{
    "cells": [
        {
            "attachments": {},
            "cell_type": "markdown",
            "id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
            "metadata": {},
            "source": [
                "## EZKL Jupyter Notebook Demo \n",
                "\n",
                "Here we demonstrate the use of the EZKL package in a Jupyter notebook whereby all components of the circuit are public or pre-committed to. This is the simplest case of using EZKL (proof of computation)."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "95613ee9",
            "metadata": {},
            "outputs": [],
            "source": [
                "# check if notebook is in colab\n",
                "try:\n",
                "    # install ezkl\n",
                "    import google.colab\n",
                "    import subprocess\n",
                "    import sys\n",
                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
                "\n",
                "# rely on local installation of ezkl if the notebook is not in colab\n",
                "except:\n",
                "    pass\n",
                "\n",
                "\n",
                "# here we create and (potentially train a model)\n",
                "\n",
                "# make sure you have the dependencies required here already installed\n",
                "from torch import nn\n",
                "import ezkl\n",
                "import os\n",
                "import json\n",
                "import torch\n",
                "\n",
                "\n",
                "# Defines the model\n",
                "# we got convs, we got relu, we got linear layers\n",
                "# What else could one want ????\n",
                "\n",
                "class MyModel(nn.Module):\n",
                "    def __init__(self):\n",
                "        super(MyModel, self).__init__()\n",
                "\n",
                "        self.conv1 = nn.Conv2d(in_channels=1, out_channels=2, kernel_size=5, stride=2)\n",
                "        self.conv2 = nn.Conv2d(in_channels=2, out_channels=3, kernel_size=5, stride=2)\n",
                "\n",
                "        self.relu = nn.ReLU()\n",
                "\n",
                "        self.d1 = nn.Linear(48, 48)\n",
                "        self.d2 = nn.Linear(48, 10)\n",
                "\n",
                "    def forward(self, x):\n",
                "        # 32x1x28x28 => 32x32x26x26\n",
                "        x = self.conv1(x)\n",
                "        x = self.relu(x)\n",
                "        x = self.conv2(x)\n",
                "        x = self.relu(x)\n",
                "\n",
                "        # flatten => 32 x (32*26*26)\n",
                "        x = x.flatten(start_dim = 1)\n",
                "\n",
                "        # 32 x (32*26*26) => 32x128\n",
                "        x = self.d1(x)\n",
                "        x = self.relu(x)\n",
                "\n",
                "        # logits => 32x10\n",
                "        logits = self.d2(x)\n",
                "\n",
                "        return logits\n",
                "\n",
                "\n",
                "circuit = MyModel()\n",
                "\n",
                "# Train the model as you like here (skipped for brevity)\n",
                "\n",
                "\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "b37637c4",
            "metadata": {},
            "outputs": [],
            "source": [
                "model_path = os.path.join('network.onnx')\n",
                "compiled_model_path = os.path.join('network.compiled')\n",
                "pk_path = os.path.join('test.pk')\n",
                "vk_path = os.path.join('test.vk')\n",
                "settings_path = os.path.join('settings.json')\n",
                "\n",
                "witness_path = os.path.join('witness.json')\n",
                "data_path = os.path.join('input.json')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "82db373a",
            "metadata": {},
            "outputs": [],
            "source": [
                "\n",
                "shape = [1, 28, 28]\n",
                "# After training, export to onnx (network.onnx) and create a data file (input.json)\n",
                "x = 0.1*torch.rand(1,*shape, requires_grad=True)\n",
                "\n",
                "# Flips the neural net into inference mode\n",
                "circuit.eval()\n",
                "\n",
                "    # Export the model\n",
                "torch.onnx.export(circuit,               # model being run\n",
                "                      x,                   # model input (or a tuple for multiple inputs)\n",
                "                      model_path,            # where to save the model (can be a file or file-like object)\n",
                "                      export_params=True,        # store the trained parameter weights inside the model file\n",
                "                      opset_version=10,          # the ONNX version to export the model to\n",
                "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
                "                      input_names = ['input'],   # the model's input names\n",
                "                      output_names = ['output'], # the model's output names\n",
                "                      dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes\n",
                "                                    'output' : {0 : 'batch_size'}})\n",
                "\n",
                "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
                "\n",
                "data = dict(input_data = [data_array])\n",
                "\n",
                "    # Serialize data into file:\n",
                "json.dump( data, open(data_path, 'w' ))\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "d5e374a2",
            "metadata": {},
            "outputs": [],
            "source": [
                "\n",
                "py_run_args = ezkl.PyRunArgs()\n",
                "py_run_args.input_visibility = \"public\"\n",
                "py_run_args.output_visibility = \"public\"\n",
                "py_run_args.param_visibility = \"fixed\" # \"fixed\" for params means that the committed to params are used for all proofs\n",
                "\n",
                "res = ezkl.gen_settings(model_path, settings_path, py_run_args=py_run_args)\n",
                "assert res == True\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "cal_path = os.path.join(\"calibration.json\")\n",
                "\n",
                "data_array = (torch.rand(20, *shape, requires_grad=True).detach().numpy()).reshape([-1]).tolist()\n",
                "\n",
                "data = dict(input_data = [data_array])\n",
                "\n",
                "# Serialize data into file:\n",
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "3aa4f090",
            "metadata": {},
            "outputs": [],
            "source": [
                "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
                "assert res == True"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "8b74dcee",
            "metadata": {},
            "outputs": [],
            "source": [
                "# srs path\n",
                "res = ezkl.get_srs( settings_path)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "18c8b7c7",
            "metadata": {},
            "outputs": [],
            "source": [
                "# now generate the witness file \n",
                "\n",
                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "b1c561a8",
            "metadata": {},
            "outputs": [],
            "source": [
                "\n",
                "# HERE WE SETUP THE CIRCUIT PARAMS\n",
                "# WE GOT KEYS\n",
                "# WE GOT CIRCUIT PARAMETERS\n",
                "# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\n",
                "\n",
                "\n",
                "\n",
                "res = ezkl.setup(\n",
                "        compiled_model_path,\n",
                "        vk_path,\n",
                "        pk_path,\n",
                "        \n",
                "    )\n",
                "\n",
                "assert res == True\n",
                "assert os.path.isfile(vk_path)\n",
                "assert os.path.isfile(pk_path)\n",
                "assert os.path.isfile(settings_path)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "c384cbc8",
            "metadata": {},
            "outputs": [],
            "source": [
                "# GENERATE A PROOF\n",
                "\n",
                "\n",
                "proof_path = os.path.join('test.pf')\n",
                "\n",
                "res = ezkl.prove(\n",
                "        witness_path,\n",
                "        compiled_model_path,\n",
                "        pk_path,\n",
                "        proof_path,\n",
                "        \n",
                "        \"single\",\n",
                "    )\n",
                "\n",
                "print(res)\n",
                "assert os.path.isfile(proof_path)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "76f00d41",
            "metadata": {},
            "outputs": [],
            "source": [
                "# VERIFY IT\n",
                "\n",
                "res = ezkl.verify(\n",
                "        proof_path,\n",
                "        settings_path,\n",
                "        vk_path,\n",
                "        \n",
                "    )\n",
                "\n",
                "assert res == True\n",
                "print(\"verified\")"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3 (ipykernel)",
            "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": "3.9.15"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
}
