ci: update version string in docs

Cargo.lock

@@ -1324,6 +1324,12 @@ version = "1.0.17"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0d6ef0072f8a535281e4876be788938b528e9a1d43900b82c2569af7da799125"
 
+[[package]]
+name = "dyn-hash"
+version = "0.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a650a461c6a8ff1ef205ed9a2ad56579309853fecefc2423f73dced342f92258"
+
 [[package]]
 name = "ecc"
 version = "0.1.0"
@@ -2270,7 +2276,7 @@ dependencies = [
 [[package]]
 name = "halo2_solidity_verifier"
 version = "0.1.0"
-source = "git+https://github.com/alexander-camuto/halo2-solidity-verifier?branch=main#eb04be1f7d005e5b9dd3ff41efa30aeb5e0c34a3"
+source = "git+https://github.com/alexander-camuto/halo2-solidity-verifier?branch=main#fd74f1da2ce51664e2d4349965987ee606551060"
 dependencies = [
  "askama",
  "blake2b_simd",
@@ -5476,8 +5482,8 @@ dependencies = [
 
 [[package]]
 name = "tract-core"
-version = "0.21.3"
-source = "git+https://github.com/sonos/tract/?rev=681a096f02c9d7d363102d9fb0e446d1710ac2c8#681a096f02c9d7d363102d9fb0e446d1710ac2c8"
+version = "0.21.5-pre"
+source = "git+https://github.com/sonos/tract/?rev=05ebf550aa9922b221af4635c21a67a8d2af12a9#05ebf550aa9922b221af4635c21a67a8d2af12a9"
 dependencies = [
  "anyhow",
  "bit-set",
@@ -5500,10 +5506,12 @@ dependencies = [
 
 [[package]]
 name = "tract-data"
-version = "0.21.3"
-source = "git+https://github.com/sonos/tract/?rev=681a096f02c9d7d363102d9fb0e446d1710ac2c8#681a096f02c9d7d363102d9fb0e446d1710ac2c8"
+version = "0.21.5-pre"
+source = "git+https://github.com/sonos/tract/?rev=05ebf550aa9922b221af4635c21a67a8d2af12a9#05ebf550aa9922b221af4635c21a67a8d2af12a9"
 dependencies = [
  "anyhow",
+ "downcast-rs",
+ "dyn-hash",
  "half 2.2.1",
  "itertools 0.12.1",
  "lazy_static",
@@ -5519,8 +5527,8 @@ dependencies = [
 
 [[package]]
 name = "tract-hir"
-version = "0.21.3"
-source = "git+https://github.com/sonos/tract/?rev=681a096f02c9d7d363102d9fb0e446d1710ac2c8#681a096f02c9d7d363102d9fb0e446d1710ac2c8"
+version = "0.21.5-pre"
+source = "git+https://github.com/sonos/tract/?rev=05ebf550aa9922b221af4635c21a67a8d2af12a9#05ebf550aa9922b221af4635c21a67a8d2af12a9"
 dependencies = [
  "derive-new",
  "log",
@@ -5529,13 +5537,14 @@ dependencies = [
 
 [[package]]
 name = "tract-linalg"
-version = "0.21.3"
-source = "git+https://github.com/sonos/tract/?rev=681a096f02c9d7d363102d9fb0e446d1710ac2c8#681a096f02c9d7d363102d9fb0e446d1710ac2c8"
+version = "0.21.5-pre"
+source = "git+https://github.com/sonos/tract/?rev=05ebf550aa9922b221af4635c21a67a8d2af12a9#05ebf550aa9922b221af4635c21a67a8d2af12a9"
 dependencies = [
  "cc",
  "derive-new",
  "downcast-rs",
  "dyn-clone",
+ "dyn-hash",
  "half 2.2.1",
  "lazy_static",
  "liquid",
@@ -5553,8 +5562,8 @@ dependencies = [
 
 [[package]]
 name = "tract-nnef"
-version = "0.21.3"
-source = "git+https://github.com/sonos/tract/?rev=681a096f02c9d7d363102d9fb0e446d1710ac2c8#681a096f02c9d7d363102d9fb0e446d1710ac2c8"
+version = "0.21.5-pre"
+source = "git+https://github.com/sonos/tract/?rev=05ebf550aa9922b221af4635c21a67a8d2af12a9#05ebf550aa9922b221af4635c21a67a8d2af12a9"
 dependencies = [
  "byteorder",
  "flate2",
@@ -5567,8 +5576,8 @@ dependencies = [
 
 [[package]]
 name = "tract-onnx"
-version = "0.21.3"
-source = "git+https://github.com/sonos/tract/?rev=681a096f02c9d7d363102d9fb0e446d1710ac2c8#681a096f02c9d7d363102d9fb0e446d1710ac2c8"
+version = "0.21.5-pre"
+source = "git+https://github.com/sonos/tract/?rev=05ebf550aa9922b221af4635c21a67a8d2af12a9#05ebf550aa9922b221af4635c21a67a8d2af12a9"
 dependencies = [
  "bytes",
  "derive-new",
@@ -5584,8 +5593,8 @@ dependencies = [
 
 [[package]]
 name = "tract-onnx-opl"
-version = "0.21.3"
-source = "git+https://github.com/sonos/tract/?rev=681a096f02c9d7d363102d9fb0e446d1710ac2c8#681a096f02c9d7d363102d9fb0e446d1710ac2c8"
+version = "0.21.5-pre"
+source = "git+https://github.com/sonos/tract/?rev=05ebf550aa9922b221af4635c21a67a8d2af12a9#05ebf550aa9922b221af4635c21a67a8d2af12a9"
 dependencies = [
  "getrandom",
  "log",

Cargo.toml

@@ -81,7 +81,7 @@ pyo3-asyncio = { version = "0.20.0", features = [
     "tokio-runtime",
 ], default_features = false, optional = true }
 pyo3-log = { version = "0.9.0", default_features = false, optional = true }
-tract-onnx = { git = "https://github.com/sonos/tract/", rev = "681a096f02c9d7d363102d9fb0e446d1710ac2c8", default_features = false, optional = true }
+tract-onnx = { git = "https://github.com/sonos/tract/", rev = "05ebf550aa9922b221af4635c21a67a8d2af12a9", default_features = false, optional = true }
 tabled = { version = "0.12.0", optional = true }
 
 objc = { version = "0.2.4", optional = true }

docs/python/requirements-docs.txt

@@ -1,4 +1,4 @@
-ezkl==0.0.0
+ezkl==11.0.4
 sphinx
 sphinx-rtd-theme
 sphinxcontrib-napoleon

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '0.0.0'
+release = '11.0.4'
 version = release
 
 

examples/notebooks/logistic_regression.ipynb

@@ -0,0 +1,279 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
+            "metadata": {},
+            "source": [
+                "## Logistic Regression\n",
+                "\n",
+                "\n",
+                "Sklearn based models are slightly finicky to get into a suitable onnx format. \n",
+                "This notebook showcases how to do so using the `hummingbird-ml` python package for a Logistic Regression model. "
+            ]
+        },
+        {
+            "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",
+                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"hummingbird-ml\"])\n",
+                "\n",
+                "# rely on local installation of ezkl if the notebook is not in colab\n",
+                "except:\n",
+                "    pass\n",
+                "\n",
+                "import os\n",
+                "import torch\n",
+                "import ezkl\n",
+                "import json\n",
+                "from hummingbird.ml import convert\n",
+                "\n",
+                "\n",
+                "# here we create and (potentially train a model)\n",
+                "\n",
+                "# make sure you have the dependencies required here already installed\n",
+                "import numpy as np\n",
+                "from sklearn.linear_model import LogisticRegression\n",
+                "X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])\n",
+                "# y = 1 * x_0 + 2 * x_1 + 3\n",
+                "y = np.dot(X, np.array([1, 2])) + 3\n",
+                "reg = LogisticRegression().fit(X, y)\n",
+                "reg.score(X, y)\n",
+                "\n",
+                "circuit = convert(reg, \"torch\", X[:1]).model\n",
+                "\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",
+                "\n",
+                "# export to onnx format\n",
+                "# !!!!!!!!!!!!!!!!! This will flash a warning but it is fine !!!!!!!!!!!!!!!!!!!!!\n",
+                "\n",
+                "# Input to the model\n",
+                "shape = X.shape[1:]\n",
+                "x = torch.rand(1, *shape, requires_grad=True)\n",
+                "torch_out = circuit(x)\n",
+                "# Export the model\n",
+                "torch.onnx.export(circuit,               # model being run\n",
+                "                  # model input (or a tuple for multiple inputs)\n",
+                "                  x,\n",
+                "                  # where to save the model (can be a file or file-like object)\n",
+                "                  \"network.onnx\",\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",
+                "d = ((x).detach().numpy()).reshape([-1]).tolist()\n",
+                "\n",
+                "data = dict(input_shapes=[shape],\n",
+                "            input_data=[d],\n",
+                "            output_data=[((o).detach().numpy()).reshape([-1]).tolist() for o in torch_out])\n",
+                "\n",
+                "# Serialize data into file:\n",
+                "json.dump(data, open(\"input.json\", 'w'))\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "d5e374a2",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "!RUST_LOG=trace\n",
+                "# TODO: Dictionary outputs\n",
+                "res = ezkl.gen_settings(model_path, settings_path)\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.randn(20, *shape).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",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "assert res == True\n"
+            ]
+        },
+        {
+            "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": "3.12.2"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 5
+}

examples/onnx/lstm_medium/input.json

@@ -0,0 +1,9 @@
+{
+  "input_data": [
+    [
+      1.514470100402832, 1.519423007965088, 1.5182757377624512,
+      1.5262789726257324, 1.5298409461975098
+    ]
+  ],
+  "output_data": [[-0.1862019]]
+}

tests/integration_tests.rs

@@ -200,7 +200,7 @@ mod native_tests {
         "1l_tiny_div",
     ];
 
-    const TESTS: [&str; 92] = [
+    const TESTS: [&str; 93] = [
         "1l_mlp", //0
         "1l_slice",
         "1l_concat",
@@ -296,7 +296,8 @@ mod native_tests {
         "reducel1",
         "reducel2", // 89
         "1l_lppool",
-        "lstm_large", // 91
+        "lstm_large",  // 91
+        "lstm_medium", // 92
     ];
 
     const WASM_TESTS: [&str; 46] = [
@@ -535,7 +536,7 @@ mod native_tests {
             }
         });
 
-            seq!(N in 0..=91 {
+            seq!(N in 0..=92 {
 
             #(#[test_case(TESTS[N])])*
             #[ignore]
@@ -623,7 +624,7 @@ mod native_tests {
             #(#[test_case(TESTS[N])])*
             fn mock_large_batch_public_outputs_(test: &str) {
                 // currently variable output rank is not supported in ONNX
-                if test != "gather_nd" && test != "lstm_large" {
+                if test != "gather_nd" && test != "lstm_large"  && test != "lstm_medium" {
                     crate::native_tests::init_binary();
                     let test_dir = TempDir::new(test).unwrap();
                     let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);

tests/py_integration_tests.rs

@@ -123,7 +123,7 @@ mod py_tests {
         }
     }
 
-    const TESTS: [&str; 32] = [
+    const TESTS: [&str; 33] = [
         "proof_splitting.ipynb", // 0
         "variance.ipynb",
         "mnist_gan.ipynb",
@@ -157,6 +157,7 @@ mod py_tests {
         "generalized_inverse.ipynb",
         "mnist_classifier.ipynb", // 30
         "world_rotation.ipynb",
+        "logistic_regression.ipynb",
     ];
 
     macro_rules! test_func {
@@ -169,7 +170,7 @@ mod py_tests {
             use super::*;
 
 
-            seq!(N in 0..=31 {
+            seq!(N in 0..=32 {
 
             #(#[test_case(TESTS[N])])*
             fn run_notebook_(test: &str) {