felt conversion nb

.github/workflows/rust.yml

@@ -650,6 +650,8 @@ jobs:
         run: python -m venv .env --clear; source .env/bin/activate; pip install -r requirements.txt; python -m ensurepip --upgrade
       - name: Build python ezkl
         run: source .env/bin/activate; unset CONDA_PREFIX; maturin develop --features python-bindings --release
+      - name: Postgres tutorials
+        run: source .env/bin/activate; cargo nextest run py_tests::tests::felt_conversion_test_ --no-capture
       - name: Postgres tutorials
         run: source .env/bin/activate; cargo nextest run py_tests::tests::postgres_ --no-capture
       - name: Tictactoe tutorials

examples/notebooks/felt_conversion_test.ipynb

@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "import torch\n",
+    "import ezkl\n",
+    "import json\n",
+    "import subprocess\n",
+    "from pathlib import Path\n",
+    "\n",
+    "\n",
+    "class Passthrough(torch.nn.Module):\n",
+    "    def __init__(self, input_size=10):\n",
+    "        super().__init__()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return x\n",
+    "\n",
+    "def generate_random_data(size=10, min_val=1, max_val=10):\n",
+    "    return [min_val + (max_val - min_val) * torch.rand(1).item() for _ in range(size)]\n",
+    "\n",
+    "def save_json(data, filename):\n",
+    "    with open(filename, 'w') as f:\n",
+    "        json.dump(data, f)\n",
+    "\n",
+    "async def run_ezkl_pipeline():\n",
+    "    gip_run_args = ezkl.PyRunArgs()\n",
+    "    gip_run_args.input_visibility = \"public\"\n",
+    "    gip_run_args.output_visibility = \"public\"   # no parameters used\n",
+    "    gip_run_args.param_visibility = \"fixed\"\n",
+    "    gip_run_args.input_scale = 19\n",
+    "    gip_run_args.param_scale = 19\n",
+    "    gip_run_args.logrows = 8\n",
+    "    run_args = ezkl.gen_settings(py_run_args=gip_run_args)\n",
+    "    ezkl.compile_circuit()\n",
+    "    await ezkl.gen_witness()\n",
+    "    ezkl.setup()\n",
+    "    ezkl.prove(proof_path=\"proof.json\")\n",
+    "    ezkl.verify()\n",
+    "\n",
+    "def verify_proof_matches_input():\n",
+    "    settings = json.load(open(\"settings.json\"))\n",
+    "    inputs = json.load(open(\"input.json\"))\n",
+    "    proof = json.load(open(\"proof.json\"))\n",
+    "\n",
+    "    input_scale = settings[\"model_input_scales\"][0]\n",
+    "    model_shapes = settings[\"model_instance_shapes\"]\n",
+    "\n",
+    "    flat_inputs = [x for arr in inputs[\"input_data\"] for x in arr]\n",
+    "    scaled_inputs = [ezkl.float_to_felt(x, input_scale) for x in flat_inputs]\n",
+    "    proof_instances = proof[\"instances\"][0]\n",
+    "\n",
+    "    def get_group_index(i):\n",
+    "        pos = 0\n",
+    "        for idx, (batch, length) in enumerate(model_shapes):\n",
+    "            next_pos = pos + (batch * length)\n",
+    "            if i < next_pos:\n",
+    "                return idx\n",
+    "            pos = next_pos\n",
+    "        raise IndexError(\"Index out of bounds\")\n",
+    "\n",
+    "    for i, (scaled, instance) in enumerate(zip(scaled_inputs, proof_instances)):\n",
+    "        group_idx = get_group_index(i)\n",
+    "        _, length = model_shapes[group_idx]\n",
+    "\n",
+    "        descaled_instance = ezkl.felt_to_float(instance, input_scale)\n",
+    "        descaled_input = ezkl.felt_to_float(scaled, input_scale)\n",
+    "        pretty_value = proof[\"pretty_public_inputs\"][\"rescaled_inputs\"][group_idx][i % length]\n",
+    "\n",
+    "        assert scaled == instance, f\"Input mismatch at index {i}: {scaled} != {instance} ({descaled_instance} != {descaled_input} OG {flat_inputs[i]} PRETTY {pretty_value})\"\n",
+    "\n",
+    "model = Passthrough()\n",
+    "torch.onnx.export(model, torch.randn(1, 10), \"network.onnx\")\n",
+    "\n",
+    "input_data = {\"input_data\": [generate_random_data()]}\n",
+    "save_json(input_data, \"input.json\")\n",
+    "save_json({\"input_data\": [generate_random_data()]}, \"calibration.json\")\n",
+    "\n",
+    "await run_ezkl_pipeline()\n",
+    "verify_proof_matches_input()\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".env",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

tests/py_integration_tests.rs

@@ -189,6 +189,17 @@ mod py_tests {
                 anvil_child.kill().unwrap();
             }
             });
+
+            #[test]
+            fn felt_conversion_test_notebook() {
+                crate::py_tests::init_binary();
+                let test_dir: TempDir = TempDir::new("felt_conversion_test").unwrap();
+                let path = test_dir.path().to_str().unwrap();
+                crate::py_tests::mv_test_(path, "felt_conversion_test.ipynb");
+                run_notebook(path, "felt_conversion_test.ipynb");
+                test_dir.close().unwrap();
+            }
+
             #[test]
             fn voice_notebook_() {
                 crate::py_tests::init_binary();