ci: update version string in docs

BREAKING CHANGE: pk and vk will not be backwards compatible

.github/workflows/benchmarks.yml

@@ -6,22 +6,13 @@ on:
         description: "Test scenario tags"
 
 jobs:
-  bench_elgamal:
-    runs-on: self-hosted
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
-        with:
-          toolchain: nightly-2023-06-27
-          override: true
-          components: rustfmt, clippy
-      - name: Bench elgamal
-        run: cargo bench --verbose --bench elgamal
 
   bench_poseidon:
     runs-on: self-hosted
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -35,6 +26,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -48,6 +41,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -61,6 +56,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -74,6 +71,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -87,6 +86,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -100,6 +101,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -113,6 +116,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -126,6 +131,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -139,6 +146,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -152,6 +161,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27

.github/workflows/engine.yml

@@ -20,6 +20,8 @@ jobs:
     if: startsWith(github.ref, 'refs/tags/')
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -51,7 +53,7 @@ jobs:
         run: |
           echo '{
             "name": "@ezkljs/engine",
-            "version": "${{ github.ref_name }}",
+            "version": "${RELEASE_TAG}",
             "dependencies": {
               "@types/json-bigint": "^1.0.1",
               "json-bigint": "^1.0.0"
@@ -190,15 +192,17 @@ jobs:
     if: startsWith(github.ref, 'refs/tags/')
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - name: Update version in package.json
         shell: bash
         env:
           RELEASE_TAG: ${{ github.ref_name }}
         run: |
-          sed -i "s|\"version\": \".*\"|\"version\": \"${{ github.ref_name }}\"|" in-browser-evm-verifier/package.json
+          sed -i "s|\"version\": \".*\"|\"version\": \"${RELEASE_TAG}\"|" in-browser-evm-verifier/package.json
       - name: Prepare tag and fetch package integrity
         run: |
-          CLEANED_TAG=${{ github.ref_name }} # Get the tag from ref_name
+          CLEANED_TAG=${RELEASE_TAG} # Get the tag from ref_name
           CLEANED_TAG="${CLEANED_TAG#v}" # Remove leading 'v'
           echo "CLEANED_TAG=${CLEANED_TAG}" >> $GITHUB_ENV # Set it as an environment variable for later steps
           ENGINE_INTEGRITY=$(npm view @ezkljs/engine@$CLEANED_TAG dist.integrity)

.github/workflows/large-tests.yml

@@ -9,6 +9,8 @@ jobs:
     runs-on: kaiju
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18

.github/workflows/pypi-gpu.yml

@@ -24,6 +24,8 @@ jobs:
         target: [x86_64]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: 3.12

.github/workflows/pypi.yml

@@ -23,6 +23,8 @@ jobs:
         target: [x86_64, universal2-apple-darwin]
     steps:
       - uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: 3.12
@@ -69,6 +71,8 @@ jobs:
         target: [x64, x86]
     steps:
       - uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: 3.12
@@ -114,6 +118,8 @@ jobs:
         target: [x86_64]
     steps:
       - uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: 3.12
@@ -228,6 +234,8 @@ jobs:
           - x86_64-unknown-linux-musl
     steps:
       - uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: 3.12
@@ -292,6 +300,8 @@ jobs:
             arch: aarch64
     steps:
       - uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: 3.12
@@ -373,7 +383,8 @@ jobs:
     needs: pypi-publish
     steps:
       - uses: actions/checkout@v4
-
+        with: 
+          persist-credentials: false
       - name: Trigger RTDs build
         uses: dfm/rtds-action@v1
         with:

.github/workflows/release.yml

@@ -50,6 +50,9 @@ jobs:
           components: rustfmt, clippy
       - name: Checkout repo
         uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
+
 
       - name: Get release version from tag
         shell: bash
@@ -132,6 +135,8 @@ jobs:
     steps:
       - name: Checkout repo
         uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
 
       - name: Get release version from tag
         shell: bash

.github/workflows/rust.yml

@@ -19,11 +19,28 @@ env:
   CARGO_TERM_COLOR: always
 
 jobs:
+
+  fr-age-test:
+    runs-on: large-self-hosted
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@v1
+        with:
+          toolchain: nightly-2024-07-18
+          override: true
+          components: rustfmt, clippy
+      - name: fr age Mock
+        run: cargo test --release --verbose tests::large_mock_::large_tests_6_expects -- --include-ignored
+
   build:
     runs-on: ubuntu-latest
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -36,6 +53,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -48,6 +67,8 @@ jobs:
     runs-on: ubuntu-latest-32-cores
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -71,6 +92,8 @@ jobs:
   #     ENABLE_ICICLE_GPU: true
   #   steps:
   #     - uses: actions/checkout@v4
+        # with:
+        #   persist-credentials: false
   #     - uses: actions-rs/toolchain@v1
   #       with:
   #         toolchain: nightly-2024-07-18
@@ -104,6 +127,8 @@ jobs:
     runs-on: non-gpu
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -137,6 +162,8 @@ jobs:
     runs-on: non-gpu
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -170,6 +197,8 @@ jobs:
     runs-on: ubuntu-latest-16-cores
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -186,6 +215,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -212,6 +243,8 @@ jobs:
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -275,6 +308,8 @@ jobs:
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -285,6 +320,8 @@ jobs:
           crate: cargo-nextest
           locked: true
       - uses: actions/checkout@v3
+        with:
+          persist-credentials: false
       - name: Use pnpm 8
         uses: pnpm/action-setup@v2
         with:
@@ -354,6 +391,8 @@ jobs:
     needs: [build, library-tests, docs]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -369,6 +408,8 @@ jobs:
       - name: Add rust-src
         run: rustup component add rust-src --toolchain nightly-2024-07-18-x86_64-unknown-linux-gnu
       - uses: actions/checkout@v3
+        with:
+          persist-credentials: false
       - name: Use pnpm 8
         uses: pnpm/action-setup@v2
         with:
@@ -431,6 +472,8 @@ jobs:
   #     ENABLE_ICICLE_GPU: true
   #   steps:
   #     - uses: actions/checkout@v4
+        # with:
+        #   persist-credentials: false
   #     - uses: actions-rs/toolchain@v1
   #       with:
   #         toolchain: nightly-2024-07-18
@@ -465,6 +508,8 @@ jobs:
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -483,6 +528,8 @@ jobs:
   #     ENABLE_ICICLE_GPU: true
   #   steps:
   #     - uses: actions/checkout@v4
+        # with:
+        #   persist-credentials: false
   #     - uses: actions-rs/toolchain@v1
   #       with:
   #         toolchain: nightly-2024-07-18
@@ -500,6 +547,8 @@ jobs:
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -517,6 +566,8 @@ jobs:
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -538,6 +589,8 @@ jobs:
     needs: [build, library-tests, docs]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -555,6 +608,8 @@ jobs:
     needs: [build, library-tests, docs]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: "3.12"
@@ -577,10 +632,12 @@ jobs:
         run: source .env/bin/activate; pip install pytest-asyncio; pytest -vv
 
   accuracy-measurement-tests:
-    runs-on: ubuntu-latest-32-cores
+    runs-on: non-gpu
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: "3.12"
@@ -628,6 +685,8 @@ jobs:
           - 5432:5432
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: "3.11"
@@ -680,6 +739,8 @@ jobs:
       runs-on: macos-latest
       steps:
         - uses: actions/checkout@v4
+          with:
+            persist-credentials: false
         - uses: actions-rs/toolchain@v1
           with:
             toolchain: nightly-2024-07-18
@@ -698,6 +759,8 @@ jobs:
 
       steps:
         - uses: actions/checkout@v4
+          with:
+            persist-credentials: false
         - uses: actions-rs/toolchain@v1
           with:
             toolchain: nightly-2024-07-18
@@ -715,6 +778,15 @@ jobs:
             rm -rf ezkl-swift-package/Sources/EzklCoreBindings
             cp -r build/EzklCoreBindings ezkl-swift-package/Sources/
 
+        - name: Copy Test Files
+          run: |
+            rm -rf ezkl-swift-package/Tests/EzklAssets/
+            mkdir -p ezkl-swift-package/Tests/EzklAssets/
+            cp tests/assets/kzg ezkl-swift-package/Tests/EzklAssets/kzg.srs
+            cp tests/assets/input.json ezkl-swift-package/Tests/EzklAssets/input.json
+            cp tests/assets/model.compiled ezkl-swift-package/Tests/EzklAssets/network.ezkl
+            cp tests/assets/settings.json ezkl-swift-package/Tests/EzklAssets/settings.json
+
         - name: Set up Xcode environment
           run: |
             sudo xcode-select -s /Applications/Xcode.app/Contents/Developer

.github/workflows/static-analysis.yml

@@ -0,0 +1,32 @@
+name: Static Analysis
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  analyze:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@v1
+        with:
+          toolchain: nightly-2024-07-18
+          override: true
+          components: rustfmt, clippy
+
+      # Run Zizmor static analysis
+
+      - name: Install Zizmor
+        run: cargo install --locked zizmor
+
+      - name: Run Zizmor Analysis
+        run: zizmor .
+
+
+    

.github/workflows/swift-pm.yml

@@ -12,15 +12,18 @@ jobs:
     runs-on: macos-latest
     env:
       EZKL_SWIFT_PACKAGE_REPO: github.com/zkonduit/ezkl-swift-package.git
+      RELEASE_TAG: ${{ github.ref_name }}
 
     steps:
       - name: Checkout EZKL
         uses: actions/checkout@v3
+        with: 
+          persist-credentials: false
 
       - name: Extract TAG from github.ref_name
         run: |
           # github.ref_name is provided by GitHub Actions and contains the tag name directly.
-          TAG="${{ github.ref_name }}"
+          TAG="${RELEASE_TAG}"
           echo "Original TAG: $TAG"
           # Remove leading 'v' if present to match the Swift Package Manager version format.
           NEW_TAG=${TAG#v}
@@ -47,7 +50,8 @@ jobs:
 
       - name: Copy Test Files
         run: |
-          rm -rf ezkl-swift-package/Tests/EzklAssets/*
+          rm -rf ezkl-swift-package/Tests/EzklAssets/
+          mkdir -p ezkl-swift-package/Tests/EzklAssets/
           cp tests/assets/kzg ezkl-swift-package/Tests/EzklAssets/kzg.srs
           cp tests/assets/input.json ezkl-swift-package/Tests/EzklAssets/input.json
           cp tests/assets/model.compiled ezkl-swift-package/Tests/EzklAssets/network.ezkl
@@ -105,7 +109,6 @@ jobs:
           cd ezkl-swift-package
           git add Sources/EzklCoreBindings Tests/EzklAssets
           git commit -m "Automatically updated EzklCoreBindings for EZKL"
-          
           if ! git push origin; then
             echo "::error::Failed to push changes to ${{ env.EZKL_SWIFT_PACKAGE_REPO }}. Please ensure that EZKL_PORTER_TOKEN has the correct permissions."
             exit 1
@@ -115,7 +118,6 @@ jobs:
         run: |
           cd ezkl-swift-package
           source $GITHUB_ENV
-          
           # Tag the latest commit on the current branch
           if git rev-parse "$TAG" >/dev/null 2>&1; then
             echo "Tag $TAG already exists locally. Skipping tag creation."

.github/workflows/tagging.yml

@@ -12,6 +12,8 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - name: Bump version and push tag
         id: tag_version
         uses: mathieudutour/github-tag-action@v6.2

Cargo.lock

@@ -2377,7 +2377,7 @@ dependencies = [
 [[package]]
 name = "halo2_gadgets"
 version = "0.2.0"
-source = "git+https://github.com/zkonduit/halo2#6d72498928cdb69ce0de9f2230d2873ca2cf5324"
+source = "git+https://github.com/zkonduit/halo2#d7ecad83c7439fa1cb450ee4a89c2d0b45604ceb"
 dependencies = [
  "arrayvec 0.7.4",
  "bitvec",
@@ -2394,7 +2394,7 @@ dependencies = [
 [[package]]
 name = "halo2_proofs"
 version = "0.3.0"
-source = "git+https://github.com/zkonduit/halo2#6d72498928cdb69ce0de9f2230d2873ca2cf5324#6d72498928cdb69ce0de9f2230d2873ca2cf5324"
+source = "git+https://github.com/zkonduit/halo2#ee4e1a09ebdb1f79f797685b78951c6034c430a6#ee4e1a09ebdb1f79f797685b78951c6034c430a6"
 dependencies = [
  "bincode",
  "blake2b_simd",

Cargo.toml

@@ -280,10 +280,10 @@ no-update = []
 
 
 [patch.'https://github.com/zkonduit/halo2']
-halo2_proofs = { git = "https://github.com/zkonduit/halo2#6d72498928cdb69ce0de9f2230d2873ca2cf5324", package = "halo2_proofs" }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2#ee4e1a09ebdb1f79f797685b78951c6034c430a6", package = "halo2_proofs" }
 
 [patch.'https://github.com/zkonduit/halo2#0654e92bdf725fd44d849bfef3643870a8c7d50b']
-halo2_proofs = { git = "https://github.com/zkonduit/halo2#6d72498928cdb69ce0de9f2230d2873ca2cf5324", package = "halo2_proofs" }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2#ee4e1a09ebdb1f79f797685b78951c6034c430a6", package = "halo2_proofs" }
 
 [patch.crates-io]
 uniffi_testing = { git = "https://github.com/ElusAegis/uniffi-rs", branch = "feat/testing-feature-build-fix" }

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '0.0.0'
+release = '18.0.0'
 version = release
 
 

examples/notebooks/felt_conversion_test.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -54,7 +54,7 @@
     "    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.get_srs(commitment=ezkl.PyCommitments.KZG)\n",
+    "    await ezkl.get_srs(commitment=ezkl.PyCommitments.KZG)\n",
     "    ezkl.compile_circuit()\n",
     "    res = await ezkl.gen_witness()\n",
     "    print(res)\n",

examples/notebooks/linear_regression.ipynb

@@ -1,279 +1,284 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
-            "metadata": {},
-            "source": [
-                "## Linear 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 ! "
-            ]
-        },
-        {
-            "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 LinearRegression\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 = LinearRegression().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 = await 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 = await ezkl.get_srs( settings_path)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "18c8b7c7",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# now generate the witness file \n",
-                "\n",
-                "res = await 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
-}
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
+   "metadata": {},
+   "source": [
+    "## Linear 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 ! "
+   ]
+  },
+  {
+   "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 LinearRegression\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 = LinearRegression().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",
+    "\n",
+    "\n",
+    "# note that you can also call the following function to generate random data for the model\n",
+    "# it is functionally equivalent to the code above\n",
+    "ezkl.gen_random_data()\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 = await 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 = await ezkl.get_srs( settings_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18c8b7c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# now generate the witness file \n",
+    "\n",
+    "res = await 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
+}

examples/notebooks/mean_postgres.ipynb

@@ -1,456 +1,459 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Mean of ERC20 transfer amounts\n",
-        "\n",
-        "This notebook shows how to calculate the mean of ERC20 transfer amounts, pulling data in from a Postgres database. First we install and get the necessary libraries running. \n",
-        "The first of which is [shovel](https://indexsupply.com/shovel/docs/#getting-started), which is a library that allows us to pull data from the Ethereum blockchain into a Postgres database.\n",
-        "\n",
-        "Make sure you install postgres if needed https://indexsupply.com/shovel/docs/#getting-started. \n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import getpass\n",
-        "import json\n",
-        "import time\n",
-        "import subprocess\n",
-        "\n",
-        "# swap out for the relevant linux/amd64, darwin/arm64, darwin/amd64, windows/amd64\n",
-        "os.system(\"curl -LO https://indexsupply.net/bin/1.0/linux/amd64/shovel\")\n",
-        "os.system(\"chmod +x shovel\")\n",
-        "\n",
-        "\n",
-        "os.environ[\"PG_URL\"] = \"postgres://\" + getpass.getuser() + \":@localhost:5432/shovel\"\n",
-        "\n",
-        "# create a config.json file with the following contents\n",
-        "config = {\n",
-        "  \"pg_url\": \"$PG_URL\",\n",
-        "  \"eth_sources\": [\n",
-        "    {\"name\": \"mainnet\", \"chain_id\": 1, \"url\": \"https://ethereum-rpc.publicnode.com\"},\n",
-        "    {\"name\": \"base\", \"chain_id\": 8453, \"url\": \"https://base-rpc.publicnode.com\"}\n",
-        "  ],\n",
-        "  \"integrations\": [{\n",
-        "    \"name\": \"usdc_transfer\",\n",
-        "    \"enabled\": True,\n",
-        "    \"sources\": [{\"name\": \"mainnet\"}, {\"name\": \"base\"}],\n",
-        "    \"table\": {\n",
-        "      \"name\": \"usdc\",\n",
-        "      \"columns\": [\n",
-        "        {\"name\": \"log_addr\",  \"type\": \"bytea\"},\n",
-        "        {\"name\": \"block_num\", \"type\": \"numeric\"},\n",
-        "        {\"name\": \"f\",         \"type\": \"bytea\"},\n",
-        "        {\"name\": \"t\",         \"type\": \"bytea\"},\n",
-        "        {\"name\": \"v\",         \"type\": \"numeric\"}\n",
-        "      ]\n",
-        "    },\n",
-        "    \"block\": [\n",
-        "      {\"name\": \"block_num\", \"column\": \"block_num\"},\n",
-        "      {\n",
-        "        \"name\": \"log_addr\",\n",
-        "        \"column\": \"log_addr\",\n",
-        "        \"filter_op\": \"contains\",\n",
-        "        \"filter_arg\": [\n",
-        "          \"a0b86991c6218b36c1d19d4a2e9eb0ce3606eb48\",\n",
-        "          \"833589fCD6eDb6E08f4c7C32D4f71b54bdA02913\"\n",
-        "        ]\n",
-        "      }\n",
-        "    ],\n",
-        "    \"event\": {\n",
-        "      \"name\": \"Transfer\",\n",
-        "      \"type\": \"event\",\n",
-        "      \"anonymous\": False,\n",
-        "      \"inputs\": [\n",
-        "        {\"indexed\": True,  \"name\": \"from\",  \"type\": \"address\", \"column\": \"f\"},\n",
-        "        {\"indexed\": True,  \"name\": \"to\",    \"type\": \"address\", \"column\": \"t\"},\n",
-        "        {\"indexed\": False, \"name\": \"value\", \"type\": \"uint256\", \"column\": \"v\"}\n",
-        "      ]\n",
-        "    }\n",
-        "  }]\n",
-        "}\n",
-        "\n",
-        "# write the config to a file\n",
-        "with open(\"config.json\", \"w\") as f:\n",
-        "  f.write(json.dumps(config))\n",
-        "\n",
-        "\n",
-        "# print the two env variables\n",
-        "os.system(\"echo $PG_URL\")\n",
-        "\n",
-        "os.system(\"createdb -h localhost -p 5432 shovel\")\n",
-        "\n",
-        "os.system(\"echo shovel is now installed. starting:\")\n",
-        "\n",
-        "command = [\"./shovel\", \"-config\", \"config.json\"]\n",
-        "proc = subprocess.Popen(command)\n",
-        "\n",
-        "os.system(\"echo shovel started.\")\n",
-        "\n",
-        "time.sleep(10)\n",
-        "\n",
-        "# after we've fetched some data -- kill the process\n",
-        "proc.terminate()\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2wIAHwqH2_mo"
-      },
-      "source": [
-        "**Import Dependencies**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "9Byiv2Nc2MsK"
-      },
-      "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",
-        "import ezkl\n",
-        "import torch\n",
-        "import datetime\n",
-        "import pandas as pd\n",
-        "import requests\n",
-        "import json\n",
-        "import os\n",
-        "\n",
-        "import logging\n",
-        "# # uncomment for more descriptive logging \n",
-        "FORMAT = '%(levelname)s %(name)s %(asctime)-15s %(filename)s:%(lineno)d %(message)s'\n",
-        "logging.basicConfig(format=FORMAT)\n",
-        "logging.getLogger().setLevel(logging.DEBUG)\n",
-        "\n",
-        "print(\"ezkl version: \", ezkl.__version__)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "osjj-0Ta3E8O"
-      },
-      "source": [
-        "**Create Computational Graph**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "x1vl9ZXF3EEW",
-        "outputId": "bda21d02-fe5f-4fb2-8106-f51a8e2e67aa"
-      },
-      "outputs": [],
-      "source": [
-        "from torch import nn\n",
-        "import torch\n",
-        "\n",
-        "\n",
-        "class Model(nn.Module):\n",
-        "    def __init__(self):\n",
-        "        super(Model, self).__init__()\n",
-        "\n",
-        "    # x is a time series \n",
-        "    def forward(self, x):\n",
-        "        return [torch.mean(x)]\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "circuit = Model()\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "x = 0.1*torch.rand(1,*[1,5], requires_grad=True)\n",
-        "\n",
-        "# # print(torch.__version__)\n",
-        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
-        "\n",
-        "print(device)\n",
-        "\n",
-        "circuit.to(device)\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",
-        "                      \"lol.onnx\",            # 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=11,          # 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",
-        "# export(circuit, input_shape=[1, 20])\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "E3qCeX-X5xqd"
-      },
-      "source": [
-        "**Set Data Source and Get Data**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "6RAMplxk5xPk",
-        "outputId": "bd2158fe-0c00-44fd-e632-6a3f70cdb7c9"
-      },
-      "outputs": [],
-      "source": [
-        "import getpass\n",
-        "# make an input.json file from the df above\n",
-        "input_filename = os.path.join('input.json')\n",
-        "\n",
-        "pg_input_file = dict(input_data = {\n",
-        "  \"host\": \"localhost\",\n",
-        "  # make sure you replace this with your own username\n",
-        "  \"user\": getpass.getuser(),\n",
-        "  \"dbname\": \"shovel\",\n",
-        "  \"password\": \"\",\n",
-        "  \"query\": \"SELECT v FROM usdc ORDER BY block_num DESC LIMIT 5\",\n",
-        "  \"port\": \"5432\",\n",
-        "})\n",
-        "\n",
-        "json_formatted_str = json.dumps(pg_input_file, indent=2)\n",
-        "print(json_formatted_str)\n",
-        "\n",
-        "\n",
-        "    # Serialize data into file:\n",
-        "json.dump(pg_input_file, open(input_filename, 'w' ))\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# this corresponds to 4 batches\n",
-        "calibration_filename = os.path.join('calibration.json')\n",
-        "\n",
-        "pg_cal_file = dict(input_data = {\n",
-        "  \"host\": \"localhost\",\n",
-        "  # make sure you replace this with your own username\n",
-        "  \"user\": getpass.getuser(),\n",
-        "  \"dbname\": \"shovel\",\n",
-        "  \"password\": \"\",\n",
-        "  \"query\": \"SELECT v FROM usdc ORDER BY block_num DESC LIMIT 20\",\n",
-        "  \"port\": \"5432\",\n",
-        "})\n",
-        "\n",
-        "    # Serialize data into file:\n",
-        "json.dump( pg_cal_file, open(calibration_filename, 'w' ))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "eLJ7oirQ_HQR"
-      },
-      "source": [
-        "**EZKL Workflow**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "rNw0C9QL6W88"
-      },
-      "outputs": [],
-      "source": [
-        "import subprocess\n",
-        "import os\n",
-        "\n",
-        "onnx_filename = os.path.join('lol.onnx')\n",
-        "compiled_filename = os.path.join('lol.compiled')\n",
-        "settings_filename = os.path.join('settings.json')\n",
-        "\n",
-        "# Generate settings using ezkl\n",
-        "res = ezkl.gen_settings(onnx_filename, settings_filename)\n",
-        "\n",
-        "assert res == True\n",
-        "\n",
-        "res = await ezkl.calibrate_settings(input_filename, onnx_filename, settings_filename, \"resources\")\n",
-        "\n",
-        "assert res == True"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "\n",
-        "ezkl.compile_circuit(onnx_filename, compiled_filename, settings_filename)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "4MmE9SX66_Il",
-        "outputId": "16403639-66a4-4280-ac7f-6966b75de5a3"
-      },
-      "outputs": [],
-      "source": [
-        "# generate settings\n",
-        "\n",
-        "\n",
-        "# show the settings.json\n",
-        "with open(\"settings.json\") as f:\n",
-        "  data = json.load(f)\n",
-        "  json_formatted_str = json.dumps(data, indent=2)\n",
-        "\n",
-        "  print(json_formatted_str)\n",
-        "\n",
-        "assert os.path.exists(\"settings.json\")\n",
-        "assert os.path.exists(\"input.json\")\n",
-        "assert os.path.exists(\"lol.onnx\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "fULvvnK7_CMb"
-      },
-      "outputs": [],
-      "source": [
-        "pk_path = os.path.join('test.pk')\n",
-        "vk_path = os.path.join('test.vk')\n",
-        "\n",
-        "\n",
-        "# setup the proof\n",
-        "res = ezkl.setup(\n",
-        "        compiled_filename,\n",
-        "        vk_path,\n",
-        "        pk_path\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_filename)\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "witness_path = \"witness.json\"\n",
-        "\n",
-        "# generate the witness\n",
-        "res = await ezkl.gen_witness(\n",
-        "        input_filename,\n",
-        "        compiled_filename,\n",
-        "        witness_path\n",
-        "    )\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Oog3j6Kd-Wed",
-        "outputId": "5839d0c1-5b43-476e-c2f8-6707de562260"
-      },
-      "outputs": [],
-      "source": [
-        "# prove the zk circuit\n",
-        "# GENERATE A PROOF\n",
-        "proof_path = os.path.join('test.pf')\n",
-        "\n",
-        "\n",
-        "proof = ezkl.prove(\n",
-        "        witness_path,\n",
-        "        compiled_filename,\n",
-        "        pk_path,\n",
-        "        proof_path,\n",
-        "        \"single\"\n",
-        "    )\n",
-        "\n",
-        "\n",
-        "print(\"proved\")\n",
-        "\n",
-        "assert os.path.isfile(proof_path)\n",
-        "\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "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"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mean of ERC20 transfer amounts\n",
+    "\n",
+    "This notebook shows how to calculate the mean of ERC20 transfer amounts, pulling data in from a Postgres database. First we install and get the necessary libraries running. \n",
+    "The first of which is [shovel](https://indexsupply.com/shovel/docs/#getting-started), which is a library that allows us to pull data from the Ethereum blockchain into a Postgres database.\n",
+    "\n",
+    "Make sure you install postgres if needed https://indexsupply.com/shovel/docs/#getting-started. \n",
+    "\n"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "import json\n",
+    "import time\n",
+    "import subprocess\n",
+    "\n",
+    "# swap out for the relevant linux/amd64, darwin/arm64, darwin/amd64, windows/amd64\n",
+    "os.system(\"curl -LO https://indexsupply.net/bin/1.0/linux/amd64/shovel\")\n",
+    "os.system(\"chmod +x shovel\")\n",
+    "\n",
+    "\n",
+    "os.environ[\"PG_URL\"] = \"postgres://\" + getpass.getuser() + \":@localhost:5432/shovel\"\n",
+    "\n",
+    "# create a config.json file with the following contents\n",
+    "config = {\n",
+    "  \"pg_url\": \"$PG_URL\",\n",
+    "  \"eth_sources\": [\n",
+    "    {\"name\": \"mainnet\", \"chain_id\": 1, \"url\": \"https://ethereum-rpc.publicnode.com\"},\n",
+    "    {\"name\": \"base\", \"chain_id\": 8453, \"url\": \"https://base-rpc.publicnode.com\"}\n",
+    "  ],\n",
+    "  \"integrations\": [{\n",
+    "    \"name\": \"usdc_transfer\",\n",
+    "    \"enabled\": True,\n",
+    "    \"sources\": [{\"name\": \"mainnet\"}, {\"name\": \"base\"}],\n",
+    "    \"table\": {\n",
+    "      \"name\": \"usdc\",\n",
+    "      \"columns\": [\n",
+    "        {\"name\": \"log_addr\",  \"type\": \"bytea\"},\n",
+    "        {\"name\": \"block_num\", \"type\": \"numeric\"},\n",
+    "        {\"name\": \"f\",         \"type\": \"bytea\"},\n",
+    "        {\"name\": \"t\",         \"type\": \"bytea\"},\n",
+    "        {\"name\": \"v\",         \"type\": \"numeric\"}\n",
+    "      ]\n",
+    "    },\n",
+    "    \"block\": [\n",
+    "      {\"name\": \"block_num\", \"column\": \"block_num\"},\n",
+    "      {\n",
+    "        \"name\": \"log_addr\",\n",
+    "        \"column\": \"log_addr\",\n",
+    "        \"filter_op\": \"contains\",\n",
+    "        \"filter_arg\": [\n",
+    "          \"a0b86991c6218b36c1d19d4a2e9eb0ce3606eb48\",\n",
+    "          \"833589fCD6eDb6E08f4c7C32D4f71b54bdA02913\"\n",
+    "        ]\n",
+    "      }\n",
+    "    ],\n",
+    "    \"event\": {\n",
+    "      \"name\": \"Transfer\",\n",
+    "      \"type\": \"event\",\n",
+    "      \"anonymous\": False,\n",
+    "      \"inputs\": [\n",
+    "        {\"indexed\": True,  \"name\": \"from\",  \"type\": \"address\", \"column\": \"f\"},\n",
+    "        {\"indexed\": True,  \"name\": \"to\",    \"type\": \"address\", \"column\": \"t\"},\n",
+    "        {\"indexed\": False, \"name\": \"value\", \"type\": \"uint256\", \"column\": \"v\"}\n",
+    "      ]\n",
+    "    }\n",
+    "  }]\n",
+    "}\n",
+    "\n",
+    "# write the config to a file\n",
+    "with open(\"config.json\", \"w\") as f:\n",
+    "  f.write(json.dumps(config))\n",
+    "\n",
+    "\n",
+    "# print the two env variables\n",
+    "os.system(\"echo $PG_URL\")\n",
+    "\n",
+    "os.system(\"createdb -h localhost -p 5432 shovel\")\n",
+    "\n",
+    "os.system(\"echo shovel is now installed. starting:\")\n",
+    "\n",
+    "command = [\"./shovel\", \"-config\", \"config.json\"]\n",
+    "proc = subprocess.Popen(command)\n",
+    "\n",
+    "os.system(\"echo shovel started.\")\n",
+    "\n",
+    "time.sleep(10)\n",
+    "\n",
+    "# after we've fetched some data -- kill the process\n",
+    "proc.terminate()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2wIAHwqH2_mo"
+   },
+   "source": [
+    "**Import Dependencies**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9Byiv2Nc2MsK"
+   },
+   "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",
+    "import ezkl\n",
+    "import torch\n",
+    "import datetime\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "import json\n",
+    "import os\n",
+    "\n",
+    "import logging\n",
+    "# # uncomment for more descriptive logging \n",
+    "FORMAT = '%(levelname)s %(name)s %(asctime)-15s %(filename)s:%(lineno)d %(message)s'\n",
+    "logging.basicConfig(format=FORMAT)\n",
+    "logging.getLogger().setLevel(logging.DEBUG)\n",
+    "\n",
+    "print(\"ezkl version: \", ezkl.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "osjj-0Ta3E8O"
+   },
+   "source": [
+    "**Create Computational Graph**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "x1vl9ZXF3EEW",
+    "outputId": "bda21d02-fe5f-4fb2-8106-f51a8e2e67aa"
+   },
+   "outputs": [],
+   "source": [
+    "from torch import nn\n",
+    "import torch\n",
+    "\n",
+    "\n",
+    "class Model(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(Model, self).__init__()\n",
+    "\n",
+    "    # x is a time series \n",
+    "    def forward(self, x):\n",
+    "        return [torch.mean(x)]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "circuit = Model()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "x = 0.1*torch.rand(1,*[1,5], requires_grad=True)\n",
+    "\n",
+    "# # print(torch.__version__)\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "print(device)\n",
+    "\n",
+    "circuit.to(device)\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",
+    "                      \"lol.onnx\",            # 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=11,          # 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",
+    "# export(circuit, input_shape=[1, 20])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "E3qCeX-X5xqd"
+   },
+   "source": [
+    "**Set Data Source and Get Data**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6RAMplxk5xPk",
+    "outputId": "bd2158fe-0c00-44fd-e632-6a3f70cdb7c9"
+   },
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "# make an input.json file from the df above\n",
+    "input_filename = os.path.join('input.json')\n",
+    "\n",
+    "pg_input_file = dict(input_data = {\n",
+    "  \"host\": \"localhost\",\n",
+    "  # make sure you replace this with your own username\n",
+    "  \"user\": getpass.getuser(),\n",
+    "  \"dbname\": \"shovel\",\n",
+    "  \"password\": \"\",\n",
+    "  \"query\": \"SELECT v FROM usdc ORDER BY block_num DESC LIMIT 5\",\n",
+    "  \"port\": \"5432\",\n",
+    "})\n",
+    "\n",
+    "json_formatted_str = json.dumps(pg_input_file, indent=2)\n",
+    "print(json_formatted_str)\n",
+    "\n",
+    "\n",
+    "    # Serialize data into file:\n",
+    "json.dump(pg_input_file, open(input_filename, 'w' ))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this corresponds to 4 batches\n",
+    "calibration_filename = os.path.join('calibration.json')\n",
+    "\n",
+    "pg_cal_file = dict(input_data = {\n",
+    "  \"host\": \"localhost\",\n",
+    "  # make sure you replace this with your own username\n",
+    "  \"user\": getpass.getuser(),\n",
+    "  \"dbname\": \"shovel\",\n",
+    "  \"password\": \"\",\n",
+    "  \"query\": \"SELECT v FROM usdc ORDER BY block_num DESC LIMIT 20\",\n",
+    "  \"port\": \"5432\",\n",
+    "})\n",
+    "\n",
+    "    # Serialize data into file:\n",
+    "json.dump( pg_cal_file, open(calibration_filename, 'w' ))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eLJ7oirQ_HQR"
+   },
+   "source": [
+    "**EZKL Workflow**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "rNw0C9QL6W88"
+   },
+   "outputs": [],
+   "source": [
+    "import subprocess\n",
+    "import os\n",
+    "\n",
+    "onnx_filename = os.path.join('lol.onnx')\n",
+    "compiled_filename = os.path.join('lol.compiled')\n",
+    "settings_filename = os.path.join('settings.json')\n",
+    "\n",
+    "# Generate settings using ezkl\n",
+    "res = ezkl.gen_settings(onnx_filename, settings_filename)\n",
+    "\n",
+    "assert res == True\n",
+    "\n",
+    "res = await ezkl.calibrate_settings(input_filename, onnx_filename, settings_filename, \"resources\")\n",
+    "\n",
+    "assert res == True\n",
+    "\n",
+    "await ezkl.get_srs(settings_filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "ezkl.compile_circuit(onnx_filename, compiled_filename, settings_filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4MmE9SX66_Il",
+    "outputId": "16403639-66a4-4280-ac7f-6966b75de5a3"
+   },
+   "outputs": [],
+   "source": [
+    "# generate settings\n",
+    "\n",
+    "\n",
+    "# show the settings.json\n",
+    "with open(\"settings.json\") as f:\n",
+    "  data = json.load(f)\n",
+    "  json_formatted_str = json.dumps(data, indent=2)\n",
+    "\n",
+    "  print(json_formatted_str)\n",
+    "\n",
+    "assert os.path.exists(\"settings.json\")\n",
+    "assert os.path.exists(\"input.json\")\n",
+    "assert os.path.exists(\"lol.onnx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "fULvvnK7_CMb"
+   },
+   "outputs": [],
+   "source": [
+    "pk_path = os.path.join('test.pk')\n",
+    "vk_path = os.path.join('test.vk')\n",
+    "\n",
+    "\n",
+    "# setup the proof\n",
+    "res = ezkl.setup(\n",
+    "        compiled_filename,\n",
+    "        vk_path,\n",
+    "        pk_path\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_filename)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "witness_path = \"witness.json\"\n",
+    "\n",
+    "# generate the witness\n",
+    "res = await ezkl.gen_witness(\n",
+    "        input_filename,\n",
+    "        compiled_filename,\n",
+    "        witness_path\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Oog3j6Kd-Wed",
+    "outputId": "5839d0c1-5b43-476e-c2f8-6707de562260"
+   },
+   "outputs": [],
+   "source": [
+    "# prove the zk circuit\n",
+    "# GENERATE A PROOF\n",
+    "proof_path = os.path.join('test.pf')\n",
+    "\n",
+    "\n",
+    "proof = ezkl.prove(\n",
+    "        witness_path,\n",
+    "        compiled_filename,\n",
+    "        pk_path,\n",
+    "        proof_path,\n",
+    "        \"single\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "print(\"proved\")\n",
+    "\n",
+    "assert os.path.isfile(proof_path)\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "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": 0
 }

examples/notebooks/mnist_gan_proof_splitting.ipynb

@@ -453,18 +453,18 @@
             "outputs": [],
             "source": [
                 "# now mock aggregate the proofs\n",
-                "proofs = []\n",
-                "for i in range(3):\n",
-                "    proof_path = os.path.join('proof_split_'+str(i)+'.json')\n",
-                "    proofs.append(proof_path)\n",
+                "# proofs = []\n",
+                "# for i in range(3):\n",
+                "#     proof_path = os.path.join('proof_split_'+str(i)+'.json')\n",
+                "#     proofs.append(proof_path)\n",
                 "\n",
-                "ezkl.mock_aggregate(proofs, logrows=23, split_proofs = True)"
+                "# ezkl.mock_aggregate(proofs, logrows=26, split_proofs = True)"
             ]
         }
     ],
     "metadata": {
         "kernelspec": {
-            "display_name": "ezkl",
+            "display_name": ".env",
             "language": "python",
             "name": "python3"
         },
@@ -478,7 +478,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.12.5"
+            "version": "3.12.7"
         },
         "orig_nbformat": 4
     },

src/bindings/python.rs

@@ -938,6 +938,45 @@ fn gen_settings(
     Ok(true)
 }
 
+/// Generates random data for the model
+///
+/// Arguments
+/// ---------
+/// model: str
+///     Path to the onnx file
+///
+/// output: str
+///     Path to create the data file
+///
+/// seed: int
+///     Random seed to use for generated data
+///
+/// variables
+/// Returns
+/// -------
+/// bool
+///
+#[pyfunction(signature = (
+    model=PathBuf::from(DEFAULT_MODEL),
+    output=PathBuf::from(DEFAULT_SETTINGS),
+    variables=Vec::from([("batch_size".to_string(), 1)]),
+    seed=DEFAULT_SEED.parse().unwrap(),
+))]
+#[gen_stub_pyfunction]
+fn gen_random_data(
+    model: PathBuf,
+    output: PathBuf,
+    variables: Vec<(String, usize)>,
+    seed: u64,
+) -> Result<bool, PyErr> {
+    crate::execute::gen_random_data(model, output, variables, seed).map_err(|e| {
+        let err_str = format!("Failed to generate settings: {}", e);
+        PyRuntimeError::new_err(err_str)
+    })?;
+
+    Ok(true)
+}
+
 /// Calibrates the circuit settings
 ///
 /// Arguments
@@ -2055,6 +2094,7 @@ fn ezkl(m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_function(wrap_pyfunction!(get_srs, m)?)?;
     m.add_function(wrap_pyfunction!(gen_witness, m)?)?;
     m.add_function(wrap_pyfunction!(gen_settings, m)?)?;
+    m.add_function(wrap_pyfunction!(gen_random_data, m)?)?;
     m.add_function(wrap_pyfunction!(calibrate_settings, m)?)?;
     m.add_function(wrap_pyfunction!(aggregate, m)?)?;
     m.add_function(wrap_pyfunction!(mock_aggregate, m)?)?;

src/circuit/modules/poseidon.rs

@@ -100,9 +100,6 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
         let rc_a = (0..WIDTH).map(|_| meta.fixed_column()).collect::<Vec<_>>();
         let rc_b = (0..WIDTH).map(|_| meta.fixed_column()).collect::<Vec<_>>();
 
-        for input in hash_inputs.iter().take(WIDTH) {
-            meta.enable_equality(*input);
-        }
         meta.enable_constant(rc_b[0]);
 
         Self::configure_with_cols(
@@ -152,9 +149,6 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
         let rc_a = (0..WIDTH).map(|_| meta.fixed_column()).collect::<Vec<_>>();
         let rc_b = (0..WIDTH).map(|_| meta.fixed_column()).collect::<Vec<_>>();
 
-        for input in hash_inputs.iter().take(WIDTH) {
-            meta.enable_equality(*input);
-        }
         meta.enable_constant(rc_b[0]);
 
         let instance = meta.instance_column();

src/circuit/ops/chip.rs

@@ -75,6 +75,16 @@ impl FromStr for CheckMode {
     }
 }
 
+impl CheckMode {
+    /// Returns the value of the check mode
+    pub fn is_safe(&self) -> bool {
+        match self {
+            CheckMode::SAFE => true,
+            CheckMode::UNSAFE => false,
+        }
+    }
+}
+
 #[allow(missing_docs)]
 /// An enum representing the tolerance we can accept for the accumulated arguments, either absolute or percentage
 #[derive(Clone, Default, Debug, PartialEq, PartialOrd, Serialize, Deserialize, Copy)]
@@ -205,15 +215,16 @@ impl DynamicLookups {
 
 /// A struct representing the selectors for the dynamic lookup tables
 #[derive(Clone, Debug, Default)]
+
 pub struct Shuffles {
     /// [Selector]s generated when configuring the layer. We use a [BTreeMap] as we expect to configure many dynamic lookup ops.
     pub input_selectors: BTreeMap<(usize, (usize, usize)), Selector>,
     /// Selectors for the dynamic lookup tables
-    pub reference_selectors: Vec<Selector>,
+    pub output_selectors: Vec<Selector>,
     /// Inputs:
     pub inputs: Vec<VarTensor>,
     /// tables
-    pub references: Vec<VarTensor>,
+    pub outputs: Vec<VarTensor>,
 }
 
 impl Shuffles {
@@ -224,9 +235,13 @@ impl Shuffles {
 
         Self {
             input_selectors: BTreeMap::new(),
-            reference_selectors: vec![],
-            inputs: vec![dummy_var.clone(), dummy_var.clone()],
-            references: vec![single_col_dummy_var.clone(), single_col_dummy_var.clone()],
+            output_selectors: vec![],
+            inputs: vec![dummy_var.clone(), dummy_var.clone(), dummy_var.clone()],
+            outputs: vec![
+                single_col_dummy_var.clone(),
+                single_col_dummy_var.clone(),
+                single_col_dummy_var.clone(),
+            ],
         }
     }
 }
@@ -364,6 +379,12 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
         if inputs[0].num_cols() != output.num_cols() {
             log::warn!("input and output shapes do not match");
         }
+        if inputs[0].num_inner_cols() != inputs[1].num_inner_cols() {
+            log::warn!("input number of inner columns do not match");
+        }
+        if inputs[0].num_inner_cols() != output.num_inner_cols() {
+            log::warn!("input and output number of inner columns do not match");
+        }
 
         for i in 0..output.num_blocks() {
             for j in 0..output.num_inner_cols() {
@@ -730,8 +751,8 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
     pub fn configure_shuffles(
         &mut self,
         cs: &mut ConstraintSystem<F>,
-        inputs: &[VarTensor; 2],
-        references: &[VarTensor; 2],
+        inputs: &[VarTensor; 3],
+        outputs: &[VarTensor; 3],
     ) -> Result<(), CircuitError>
     where
         F: Field,
@@ -742,14 +763,14 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
             }
         }
 
-        for t in references.iter() {
+        for t in outputs.iter() {
             if !t.is_advice() || t.num_inner_cols() > 1 {
                 return Err(CircuitError::WrongDynamicColumnType(t.name().to_string()));
             }
         }
 
         // assert all tables have the same number of blocks
-        if references
+        if outputs
             .iter()
             .map(|t| t.num_blocks())
             .collect::<Vec<_>>()
@@ -757,23 +778,23 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
             .any(|w| w[0] != w[1])
         {
             return Err(CircuitError::WrongDynamicColumnType(
-                "references inner cols".to_string(),
+                "outputs inner cols".to_string(),
             ));
         }
 
         let one = Expression::Constant(F::ONE);
 
-        for q in 0..references[0].num_blocks() {
-            let s_reference = cs.complex_selector();
+        for q in 0..outputs[0].num_blocks() {
+            let s_output = cs.complex_selector();
 
             for x in 0..inputs[0].num_blocks() {
                 for y in 0..inputs[0].num_inner_cols() {
                     let s_input = cs.complex_selector();
 
-                    cs.lookup_any("lookup", |cs| {
+                    cs.lookup_any("shuffle", |cs| {
                         let s_inputq = cs.query_selector(s_input);
                         let mut expression = vec![];
-                        let s_referenceq = cs.query_selector(s_reference);
+                        let s_outputq = cs.query_selector(s_output);
                         let mut input_queries = vec![one.clone()];
 
                         for input in inputs {
@@ -785,9 +806,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
                             });
                         }
 
-                        let mut ref_queries = vec![one.clone()];
-                        for reference in references {
-                            ref_queries.push(match reference {
+                        let mut output_queries = vec![one.clone()];
+                        for output in outputs {
+                            output_queries.push(match output {
                                 VarTensor::Advice { inner: advices, .. } => {
                                     cs.query_advice(advices[q][0], Rotation(0))
                                 }
@@ -796,7 +817,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
                         }
 
                         let lhs = input_queries.into_iter().map(|c| c * s_inputq.clone());
-                        let rhs = ref_queries.into_iter().map(|c| c * s_referenceq.clone());
+                        let rhs = output_queries.into_iter().map(|c| c * s_outputq.clone());
                         expression.extend(lhs.zip(rhs));
 
                         expression
@@ -807,13 +828,13 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
                         .or_insert(s_input);
                 }
             }
-            self.shuffles.reference_selectors.push(s_reference);
+            self.shuffles.output_selectors.push(s_output);
         }
 
         // if we haven't previously initialized the input/output, do so now
-        if self.shuffles.references.is_empty() {
-            debug!("assigning shuffles reference");
-            self.shuffles.references = references.to_vec();
+        if self.shuffles.outputs.is_empty() {
+            debug!("assigning shuffles output");
+            self.shuffles.outputs = outputs.to_vec();
         }
         if self.shuffles.inputs.is_empty() {
             debug!("assigning shuffles input");

src/circuit/ops/errors.rs

@@ -100,4 +100,7 @@ pub enum CircuitError {
     #[error("invalid input type {0}")]
     /// Invalid input type
     InvalidInputType(String),
+    #[error("an element is missing from the shuffled version of the tensor")]
+    /// An element is missing from the shuffled version of the tensor
+    MissingShuffleElement,
 }

src/circuit/ops/hybrid.rs

@@ -1,7 +1,7 @@
 use super::*;
 use crate::{
     circuit::{layouts, utils, Tolerance},
-    fieldutils::integer_rep_to_felt,
+    fieldutils::{integer_rep_to_felt, IntegerRep},
     graph::multiplier_to_scale,
     tensor::{self, Tensor, TensorType, ValTensor},
 };
@@ -250,8 +250,8 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                 config,
                 region,
                 values[..].try_into()?,
-                integer_rep_to_felt(input_scale.0 as i128),
-                integer_rep_to_felt(output_scale.0 as i128),
+                integer_rep_to_felt(input_scale.0 as IntegerRep),
+                integer_rep_to_felt(output_scale.0 as IntegerRep),
             )?,
             HybridOp::Div { denom, .. } => {
                 if denom.0.fract() == 0.0 {
@@ -259,7 +259,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                         config,
                         region,
                         values[..].try_into()?,
-                        integer_rep_to_felt(denom.0 as i128),
+                        integer_rep_to_felt(denom.0 as IntegerRep),
                     )?
                 } else {
                     layouts::nonlinearity(

src/circuit/ops/layouts.rs

@@ -43,7 +43,7 @@ const ASCII_ALPHABET: &str = "abcdefghijklmnopqrstuvwxyz";
 /// use ezkl::circuit::BaseConfig;
 /// use ezkl::tensor::ValTensor;
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///    Some(&[1, 2, 3, 2, 3, 4, 3, 4, 5]),
 /// &[3, 3],
@@ -160,9 +160,26 @@ pub(crate) fn div<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
         .into()
     };
     claimed_output.reshape(input_dims)?;
-    region.assign(&config.custom_gates.output, &claimed_output)?;
+    let claimed_output = region.assign(&config.custom_gates.output, &claimed_output)?;
     region.increment(claimed_output.len());
 
+    // here we decompose and extract the sign of the input
+    let sign = sign(config, region, &[claimed_output.clone()])?;
+    // check if x is too large only if the decomp would support overflow in the previous op
+    if (IntegerRep::MAX).abs() < ((region.base() as i128).pow(region.legs() as u32)) - 1 {
+        let abs_value = pairwise(
+            config,
+            region,
+            &[claimed_output.clone(), sign],
+            BaseOp::Mult,
+        )?;
+        let max_val = create_constant_tensor(integer_rep_to_felt(IntegerRep::MAX), 1);
+        let less_than_max = less(config, region, &[abs_value.clone(), max_val])?;
+        // assert the result is 1
+        let comparison_unit = create_constant_tensor(F::ONE, less_than_max.len());
+        enforce_equality(config, region, &[abs_value, comparison_unit])?;
+    }
+
     let product = pairwise(
         config,
         region,
@@ -235,6 +252,30 @@ pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
         &[equal_zero_mask.clone(), equal_inverse_mask],
     )?;
 
+    let masked_output = pairwise(
+        config,
+        region,
+        &[claimed_output.clone(), not_equal_zero_mask.clone()],
+        BaseOp::Mult,
+    )?;
+
+    // here we decompose and extract the sign of the input
+    let sign = sign(config, region, &[masked_output.clone()])?;
+    // check if x is too large only if the decomp would support overflow in the previous op
+    if (IntegerRep::MAX).abs() < ((region.base() as i128).pow(region.legs() as u32)) - 1 {
+        let abs_value = pairwise(
+            config,
+            region,
+            &[claimed_output.clone(), sign],
+            BaseOp::Mult,
+        )?;
+        let max_val = create_constant_tensor(integer_rep_to_felt(IntegerRep::MAX), 1);
+        let less_than_max = less(config, region, &[abs_value.clone(), max_val])?;
+        // assert the result is 1
+        let comparison_unit = create_constant_tensor(F::ONE, less_than_max.len());
+        enforce_equality(config, region, &[abs_value, comparison_unit])?;
+    }
+
     let err_func = |config: &BaseConfig<F>,
                     region: &mut RegionCtx<F>,
                     x: &ValTensor<F>|
@@ -266,7 +307,7 @@ pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::circuit::BaseConfig;
 /// use ezkl::tensor::ValTensor;
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[1, 2, 3, 2, 3, 4, 3, 4, 9]),
 ///    &[3, 3],
@@ -338,7 +379,7 @@ pub fn sqrt<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::circuit::BaseConfig;
 /// use ezkl::tensor::ValTensor;
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///    Some(&[1, 2, 3, 2, 3, 4, 3, 4, 5]),
 /// &[3, 3],
@@ -377,7 +418,7 @@ pub fn rsqrt<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::circuit::layouts::dot;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[5, 2, 3, 0, 4, -1, 3, 1, 6]),
@@ -404,22 +445,6 @@ pub fn dot<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 
     let mut values = values.clone();
 
-    // this section has been optimized to death, don't mess with it
-    let mut removal_indices = values[0].get_const_zero_indices();
-    let second_zero_indices = values[1].get_const_zero_indices();
-    removal_indices.extend(second_zero_indices);
-    removal_indices.par_sort_unstable();
-    removal_indices.dedup();
-
-    // if empty return a const
-    if removal_indices.len() == values[0].len() {
-        return Ok(create_zero_tensor(1));
-    }
-
-    // is already sorted
-    values[0].remove_indices(&mut removal_indices, true)?;
-    values[1].remove_indices(&mut removal_indices, true)?;
-
     let mut inputs = vec![];
     let block_width = config.custom_gates.output.num_inner_cols();
 
@@ -492,7 +517,7 @@ pub fn dot<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// // matmul case
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
@@ -906,7 +931,6 @@ fn _sort_ascending<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     let window_b = assigned_sort.get_slice(&[1..assigned_sort.len()])?;
 
     let is_greater = greater_equal(config, region, &[window_b.clone(), window_a.clone()])?;
-
     let unit = create_unit_tensor(is_greater.len());
 
     enforce_equality(config, region, &[unit, is_greater])?;
@@ -945,7 +969,7 @@ fn _select_topk<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[2, 15, 2, 1, 1, 0]),
@@ -1168,30 +1192,38 @@ pub(crate) fn dynamic_lookup<F: PrimeField + TensorType + PartialOrd + std::hash
 }
 
 /// Shuffle arg
+/// 1. the input is a set of pairs (index_input, value_input) -- looked up against a (dynamic) set of values (index_output, value_output).
+/// 2. index_input is copy constrained to values in a fixed column and is thus a fixed set of incrementing values over the input
+/// 3. value_input is just the input that we are ascertaining is shuffled
+/// 4. index_output is (typically) a prover generated witness committed to in an advice column
+/// 5. value_output is (typically) a prover generated witness committed to in an advice column
+/// 6. Given the above, and given the fixed index_input , we go through every (index_input, value_input) pair and ascertain that it is contained in the input.
+/// Given the fixed incrementing index index_input, we avoid multiplicity in the output by leveraging this surrogate index: if index_output isn't matched to the exact value where for `index_input=index_output` -> `value_input=value_output`, then the lookup fails
 pub(crate) fn shuffles<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
+    output: &[ValTensor<F>; 1],
     input: &[ValTensor<F>; 1],
-    reference: &[ValTensor<F>; 1],
 ) -> Result<ValTensor<F>, CircuitError> {
     let shuffle_index = region.shuffle_index();
-    let (input, reference) = (input[0].clone(), reference[0].clone());
+    let (output, input) = (output[0].clone(), input[0].clone());
 
     // assert input and reference are same length
-    if input.len() != reference.len() {
+    if output.len() != input.len() {
         return Err(CircuitError::MismatchedShuffleLength(
+            output.len(),
             input.len(),
-            reference.len(),
         ));
     }
 
-    let (reference, flush_len_ref) =
-        region.assign_shuffle(&config.shuffles.references[0], &reference)?;
-    let reference_len = reference.len();
+    let (output, flush_len_ref) = region.assign_shuffle(&config.shuffles.outputs[0], &output)?;
+    let output_len = output.len();
+    let input = region.assign(&config.shuffles.inputs[0], &input)?;
 
     // now create a vartensor of constants for the shuffle index
-    let index = create_constant_tensor(F::from(shuffle_index as u64), reference_len);
-    let (index, flush_len_index) = region.assign_shuffle(&config.shuffles.references[1], &index)?;
+    let index = create_constant_tensor(F::from(shuffle_index as u64), output_len);
+    let (index, flush_len_index) = region.assign_shuffle(&config.shuffles.outputs[1], &index)?;
+    region.assign(&config.shuffles.inputs[1], &index)?;
 
     if flush_len_index != flush_len_ref {
         return Err(CircuitError::MismatchedShuffleLength(
@@ -1200,18 +1232,71 @@ pub(crate) fn shuffles<F: PrimeField + TensorType + PartialOrd + std::hash::Hash
         ));
     }
 
-    let input = region.assign(&config.shuffles.inputs[0], &input)?;
-    region.assign(&config.shuffles.inputs[1], &index)?;
+    // now found the position of each element of the reference to the input
+
+    let is_known = !output.any_unknowns()? && !input.any_unknowns()?;
+
+    let claimed_index_output = if is_known {
+        let input = input.int_evals()?;
+        let output = output.int_evals()?;
+
+        // Keep track of which positions we've used for each value
+        let mut used_positions: HashMap<usize, bool> = HashMap::new();
+
+        let index_output = output
+            .iter()
+            .map(|x| {
+                // Find all positions of the current element
+                let positions: Vec<usize> = input
+                    .iter()
+                    .enumerate()
+                    .filter(|(_, y)| *y == x)
+                    .map(|(i, _)| i)
+                    .collect();
+
+                // Find the first unused position for this element
+                let pos = positions
+                    .iter()
+                    .find(|&&i| !used_positions.get(&i).unwrap_or(&false))
+                    .unwrap_or(&0);
+
+                // Mark this position as used
+                used_positions.insert(*pos, true);
+
+                Ok::<_, CircuitError>(Value::known(F::from(*pos as u64)))
+            })
+            .collect::<Result<Vec<_>, _>>()?;
+
+        // Verify all indices were used exactly once
+        if !used_positions.values().all(|&x| x) {
+            return Err(CircuitError::MissingShuffleElement);
+        }
+
+        Tensor::from(index_output.into_iter()).into()
+    } else {
+        Tensor::new(
+            Some(&vec![Value::<F>::unknown(); output_len]),
+            &[output_len],
+        )?
+        .into()
+    };
+
+    region.assign_shuffle(&config.shuffles.outputs[2], &claimed_index_output)?;
+
+    // the incrementing index is the set of numbered values for the input tensor 0...n, and is FIXED
+    let incrementing_index: ValTensor<F> =
+        Tensor::from((0..output.len() as u64).map(|x| ValType::Constant(F::from(x)))).into();
+    region.assign(&config.shuffles.inputs[2], &incrementing_index)?;
 
     let mut shuffle_block = 0;
 
     if !region.is_dummy() {
-        (0..reference_len)
+        (0..output_len)
             .map(|i| {
-                let (x, _, z) = config.shuffles.references[0]
+                let (x, _, z) = config.shuffles.outputs[0]
                     .cartesian_coord(region.combined_dynamic_shuffle_coord() + i + flush_len_ref);
                 shuffle_block = x;
-                let ref_selector = config.shuffles.reference_selectors[shuffle_block];
+                let ref_selector = config.shuffles.output_selectors[shuffle_block];
                 region.enable(Some(&ref_selector), z)?;
                 Ok(())
             })
@@ -1220,7 +1305,7 @@ pub(crate) fn shuffles<F: PrimeField + TensorType + PartialOrd + std::hash::Hash
 
     if !region.is_dummy() {
         // Enable the selectors
-        (0..reference_len)
+        (0..output_len)
             .map(|i| {
                 let (x, y, z) =
                     config.custom_gates.inputs[0].cartesian_coord(region.linear_coord() + i);
@@ -1237,11 +1322,11 @@ pub(crate) fn shuffles<F: PrimeField + TensorType + PartialOrd + std::hash::Hash
             .collect::<Result<Vec<_>, CircuitError>>()?;
     }
 
-    region.increment_shuffle_col_coord(reference_len + flush_len_ref);
+    region.increment_shuffle_col_coord(output_len + flush_len_ref);
     region.increment_shuffle_index(1);
-    region.increment(reference_len);
+    region.increment(output_len);
 
-    Ok(input)
+    Ok(output)
 }
 
 /// One hot accumulated layout
@@ -1698,6 +1783,8 @@ pub(crate) fn get_missing_set_elements<
     // assign the claimed output
     claimed_output = region.assign(&config.custom_gates.output, &claimed_output)?;
 
+    region.increment(claimed_output.len());
+
     // input and claimed output should be the shuffles of fullset
     // concatentate input and claimed output
     let input_and_claimed_output = input.concat(claimed_output.clone())?;
@@ -1891,7 +1978,7 @@ pub(crate) fn scatter_nd<F: PrimeField + TensorType + PartialOrd + std::hash::Ha
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[2, 15, 2, 1, 1, 0]),
@@ -1989,7 +2076,7 @@ pub fn sum<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[2, 15, 2, 1, 1, 0]),
@@ -2144,7 +2231,7 @@ fn axes_wise_op<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[2, 15, 2, 1, 1, 0]),
@@ -2180,7 +2267,7 @@ pub fn prod_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[2, 15, 2, 1, 1, 0]),
@@ -2216,7 +2303,7 @@ pub fn sum_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[2, 15, 2, 1, 1, 0]),
@@ -2258,7 +2345,7 @@ pub fn argmax_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[2, 15, 2, 1, 1, 0]),
 ///     &[2, 3],
@@ -2294,7 +2381,7 @@ pub fn max_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[2, 15, 2, 1, 1, 0]),
@@ -2340,7 +2427,7 @@ pub fn argmin_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[2, 15, 2, 1, 1, 0]),
@@ -2381,25 +2468,6 @@ pub(crate) fn pairwise<F: PrimeField + TensorType + PartialOrd + std::hash::Hash
     lhs.expand(&broadcasted_shape)?;
     rhs.expand(&broadcasted_shape)?;
 
-    // original values
-    let orig_lhs = lhs.clone();
-    let orig_rhs = rhs.clone();
-
-    let first_zero_indices = HashSet::from_iter(lhs.get_const_zero_indices());
-    let second_zero_indices = HashSet::from_iter(rhs.get_const_zero_indices());
-
-    let removal_indices = match op {
-        BaseOp::Add | BaseOp::Mult => {
-            // join the zero indices
-            first_zero_indices
-                .union(&second_zero_indices)
-                .cloned()
-                .collect()
-        }
-        BaseOp::Sub => second_zero_indices.clone(),
-        _ => return Err(CircuitError::UnsupportedOp),
-    };
-
     if lhs.len() != rhs.len() {
         return Err(CircuitError::DimMismatch(format!(
             "pairwise {} layout",
@@ -2411,11 +2479,7 @@ pub(crate) fn pairwise<F: PrimeField + TensorType + PartialOrd + std::hash::Hash
         .iter()
         .enumerate()
         .map(|(i, input)| {
-            let res = region.assign_with_omissions(
-                &config.custom_gates.inputs[i],
-                input,
-                &removal_indices,
-            )?;
+            let res = region.assign(&config.custom_gates.inputs[i], input)?;
 
             Ok(res.get_inner()?)
         })
@@ -2434,12 +2498,8 @@ pub(crate) fn pairwise<F: PrimeField + TensorType + PartialOrd + std::hash::Hash
         halo2_proofs::plonk::Error::Synthesis
     })?;
 
-    let assigned_len = op_result.len() - removal_indices.len();
-    let mut output = region.assign_with_omissions(
-        &config.custom_gates.output,
-        &op_result.into(),
-        &removal_indices,
-    )?;
+    let assigned_len = op_result.len();
+    let mut output = region.assign(&config.custom_gates.output, &op_result.into())?;
 
     // Enable the selectors
     if !region.is_dummy() {
@@ -2457,44 +2517,6 @@ pub(crate) fn pairwise<F: PrimeField + TensorType + PartialOrd + std::hash::Hash
     }
     region.increment(assigned_len);
 
-    let a_tensor = orig_lhs.get_inner_tensor()?;
-    let b_tensor = orig_rhs.get_inner_tensor()?;
-
-    // infill the zero indices with the correct values from values[0] or values[1]
-    if !removal_indices.is_empty() {
-        output
-            .get_inner_tensor_mut()?
-            .par_enum_map_mut_filtered(&removal_indices, |i| {
-                let val = match op {
-                    BaseOp::Add => {
-                        let a_is_null = first_zero_indices.contains(&i);
-                        let b_is_null = second_zero_indices.contains(&i);
-
-                        if a_is_null && b_is_null {
-                            ValType::Constant(F::ZERO)
-                        } else if a_is_null {
-                            b_tensor[i].clone()
-                        } else {
-                            a_tensor[i].clone()
-                        }
-                    }
-                    BaseOp::Sub => {
-                        let a_is_null = first_zero_indices.contains(&i);
-                        // by default b is null in this case for sub
-                        if a_is_null {
-                            ValType::Constant(F::ZERO)
-                        } else {
-                            a_tensor[i].clone()
-                        }
-                    }
-                    BaseOp::Mult => ValType::Constant(F::ZERO),
-                    // can safely panic as the prior check ensures this is not called
-                    _ => unreachable!(),
-                };
-                Ok::<_, TensorError>(val)
-            })?;
-    }
-
     output.reshape(&broadcasted_shape)?;
 
     let end = global_start.elapsed();
@@ -2522,7 +2544,7 @@ pub(crate) fn pairwise<F: PrimeField + TensorType + PartialOrd + std::hash::Hash
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 /// Some(&[2, 15, 2, 1, 1, 0]),
@@ -2579,7 +2601,7 @@ pub(crate) fn expand<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128, 2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let a = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///   Some(&[1, 12, 6, 4, 5, 6]),
@@ -2607,7 +2629,8 @@ pub fn greater<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 
     let diff = pairwise(config, region, &[lhs, rhs], BaseOp::Sub)?;
     let sign = sign(config, region, &[diff])?;
-    equals(config, region, &[sign, create_unit_tensor(1)])
+    let eq = equals(config, region, &[sign, create_unit_tensor(1)])?;
+    Ok(eq)
 }
 
 /// Greater equals than operation.
@@ -2626,7 +2649,7 @@ pub fn greater<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128, 2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 ///
 /// let a = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
@@ -2676,7 +2699,7 @@ pub fn greater_equal<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128, 2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let a = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///  Some(&[1, 0, 5, 4, 5, 1]),
@@ -2717,7 +2740,7 @@ pub fn less<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let a = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///  Some(&[1, 0, 5, 4, 5, 1]),
@@ -2758,7 +2781,7 @@ pub fn less_equal<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let a = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///  Some(&[1, 1, 1, 1, 1, 0]),
@@ -2802,7 +2825,7 @@ pub fn and<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let a = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///   Some(&[1, 1, 1, 1, 1, 0]),
@@ -2850,7 +2873,7 @@ pub fn or<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let a = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 /// Some(&[1, 1, 1, 1, 1, 0]),
@@ -2881,6 +2904,7 @@ pub(crate) fn equals_zero<F: PrimeField + TensorType + PartialOrd + std::hash::H
 ) -> Result<ValTensor<F>, CircuitError> {
     let values = values[0].clone();
     let values_inverse = values.inverse()?;
+
     let product_values_and_invert = pairwise(
         config,
         region,
@@ -2924,7 +2948,7 @@ pub(crate) fn equals_zero<F: PrimeField + TensorType + PartialOrd + std::hash::H
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let a = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///  Some(&[1, 1, 1, 1, 1, 0]),
@@ -2980,7 +3004,7 @@ pub fn xor<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///    Some(&[1, 1, 1, 1, 1, 0]),
@@ -3023,7 +3047,7 @@ pub fn not<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let mask = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///    Some(&[1, 0, 1, 0, 1, 0]),
@@ -3081,7 +3105,7 @@ pub fn iff<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[2, 1, 2, 1, 1, 1]),
@@ -3113,7 +3137,7 @@ pub fn neg<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
@@ -3195,7 +3219,7 @@ pub fn sumpool<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
@@ -3249,32 +3273,30 @@ pub fn max_pool<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
         .multi_cartesian_product()
         .collect::<Vec<_>>();
 
-    output
-        .iter_mut()
-        .enumerate()
-        .map(|(flat_index, o)| {
-            let coord = &cartesian_coord[flat_index];
-            let (b, i) = (coord[0], coord[1]);
+    let inner_loop_function = |idx: usize, region: &mut RegionCtx<F>| {
+        let coord = &cartesian_coord[idx];
+        let (b, i) = (coord[0], coord[1]);
 
-            let mut slice = vec![b..b + 1, i..i + 1];
-            slice.extend(
-                coord[2..]
-                    .iter()
-                    .zip(stride.iter())
-                    .zip(pool_dims.iter())
-                    .map(|((c, s), k)| {
-                        let start = c * s;
-                        let end = start + k;
-                        start..end
-                    }),
-            );
+        let mut slice = vec![b..b + 1, i..i + 1];
+        slice.extend(
+            coord[2..]
+                .iter()
+                .zip(stride.iter())
+                .zip(pool_dims.iter())
+                .map(|((c, s), k)| {
+                    let start = c * s;
+                    let end = start + k;
+                    start..end
+                }),
+        );
 
-            let slice = padded_image.get_slice(&slice)?;
-            let max_w = max(config, region, &[slice])?;
-            *o = max_w.get_inner_tensor()?[0].clone();
-            Ok(())
-        })
-        .collect::<Result<Vec<_>, CircuitError>>()?;
+        let slice = padded_image.get_slice(&slice)?;
+        let max_w = max(config, region, &[slice])?;
+
+        Ok::<_, CircuitError>(max_w.get_inner_tensor()?[0].clone())
+    };
+
+    region.apply_in_loop(&mut output, inner_loop_function)?;
 
     let res: ValTensor<F> = output.into();
 
@@ -3296,7 +3318,7 @@ pub fn max_pool<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::tensor::ValTensor;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let c = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(Some(&[6, 0, 12, 4, 0, 8, 0, 0, 3, 0, 0, 2]), &[1, 2, 2, 3]).unwrap());
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
@@ -3524,7 +3546,7 @@ pub fn deconv<
 /// use ezkl::circuit::BaseConfig;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[5, 2, 3, 0, 4, -1, 3, 1, 6]),
@@ -4038,7 +4060,7 @@ pub(crate) fn range_check<F: PrimeField + TensorType + PartialOrd + std::hash::H
     }
 
     let is_assigned = !w.any_unknowns()?;
-    if is_assigned && region.check_range() {
+    if is_assigned && region.check_range() && config.check_mode.is_safe() {
         // assert is within range
         let int_values = w.int_evals()?;
         for v in int_values.iter() {
@@ -4248,7 +4270,7 @@ pub(crate) fn argmin<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::circuit::region::RegionSettings;
 /// use ezkl::circuit::BaseConfig;
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///    Some(&[5, 2, 3, 0]),
 ///   &[1, 1, 2, 2],
@@ -4301,7 +4323,7 @@ pub fn max_comp<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::circuit::region::RegionSettings;
 /// use ezkl::circuit::BaseConfig;
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///   Some(&[5, 2, 3, 0]),
 ///  &[1, 1, 2, 2],
@@ -4340,10 +4362,7 @@ pub(crate) fn max<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
 ) -> Result<ValTensor<F>, CircuitError> {
-    let input_len = values[0].len();
-    _sort_ascending(config, region, values)?
-        .get_slice(&[input_len - 1..input_len])
-        .map_err(|e| e.into())
+    Ok(_sort_ascending(config, region, values)?.last()?)
 }
 
 /// min layout
@@ -4352,9 +4371,7 @@ pub(crate) fn min<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
 ) -> Result<ValTensor<F>, CircuitError> {
-    _sort_ascending(config, region, values)?
-        .get_slice(&[0..1])
-        .map_err(|e| e.into())
+    Ok(_sort_ascending(config, region, values)?.first()?)
 }
 
 /// floor layout
@@ -4377,7 +4394,7 @@ pub(crate) fn min<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::circuit::region::RegionSettings;
 /// use ezkl::circuit::BaseConfig;
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 /// Some(&[3, -2, -3, 1]),
 /// &[1, 1, 2, 2],
@@ -4489,7 +4506,7 @@ pub fn floor<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::circuit::region::RegionSettings;
 /// use ezkl::circuit::BaseConfig;
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///  Some(&[3, -2, 3, 1]),
 /// &[1, 1, 2, 2],
@@ -4602,7 +4619,7 @@ pub fn ceil<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::circuit::region::RegionSettings;
 /// use ezkl::circuit::BaseConfig;
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 /// Some(&[3, 2, 3, 1]),
 /// &[1, 1, 2, 2],
@@ -4889,7 +4906,7 @@ pub fn ln<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::circuit::region::RegionSettings;
 /// use ezkl::circuit::BaseConfig;
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 /// Some(&[3, -2, 3, 1]),
 /// &[1, 1, 2, 2],
@@ -5032,7 +5049,7 @@ pub fn round<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::circuit::region::RegionSettings;
 /// use ezkl::circuit::BaseConfig;
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 /// Some(&[3, -2, 3, 1]),
 /// &[1, 1, 2, 2],
@@ -5267,7 +5284,24 @@ pub(crate) fn decompose<F: PrimeField + TensorType + PartialOrd + std::hash::Has
     let rest = claimed_output.get_slice(&rest_slice)?;
 
     let sign = range_check(config, region, &[sign], &(-1, 1))?;
-    let rest = range_check(config, region, &[rest], &(0, (*base - 1) as i128))?;
+
+    // isZero(input) * sign == 0.
+    {
+        let is_zero = equals_zero(config, region, &[input.clone()])?;
+        // take the product of the sign and is_zero
+        let sign_is_zero = pairwise(config, region, &[sign.clone(), is_zero], BaseOp::Mult)?;
+        // constrain the sign_is_zero to be 0
+        enforce_equality(
+            config,
+            region,
+            &[
+                sign_is_zero.clone(),
+                create_constant_tensor(F::ZERO, sign_is_zero.len()),
+            ],
+        )?;
+    }
+
+    let rest = range_check(config, region, &[rest], &(0, (*base - 1) as IntegerRep))?;
 
     // equation needs to be constructed as ij,ij->i but for arbitrary n dims we need to construct this dynamically
     // indices should map in order of the alphabet
@@ -5531,7 +5565,7 @@ pub(crate) fn percent<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>
 /// use ezkl::circuit::BaseConfig;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[2, 2, 3, 2, 2, 0]),
@@ -5582,7 +5616,7 @@ pub fn softmax<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
 /// use ezkl::circuit::BaseConfig;
 ///
 /// let dummy_config = BaseConfig::dummy(12, 2);
-/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(65536, 4));
 ///
 /// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
 ///     Some(&[100, 200, 300, 400, 500, 600]),

src/circuit/tests.rs

@@ -1040,6 +1040,10 @@ mod conv {
             let a = VarTensor::new_advice(cs, K, 1, (LEN + 1) * LEN);
             let b = VarTensor::new_advice(cs, K, 1, (LEN + 1) * LEN);
             let output = VarTensor::new_advice(cs, K, 1, (LEN + 1) * LEN);
+
+            // column for constants
+            let _constant = VarTensor::constant_cols(cs, K, 8, false);
+
             Self::Config::configure(cs, &[a, b], &output, CheckMode::SAFE)
         }
 
@@ -1171,7 +1175,7 @@ mod conv_col_ultra_overflow {
 
     use super::*;
 
-    const K: usize = 4;
+    const K: usize = 6;
     const LEN: usize = 10;
 
     #[derive(Clone)]
@@ -1191,9 +1195,10 @@ mod conv_col_ultra_overflow {
         }
 
         fn configure(cs: &mut ConstraintSystem<F>) -> Self::Config {
-            let a = VarTensor::new_advice(cs, K, 1, LEN * LEN * LEN);
-            let b = VarTensor::new_advice(cs, K, 1, LEN * LEN * LEN);
-            let output = VarTensor::new_advice(cs, K, 1, LEN * LEN * LEN);
+            let a = VarTensor::new_advice(cs, K, 1, LEN * LEN * LEN * LEN);
+            let b = VarTensor::new_advice(cs, K, 1, LEN * LEN * LEN * LEN);
+            let output = VarTensor::new_advice(cs, K, 1, LEN * LEN * LEN * LEN);
+            let _constant = VarTensor::constant_cols(cs, K, LEN * LEN * LEN * LEN, false);
             Self::Config::configure(cs, &[a, b], &output, CheckMode::SAFE)
         }
 
@@ -1776,13 +1781,18 @@ mod shuffle {
 
             let d = VarTensor::new_advice(cs, K, 1, LEN);
             let e = VarTensor::new_advice(cs, K, 1, LEN);
+            let f: VarTensor = VarTensor::new_advice(cs, K, 1, LEN);
 
             let _constant = VarTensor::constant_cols(cs, K, LEN * NUM_LOOP, false);
 
             let mut config =
                 Self::Config::configure(cs, &[a.clone(), b.clone()], &c, CheckMode::SAFE);
             config
-                .configure_shuffles(cs, &[a.clone(), b.clone()], &[d.clone(), e.clone()])
+                .configure_shuffles(
+                    cs,
+                    &[a.clone(), b.clone(), c.clone()],
+                    &[d.clone(), e.clone(), f.clone()],
+                )
                 .unwrap();
             config
         }

src/commands.rs

@@ -83,13 +83,15 @@ pub const DEFAULT_VK_SOL: &str = "vk.sol";
 /// Default VK abi path
 pub const DEFAULT_VK_ABI: &str = "vk.abi";
 /// Default scale rebase multipliers for calibration
-pub const DEFAULT_SCALE_REBASE_MULTIPLIERS: &str = "1,2,10";
+pub const DEFAULT_SCALE_REBASE_MULTIPLIERS: &str = "1,10";
 /// Default use reduced srs for verification
 pub const DEFAULT_USE_REDUCED_SRS_FOR_VERIFICATION: &str = "false";
 /// Default only check for range check rebase
 pub const DEFAULT_ONLY_RANGE_CHECK_REBASE: &str = "false";
 /// Default commitment
 pub const DEFAULT_COMMITMENT: &str = "kzg";
+/// Default seed used to generate random data
+pub const DEFAULT_SEED: &str = "21242";
 
 #[cfg(feature = "python-bindings")]
 /// Converts TranscriptType into a PyObject (Required for TranscriptType to be compatible with Python)
@@ -422,7 +424,21 @@ pub enum Commands {
         #[clap(flatten)]
         args: RunArgs,
     },
-
+    /// Generate random data for a model
+    GenRandomData {
+        /// The path to the .onnx model file
+        #[arg(short = 'M', long, default_value = DEFAULT_MODEL, value_hint = clap::ValueHint::FilePath)]
+        model: Option<PathBuf>,
+        /// The path to the .json data file
+        #[arg(short = 'D', long, default_value = DEFAULT_DATA, value_hint = clap::ValueHint::FilePath)]
+        data: Option<PathBuf>,
+        /// Hand-written parser for graph variables, eg. batch_size=1
+        #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(short = 'V', long, value_parser = crate::parse_key_val::<String, usize>, default_value = "batch_size->1", value_delimiter = ',', value_hint = clap::ValueHint::Other))]
+        variables: Vec<(String, usize)>,
+        /// random seed for reproducibility (optional)
+        #[arg(long, value_hint = clap::ValueHint::Other, default_value = DEFAULT_SEED)]
+        seed: u64,
+    },
     /// Calibrates the proving scale, lookup bits and logrows from a circuit settings file.
     CalibrateSettings {
         /// The path to the .json calibration data file.

src/execute.rs

@@ -65,6 +65,8 @@ use std::str::FromStr;
 use std::time::Duration;
 use tabled::Tabled;
 use thiserror::Error;
+use tract_onnx::prelude::IntoTensor;
+use tract_onnx::prelude::Tensor as TractTensor;
 
 use lazy_static::lazy_static;
 
@@ -134,6 +136,17 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
             settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
             args,
         ),
+        Commands::GenRandomData {
+            model,
+            data,
+            variables,
+            seed,
+        } => gen_random_data(
+            model.unwrap_or(DEFAULT_MODEL.into()),
+            data.unwrap_or(DEFAULT_DATA.into()),
+            variables,
+            seed,
+        ),
         Commands::CalibrateSettings {
             model,
             settings_path,
@@ -828,6 +841,71 @@ pub(crate) fn gen_circuit_settings(
     Ok(String::new())
 }
 
+/// Generate a circuit settings file
+pub(crate) fn gen_random_data(
+    model_path: PathBuf,
+    data_path: PathBuf,
+    variables: Vec<(String, usize)>,
+    seed: u64,
+) -> Result<String, EZKLError> {
+    let mut file = std::fs::File::open(&model_path).map_err(|e| {
+        crate::graph::errors::GraphError::ReadWriteFileError(
+            model_path.display().to_string(),
+            e.to_string(),
+        )
+    })?;
+
+    let (tract_model, _symbol_values) = Model::load_onnx_using_tract(&mut file, &variables)?;
+
+    let input_facts = tract_model
+        .input_outlets()
+        .map_err(|e| EZKLError::from(e.to_string()))?
+        .iter()
+        .map(|&i| tract_model.outlet_fact(i))
+        .collect::<tract_onnx::prelude::TractResult<Vec<_>>>()
+        .map_err(|e| EZKLError::from(e.to_string()))?;
+
+    /// Generates a random tensor of a given size and type.
+    fn random(
+        sizes: &[usize],
+        datum_type: tract_onnx::prelude::DatumType,
+        seed: u64,
+    ) -> TractTensor {
+        use rand::{Rng, SeedableRng};
+        let mut rng = rand::rngs::StdRng::seed_from_u64(seed);
+
+        let mut tensor = TractTensor::zero::<f32>(sizes).unwrap();
+        let slice = tensor.as_slice_mut::<f32>().unwrap();
+        slice.iter_mut().for_each(|x| *x = rng.gen());
+        tensor.cast_to_dt(datum_type).unwrap().into_owned()
+    }
+
+    fn tensor_for_fact(fact: &tract_onnx::prelude::TypedFact, seed: u64) -> TractTensor {
+        if let Some(value) = &fact.konst {
+            return value.clone().into_tensor();
+        }
+
+        random(
+            fact.shape
+                .as_concrete()
+                .expect("Expected concrete shape, found: {fact:?}"),
+            fact.datum_type,
+            seed,
+        )
+    }
+
+    let generated = input_facts
+        .iter()
+        .map(|v| tensor_for_fact(v, seed))
+        .collect_vec();
+
+    let data = GraphData::from_tract_data(&generated)?;
+
+    data.save(data_path)?;
+
+    Ok(String::new())
+}
+
 // not for wasm targets
 pub(crate) fn init_spinner() -> ProgressBar {
     let pb = indicatif::ProgressBar::new_spinner();

src/fieldutils.rs

@@ -5,7 +5,7 @@ use halo2curves::ff::PrimeField;
 /// Integer representation of a PrimeField element.
 pub type IntegerRep = i128;
 
-/// Converts an i64 to a PrimeField element.
+/// Converts an integer rep to a PrimeField element.
 pub fn integer_rep_to_felt<F: PrimeField>(x: IntegerRep) -> F {
     if x >= 0 {
         F::from_u128(x as u128)
@@ -69,7 +69,7 @@ mod test {
     fn felttointegerrep() {
         for x in -(2_i128.pow(16))..(2_i128.pow(16)) {
             let fieldx: F = integer_rep_to_felt::<F>(x);
-            let xf: i128 = felt_to_integer_rep::<F>(fieldx);
+            let xf: IntegerRep = felt_to_integer_rep::<F>(fieldx);
             assert_eq!(x, xf);
         }
     }

src/graph/input.rs

@@ -14,7 +14,6 @@ use pyo3::prelude::*;
 use pyo3::types::PyDict;
 #[cfg(feature = "python-bindings")]
 use pyo3::ToPyObject;
-use serde::ser::SerializeStruct;
 use serde::{Deserialize, Deserializer, Serialize, Serializer};
 use std::io::BufReader;
 use std::io::BufWriter;
@@ -515,7 +514,7 @@ impl<'de> Deserialize<'de> for DataSource {
 
 /// Input to graph as a datasource
 /// Always use JSON serialization for GraphData. Seriously.
-#[derive(Clone, Debug, Deserialize, Default, PartialEq)]
+#[derive(Clone, Debug, Deserialize, Default, PartialEq, Serialize)]
 pub struct GraphData {
     /// Inputs to the model / computational graph (can be empty vectors if inputs are coming from on-chain).
     pub input_data: DataSource,
@@ -557,6 +556,34 @@ impl GraphData {
         Ok(inputs)
     }
 
+    // not wasm
+    #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
+    /// Convert the tract data to tract data
+    pub fn from_tract_data(tensors: &[TractTensor]) -> Result<Self, GraphError> {
+        use tract_onnx::prelude::DatumType;
+
+        let mut input_data = vec![];
+        for tensor in tensors {
+            match tensor.datum_type() {
+                tract_onnx::prelude::DatumType::Bool => {
+                    let tensor = tensor.to_array_view::<bool>()?;
+                    let tensor = tensor.iter().map(|e| FileSourceInner::Bool(*e)).collect();
+                    input_data.push(tensor);
+                }
+                _ => {
+                    let cast_tensor = tensor.cast_to_dt(DatumType::F64)?;
+                    let tensor = cast_tensor.to_array_view::<f64>()?;
+                    let tensor = tensor.iter().map(|e| FileSourceInner::Float(*e)).collect();
+                    input_data.push(tensor);
+                }
+            }
+        }
+        Ok(GraphData {
+            input_data: DataSource::File(input_data),
+            output_data: None,
+        })
+    }
+
     ///
     pub fn new(input_data: DataSource) -> Self {
         GraphData {
@@ -741,18 +768,6 @@ impl ToPyObject for FileSourceInner {
     }
 }
 
-impl Serialize for GraphData {
-    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
-    where
-        S: Serializer,
-    {
-        let mut state = serializer.serialize_struct("GraphData", 4)?;
-        state.serialize_field("input_data", &self.input_data)?;
-        state.serialize_field("output_data", &self.output_data)?;
-        state.end()
-    }
-}
-
 #[cfg(test)]
 mod tests {
     use super::*;

src/graph/mod.rs

@@ -280,7 +280,13 @@ impl GraphWitness {
         })?;
 
         let reader = std::io::BufReader::with_capacity(*EZKL_BUF_CAPACITY, file);
-        serde_json::from_reader(reader).map_err(|e| e.into())
+        let witness: GraphWitness =
+            serde_json::from_reader(reader).map_err(Into::<GraphError>::into)?;
+
+        // check versions match
+        crate::check_version_string_matches(witness.version.as_deref().unwrap_or(""));
+
+        Ok(witness)
     }
 
     /// Save the model input to a file
@@ -572,10 +578,14 @@ impl GraphSettings {
         // buf reader
         let reader =
             std::io::BufReader::with_capacity(*EZKL_BUF_CAPACITY, std::fs::File::open(path)?);
-        serde_json::from_reader(reader).map_err(|e| {
+        let settings: GraphSettings = serde_json::from_reader(reader).map_err(|e| {
             error!("failed to load settings file at {}", e);
             std::io::Error::new(std::io::ErrorKind::Other, e)
-        })
+        })?;
+
+        crate::check_version_string_matches(&settings.version);
+
+        Ok(settings)
     }
 
     /// Export the ezkl configuration as json
@@ -697,6 +707,9 @@ impl GraphCircuit {
         let reader = std::io::BufReader::with_capacity(*EZKL_BUF_CAPACITY, f);
         let result: GraphCircuit = bincode::deserialize_from(reader)?;
 
+        // check the versions matche
+        crate::check_version_string_matches(&result.core.settings.version);
+
         Ok(result)
     }
 }

src/graph/model.rs

@@ -621,16 +621,16 @@ impl Model {
     /// * `scale` - The scale to use for quantization.
     /// * `public_params` - Whether to make the params public.
     #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-    fn load_onnx_using_tract(
+    pub(crate) fn load_onnx_using_tract(
         reader: &mut dyn std::io::Read,
-        run_args: &RunArgs,
+        variables: &[(String, usize)],
     ) -> Result<TractResult, GraphError> {
         use tract_onnx::tract_hir::internal::GenericFactoid;
 
         let mut model = tract_onnx::onnx().model_for_read(reader)?;
 
         let variables: std::collections::HashMap<String, usize> =
-            std::collections::HashMap::from_iter(run_args.variables.clone());
+            std::collections::HashMap::from_iter(variables.iter().map(|(k, v)| (k.clone(), *v)));
 
         for (i, id) in model.clone().inputs.iter().enumerate() {
             let input = model.node_mut(id.node);
@@ -655,7 +655,7 @@ impl Model {
         }
 
         let mut symbol_values = SymbolValues::default();
-        for (symbol, value) in run_args.variables.iter() {
+        for (symbol, value) in variables.iter() {
             let symbol = model.symbols.sym(symbol);
             symbol_values = symbol_values.with(&symbol, *value as i64);
             debug!("set {} to {}", symbol, value);
@@ -683,7 +683,7 @@ impl Model {
     ) -> Result<ParsedNodes, GraphError> {
         let start_time = instant::Instant::now();
 
-        let (model, symbol_values) = Self::load_onnx_using_tract(reader, run_args)?;
+        let (model, symbol_values) = Self::load_onnx_using_tract(reader, &run_args.variables)?;
 
         let scales = VarScales::from_args(run_args);
         let nodes = Self::nodes_from_graph(
@@ -964,7 +964,7 @@ impl Model {
             GraphError::ReadWriteFileError(model_path.display().to_string(), e.to_string())
         })?;
 
-        let (model, _) = Model::load_onnx_using_tract(&mut file, run_args)?;
+        let (model, _) = Model::load_onnx_using_tract(&mut file, &run_args.variables)?;
 
         let datum_types: Vec<DatumType> = model
             .input_outlets()?
@@ -1045,8 +1045,8 @@ impl Model {
         if settings.requires_shuffle() {
             base_gate.configure_shuffles(
                 meta,
-                vars.advices[0..2].try_into()?,
-                vars.advices[3..5].try_into()?,
+                vars.advices[0..3].try_into()?,
+                vars.advices[3..6].try_into()?,
             )?;
         }
 

src/graph/vars.rs

@@ -435,7 +435,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ModelVars<F> {
             .collect_vec();
 
         if requires_dynamic_lookup || requires_shuffle {
-            let num_cols = if requires_dynamic_lookup { 3 } else { 2 };
+            let num_cols = 3;
             for _ in 0..num_cols {
                 let dynamic_lookup =
                     VarTensor::new_advice(cs, logrows, 1, dynamic_lookup_and_shuffle_size);

src/lib.rs

@@ -420,3 +420,30 @@ where
     let b = s[pos + 2..].parse()?;
     Ok((a, b))
 }
+
+/// Check if the version string matches the artifact version
+/// If the version string does not match the artifact version, log a warning
+pub fn check_version_string_matches(artifact_version: &str) {
+    if artifact_version == "0.0.0"
+        || artifact_version == "source - no compatibility guaranteed"
+        || artifact_version.is_empty()
+    {
+        log::warn!("Artifact version is 0.0.0, skipping version check");
+        return;
+    }
+
+    let version = crate::version();
+
+    if version == "source - no compatibility guaranteed" {
+        log::warn!("Compiled source version is not guaranteed to match artifact version");
+        return;
+    }
+
+    if version != artifact_version {
+        log::warn!(
+            "Version mismatch: CLI version is {} but artifact version is {}",
+            version,
+            artifact_version
+        );
+    }
+}

src/tensor/ops.rs

@@ -27,7 +27,7 @@ pub fn get_rep(
     n: usize,
 ) -> Result<Vec<IntegerRep>, DecompositionError> {
     // check if x is too large
-    if x.abs() > (base.pow(n as u32) as IntegerRep) - 1 {
+    if (*x).abs() > ((base as i128).pow(n as u32)) - 1 {
         return Err(DecompositionError::TooLarge(*x, base, n));
     }
     let mut rep = vec![0; n + 1];
@@ -43,8 +43,8 @@ pub fn get_rep(
     let mut x = x.abs();
     //
     for i in (1..rep.len()).rev() {
-        rep[i] = x % base as i128;
-        x /= base as i128;
+        rep[i] = x % base as IntegerRep;
+        x /= base as IntegerRep;
     }
 
     Ok(rep)
@@ -127,7 +127,7 @@ pub fn decompose(
         .flatten()
         .collect::<Vec<IntegerRep>>();
 
-    let output = Tensor::<i128>::new(Some(&resp), &dims)?;
+    let output = Tensor::<IntegerRep>::new(Some(&resp), &dims)?;
 
     Ok(output)
 }

src/tensor/var.rs

@@ -558,7 +558,7 @@ impl VarTensor {
 
                 // duplicates every nth element to adjust for column overflow
                 let v = v.duplicate_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();
-                let mut res: ValTensor<F> = {
+                let mut res: ValTensor<F> =
                     v.enum_map(|coord, k| {
 
                     let step = self.num_inner_cols();
@@ -579,12 +579,18 @@ impl VarTensor {
                         prev_cell = Some(cell.clone());
                     } else if coord > 0 && at_beginning_of_column  {
                         if let Some(prev_cell) = prev_cell.as_ref() {
-                            let cell = cell.cell().ok_or({
+                            let cell = if let Some(cell) = cell.cell() {
+                                cell
+                            } else {
                                 error!("Error getting cell: {:?}", (x,y));
-                                halo2_proofs::plonk::Error::Synthesis})?;
-                            let prev_cell = prev_cell.cell().ok_or({
-                                error!("Error getting cell: {:?}", (x,y));
-                                halo2_proofs::plonk::Error::Synthesis})?;
+                                return Err(halo2_proofs::plonk::Error::Synthesis);
+                            };
+                            let prev_cell = if let Some(prev_cell) = prev_cell.cell() {
+                                prev_cell
+                            } else {
+                                error!("Error getting prev cell: {:?}", (x,y));
+                                return Err(halo2_proofs::plonk::Error::Synthesis);
+                            };
                             region.constrain_equal(prev_cell,cell)?;
                         } else {
                             error!("Previous cell was not set");
@@ -594,7 +600,8 @@ impl VarTensor {
 
                     Ok(cell)
 
-                })?.into()};
+                })?.into();
+
                 let total_used_len = res.len();
                 res.remove_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();
 

tests/integration_tests.rs

@@ -187,13 +187,14 @@ mod native_tests {
 
     const PF_FAILURE_AGGR: &str = "examples/test_failure_aggr_proof.json";
 
-    const LARGE_TESTS: [&str; 6] = [
+    const LARGE_TESTS: [&str; 7] = [
         "self_attention",
         "nanoGPT",
         "multihead_attention",
         "mobilenet",
         "mnist_gan",
         "smallworm",
+        "fr_age",
     ];
 
     const ACCURACY_CAL_TESTS: [&str; 6] = [
@@ -395,29 +396,29 @@ mod native_tests {
     const TESTS_AGGR: [&str; 3] = ["1l_mlp", "1l_flatten", "1l_average"];
 
     const TESTS_EVM: [&str; 23] = [
-        "1l_mlp",
-        "1l_flatten",
-        "1l_average",
-        "1l_reshape",
-        "1l_sigmoid",
-        "1l_div",
-        "1l_sqrt",
-        "1l_prelu",
-        "1l_var",
-        "1l_leakyrelu",
-        "1l_gelu_noappx",
-        "1l_relu",
-        "1l_tanh",
-        "2l_relu_sigmoid_small",
-        "2l_relu_small",
-        "min",
-        "max",
-        "1l_max_pool",
-        "idolmodel",
-        "1l_identity",
-        "lstm",
-        "rnn",
-        "quantize_dequantize",
+        "1l_mlp",                // 0
+        "1l_flatten",            // 1
+        "1l_average",            // 2
+        "1l_reshape",            // 3
+        "1l_sigmoid",            // 4
+        "1l_div",                // 5
+        "1l_sqrt",               // 6
+        "1l_prelu",              // 7
+        "1l_var",                // 8
+        "1l_leakyrelu",          // 9
+        "1l_gelu_noappx",        // 10
+        "1l_relu",               // 11
+        "1l_tanh",               // 12
+        "2l_relu_sigmoid_small", // 13
+        "2l_relu_small",         // 14
+        "min",                   // 15
+        "max",                   // 16
+        "1l_max_pool",           // 17
+        "idolmodel",             // 18
+        "1l_identity",           // 19
+        "lstm",                  // 20
+        "rnn",                   // 21
+        "quantize_dequantize",   // 22
     ];
 
     const TESTS_EVM_AGGR: [&str; 18] = [
@@ -541,7 +542,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "public", "fixed", "public", 1, "accuracy", None, 0.0, false);
+                mock(path, test.to_string(), "public", "fixed", "public", 1, "accuracy", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
         });
@@ -606,7 +607,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -616,7 +617,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, 0.0, true);
+                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, 0.0, true, Some(8194), Some(4));
                 test_dir.close().unwrap();
             }
 
@@ -627,7 +628,7 @@ mod native_tests {
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
                 // gen random number between 0.0 and 1.0
                 let tolerance = rand::thread_rng().gen_range(0.0..1.0) * 100.0;
-                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, tolerance, false);
+                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, tolerance, false, Some(8194), Some(5));
                 test_dir.close().unwrap();
             }
 
@@ -642,7 +643,7 @@ mod native_tests {
                     let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
                     let large_batch_dir = &format!("large_batches_{}", test);
                     crate::native_tests::mk_data_batches_(path, test, &large_batch_dir, 10);
-                    mock(path, large_batch_dir.to_string(), "private", "private", "public", 10, "resources", None, 0.0, false);
+                    mock(path, large_batch_dir.to_string(), "private", "private", "public", 10, "resources", None, 0.0, false, None, None);
                     test_dir.close().unwrap();
                 }
             }
@@ -652,7 +653,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "public", "private", "private", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "public", "private", "private", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -661,7 +662,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "public", "hashed", "private", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "public", "hashed", "private", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -670,7 +671,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "fixed", "private", "private", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "fixed", "private", "private", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -679,7 +680,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "private", "private", "fixed", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "private", "private", "fixed", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -688,7 +689,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "private", "fixed", "private", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "private", "fixed", "private", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -697,7 +698,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "hashed", "private", "public", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "hashed", "private", "public", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -706,7 +707,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "polycommit", "private", "public", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "polycommit", "private", "public", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -716,7 +717,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "private", "hashed", "public", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "private", "hashed", "public", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -726,7 +727,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "private", "polycommit", "public", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "private", "polycommit", "public", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -735,7 +736,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "public", "private", "hashed", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "public", "private", "hashed", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -745,7 +746,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "public", "private", "polycommit", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "public", "private", "polycommit", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -754,7 +755,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "public", "fixed", "hashed", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "public", "fixed", "hashed", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -764,7 +765,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "public", "polycommit", "hashed", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "public", "polycommit", "hashed", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -774,7 +775,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "polycommit", "polycommit", "polycommit", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "polycommit", "polycommit", "polycommit", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -784,7 +785,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "hashed", "private", "hashed", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "hashed", "private", "hashed", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -794,7 +795,7 @@ mod native_tests {
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
                 // needs an extra row for the large model
-                mock(path, test.to_string(),"hashed", "hashed", "public", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(),"hashed", "hashed", "public", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -804,7 +805,7 @@ mod native_tests {
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
                 // needs an extra row for the large model
-                mock(path, test.to_string(),"hashed", "hashed", "hashed", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(),"hashed", "hashed", "hashed", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
 
@@ -963,7 +964,7 @@ mod native_tests {
 
             });
 
-            seq!(N in 0..=5 {
+            seq!(N in 0..=6 {
 
             #(#[test_case(LARGE_TESTS[N])])*
             #[ignore]
@@ -981,7 +982,7 @@ mod native_tests {
                 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);
-                mock(path, test.to_string(), "private", "fixed", "public", 1, "resources", None, 0.0, false);
+                mock(path, test.to_string(), "private", "fixed", "public", 1, "resources", None, 0.0, false, None, None);
                 test_dir.close().unwrap();
             }
         });
@@ -1459,6 +1460,8 @@ mod native_tests {
         scales_to_use: Option<Vec<u32>>,
         tolerance: f32,
         bounded_lookup_log: bool,
+        decomp_base: Option<usize>,
+        decomp_legs: Option<usize>,
     ) {
         let mut tolerance = tolerance;
         gen_circuit_settings_and_witness(
@@ -1475,6 +1478,8 @@ mod native_tests {
             Commitments::KZG,
             2,
             bounded_lookup_log,
+            decomp_base,
+            decomp_legs,
         );
 
         if tolerance > 0.0 {
@@ -1616,6 +1621,8 @@ mod native_tests {
         commitment: Commitments,
         lookup_safety_margin: usize,
         bounded_lookup_log: bool,
+        decomp_base: Option<usize>,
+        decomp_legs: Option<usize>,
     ) {
         let mut args = vec![
             "gen-settings".to_string(),
@@ -1634,6 +1641,14 @@ mod native_tests {
             format!("--commitment={}", commitment),
         ];
 
+        if let Some(decomp_base) = decomp_base {
+            args.push(format!("--decomp-base={}", decomp_base));
+        }
+
+        if let Some(decomp_legs) = decomp_legs {
+            args.push(format!("--decomp-legs={}", decomp_legs));
+        }
+
         if bounded_lookup_log {
             args.push("--bounded-log-lookup".to_string());
         }
@@ -1751,6 +1766,8 @@ mod native_tests {
             Commitments::KZG,
             2,
             false,
+            None,
+            None,
         );
 
         println!(
@@ -2035,6 +2052,8 @@ mod native_tests {
             commitment,
             lookup_safety_margin,
             false,
+            None,
+            None,
         );
 
         let settings_path = format!("{}/{}/settings.json", test_dir, example_name);
@@ -2467,6 +2486,8 @@ mod native_tests {
             Commitments::KZG,
             2,
             false,
+            None,
+            None,
         );
 
         let model_path = format!("{}/{}/network.compiled", test_dir, example_name);

tests/python/binding_tests.py

@@ -873,6 +873,7 @@ def get_examples():
         'linear_regression',
         "mnist_gan",
         "smallworm",
+        "fr_age"
     ]
     examples = []
     for subdir, _, _ in os.walk(os.path.join(examples_path, "onnx")):