mirror of
https://github.com/zkonduit/ezkl.git
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450 lines
17 KiB
Plaintext
450 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
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"metadata": {},
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"source": [
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"## Support Vector Machines\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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"cell_type": "code",
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"execution_count": null,
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"id": "95613ee9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# check if notebook is in colab\n",
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"try:\n",
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" # install ezkl\n",
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" import google.colab\n",
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" import subprocess\n",
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" import sys\n",
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" subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
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" subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
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" subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"sk2torch\"])\n",
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"\n",
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"# rely on local installation of ezkl if the notebook is not in colab\n",
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"except:\n",
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" pass\n",
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"\n",
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"\n",
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"# here we create and (potentially train a model)\n",
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"\n",
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"# make sure you have the dependencies required here already installed\n",
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"import json\n",
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"import numpy as np\n",
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"from sklearn.svm import SVC\n",
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"import sk2torch\n",
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"import torch\n",
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"import ezkl\n",
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"import os\n",
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"\n",
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"\n",
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"# Create a dataset of two Gaussians. There will be some overlap\n",
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"# between the two classes, which adds some uncertainty to the model.\n",
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"xs = np.concatenate(\n",
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" [\n",
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" np.random.random(size=(256, 2)) + [1, 0],\n",
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" np.random.random(size=(256, 2)) + [-1, 0],\n",
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" ],\n",
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" axis=0,\n",
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")\n",
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"ys = np.array([False] * 256 + [True] * 256)\n",
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"\n",
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"# Train an SVM on the data and wrap it in PyTorch.\n",
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"sk_model = SVC(probability=True)\n",
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"sk_model.fit(xs, ys)\n",
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"model = sk2torch.wrap(sk_model)\n",
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"\n",
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"\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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"cell_type": "code",
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"execution_count": null,
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"id": "b37637c4",
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"metadata": {},
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"outputs": [],
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"source": [
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"model_path = os.path.join('network.onnx')\n",
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"compiled_model_path = os.path.join('network.compiled')\n",
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"pk_path = os.path.join('test.pk')\n",
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"vk_path = os.path.join('test.vk')\n",
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"settings_path = os.path.join('settings.json')\n",
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"\n",
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"witness_path = os.path.join('witness.json')\n",
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"data_path = os.path.join('input.json')"
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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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"id": "7f0ca328",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"# Create a coordinate grid to compute a vector field on.\n",
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"spaced = np.linspace(-2, 2, num=25)\n",
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"grid_xs = torch.tensor([[x, y] for x in spaced for y in spaced], requires_grad=True)\n",
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"\n",
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"\n",
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"# Compute the gradients of the SVM output.\n",
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"outputs = model.predict_proba(grid_xs)[:, 1]\n",
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"(input_grads,) = torch.autograd.grad(outputs.sum(), (grid_xs,))\n",
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"\n",
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"\n",
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"# Create a quiver plot of the vector field.\n",
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"plt.quiver(\n",
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" grid_xs[:, 0].detach().numpy(),\n",
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" grid_xs[:, 1].detach().numpy(),\n",
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" input_grads[:, 0].detach().numpy(),\n",
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" input_grads[:, 1].detach().numpy(),\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": null,
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"id": "82db373a",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"# export to onnx format\n",
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"# !!!!!!!!!!!!!!!!! This will flash a warning but it is fine !!!!!!!!!!!!!!!!!!!!!\n",
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"\n",
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"# Input to the model\n",
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"shape = xs.shape[1:]\n",
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"x = grid_xs[0:1]\n",
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"torch_out = model.predict(x)\n",
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"# Export the model\n",
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"torch.onnx.export(model, # model being run\n",
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" # model input (or a tuple for multiple inputs)\n",
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" x,\n",
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" # where to save the model (can be a file or file-like object)\n",
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" \"network.onnx\",\n",
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" export_params=True, # store the trained parameter weights inside the model file\n",
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" opset_version=10, # the ONNX version to export the model to\n",
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" do_constant_folding=True, # whether to execute constant folding for optimization\n",
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" input_names=['input'], # the model's input names\n",
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" output_names=['output'], # the model's output names\n",
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" dynamic_axes={'input': {0: 'batch_size'}, # variable length axes\n",
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" 'output': {0: 'batch_size'}})\n",
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"\n",
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"d = ((x).detach().numpy()).reshape([-1]).tolist()\n",
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"\n",
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"data = dict(input_shapes=[shape],\n",
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" input_data=[d],\n",
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" output_data=[o.reshape([-1]).tolist() for o in torch_out])\n",
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"\n",
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"# Serialize data into file:\n",
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"json.dump(data, open(\"input.json\", 'w'))\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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"id": "d5e374a2",
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"metadata": {},
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"outputs": [],
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"source": [
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"!RUST_LOG=trace\n",
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"# TODO: Dictionary outputs\n",
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"res = ezkl.gen_settings(model_path, settings_path)\n",
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"assert res == True\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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"cal_path = os.path.join(\"calibration.json\")\n",
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"\n",
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"data_array = ((grid_xs[0:20]).detach().numpy()).reshape([-1]).tolist()\n",
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"\n",
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"data = dict(input_data = [data_array])\n",
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"\n",
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"# Serialize data into file:\n",
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"json.dump(data, open(cal_path, 'w'))\n",
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"\n",
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"\n",
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"ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
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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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"id": "3aa4f090",
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"metadata": {},
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"outputs": [],
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"source": [
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"res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
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"assert res == True"
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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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"id": "8b74dcee",
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"metadata": {},
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"outputs": [],
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"source": [
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"# srs path\n",
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"res = ezkl.get_srs( 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": null,
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"id": "18c8b7c7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# now generate the witness file \n",
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"\n",
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"res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
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"assert os.path.isfile(witness_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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"id": "b1c561a8",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"# HERE WE SETUP THE CIRCUIT PARAMS\n",
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"# WE GOT KEYS\n",
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"# WE GOT CIRCUIT PARAMETERS\n",
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"# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\n",
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"\n",
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"\n",
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"\n",
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"res = ezkl.setup(\n",
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" compiled_model_path,\n",
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" vk_path,\n",
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" pk_path,\n",
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" \n",
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" )\n",
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"\n",
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"assert res == True\n",
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"assert os.path.isfile(vk_path)\n",
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"assert os.path.isfile(pk_path)\n",
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"assert os.path.isfile(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": null,
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"id": "c384cbc8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# GENERATE A PROOF\n",
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"\n",
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"\n",
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"proof_path = os.path.join('test.pf')\n",
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"\n",
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"res = ezkl.prove(\n",
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" witness_path,\n",
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" compiled_model_path,\n",
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" pk_path,\n",
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" proof_path,\n",
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" \n",
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" \"single\",\n",
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" )\n",
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"\n",
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"print(res)\n",
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"assert os.path.isfile(proof_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": null,
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"id": "76f00d41",
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"metadata": {},
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"outputs": [],
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"source": [
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"# VERIFY IT\n",
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"\n",
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"res = ezkl.verify(\n",
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" proof_path,\n",
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" settings_path,\n",
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" vk_path,\n",
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" \n",
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" )\n",
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"\n",
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"assert res == True\n",
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"print(\"verified\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "760580d4",
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"metadata": {},
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"source": [
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"# Linear SVC"
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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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"id": "481824fe",
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"metadata": {},
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"outputs": [],
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"source": [
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"### Linear SVC\n",
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"\n",
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"# check if notebook is in colab\n",
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"try:\n",
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" # install ezkl\n",
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" import google.colab\n",
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" import subprocess\n",
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" import sys\n",
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" subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
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" subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
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" subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"sk2torch\"])\n",
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"\n",
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"# rely on local installation of ezkl if the notebook is not in colab\n",
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"except:\n",
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" pass\n",
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"\n",
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"\n",
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"# here we create and (potentially train a model)\n",
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"\n",
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"# make sure you have the dependencies required here already installed\n",
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"import json\n",
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"import numpy as np\n",
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"from sklearn.svm import LinearSVC\n",
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"import sk2torch\n",
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"import torch\n",
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"import ezkl\n",
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"import os\n",
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"\n",
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"\n",
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"xs = np.concatenate(\n",
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" [\n",
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" np.random.random(size=(256, 2)) + [1, 0],\n",
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" np.random.random(size=(256, 2)) + [-1, 0],\n",
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" ],\n",
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" axis=0,\n",
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")\n",
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"ys = np.array([False] * 256 + [True] * 256)\n",
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"\n",
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"# Train an SVM on the data and wrap it in PyTorch.\n",
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"sk_model = LinearSVC()\n",
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"sk_model.fit(xs, ys)\n",
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"model = sk2torch.wrap(sk_model)\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": null,
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"id": "7d1d47fe",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"# export to onnx format\n",
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"# !!!!!!!!!!!!!!!!! This will flash a warning but it is fine !!!!!!!!!!!!!!!!!!!!!\n",
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"\n",
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"# Input to the model\n",
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"shape = xs.shape[1:]\n",
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"x = grid_xs[0:1]\n",
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"torch_out = model.predict(x)\n",
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"# Export the model\n",
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"torch.onnx.export(model, # model being run\n",
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" # model input (or a tuple for multiple inputs)\n",
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" x,\n",
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" # where to save the model (can be a file or file-like object)\n",
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" \"network.onnx\",\n",
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" export_params=True, # store the trained parameter weights inside the model file\n",
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" opset_version=10, # the ONNX version to export the model to\n",
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" do_constant_folding=True, # whether to execute constant folding for optimization\n",
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" input_names=['input'], # the model's input names\n",
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" output_names=['output'], # the model's output names\n",
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" dynamic_axes={'input': {0: 'batch_size'}, # variable length axes\n",
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" 'output': {0: 'batch_size'}})\n",
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"\n",
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"d = ((x).detach().numpy()).reshape([-1]).tolist()\n",
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"\n",
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"data = dict(input_shapes=[shape],\n",
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" input_data=[d],\n",
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" output_data=[o.reshape([-1]).tolist() for o in torch_out])\n",
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"\n",
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"# Serialize data into file:\n",
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"json.dump(data, open(\"input.json\", 'w'))\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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"id": "7c00b3f6",
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"metadata": {},
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"outputs": [],
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"source": [
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"model_path = os.path.join('network.onnx')\n",
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"compiled_model_path = os.path.join('network.compiled')\n",
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"pk_path = os.path.join('test.pk')\n",
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"vk_path = os.path.join('test.vk')\n",
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"settings_path = os.path.join('settings.json')\n",
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"\n",
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"witness_path = os.path.join('witness.json')\n",
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"data_path = os.path.join('input.json')"
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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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"id": "69536185",
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"metadata": {},
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"outputs": [],
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"source": [
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"!RUST_LOG=trace\n",
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"# TODO: Dictionary outputs\n",
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"res = ezkl.gen_settings(model_path, settings_path)\n",
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"assert res == True\n",
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"\n",
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"res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
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"assert res == True"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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|
"pygments_lexer": "ipython3",
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"version": "3.9.15"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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