872aa6deab40635ed90f23', '0100000000000000000000000000000000000000000000000000000000000000', 'a503000000000000000000000000000000000000000000000000000000000000']], 'proof': '0x2ea53767627a825d488d58596fc992c33ac4780a4d585c0ae3a2664fe513ce900c79099b797ba238d29133da21f2ff81a4eb0312d8084b2f2bbd725c54f5e0c32bcf23f22b94c1764f8bfc2c66b97768bb62e3e69f0e11ced087b0eb74370a9f20e0ff4f1bdb3c4f58a80940b7b2df8024be0100008a578925e93bcad8fd274a1009140f48bb12422b923c1f33e6e8ede158c081a1a21b4f42ce1caa64749de71c42fac312d374a9ee18c477933db3cba1dfc1dd95fdf8369adbc3b00bd82c0e261eee129ba354368a9bed9a1e7e290052e6a230f5c4cb94c136f76fdca2e357045f74e768829996e853d7a7e947dcba3d38d74b9b735025bde53143cfa5a0da29ef4bae4e2a3885f3a37bbf7a7dbcc39fea652a70092999d921d15b880be3341e1f0ffc2c39667b79956d5fbe0f560d2315f58adcd6d872dc23553a236cfdf10098df0c5366a8e27d180e589eac25a3d1265fa16294bd7d8899aafda60cb7910c7c100322b35ff4f00115f418dcb716c36e0f205828f3b39ea83bf6264612a51c7f39bb07de1460fa21d5cbde62cabe68bc346727e3be736d79b7e37a19715211c8cec05a5a4381de2be303f83a67a94cd4c5f69aa413febabf0f35319ef1732757b54ce2e93afdaa825e599285efee173ab03612e8a06b0febd45836d078ed16b1b7a4599f72faf2aee13ddb7e401b4443e35b6ca8de192247eb42eedfee450b11539523ae877e13a6ba7cd38e07d720f995371dbe9c7815120b242f8419fb23f7c0b89c50dd33abe1000627d50743bce108ee05c8d331c74ed67d9d9ed2421c7d1eefdc098191560237e346081ca82a5b3c8ed2c03cb095e69a41a115a4751d9ee08de6fda97f313740e6c2f7dbd97fec773270a423b46d0f0e1bc0f785d6036cd843faa1dcfba1af2963625cea0ff80fa31ccd944ab851eae6632dba7fcc2cc993d9c3d578c294ab9ec7f3661a1c7324df89a0e0771f6799f6c058e9872815e4c6df4b1f901f7230188f9a9d128274d7f21a5635319d9551db501411358d0de7665252ce5b3dd59182b5153a20e12180903ca5dfda504e92c287d0b4dd0a062a40852126acd8f81da3610150c01519e0c9a045f695cd6ddcbe683342a8df0df45d855befa336e9d5df3dd7281f6568128fd426ac6c0eea033d1383478cbe1d8f841b4f4f91a03f0e98c613e7bda6e05a3b92111c9bf473662f9324da4f5f2029ab20e66b04f18419578b47f6a25d6ed138ba1b37900454ee7d8bcb20cb85281d090b3c6b8e18fe9eaf71a8003e29a56dd3f8cc86a71a82f7a5af07993a57070cfc95f3c9b233053d8381982ae9200cdb6e7d108d11f885d62fe03d3f813723fcb531459a1970d207d0e6b05884fccf8b633a7c109bc5f349478e856f7acc2c4716bf24b5b78705dc160b8ddd378d35796611b485fb07231b1fa2ab187df61464755176c206571a1231377be5b8c5db9ba5003c88a4ffc92be628a097de8c030c8de60eb45ffcd5874c3ccdfc3dd84eed5b07569749e49158b2da657fb7a10040bcad71328961a3bee788397d99255ffe369e54bd0e587b38f16f1a18d1e31fa30dbcbf5ae05d95b6c576f2dea328802056cdddb82972810bd09fe22c655c05e1e7d8b16da06f5b8c87b672593650de90da924cbcaeb935c45bf4dcd5a9bb0e504a14fdc66853e1a6f5363688ecdad9853a59896678db5cec495bb3c6768b2d62c6f3648b6cefab038b5b1a9a216b476216ea45d225a5b7803b9370d5ed2223fe9fffad65661a9eae60b69484e6df453d7788b527922ff68b3bb1d0199b66183336ea841f590fd957710aaefc3f54e9fe090b9e56affcb54418bb214e83962169555d408389b44dc540898df1cc911ece449d7c90a45c59c23656486316db2f4f754a356785c1ccd80b10e7df60bfcdf7c7e69d4ca65606fe3b3ba26f324317977076ad484a2fa23b0d15c8caf7645723e764e54165705845ef94595ab47525f6feef178a4efc7052b2f9f27eaa94cd3142269b9b42fd8c898f58ee33482b04e428c83bc16e5f68b63d64a2596f3658f37ccd4a68aba899b42856b41545052405b76be953a8364332194987ccd4ef7a6b2db6e7ae9f32dc6357978852f5410bb1d50f585bdc6e4e11f5b9847bd8fd062f7b0d31ac0f186266482365d8deb4184150e0d4aa07cca97b6038cd2413843e96539e7edb124a9f8fc7e831be4c66080d684c78a0463ba2e45282d7f7a8ce53a70414e1291fbffdd0907172d4dcb41f0dac891370295813a4611f4050997ed4b5852560e1515b8dbd3499da3370b508add8c0a5c9446a1894f52449a17a1885aa4754e608afa6e5ddae8fd0510be425f85f941d85ba0113394f22adbcac53f3d83df752f4cb7f85de4f29f21c10451372f27b71c8d2da241d2e371a589f6056c56c6480aef32d4da7195ed242bdc720f1bf6376a3957b8502a3e297ea4108c0f29ed3dcd754d70405d0524cb984c70036bb6c5df7b9cc7c8278cec218358bc61328e55869f83532dd7f8302f1ab82027d5ff0dcb3565bdc3539eb90f53564e9678f2c2fc862c986da1d3da040b6f421b1f74673f52944a0ed3c58670dd07ccd5c4f4330f050a02a716b8f99195bda2815fdb9996a0b5dcc5b309bb5002dbf72ca9ca88a8f0e3d5d3160019e484d960ad66ce257e41c1a192c06a8eb2d82dfa71437d159f9cbe82bd07d9912331a221be315dd461b9537eae9c595518a57b4f63174b82bc91b3dabc2c5437dd107241dfb07304695fa7d1d578f4606ce5491a6f61da6a6188737fbc4fb045531b5fd0789f87fe2d80844125c14134b782e9cf91ba6477dce95300e484099b080ad561a09be1c7005aae2adae91430f6165573e94c2036fc17fad0e85e5e5e74b4e450dec77b3cbcf6d3a0d0db53b00f1211605204a8eca5960d9de9e449a4e831a521ffcdbccbfaf97cc0de5a00b6fd6dbed9d6b241fb0ec485c4ce717bc86c2fcf303597fe6ae5d97fa16fc13f11b0a3997dce7b7b9b1ab7f0e04103395631275ea0cb6669f9b73d50d742def1eead1739d6e3f0f28ddc98e203c9057266c188f7a1a703907964c0bed6b6bf7e1a4af06817f902742a224ad077aa923778befd4e31c64ad7993766864eb0dc29f0ed02961550c2c4c77ad53c818f72a84ea33fc6f24d5ad14c833e2962e3f65811bc1abd026b9c103e766112a6c776071852408e91b7391be1e664a42ad74898dd824db764bf383c2e83f4402692eba767ee739cc000a68b91e06b009aaa335460b68de0673a083726ec86435d72cb3ea417311181e664720c45a4baa48244bdbda200219147919be3c875b83768886d790399225116a06ea1247e459674e762f5bde6a1d431f36c974efbd7720955ca2037f589d2078f049741935f641bafb963b94f9bb40bc473ee55512e059271d95225bca9922f0a100b7ed137bc35ec76738ead38a9476cf8ac110afb1269a646a0f1866ab0b4491b324e32c6a2f46b4a847489c66a088ffbf3d570b658516f07d990fc89e2f956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'transcript_type': 'EVM'}\n",
|
|
"Time gen prf: 75.34422492980957 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",
|
|
"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": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Verifier gets result: [14.578125]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Verifier verifies\n",
|
|
"res = verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)\n",
|
|
"print(\"Verifier gets result:\", res)"
|
|
]
|
|
},
|
|
{
|
|
"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
|
|
}
|