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feat: no manually setting selected_columns

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mhchia
2024-09-13 18:27:02 +08:00
parent c449ad9858
commit 3668dbcf63
4 changed files with 171 additions and 326 deletions
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73
public/assets/generate_data_commitment.ipynb
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@@ -9,48 +9,45 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Requirement already satisfied: zkstats==0.1.5 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (0.1.5)\n", "Requirement already satisfied: zkstats==0.1.10 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (0.1.10)\n",
"Requirement already satisfied: click<9.0.0,>=8.1.7 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from zkstats==0.1.5) (8.1.7)\n", "Requirement already satisfied: click<9.0.0,>=8.1.7 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from zkstats==0.1.10) (8.1.7)\n",
"Requirement already satisfied: ezkl==9.1.0 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from zkstats==0.1.5) (9.1.0)\n", "Requirement already satisfied: ezkl==9.1.0 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from zkstats==0.1.10) (9.1.0)\n",
"Requirement already satisfied: matplotlib<4.0.0,>=3.8.2 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from zkstats==0.1.5) (3.8.3)\n", "Requirement already satisfied: matplotlib<4.0.0,>=3.8.2 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from zkstats==0.1.10) (3.9.2)\n",
"Requirement already satisfied: numpy<2.0.0,>=1.26.2 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from zkstats==0.1.5) (1.26.4)\n", "Requirement already satisfied: numpy<2.0.0,>=1.26.2 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from zkstats==0.1.10) (1.26.4)\n",
"Requirement already satisfied: onnx<2.0.0,>=1.15.0 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from zkstats==0.1.5) (1.15.0)\n", "Requirement already satisfied: onnx<2.0.0,>=1.15.0 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from zkstats==0.1.10) (1.16.2)\n",
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"Requirement already satisfied: statistics<2.0.0,>=1.0.3 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from zkstats==0.1.5) (1.0.3.5)\n", "Requirement already satisfied: statistics<2.0.0,>=1.0.3 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from zkstats==0.1.10) (1.0.3.5)\n",
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"Requirement already satisfied: MarkupSafe>=2.0 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from jinja2->torch<3.0.0,>=2.1.1->zkstats==0.1.5) (2.1.5)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from jinja2->torch==2.2.0->zkstats==0.1.10) (2.1.5)\n",
"Requirement already satisfied: mpmath>=0.19 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from sympy->torch<3.0.0,>=2.1.1->zkstats==0.1.5) (1.3.0)\n", "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (from sympy->torch==2.2.0->zkstats==0.1.10) (1.3.0)\n",
"\n",
"\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;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\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",
"Note: you may need to restart the kernel to use updated packages.\n" "Note: you may need to restart the kernel to use updated packages.\n"
] ]
} }
], ],
"source": [ "source": [
"%pip install zkstats==0.1.5" "%pip install zkstats==0.1.10"
] ]
}, },
{ {
@@ -101,7 +98,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.12.1" "version": "3.12.5"
} }
}, },
"nbformat": 4, "nbformat": 4,

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"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Requirement already satisfied: torch in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (2.2.2)\n", "Requirement already satisfied: torch in /Users/mhchia/Library/Caches/pypoetry/virtualenvs/zkstats-brXmXluj-py3.12/lib/python3.12/site-packages (2.2.0)\n",
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"\n",
"\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;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.1\u001b[0m\n",
"\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",
"Note: you may need to restart the kernel to use updated packages.\n" "Note: you may need to restart the kernel to use updated packages.\n"
] ]
} }
], ],
"source": [ "source": [
"%pip install torch zkstats==0.1.8" "%pip install torch zkstats==0.1.10"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"import json\n",
"import os\n", "import os\n",
"from zkstats.core import (\n", "from zkstats.core import (\n",
" setup,\n", " setup,\n",
@@ -76,7 +72,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -92,17 +88,6 @@
"User select the columns" "User select the columns"
] ]
}, },
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# FIXME: this should be provided by users\n",
"# selected_columns = [\"x\", \"y\"]\n",
"selected_columns = [\"x\"]"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@@ -112,10 +97,10 @@
"A computation should be of type `TComputation`. For example, the following code snippet defines a computation that computes the sum of the private data.\n", "A computation should be of type `TComputation`. For example, the following code snippet defines a computation that computes the sum of the private data.\n",
"\n", "\n",
"```python\n", "```python\n",
"def computation(state: State, x: list[torch.Tensor]):\n", "def computation(state: State, args: Args):\n",
" out_0 = state.median(x[0])\n", " x = args[\"x\"]\n",
" out_1 = state.median(x[1])\n", " y = args[\"y\"]\n",
" return state.mean(torch.cat([out_0.unsqueeze(0), out_1.unsqueeze(0)]).reshape(-1,1))\n", " return state.mean(x), state.mean(y)\n",
"```\n", "```\n",
"\n", "\n",
"FIXME: The following code snippet is entirely from the user. You MUST check\n", "FIXME: The following code snippet is entirely from the user. You MUST check\n",
@@ -125,7 +110,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -135,7 +120,8 @@
"\n", "\n",
"def computation(state: State, args: Args):\n", "def computation(state: State, args: Args):\n",
" x = args[\"x\"]\n", " x = args[\"x\"]\n",
" return state.mean(x), state.median(x)" " y = args[\"y\"]\n",
" return state.mean(x), state.mean(y)"
] ]
}, },
{ {
@@ -147,7 +133,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -166,9 +152,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 20,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==== setting up ezkl ====\n"
]
},
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
@@ -183,17 +176,16 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"==== setting up ezkl ====\n", "Time setup: 0.907416820526123 seconds\n"
"Time setup: 0.5496859550476074 seconds\n"
] ]
}, },
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"[51.5, 46.25]" "[51.5390625, 4.0859375]"
] ]
}, },
"execution_count": 7, "execution_count": 20,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@@ -223,27 +215,49 @@
"\n", "\n",
"from zkstats.core import verifier_define_calculation, verifier_verify\n", "from zkstats.core import verifier_define_calculation, verifier_verify\n",
"\n", "\n",
"verifier_model_path = f\"{output_dir}/verifier_model.onnx\"\n",
"verifier_compiled_model_path = f\"{output_dir}/verifier_model.compiled\"\n",
"verifier_vk_path = f\"{output_dir}/verifier_model.vk\"\n",
"verifier_pk_path = f\"{output_dir}/verifier_model.pk\"\n",
"dummy_data_path = f\"{output_dir}/dummy_data.json\"\n",
"sel_dummy_data_path = f\"{output_dir}/sel_dummy_data.json\"\n",
"\n", "\n",
"# NOTE: generate the verifier model with the `precal_witness_path` provided by the prover\n", "def generate_verifier_model_and_setup(\n",
"_, verifier_model = computation_to_model(computation, precal_witness_path, isProver=False, selected_columns=selected_columns)\n", " output_dir: str,\n",
" computation, # from myself\n",
" precal_witness_path, # from prover\n",
" data_shape, # from prover\n",
"):\n",
" # Generate the verifier model with the `precal_witness_path` provided by the prover\n",
" selected_columns, _, verifier_model = computation_to_model(computation, precal_witness_path, data_shape, isProver=False)\n",
"\n",
" verifier_model_path = f\"{output_dir}/verifier_model.onnx\"\n",
" verifier_compiled_model_path = f\"{output_dir}/verifier_model.compiled\"\n",
"\n",
" verifier_vk_path = f\"{output_dir}/verifier_model.vk\"\n",
" verifier_pk_path = f\"{output_dir}/verifier_model.pk\"\n",
" dummy_data_path = f\"{output_dir}/dummy_data.json\"\n",
" sel_dummy_data_path = f\"{output_dir}/sel_dummy_data.json\"\n",
"\n",
" # Create dummy data with the same shape as the original data\n",
" create_dummy(data_shape, dummy_data_path)\n",
"\n",
" # Generate the verifier model given the dummy data and the selected columns\n",
" verifier_define_calculation(dummy_data_path, selected_columns, sel_dummy_data_path, verifier_model, verifier_model_path)\n",
"\n",
" # Generate the verification key\n",
" setup(verifier_model_path, verifier_compiled_model_path, settings_path, verifier_vk_path, verifier_pk_path)\n",
"\n",
" return selected_columns, verifier_vk_path\n",
"\n",
"# Determine which srs to use with the logrows in the settings.json\n", "# Determine which srs to use with the logrows in the settings.json\n",
"with open(settings_path, \"r\") as f:\n", "with open(settings_path, \"r\") as f:\n",
" settings = json.load(f)\n", " settings = json.load(f)\n",
"logrows = int(settings[\"run_args\"][\"logrows\"])\n", "logrows = int(settings[\"run_args\"][\"logrows\"])\n",
"srs_path = f'~/.ezkl/srs/kzg{logrows}.srs'\n", "srs_path = f'~/.ezkl/srs/kzg{logrows}.srs'\n",
"\n", "\n",
"# create dummy data with the same shape as the original data\n", "# Call the function with the necessary parameters\n",
"create_dummy(data_shape, dummy_data_path)\n", "selected_columns, verifier_vk_path = generate_verifier_model_and_setup(\n",
"# generate the verifier model given the dummy data and the selected columns\n", " output_dir,\n",
"verifier_define_calculation(dummy_data_path, selected_columns, sel_dummy_data_path, verifier_model, verifier_model_path)\n", " computation,\n",
"# generate the verification key\n", " precal_witness_path,\n",
"setup(verifier_model_path, verifier_compiled_model_path, settings_path, verifier_vk_path, verifier_pk_path)\n", " data_shape,\n",
")\n",
"\n",
"# verify the proof\n", "# verify the proof\n",
"verifier_verify(proof_path, settings_path, verifier_vk_path, selected_columns, data_commitment_path)" "verifier_verify(proof_path, settings_path, verifier_vk_path, selected_columns, data_commitment_path)"
] ]
@@ -257,23 +271,21 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Model onnx:\t\t /Users/mhchia/projects/work/pse/demo-next/public/assets/out/verifier_model.onnx\n",
"Settings:\t\t /Users/mhchia/projects/work/pse/demo-next/public/assets/out/settings.json\n", "Settings:\t\t /Users/mhchia/projects/work/pse/demo-next/public/assets/out/settings.json\n",
"Proof:\t\t\t /Users/mhchia/projects/work/pse/demo-next/public/assets/out/model.pf\n", "Proof:\t\t\t /Users/mhchia/projects/work/pse/demo-next/public/assets/out/model.pf\n",
"Verification key:\t /Users/mhchia/projects/work/pse/demo-next/public/assets/out/verifier_model.vk\n", "Verification key:\t /Users/mhchia/projects/work/pse/demo-next/public/assets/out/verifier_model.vk\n",
"Srs path:\t\t ~/.ezkl/srs/kzg11.srs\n" "Srs path:\t\t ~/.ezkl/srs/kzg12.srs\n"
] ]
} }
], ],
"source": [ "source": [
"print(\"Model onnx:\\t\\t\", verifier_model_path)\n",
"print(\"Settings:\\t\\t\", settings_path)\n", "print(\"Settings:\\t\\t\", settings_path)\n",
"print(\"Proof:\\t\\t\\t\", proof_path)\n", "print(\"Proof:\\t\\t\\t\", proof_path)\n",
"print(\"Verification key:\\t\", verifier_vk_path)\n", "print(\"Verification key:\\t\", verifier_vk_path)\n",
@@ -297,7 +309,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.12.4" "version": "3.12.5"
} }
}, },
"nbformat": 4, "nbformat": 4,

21
src/app/page.tsx
View File

@@ -18,8 +18,7 @@ const possibleScales = Array.from({ length: 20 }, (_, i) => i);
// template notebook settings // template notebook settings
const templateNotebook = `${assetsURL}/template.ipynb`; const templateNotebook = `${assetsURL}/template.ipynb`;
const cellForSelectedColumns = 4; const cellForUserComputation = 4;
const cellForUserComputation = 6;
/** /**
* Generate the notebook for the computation * Generate the notebook for the computation
@@ -27,16 +26,11 @@ const cellForUserComputation = 6;
*/ */
export async function generateJupyterNotebookForComputation( export async function generateJupyterNotebookForComputation(
computation: string, computation: string,
selectedColumns: string[],
templateURL: string, templateURL: string,
) { ) {
const splitCode = computation.split("\n").map((line) => `${line}\n`); const splitCode = computation.split("\n").map((line) => `${line}\n`);
const response = await fetch(templateURL); const response = await fetch(templateURL);
const template = await response.json(); const template = await response.json();
// Fill the selected columns in the notebook
const selectedColumnsCell = template.cells[cellForSelectedColumns];
// Put `selected_columns = ["col1", "col2", ...]` in the notebook
selectedColumnsCell.source = `selected_columns = ${JSON.stringify(selectedColumns)}`;
// Fill the user computation in the notebook // Fill the user computation in the notebook
const codeCell = template.cells[cellForUserComputation]; const codeCell = template.cells[cellForUserComputation];
codeCell.source = splitCode; codeCell.source = splitCode;
@@ -46,15 +40,14 @@ export async function generateJupyterNotebookForComputation(
async function exampleDownloadNotebook() { async function exampleDownloadNotebook() {
const name = "mean"; const name = "mean";
const date = Date.now(); const date = Date.now();
const selectedColumns = ["x", "y"];
const computation = `import torch const computation = `import torch
from zkstats.computation import State from zkstats.computation import State, Args
def computation(state: State, x: list[torch.Tensor]): def computation(state: State, args: Args):
out_0 = state.median(x[0]) x = args["x"]
out_1 = state.median(x[1]) y = args["y"]
return state.mean(torch.cat([out_0.unsqueeze(0), out_1.unsqueeze(0)]).reshape(-1,1))` return state.mean(x), state.mean(y)`
const notebook = await generateJupyterNotebookForComputation(computation, selectedColumns, templateNotebook) const notebook = await generateJupyterNotebookForComputation(computation, templateNotebook)
// Download for testing // Download for testing
const element = document.createElement("a"); const element = document.createElement("a");
const file = new Blob([notebook], {type: 'text/plain'}); const file = new Blob([notebook], {type: 'text/plain'});