refactor: DataSource enum -> struct

BREAKING CHANGE: in python the order of arguments for evm related functions has changed

.cargo/config.toml

@@ -2,3 +2,16 @@
 runner = 'wasm-bindgen-test-runner'
 rustflags = ["-C", "target-feature=+atomics,+bulk-memory,+mutable-globals","-C",
  "link-arg=--max-memory=4294967296"]
+
+
+[target.x86_64-apple-darwin]
+rustflags = [
+  "-C", "link-arg=-undefined",
+  "-C", "link-arg=dynamic_lookup",
+]
+
+[target.aarch64-apple-darwin]
+rustflags = [
+  "-C", "link-arg=-undefined",
+  "-C", "link-arg=dynamic_lookup",
+]

.github/workflows/benchmarks.yml

@@ -6,23 +6,16 @@ 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:
+    permissions:
+      contents: read
     runs-on: self-hosted
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
           toolchain: nightly-2023-06-27
           override: true
@@ -31,11 +24,15 @@ jobs:
         run: cargo bench --verbose --bench poseidon
 
   bench_einsum_accum_matmul:
+    permissions:
+      contents: read
     runs-on: self-hosted
     needs: [bench_poseidon]
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
           toolchain: nightly-2023-06-27
           override: true
@@ -44,11 +41,15 @@ jobs:
         run: cargo bench --verbose --bench accum_einsum_matmul
 
   bench_accum_matmul_relu:
+    permissions:
+      contents: read
     runs-on: self-hosted
     needs: [bench_poseidon]
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
           toolchain: nightly-2023-06-27
           override: true
@@ -57,11 +58,15 @@ jobs:
         run: cargo bench --verbose --bench accum_matmul_relu
 
   bench_accum_matmul_relu_overflow:
+    permissions:
+      contents: read
     runs-on: self-hosted
     needs: [bench_poseidon]
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
           toolchain: nightly-2023-06-27
           override: true
@@ -70,11 +75,15 @@ jobs:
         run: cargo bench --verbose --bench accum_matmul_relu_overflow
 
   bench_relu:
+    permissions:
+      contents: read
     runs-on: self-hosted
     needs: [bench_poseidon]
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
           toolchain: nightly-2023-06-27
           override: true
@@ -83,11 +92,15 @@ jobs:
         run: cargo bench --verbose --bench relu
 
   bench_accum_dot:
+    permissions:
+      contents: read
     runs-on: self-hosted
     needs: [bench_poseidon]
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
           toolchain: nightly-2023-06-27
           override: true
@@ -96,11 +109,15 @@ jobs:
         run: cargo bench --verbose --bench accum_dot
 
   bench_accum_conv:
+    permissions:
+      contents: read
     runs-on: self-hosted
     needs: [bench_poseidon]
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
           toolchain: nightly-2023-06-27
           override: true
@@ -109,11 +126,15 @@ jobs:
         run: cargo bench --verbose --bench accum_conv
 
   bench_accum_sumpool:
+    permissions:
+      contents: read
     runs-on: self-hosted
     needs: [bench_poseidon]
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
           toolchain: nightly-2023-06-27
           override: true
@@ -122,11 +143,15 @@ jobs:
         run: cargo bench --verbose --bench accum_sumpool
 
   bench_pairwise_add:
+    permissions:
+      contents: read
     runs-on: self-hosted
     needs: [bench_poseidon]
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
           toolchain: nightly-2023-06-27
           override: true
@@ -135,11 +160,15 @@ jobs:
         run: cargo bench --verbose --bench pairwise_add
 
   bench_accum_sum:
+    permissions:
+      contents: read
     runs-on: self-hosted
     needs: [bench_poseidon]
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
           toolchain: nightly-2023-06-27
           override: true
@@ -148,11 +177,15 @@ jobs:
         run: cargo bench --verbose --bench accum_sum
 
   bench_pairwise_pow:
+    permissions:
+      contents: read
     runs-on: self-hosted
     needs: [bench_poseidon]
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
           toolchain: nightly-2023-06-27
           override: true

.github/workflows/engine.yml

@@ -15,17 +15,24 @@ defaults:
     working-directory: .
 jobs:
   publish-wasm-bindings:
+    permissions:
+      contents: read
+      packages: write
     name: publish-wasm-bindings
+    env:
+      RELEASE_TAG: ${{ github.ref_name }}
     runs-on: ubuntu-latest
     if: startsWith(github.ref, 'refs/tags/')
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
         with:
-          toolchain: nightly-2024-07-18
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+        with:
+          toolchain: nightly-2025-02-17
           override: true
           components: rustfmt, clippy
-      - uses: jetli/wasm-pack-action@v0.4.0
+      - uses: jetli/wasm-pack-action@0d096b08b4e5a7de8c28de67e11e945404e9eefa  #v0.4.0
         with:
           # Pin to version 0.12.1
           version: 'v0.12.1'
@@ -33,7 +40,7 @@ jobs:
         run: rustup target add wasm32-unknown-unknown
 
       - name: Add rust-src
-        run: rustup component add rust-src --toolchain nightly-2024-07-18-x86_64-unknown-linux-gnu
+        run: rustup component add rust-src --toolchain nightly-2025-02-17-x86_64-unknown-linux-gnu
       - name: Install binaryen
         run: |
           set -e
@@ -42,41 +49,41 @@ jobs:
           wasm-opt --version
       - name: Build wasm files for both web and nodejs compilation targets
         run: |
-          wasm-pack build --release --target nodejs --out-dir ./pkg/nodejs . -- -Z build-std="panic_abort,std"
-          wasm-pack build --release --target web --out-dir ./pkg/web . -- -Z build-std="panic_abort,std" --features web
+            wasm-pack build --release --target nodejs --out-dir ./pkg/nodejs . -- -Z build-std="panic_abort,std"
+            wasm-pack build --release --target web --out-dir ./pkg/web . -- -Z build-std="panic_abort,std" --features web
       - name: Create package.json in pkg folder
         shell: bash
-        env:
-          RELEASE_TAG: ${{ github.ref_name }}
         run: |
-          echo '{
-            "name": "@ezkljs/engine",
-            "version": "${{ github.ref_name }}",
-            "dependencies": {
-              "@types/json-bigint": "^1.0.1",
-              "json-bigint": "^1.0.0"
-            },
-            "files": [
-              "nodejs/ezkl_bg.wasm",
-              "nodejs/ezkl.js",
-              "nodejs/ezkl.d.ts",
-              "nodejs/package.json",
-              "nodejs/utils.js",
-              "web/ezkl_bg.wasm",
-              "web/ezkl.js",
-              "web/ezkl.d.ts",
-              "web/snippets/**/*",
-              "web/package.json",
-              "web/utils.js",
-              "ezkl.d.ts"
-            ],
-            "main": "nodejs/ezkl.js",
-            "module": "web/ezkl.js",
-            "types": "nodejs/ezkl.d.ts",
-            "sideEffects": [
-              "web/snippets/*"
-            ]
-          }' > pkg/package.json
+            cat > pkg/package.json << EOF
+            {
+              "name": "@ezkljs/engine",
+              "version": "$RELEASE_TAG",
+              "dependencies": {
+                "@types/json-bigint": "^1.0.1",
+                "json-bigint": "^1.0.0"
+              },
+              "files": [
+                "nodejs/ezkl_bg.wasm",
+                "nodejs/ezkl.js",
+                "nodejs/ezkl.d.ts",
+                "nodejs/package.json",
+                "nodejs/utils.js",
+                "web/ezkl_bg.wasm",
+                "web/ezkl.js",
+                "web/ezkl.d.ts",
+                "web/snippets/**/*",
+                "web/package.json",
+                "web/utils.js",
+                "ezkl.d.ts"
+              ],
+              "main": "nodejs/ezkl.js",
+              "module": "web/ezkl.js",
+              "types": "nodejs/ezkl.d.ts",
+              "sideEffects": [
+                "web/snippets/*"
+              ]
+            }
+            EOF
 
       - name: Replace memory definition in nodejs
         run: |
@@ -169,7 +176,7 @@ jobs:
           curl -s "https://raw.githubusercontent.com/zkonduit/ezkljs-engine/main/README.md" > ./pkg/README.md
 
       - name: Set up Node.js
-        uses: actions/setup-node@v3
+        uses: actions/setup-node@1a4442cacd436585916779262731d5b162bc6ec7  #v3.8.2
         with:
           node-version: "18.12.1"
           registry-url: "https://registry.npmjs.org"
@@ -184,21 +191,26 @@ jobs:
 
 
   in-browser-evm-ver-publish:
+    permissions:
+      contents: read
+      packages: write
     name: publish-in-browser-evm-verifier-package
     needs: [publish-wasm-bindings]
     runs-on: ubuntu-latest
+    env:
+        RELEASE_TAG: ${{ github.ref_name }}
     if: startsWith(github.ref, 'refs/tags/')
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        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)
@@ -218,13 +230,13 @@ jobs:
           NR==30{$0="    specifier: \"" tag "\""}
           NR==31{$0="    version: \"" tag "\""}
           NR==400{$0="  /@ezkljs/engine@" tag ":"}
-          NR==401{$0="    resolution: {integrity: \"" integrity "\"}"} 1' in-browser-evm-verifier/pnpm-lock.yaml > temp.yaml && mv temp.yaml in-browser-evm-verifier/pnpm-lock.yaml  
+          NR==401{$0="    resolution: {integrity: \"" integrity "\"}"} 1' in-browser-evm-verifier/pnpm-lock.yaml > temp.yaml && mv temp.yaml in-browser-evm-verifier/pnpm-lock.yaml
       - name: Use pnpm 8
-        uses: pnpm/action-setup@v2
+        uses: pnpm/action-setup@eae0cfeb286e66ffb5155f1a79b90583a127a68b  #v2.4.1
         with:
           version: 8
       - name: Set up Node.js
-        uses: actions/setup-node@v3
+        uses: actions/setup-node@1a4442cacd436585916779262731d5b162bc6ec7  #v3.8.2
         with:
           node-version: "18.12.1"
           registry-url: "https://registry.npmjs.org"
@@ -235,4 +247,4 @@ jobs:
           pnpm run build
           pnpm publish --no-git-checks
         env:
-          NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}  
+          NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}

.github/workflows/large-tests.yml

@@ -6,12 +6,16 @@ on:
         description: "Test scenario tags"
 jobs:
   large-tests:
+    permissions:
+      contents: read
     runs-on: kaiju
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
         with:
-          toolchain: nightly-2024-07-18
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+        with:
+          toolchain: nightly-2025-02-17
           override: true
           components: rustfmt, clippy
       - name: nanoGPT Mock

.github/workflows/pypi-gpu.yml

@@ -18,38 +18,46 @@ defaults:
 jobs:
 
   linux:
+    permissions:
+      contents: read
+      packages: write
     runs-on: GPU
     strategy:
       matrix:
         target: [x86_64]
+    env:
+      RELEASE_TAG: ${{ github.ref_name }}
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions/setup-python@b64ffcaf5b410884ad320a9cfac8866006a109aa  #v4.8.0
         with:
           python-version: 3.12
           architecture: x64
 
-      - name: Set pyproject.toml version to match github tag
+      - name: Set pyproject.toml version to match github tag and rename ezkl to ezkl-gpu
         shell: bash
         run: |
             mv pyproject.toml pyproject.toml.orig
-            sed "s/ezkl/ezkl-gpu/" pyproject.toml.orig >pyproject.toml
+            sed "s/ezkl/ezkl-gpu/" pyproject.toml.orig > pyproject.toml.tmp
+            sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" pyproject.toml.tmp > pyproject.toml
 
-      - uses: actions-rs/toolchain@v1
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
           toolchain: nightly-2023-06-27
           override: true
           components: rustfmt, clippy
 
-      - name: Set Cargo.toml version to match github tag
+      - name: Set Cargo.toml version to match github tag and rename ezkl to ezkl-gpu
         shell: bash
-        env:
-          RELEASE_TAG: ${{ github.ref_name }}
+        # the ezkl substitution here looks for the first instance of name = "ezkl" and changes it to "ezkl-gpu"
         run: |
           mv Cargo.toml Cargo.toml.orig
-          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.toml.orig >Cargo.toml
+          sed "0,/name = \"ezkl\"/s/name = \"ezkl\"/name = \"ezkl-gpu\"/" Cargo.toml.orig > Cargo.toml.tmp
+          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.toml.tmp > Cargo.toml
           mv Cargo.lock Cargo.lock.orig
-          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.lock.orig >Cargo.lock
+          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.lock.orig > Cargo.lock
 
       - name: Install required libraries
         shell: bash
@@ -57,7 +65,7 @@ jobs:
           sudo apt-get update && sudo apt-get install -y openssl pkg-config libssl-dev
 
       - name: Build wheels
-        uses: PyO3/maturin-action@v1
+        uses: PyO3/maturin-action@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
         with:
           target: ${{ matrix.target }}
           manylinux: auto
@@ -70,7 +78,7 @@ jobs:
           pip install ezkl-gpu --no-index --find-links dist --force-reinstall
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08  #v4.6.0
         with:
           name: wheels
           path: dist
@@ -86,7 +94,7 @@ jobs:
     # needs: [ macos, windows, linux, linux-cross, musllinux, musllinux-cross ]
     needs: [linux]
     steps:
-      - uses: actions/download-artifact@v3
+      - uses: actions/download-artifact@fa0a91b85d4f404e444e00e005971372dc801d16  #v4.1.8
         with:
           name: wheels
       - name: List Files
@@ -98,14 +106,14 @@ jobs:
       # publishes to PyPI
       - name: Publish package distributions to PyPI
         continue-on-error: true
-        uses: pypa/gh-action-pypi-publish@release/v1
+        uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc  #v1.12.4
         with:
-          packages-dir: ./
+          packages-dir: ./wheels
 
       # publishes to TestPyPI
       - name: Publish package distribution to TestPyPI
         continue-on-error: true
-        uses: pypa/gh-action-pypi-publish@release/v1
+        uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc  #v1.12.4
         with:
           repository-url: https://test.pypi.org/legacy/
-          packages-dir: ./
+          packages-dir: ./wheels

.github/workflows/pypi.yml

@@ -16,36 +16,53 @@ defaults:
 
 jobs:
   macos:
+    permissions:
+      contents: read
     runs-on: macos-latest
     if: startsWith(github.ref, 'refs/tags/')
     strategy:
       matrix:
         target: [x86_64, universal2-apple-darwin]
+    env:
+        RELEASE_TAG: ${{ github.ref_name }}
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions/setup-python@b64ffcaf5b410884ad320a9cfac8866006a109aa  #v4.8.0
         with:
           python-version: 3.12
           architecture: x64
 
+      - name: Set pyproject.toml version to match github tag
+        shell: bash
+        run: |
+          mv pyproject.toml pyproject.toml.orig
+          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" pyproject.toml.orig >pyproject.toml
+
       - name: Set Cargo.toml version to match github tag
         shell: bash
-        env:
-          RELEASE_TAG: ${{ github.ref_name }}
         run: |
           mv Cargo.toml Cargo.toml.orig
           sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.toml.orig >Cargo.toml
           mv Cargo.lock Cargo.lock.orig
           sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.lock.orig >Cargo.lock
 
-      - uses: actions-rs/toolchain@v1
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
-          toolchain: nightly-2024-07-18
+          toolchain: nightly-2025-02-17
           override: true
           components: rustfmt, clippy
 
       - name: Build wheels
-        uses: PyO3/maturin-action@v1
+        if: matrix.target == 'universal2-apple-darwin'
+        uses: PyO3/maturin-action@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
+        with:
+          target: ${{ matrix.target }}
+          args: --release --out dist --features python-bindings
+      - name: Build wheels
+        if: matrix.target == 'x86_64'
+        uses: PyO3/maturin-action@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
         with:
           target: ${{ matrix.target }}
           args: --release --out dist --features python-bindings
@@ -56,24 +73,36 @@ jobs:
           python -c "import ezkl"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08  #v4.6.0
         with:
-          name: wheels
+          name: dist-macos-${{ matrix.target }}
           path: dist
 
   windows:
+    permissions:
+      contents: read
     runs-on: windows-latest
     if: startsWith(github.ref, 'refs/tags/')
     strategy:
       matrix:
         target: [x64, x86]
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions/setup-python@b64ffcaf5b410884ad320a9cfac8866006a109aa  #v4.8.0
         with:
           python-version: 3.12
           architecture: ${{ matrix.target }}
 
+      - name: Set pyproject.toml version to match github tag
+        shell: bash
+        env:
+          RELEASE_TAG: ${{ github.ref_name }}
+        run: |
+          mv pyproject.toml pyproject.toml.orig
+          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" pyproject.toml.orig >pyproject.toml
+
       - name: Set Cargo.toml version to match github tag
         shell: bash
         env:
@@ -84,14 +113,14 @@ jobs:
           mv Cargo.lock Cargo.lock.orig
           sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.lock.orig >Cargo.lock
 
-      - uses: actions-rs/toolchain@v1
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
-          toolchain: nightly-2024-07-18
+          toolchain: nightly-2025-02-17
           override: true
           components: rustfmt, clippy
 
       - name: Build wheels
-        uses: PyO3/maturin-action@v1
+        uses: PyO3/maturin-action@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
         with:
           target: ${{ matrix.target }}
           args: --release --out dist --features python-bindings
@@ -101,24 +130,36 @@ jobs:
           python -c "import ezkl"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08  #v4.6.0 #v4.6.0
         with:
-          name: wheels
+          name: dist-windows-${{ matrix.target }}
           path: dist
 
   linux:
+    permissions:
+      contents: read
     runs-on: ubuntu-latest
     if: startsWith(github.ref, 'refs/tags/')
     strategy:
       matrix:
         target: [x86_64]
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions/setup-python@b64ffcaf5b410884ad320a9cfac8866006a109aa  #v4.8.0
         with:
           python-version: 3.12
           architecture: x64
 
+      - name: Set pyproject.toml version to match github tag
+        shell: bash
+        env:
+          RELEASE_TAG: ${{ github.ref_name }}
+        run: |
+          mv pyproject.toml pyproject.toml.orig
+          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" pyproject.toml.orig >pyproject.toml
+
       - name: Set Cargo.toml version to match github tag
         shell: bash
         env:
@@ -129,14 +170,13 @@ jobs:
           mv Cargo.lock Cargo.lock.orig
           sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.lock.orig >Cargo.lock
 
-
       - name: Install required libraries
         shell: bash
         run: |
           sudo apt-get update && sudo apt-get install -y openssl pkg-config libssl-dev
 
       - name: Build wheels
-        uses: PyO3/maturin-action@v1
+        uses: PyO3/maturin-action@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
         with:
           target: ${{ matrix.target }}
           manylinux: auto
@@ -163,63 +203,14 @@ jobs:
           python -c "import ezkl"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08  #v4.6.0
         with:
-          name: wheels
+          name: dist-linux-${{ matrix.target }}
           path: dist
 
-  # TODO: There's a problem with the maturin-action toolchain for arm arch leading to failed builds
-  # linux-cross:
-  #   runs-on: ubuntu-latest
-  #   strategy:
-  #     matrix:
-  #       target: [aarch64, armv7]
-  #   steps:
-  #   - uses: actions/checkout@v4
-  #   - uses: actions/setup-python@v4
-  #     with:
-  #       python-version: 3.12
-
-  #   - name: Install cross-compilation tools for aarch64
-  #     if: matrix.target == 'aarch64'
-  #     run: |
-  #       sudo apt-get update
-  #       sudo apt-get install -y gcc make gcc-aarch64-linux-gnu binutils-aarch64-linux-gnu libc6-dev-arm64-cross libusb-1.0-0-dev libatomic1-arm64-cross
-
-  #   - name: Install cross-compilation tools for armv7
-  #     if: matrix.target == 'armv7'
-  #     run: |
-  #       sudo apt-get update
-  #       sudo apt-get install -y gcc make gcc-arm-linux-gnueabihf binutils-arm-linux-gnueabihf libc6-dev-armhf-cross libusb-1.0-0-dev libatomic1-armhf-cross
-
-  #   - name: Build wheels
-  #     uses: PyO3/maturin-action@v1
-  #     with:
-  #       target: ${{ matrix.target }}
-  #       manylinux: auto
-  #       args: --release --out dist --features python-bindings
-
-  #   - uses: uraimo/run-on-arch-action@v2.5.0
-  #     name: Install built wheel
-  #     with:
-  #       arch: ${{ matrix.target }}
-  #       distro: ubuntu20.04
-  #       githubToken: ${{ github.token }}
-  #       install: |
-  #         apt-get update
-  #         apt-get install -y --no-install-recommends python3 python3-pip
-  #         pip3 install -U pip
-  #       run: |
-  #         pip3 install ezkl --no-index --find-links dist/ --force-reinstall
-  #         python3 -c "import ezkl"
-
-  #   - name: Upload wheels
-  #     uses: actions/upload-artifact@v3
-  #     with:
-  #       name: wheels
-  #       path: dist
-
   musllinux:
+    permissions:
+      contents: read
     runs-on: ubuntu-latest
     if: startsWith(github.ref, 'refs/tags/')
     strategy:
@@ -227,12 +218,22 @@ jobs:
         target:
           - x86_64-unknown-linux-musl
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions/setup-python@b64ffcaf5b410884ad320a9cfac8866006a109aa  #v4.8.0
         with:
           python-version: 3.12
           architecture: x64
 
+      - name: Set pyproject.toml version to match github tag
+        shell: bash
+        env:
+          RELEASE_TAG: ${{ github.ref_name }}
+        run: |
+          mv pyproject.toml pyproject.toml.orig
+          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" pyproject.toml.orig >pyproject.toml
+
       - name: Set Cargo.toml version to match github tag
         shell: bash
         env:
@@ -249,7 +250,7 @@ jobs:
           sudo apt-get update && sudo apt-get install -y pkg-config libssl-dev
 
       - name: Build wheels
-        uses: PyO3/maturin-action@v1
+        uses: PyO3/maturin-action@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
         with:
           target: ${{ matrix.target }}
           manylinux: musllinux_1_2
@@ -257,7 +258,7 @@ jobs:
 
       - name: Install built wheel
         if: matrix.target == 'x86_64-unknown-linux-musl'
-        uses: addnab/docker-run-action@v3
+        uses: addnab/docker-run-action@3e77f186b7a929ef010f183a9e24c0f9955ea609
         with:
           image: alpine:latest
           options: -v ${{ github.workspace }}:/io -w /io
@@ -270,12 +271,14 @@ jobs:
             python3 -c "import ezkl"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08  #v4.6.0
         with:
-          name: wheels
+          name: dist-musllinux-${{ matrix.target }}
           path: dist
 
   musllinux-cross:
+    permissions:
+      contents: read
     runs-on: ubuntu-latest
     if: startsWith(github.ref, 'refs/tags/')
     strategy:
@@ -283,14 +286,22 @@ jobs:
         platform:
           - target: aarch64-unknown-linux-musl
             arch: aarch64
-          - target: armv7-unknown-linux-musleabihf
-            arch: armv7
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions/setup-python@b64ffcaf5b410884ad320a9cfac8866006a109aa  #v4.8.0
         with:
           python-version: 3.12
 
+      - name: Set pyproject.toml version to match github tag
+        shell: bash
+        env:
+          RELEASE_TAG: ${{ github.ref_name }}
+        run: |
+          mv pyproject.toml pyproject.toml.orig
+          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" pyproject.toml.orig >pyproject.toml
+
       - name: Set Cargo.toml version to match github tag
         shell: bash
         env:
@@ -302,13 +313,13 @@ jobs:
           sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.lock.orig >Cargo.lock
 
       - name: Build wheels
-        uses: PyO3/maturin-action@v1
+        uses: PyO3/maturin-action@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
         with:
           target: ${{ matrix.platform.target }}
           manylinux: musllinux_1_2
           args: --release --out dist --features python-bindings
 
-      - uses: uraimo/run-on-arch-action@v2.5.0
+      - uses: uraimo/run-on-arch-action@5397f9e30a9b62422f302092631c99ae1effcd9e  #v2.8.1
         name: Install built wheel
         with:
           arch: ${{ matrix.platform.arch }}
@@ -323,9 +334,9 @@ jobs:
             python3 -c "import ezkl"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08  #v4.6.0
         with:
-          name: wheels
+          name: dist-musllinux-${{ matrix.platform.target }}
           path: dist
 
   pypi-publish:
@@ -334,44 +345,43 @@ jobs:
     permissions:
       id-token: write
     if: "startsWith(github.ref, 'refs/tags/')"
-    # TODO: Uncomment if linux-cross is working
-    # needs: [ macos, windows, linux, linux-cross, musllinux, musllinux-cross ]
     needs: [macos, windows, linux, musllinux, musllinux-cross]
     steps:
-      - uses: actions/download-artifact@v3
+      - uses: actions/download-artifact@fa0a91b85d4f404e444e00e005971372dc801d16  #v4.1.8
         with:
-          name: wheels
+          pattern: dist-*
+          merge-multiple: true
+          path: wheels
       - name: List Files
         run: ls -R
 
-      # Both publish steps will fail if there is no trusted publisher setup
-      # On failure the publish step will then simply continue to the next one
+      # # publishes to TestPyPI
+      # - name: Publish package distribution to TestPyPI
+      #   uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc  #v1.12.4
+      #   with:
+      #     repository-url: https://test.pypi.org/legacy/
+      #     packages-dir: ./
 
       # publishes to PyPI
       - name: Publish package distributions to PyPI
-        continue-on-error: true
-        uses: pypa/gh-action-pypi-publish@release/v1
+        uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc  #v1.12.4
         with:
-          packages-dir: ./
+          packages-dir: ./wheels
 
-      # publishes to TestPyPI
-      - name: Publish package distribution to TestPyPI
-        continue-on-error: true
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          repository-url: https://test.pypi.org/legacy/
-          packages-dir: ./
 
   doc-publish:
+    permissions:
+      contents: read
     name: Trigger ReadTheDocs Build
     runs-on: ubuntu-latest
     needs: pypi-publish
     steps:
       - uses: actions/checkout@v4
-
+        with:
+          persist-credentials: false
       - name: Trigger RTDs build
-        uses: dfm/rtds-action@v1
+        uses: dfm/rtds-action@618148c547f4b56cdf4fa4dcf3a94c91ce025f2d
         with:
           webhook_url: ${{ secrets.RTDS_WEBHOOK_URL }}
           webhook_token: ${{ secrets.RTDS_WEBHOOK_TOKEN }}
-          commit_ref: ${{ github.ref_name }}
+          commit_ref: ${{ github.ref_name }}

.github/workflows/release.yml

@@ -10,6 +10,9 @@ on:
       - "*"
 jobs:
   create-release:
+    permissions:
+      contents: read
+      packages: write
     name: create-release
     runs-on: ubuntu-22.04
     if: startsWith(github.ref, 'refs/tags/')
@@ -27,12 +30,15 @@ jobs:
 
       - name: Create Github Release
         id: create-release
-        uses: softprops/action-gh-release@v1
+        uses: softprops/action-gh-release@c95fe1489396fe8a9eb87c0abf8aa5b2ef267fda  #v2.2.1
         with:
           token: ${{ secrets.RELEASE_TOKEN }}
           tag_name: ${{ env.EZKL_VERSION }}
 
   build-release-gpu:
+    permissions:
+      contents: read
+      packages: write
     name: build-release-gpu
     needs: ["create-release"]
     runs-on: GPU
@@ -43,13 +49,16 @@ jobs:
       RUST_BACKTRACE: 1
       PCRE2_SYS_STATIC: 1
     steps:
-      - uses: actions-rs/toolchain@v1
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
         with:
-          toolchain: nightly-2024-07-18
+          toolchain: nightly-2025-02-17
           override: true
           components: rustfmt, clippy
       - name: Checkout repo
-        uses: actions/checkout@v4
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+
 
       - name: Get release version from tag
         shell: bash
@@ -81,7 +90,7 @@ jobs:
           echo "ASSET=build-artifacts/ezkl-linux-gpu.tar.gz" >> $GITHUB_ENV
 
       - name: Upload release archive
-        uses: actions/upload-release-asset@v1.0.2
+        uses: actions/upload-release-asset@e8f9f06c4b078e705bd2ea027f0926603fc9b4d5  #v1.0.2
         env:
           GITHUB_TOKEN: ${{ secrets.RELEASE_TOKEN }}
         with:
@@ -91,6 +100,10 @@ jobs:
           asset_content_type: application/octet-stream
 
   build-release:
+    permissions:
+      contents: read
+      packages: write
+      issues: write
     name: build-release
     needs: ["create-release"]
     runs-on: ${{ matrix.os }}
@@ -106,32 +119,34 @@ jobs:
         include:
           - build: windows-msvc
             os: windows-latest
-            rust: nightly-2024-07-18
+            rust: nightly-2025-02-17
             target: x86_64-pc-windows-msvc
           - build: macos
             os: macos-13
-            rust: nightly-2024-07-18
+            rust: nightly-2025-02-17
             target: x86_64-apple-darwin
           - build: macos-aarch64
             os: macos-13
-            rust: nightly-2024-07-18
+            rust: nightly-2025-02-17
             target: aarch64-apple-darwin
           - build: linux-musl
             os: ubuntu-22.04
-            rust: nightly-2024-07-18
+            rust: nightly-2025-02-17
             target: x86_64-unknown-linux-musl
           - build: linux-gnu
             os: ubuntu-22.04
-            rust: nightly-2024-07-18
+            rust: nightly-2025-02-17
             target: x86_64-unknown-linux-gnu
           - build: linux-aarch64
             os: ubuntu-22.04
-            rust: nightly-2024-07-18
+            rust: nightly-2025-02-17
             target: aarch64-unknown-linux-gnu
 
     steps:
       - name: Checkout repo
-        uses: actions/checkout@v4
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
 
       - name: Get release version from tag
         shell: bash
@@ -155,7 +170,7 @@ jobs:
           fi
 
       - name: Install Rust
-        uses: dtolnay/rust-toolchain@nightly
+        uses: dtolnay/rust-toolchain@4f94fbe7e03939b0e674bcc9ca609a16088f63ff  #nightly branch, TODO: update when required
         with:
           target: ${{ matrix.target }}
 
@@ -181,14 +196,18 @@ jobs:
           echo "target flag is: ${{ env.TARGET_FLAGS }}"
           echo "target dir is: ${{ env.TARGET_DIR }}"
 
-      - name: Build release binary (no asm)
-        if: matrix.build != 'linux-gnu'
+      - name: Build release binary (no asm or metal)
+        if: matrix.build != 'linux-gnu' && matrix.build != 'macos-aarch64'
         run: ${{ env.CARGO }} build --release ${{ env.TARGET_FLAGS }} -Z sparse-registry
 
       - name: Build release binary (asm)
         if: matrix.build == 'linux-gnu'
         run: ${{ env.CARGO }} build --release ${{ env.TARGET_FLAGS }} -Z sparse-registry --features asm
 
+      - name: Build release binary (metal)
+        if: matrix.build == 'macos-aarch64'
+        run: ${{ env.CARGO }} build --release ${{ env.TARGET_FLAGS }} -Z sparse-registry --features macos-metal
+
       - name: Strip release binary
         if: matrix.build != 'windows-msvc' && matrix.build != 'linux-aarch64'
         run: strip "target/${{ matrix.target }}/release/ezkl"
@@ -214,7 +233,7 @@ jobs:
           echo "ASSET=build-artifacts/ezkl-win.zip" >> $GITHUB_ENV
 
       - name: Upload release archive
-        uses: actions/upload-release-asset@v1.0.2
+        uses: actions/upload-release-asset@e8f9f06c4b078e705bd2ea027f0926603fc9b4d5  #v1.0.2
         env:
           GITHUB_TOKEN: ${{ secrets.RELEASE_TOKEN }}
         with:

.github/workflows/static-analysis.yml

@@ -0,0 +1,32 @@
+name: Static Analysis
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  analyze:
+    permissions:
+      contents: read
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+        with:
+          toolchain: nightly-2025-02-17
+          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

@@ -0,0 +1,134 @@
+name: Build and Publish EZKL iOS SPM package
+
+on:
+  push:
+    tags:
+      # Only support SemVer versioning tags
+      - 'v[0-9]+.[0-9]+.[0-9]+'
+      - '[0-9]+.[0-9]+.[0-9]+'
+
+jobs:
+  build-and-update:
+    permissions:
+      contents: read
+      packages: write
+    runs-on: macos-latest
+    env:
+      EZKL_SWIFT_PACKAGE_REPO: github.com/zkonduit/ezkl-swift-package.git
+      RELEASE_TAG: ${{ github.ref_name }}
+
+    steps:
+      - name: Checkout EZKL
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
+
+      - name: Extract TAG from github.ref_name
+        run: |
+          # github.ref_name is provided by GitHub Actions and contains the tag name directly.
+          TAG="${RELEASE_TAG}"
+          echo "Original TAG: $TAG"
+          # Remove leading 'v' if present to match the Swift Package Manager version format.
+          NEW_TAG=${TAG#v}
+          echo "Stripped TAG: $NEW_TAG"
+          echo "TAG=$NEW_TAG" >> $GITHUB_ENV
+
+      - name: Install Rust (nightly)
+        uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+        with:
+          toolchain: nightly
+          override: true
+
+      - name: Build EzklCoreBindings
+        run: CONFIGURATION=release cargo run --bin ios_gen_bindings --features "ios-bindings uuid camino uniffi_bindgen" --no-default-features
+
+      - name: Clone ezkl-swift-package repository
+        run: |
+          git clone https://${{ env.EZKL_SWIFT_PACKAGE_REPO }}
+
+      - name: Copy EzklCoreBindings
+        run: |
+          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: Check for changes
+        id: check_changes
+        run: |
+          cd ezkl-swift-package
+          if git diff --quiet Sources/EzklCoreBindings Tests/EzklAssets; then
+            echo "no_changes=true" >> $GITHUB_OUTPUT
+          else
+            echo "no_changes=false" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Set up Xcode environment
+        if: steps.check_changes.outputs.no_changes == 'false'
+        run: |
+          sudo xcode-select -s /Applications/Xcode.app/Contents/Developer
+          sudo xcodebuild -license accept
+
+      - name: Run Package Tests
+        if: steps.check_changes.outputs.no_changes == 'false'
+        run: |
+          cd ezkl-swift-package
+          xcodebuild test \
+            -scheme EzklPackage \
+            -destination 'platform=iOS Simulator,name=iPhone 15 Pro,OS=17.5' \
+            -resultBundlePath ../testResults
+
+      - name: Run Example App Tests
+        if: steps.check_changes.outputs.no_changes == 'false'
+        run: |
+          cd ezkl-swift-package/Example
+          xcodebuild test \
+            -project Example.xcodeproj \
+            -scheme EzklApp \
+            -destination 'platform=iOS Simulator,name=iPhone 15 Pro,OS=17.5' \
+            -parallel-testing-enabled NO \
+            -resultBundlePath ../../exampleTestResults \
+            -skip-testing:EzklAppUITests/EzklAppUITests/testButtonClicksInOrder
+
+      - name: Setup Git
+        run: |
+          cd ezkl-swift-package
+          git config user.name "GitHub Action"
+          git config user.email "action@github.com"
+          git remote set-url origin https://zkonduit:${EZKL_SWIFT_PACKAGE_REPO_TOKEN}@${{ env.EZKL_SWIFT_PACKAGE_REPO }}
+        env:
+          EZKL_SWIFT_PACKAGE_REPO_TOKEN: ${{ secrets.EZKL_PORTER_TOKEN }}
+
+      - name: Commit and Push Changes
+        if: steps.check_changes.outputs.no_changes == 'false'
+        run: |
+          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
+          fi
+
+      - name: Tag the latest commit
+        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 $TAG already exists locally. Skipping tag creation."
+          else
+            git tag "$TAG"
+          fi
+
+          if ! git push origin "$TAG"; then
+            echo "::error::Failed to push tag '$TAG' to ${{ env.EZKL_SWIFT_PACKAGE_REPO }}. Please ensure EZKL_PORTER_TOKEN has correct permissions."
+            exit 1
+          fi

.github/workflows/tagging.yml

@@ -11,10 +11,12 @@ jobs:
       contents: write
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+        with:
+          persist-credentials: false
       - name: Bump version and push tag
         id: tag_version
-        uses: mathieudutour/github-tag-action@v6.2
+        uses: mathieudutour/github-tag-action@a22cf08638b34d5badda920f9daf6e72c477b07b  #v6.2
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
 
@@ -44,7 +46,7 @@ jobs:
           git tag $RELEASE_TAG
 
       - name: Push changes
-        uses: ad-m/github-push-action@master
+        uses: ad-m/github-push-action@77c5b412c50b723d2a4fbc6d71fb5723bcd439aa  #master
         env:
           RELEASE_TAG: ${{ steps.tag_version.outputs.new_tag }}
         with:

.github/workflows/update-ios-package.yml

@@ -1,75 +0,0 @@
-name: Build and Publish EZKL iOS SPM package
-
-on:
-  workflow_dispatch:
-    inputs:
-      tag:
-        description: "The tag to release"
-        required: true
-  push:
-    tags:
-      - "*"
-
-jobs:
-  build-and-update:
-    runs-on: macos-latest
-
-    steps:
-      - name: Checkout EZKL
-        uses: actions/checkout@v3
-
-      - name: Install Rust
-        uses: actions-rs/toolchain@v1
-        with:
-          toolchain: nightly
-          override: true
-
-      - name: Build EzklCoreBindings
-        run: CONFIGURATION=release cargo run --bin ios_gen_bindings --features "ios-bindings uuid camino uniffi_bindgen" --no-default-features
-
-      - name: Clone ezkl-swift-package repository
-        run: |
-          git clone https://github.com/zkonduit/ezkl-swift-package.git
-
-      - name: Copy EzklCoreBindings
-        run: |
-          rm -rf ezkl-swift-package/Sources/EzklCoreBindings
-          cp -r build/EzklCoreBindings ezkl-swift-package/Sources/
-
-      - name: Set up Xcode environment
-        run: |
-          sudo xcode-select -s /Applications/Xcode.app/Contents/Developer
-          sudo xcodebuild -license accept
-
-      - name: Run Package Tests
-        run: |
-          cd ezkl-swift-package
-          xcodebuild test \
-            -scheme EzklPackage \
-            -destination 'platform=iOS Simulator,name=iPhone 15 Pro,OS=17.5' \
-            -resultBundlePath ../testResults
-
-      - name: Run Example App Tests
-        run: |
-          cd ezkl-swift-package/Example
-          xcodebuild test \
-            -project Example.xcodeproj \
-            -scheme EzklApp \
-            -destination 'platform=iOS Simulator,name=iPhone 15 Pro,OS=17.5' \
-            -parallel-testing-enabled NO \
-            -resultBundlePath ../../exampleTestResults \
-            -skip-testing:EzklAppUITests/EzklAppUITests/testButtonClicksInOrder
-
-      - name: Commit and Push Changes to feat/ezkl-direct-integration
-        run: |
-          cd ezkl-swift-package
-          git config user.name "GitHub Action"
-          git config user.email "action@github.com"
-          git add Sources/EzklCoreBindings
-          git commit -m "Automatically updated EzklCoreBindings for EZKL"
-          git tag ${{ github.event.inputs.tag }}
-          git remote set-url origin https://zkonduit:${EZKL_PORTER_TOKEN}@github.com/zkonduit/ezkl-swift-package.git
-          git push origin
-          git push origin --tags
-        env:
-          EZKL_PORTER_TOKEN: ${{ secrets.EZKL_PORTER_TOKEN }}

.gitignore

@@ -9,6 +9,7 @@ pkg
 !AttestData.sol
 !VerifierBase.sol
 !LoadInstances.sol
+!AttestData.t.sol
 *.pf
 *.vk
 *.pk
@@ -27,7 +28,6 @@ __pycache__/
 *.pyc
 *.pyo
 *.py[cod]
-bin/
 build/
 develop-eggs/
 dist/
@@ -49,4 +49,6 @@ timingData.json
 !tests/assets/pk.key
 !tests/assets/vk.key
 docs/python/build
-!tests/assets/vk_aggr.key
+!tests/assets/vk_aggr.key
+cache
+out

Cargo.toml

@@ -16,11 +16,13 @@ crate-type = ["cdylib", "rlib", "staticlib"]
 
 
 [dependencies]
-halo2_gadgets = { git = "https://github.com/zkonduit/halo2", branch = "ac/optional-selector-poly" }
+halo2_gadgets = { git = "https://github.com/zkonduit/halo2" }
 halo2curves = { git = "https://github.com/privacy-scaling-explorations/halo2curves", rev = "b753a832e92d5c86c5c997327a9cf9de86a18851", features = [
     "derive_serde",
 ] }
-halo2_proofs = { git = "https://github.com/zkonduit/halo2", package = "halo2_proofs", branch = "ac/cache-lookup-commitments", features = ["circuit-params"] }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2", package = "halo2_proofs", features = [
+    "circuit-params",
+] }
 rand = { version = "0.8", default-features = false }
 itertools = { version = "0.10.3", default-features = false }
 clap = { version = "4.5.3", features = ["derive"], optional = true }
@@ -33,20 +35,16 @@ halo2_wrong_ecc = { git = "https://github.com/zkonduit/halo2wrong", branch = "ac
 snark-verifier = { git = "https://github.com/zkonduit/snark-verifier", branch = "ac/chunked-mv-lookup", features = [
     "derive_serde",
 ] }
-halo2_solidity_verifier = { git = "https://github.com/alexander-camuto/halo2-solidity-verifier", branch = "ac/update-h2-curves", optional = true }
+halo2_solidity_verifier = { git = "https://github.com/zkonduit/verification-ezkl", branch = "vka-hash", optional = true }
 maybe-rayon = { version = "0.1.1", default-features = false }
 bincode = { version = "1.3.3", default-features = false }
 unzip-n = "0.1.2"
 num = "0.4.1"
-portable-atomic = { version = "1.6.0", optional = true }
 tosubcommand = { git = "https://github.com/zkonduit/enum_to_subcommand", package = "tosubcommand", optional = true }
 semver = { version = "1.0.22", optional = true }
 
 [target.'cfg(not(target_arch = "wasm32"))'.dependencies]
-serde_json = { version = "1.0.97", features = [
-    "float_roundtrip",
-    "raw_value",
-] }
+serde_json = { version = "1.0.97", features = ["float_roundtrip", "raw_value"] }
 
 # evm related deps
 alloy = { git = "https://github.com/alloy-rs/alloy", version = "0.1.0", rev = "5fbf57bac99edef9d8475190109a7ea9fb7e5e83", features = [
@@ -56,29 +54,42 @@ alloy = { git = "https://github.com/alloy-rs/alloy", version = "0.1.0", rev = "5
     "rpc-types-eth",
     "signer-wallet",
     "node-bindings",
-    
+
+], optional = true }
+foundry-compilers = { version = "0.4.1", features = [
+    "svm-solc",
 ], optional = true }
-foundry-compilers = { version = "0.4.1", features = ["svm-solc"], optional = true }
 ethabi = { version = "18", optional = true }
 indicatif = { version = "0.17.5", features = ["rayon"], optional = true }
 gag = { version = "1.0.0", default-features = false, optional = true }
 instant = { version = "0.1" }
-reqwest = { version = "0.12.4", default-features = false, features = ["default-tls", "multipart", "stream"], optional = true }
+reqwest = { version = "0.12.4", default-features = false, features = [
+    "default-tls",
+    "multipart",
+    "stream",
+], optional = true }
 openssl = { version = "0.10.55", features = ["vendored"], optional = true }
-tokio-postgres = { version = "0.7.10", optional = true }
-pg_bigdecimal = { version = "0.1.5", optional = true }
 lazy_static = { version = "1.4.0", optional = true }
 colored_json = { version = "3.0.1", default-features = false, optional = true }
-regex = { version = "1", default-features = false, optional = true }
-tokio = { version = "1.35.0", default-features = false, features = ["macros", "rt-multi-thread"], optional = true }
-pyo3 = { version = "0.21.2", features = ["extension-module", "abi3-py37", "macros"], default-features = false, optional = true }
-pyo3-asyncio = { git = "https://github.com/jopemachine/pyo3-asyncio/", branch="migration-pyo3-0.21", features = ["attributes", "tokio-runtime"], default-features = false, optional = true }
-pyo3-log = { version = "0.10.0", default-features = false, optional = true }
-tract-onnx = { git = "https://github.com/sonos/tract/", rev = "40c64319291184814d9fea5fdf4fa16f5a4f7116", default-features = false, optional = true }
+tokio = { version = "1.35.0", default-features = false, features = [
+    "macros",
+    "rt-multi-thread",
+], optional = true }
+pyo3 = { version = "0.24.2", features = [
+    "extension-module",
+    "abi3-py37",
+    "macros",
+], default-features = false, optional = true }
+pyo3-async-runtimes = { git = "https://github.com/PyO3/pyo3-async-runtimes", version = "0.24.0", features = [
+    "attributes",
+    "tokio-runtime",
+], default-features = false, optional = true }
+pyo3-log = { version = "0.12.0", default-features = false, optional = true }
+tract-onnx = { git = "https://github.com/sonos/tract/", rev = "37132e0397d0a73e5bd3a8615d932dabe44f6736", default-features = false, optional = true }
 tabled = { version = "0.12.0", optional = true }
-metal = { git = "https://github.com/gfx-rs/metal-rs", optional = true }
 objc = { version = "0.2.4", optional = true }
 mimalloc = { version = "0.1", optional = true }
+pyo3-stub-gen = { version = "0.6.0", optional = true }
 
 # universal bindings
 uniffi = { version = "=0.28.0", optional = true }
@@ -131,6 +142,10 @@ shellexpand = "3.1.0"
 runner = 'wasm-bindgen-test-runner'
 
 
+[[bench]]
+name = "zero_finder"
+harness = false
+
 [[bench]]
 name = "accum_dot"
 harness = false
@@ -195,12 +210,22 @@ required-features = ["ezkl"]
 name = "ios_gen_bindings"
 required-features = ["ios-bindings", "uuid", "camino", "uniffi_bindgen"]
 
+[[bin]]
+name = "py_stub_gen"
+required-features = ["python-bindings"]
+
 [features]
 web = ["wasm-bindgen-rayon"]
-default = ["ezkl", "mv-lookup", "precompute-coset", "no-banner", "parallel-poly-read"]
+default = [
+    "eth-mv-lookup",
+    "ezkl",
+    "precompute-coset",
+    "no-banner",
+    "parallel-poly-read",
+]
 onnx = ["dep:tract-onnx"]
-python-bindings = ["pyo3", "pyo3-log", "pyo3-asyncio"]
-ios-bindings = ["mv-lookup", "precompute-coset", "parallel-poly-read", "uniffi"]
+python-bindings = ["pyo3", "pyo3-log", "pyo3-async-runtimes", "pyo3-stub-gen"]
+ios-bindings = ["eth-mv-lookup", "precompute-coset", "parallel-poly-read", "uniffi"]
 ios-bindings-test = ["ios-bindings", "uniffi/bindgen-tests"]
 ezkl = [
     "onnx",
@@ -209,33 +234,48 @@ ezkl = [
     "tabled/color",
     "serde_json/std",
     "colored_json",
-    "dep:alloy",
-    "dep:foundry-compilers",
     "dep:ethabi",
     "dep:indicatif",
     "dep:gag",
     "dep:reqwest",
-    "dep:openssl",
-    "dep:tokio-postgres",
-    "dep:pg_bigdecimal",
     "dep:lazy_static",
-    "dep:regex",
     "dep:tokio",
+    "dep:openssl",
     "dep:mimalloc",
     "dep:chrono",
     "dep:sha256",
-    "dep:portable-atomic",
     "dep:clap_complete",
-    "dep:halo2_solidity_verifier",
     "dep:semver",
     "dep:clap",
     "dep:tosubcommand",
 ]
-parallel-poly-read = ["halo2_proofs/circuit-params", "halo2_proofs/parallel-poly-read"]
+eth = [
+    "dep:alloy",
+    "dep:foundry-compilers",
+    "dep:ethabi",
+]
+solidity-verifier = [
+    "dep:halo2_solidity_verifier",
+]
+solidity-verifier-mv-lookup = [
+    "halo2_solidity_verifier/mv-lookup",
+]
+eth-mv-lookup = [
+    "solidity-verifier-mv-lookup",
+    "mv-lookup",
+    "eth",
+]
+eth-original-lookup = [
+    "eth",
+    "solidity-verifier",
+]
+parallel-poly-read = [
+    "halo2_proofs/circuit-params",
+    "halo2_proofs/parallel-poly-read",
+]
 mv-lookup = [
     "halo2_proofs/mv-lookup",
     "snark-verifier/mv-lookup",
-    "halo2_solidity_verifier/mv-lookup",
 ]
 asm = ["halo2curves/asm", "halo2_proofs/asm"]
 precompute-coset = ["halo2_proofs/precompute-coset"]
@@ -244,13 +284,9 @@ icicle = ["halo2_proofs/icicle_gpu"]
 empty-cmd = []
 no-banner = []
 no-update = []
+macos-metal = ["halo2_proofs/macos"]
+ios-metal = ["halo2_proofs/ios"]
 
-# icicle patch to 0.1.0 if feature icicle is enabled
-[patch.'https://github.com/ingonyama-zk/icicle']
-icicle = { git = "https://github.com/ingonyama-zk/icicle?rev=45b00fb", package = "icicle", branch = "fix/vhnat/ezkl-build-fix" }
-
-[patch.'https://github.com/zkonduit/halo2']
-halo2_proofs = { git = "https://github.com/zkonduit/halo2?branch=ac/cache-lookup-commitments#8b13a0d2a7a34d8daab010dadb2c47dfa47d37d0", package = "halo2_proofs", branch = "ac/cache-lookup-commitments" }
 
 [patch.crates-io]
 uniffi_testing = { git = "https://github.com/ElusAegis/uniffi-rs", branch = "feat/testing-feature-build-fix" }
@@ -261,3 +297,10 @@ lto = "fat"
 codegen-units = 1
 # panic = "abort"
 
+
+[profile.test-runs]
+inherits = "dev"
+opt-level = 3
+
+[package.metadata.wasm-pack.profile.release]
+wasm-opt = ["-O4", "--flexible-inline-max-function-size", "4294967295"]

README.md

@@ -43,7 +43,7 @@ The generated proofs can then be verified with much less computational resources
 
 ----------------------
 
-### getting started ⚙️
+### Getting Started ⚙️
 
 The easiest way to get started is to try out a notebook. 
 
@@ -76,12 +76,12 @@ For more details visit the [docs](https://docs.ezkl.xyz). The CLI is faster than
 
 Build the auto-generated rust documentation and open the docs in your browser locally. `cargo doc --open`
 
-#### In-browser EVM verifier
+#### In-browser EVM Verifier
 
 As an alternative to running the native Halo2 verifier as a WASM binding in the browser, you can use the in-browser EVM verifier. The source code of which you can find in the `in-browser-evm-verifier` directory and a README with instructions on how to use it.
 
 
-### building the project 🔨
+### Building the Project 🔨
 
 #### Rust CLI
 
@@ -96,7 +96,7 @@ cargo install --locked --path .
 
 
 
-#### building python bindings
+#### Building Python Bindings
 Python bindings exists and can be built using `maturin`. You will need `rust` and `cargo` to be installed.
 
 ```bash
@@ -126,7 +126,7 @@ unset ENABLE_ICICLE_GPU
 
 **NOTE:** Even with the above environment variable set, icicle is disabled for circuits where k <= 8. To change the value of `k` where icicle is enabled, you can set the environment variable `ICICLE_SMALL_K`.
 
-### contributing 🌎
+### Contributing 🌎
 
 If you're interested in contributing and are unsure where to start, reach out to one of the maintainers:
 
@@ -144,13 +144,21 @@ More broadly:
 
 Any contribution intentionally submitted for inclusion in the work by you shall be licensed to Zkonduit Inc. under the terms and conditions specified in the [CLA](https://github.com/zkonduit/ezkl/blob/main/cla.md), which you agree to by intentionally submitting a contribution. In particular, you have the right to submit the contribution and we can distribute it, among other terms and conditions. 
 
-### no security guarantees
 
-Ezkl is unaudited, beta software undergoing rapid development. There may be bugs. No guarantees of security are made and it should not be relied on in production.
+### Audits & Security
 
-> NOTE: Because operations are quantized when they are converted from an onnx file to a zk-circuit, outputs in python and ezkl may differ slightly. 
+[v21.0.0](https://github.com/zkonduit/ezkl/releases/tag/v21.0.0) has been audited by Trail of Bits, the report can be found [here](https://github.com/trailofbits/publications/blob/master/reviews/2025-03-zkonduit-ezkl-securityreview.pdf).
 
-### no warranty
+> NOTE: Because operations are quantized when they are converted from an onnx file to a zk-circuit, outputs in python and ezkl may differ slightly.
 
-Copyright (c) 2024 Zkonduit Inc. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+
+Check out `docs/advanced_security` for more advanced information on potential threat vectors that are specific to zero-knowledge inference, quantization, and to machine learning models generally.
+
+
+### No Warranty
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+Copyright (c) 2025 Zkonduit Inc. 
+

abis/DataAttestation.json

@@ -1,167 +1,312 @@
 [
-	{
-		"inputs": [
-			{
-				"internalType": "address[]",
-				"name": "_contractAddresses",
-				"type": "address[]"
-			},
-			{
-				"internalType": "bytes[][]",
-				"name": "_callData",
-				"type": "bytes[][]"
-			},
-			{
-				"internalType": "uint256[][]",
-				"name": "_decimals",
-				"type": "uint256[][]"
-			},
-			{
-				"internalType": "uint256[]",
-				"name": "_scales",
-				"type": "uint256[]"
-			},
-			{
-				"internalType": "uint8",
-				"name": "_instanceOffset",
-				"type": "uint8"
-			},
-			{
-				"internalType": "address",
-				"name": "_admin",
-				"type": "address"
-			}
-		],
-		"stateMutability": "nonpayable",
-		"type": "constructor"
-	},
-	{
-		"inputs": [
-			{
-				"internalType": "uint256",
-				"name": "",
-				"type": "uint256"
-			}
-		],
-		"name": "accountCalls",
-		"outputs": [
-			{
-				"internalType": "address",
-				"name": "contractAddress",
-				"type": "address"
-			},
-			{
-				"internalType": "uint256",
-				"name": "callCount",
-				"type": "uint256"
-			}
-		],
-		"stateMutability": "view",
-		"type": "function"
-	},
-	{
-		"inputs": [],
-		"name": "admin",
-		"outputs": [
-			{
-				"internalType": "address",
-				"name": "",
-				"type": "address"
-			}
-		],
-		"stateMutability": "view",
-		"type": "function"
-	},
-	{
-		"inputs": [],
-		"name": "instanceOffset",
-		"outputs": [
-			{
-				"internalType": "uint8",
-				"name": "",
-				"type": "uint8"
-			}
-		],
-		"stateMutability": "view",
-		"type": "function"
-	},
-	{
-		"inputs": [
-			{
-				"internalType": "uint256",
-				"name": "",
-				"type": "uint256"
-			}
-		],
-		"name": "scales",
-		"outputs": [
-			{
-				"internalType": "uint256",
-				"name": "",
-				"type": "uint256"
-			}
-		],
-		"stateMutability": "view",
-		"type": "function"
-	},
-	{
-		"inputs": [
-			{
-				"internalType": "address[]",
-				"name": "_contractAddresses",
-				"type": "address[]"
-			},
-			{
-				"internalType": "bytes[][]",
-				"name": "_callData",
-				"type": "bytes[][]"
-			},
-			{
-				"internalType": "uint256[][]",
-				"name": "_decimals",
-				"type": "uint256[][]"
-			}
-		],
-		"name": "updateAccountCalls",
-		"outputs": [],
-		"stateMutability": "nonpayable",
-		"type": "function"
-	},
-	{
-		"inputs": [
-			{
-				"internalType": "address",
-				"name": "_admin",
-				"type": "address"
-			}
-		],
-		"name": "updateAdmin",
-		"outputs": [],
-		"stateMutability": "nonpayable",
-		"type": "function"
-	},
-	{
-		"inputs": [
-			{
-				"internalType": "address",
-				"name": "verifier",
-				"type": "address"
-			},
-			{
-				"internalType": "bytes",
-				"name": "encoded",
-				"type": "bytes"
-			}
-		],
-		"name": "verifyWithDataAttestation",
-		"outputs": [
-			{
-				"internalType": "bool",
-				"name": "",
-				"type": "bool"
-			}
-		],
-		"stateMutability": "view",
-		"type": "function"
-	}
+    {
+        "inputs": [
+            {
+                "internalType": "address",
+                "name": "_contractAddresses",
+                "type": "address"
+            },
+            {
+                "internalType": "bytes",
+                "name": "_callData",
+                "type": "bytes"
+            },
+            {
+                "internalType": "uint256[]",
+                "name": "_decimals",
+                "type": "uint256[]"
+            },
+            {
+                "internalType": "uint256[]",
+                "name": "_bits",
+                "type": "uint256[]"
+            },
+            {
+                "internalType": "uint8",
+                "name": "_instanceOffset",
+                "type": "uint8"
+            }
+        ],
+        "stateMutability": "nonpayable",
+        "type": "constructor"
+    },
+    {
+        "inputs": [],
+        "name": "HALF_ORDER",
+        "outputs": [
+            {
+                "internalType": "uint256",
+                "name": "",
+                "type": "uint256"
+            }
+        ],
+        "stateMutability": "view",
+        "type": "function"
+    },
+    {
+        "inputs": [],
+        "name": "ORDER",
+        "outputs": [
+            {
+                "internalType": "uint256",
+                "name": "",
+                "type": "uint256"
+            }
+        ],
+        "stateMutability": "view",
+        "type": "function"
+    },
+    {
+        "inputs": [
+            {
+                "internalType": "uint256[]",
+                "name": "instances",
+                "type": "uint256[]"
+            }
+        ],
+        "name": "attestData",
+        "outputs": [],
+        "stateMutability": "view",
+        "type": "function"
+    },
+    {
+        "inputs": [],
+        "name": "callData",
+        "outputs": [
+            {
+                "internalType": "bytes",
+                "name": "",
+                "type": "bytes"
+            }
+        ],
+        "stateMutability": "view",
+        "type": "function"
+    },
+    {
+        "inputs": [],
+        "name": "contractAddress",
+        "outputs": [
+            {
+                "internalType": "address",
+                "name": "",
+                "type": "address"
+            }
+        ],
+        "stateMutability": "view",
+        "type": "function"
+    },
+    {
+        "inputs": [
+            {
+                "internalType": "bytes",
+                "name": "encoded",
+                "type": "bytes"
+            }
+        ],
+        "name": "getInstancesCalldata",
+        "outputs": [
+            {
+                "internalType": "uint256[]",
+                "name": "instances",
+                "type": "uint256[]"
+            }
+        ],
+        "stateMutability": "pure",
+        "type": "function"
+    },
+    {
+        "inputs": [
+            {
+                "internalType": "bytes",
+                "name": "encoded",
+                "type": "bytes"
+            }
+        ],
+        "name": "getInstancesMemory",
+        "outputs": [
+            {
+                "internalType": "uint256[]",
+                "name": "instances",
+                "type": "uint256[]"
+            }
+        ],
+        "stateMutability": "pure",
+        "type": "function"
+    },
+    {
+        "inputs": [
+            {
+                "internalType": "uint256",
+                "name": "index",
+                "type": "uint256"
+            }
+        ],
+        "name": "getScalars",
+        "outputs": [
+            {
+                "components": [
+                    {
+                        "internalType": "uint256",
+                        "name": "decimals",
+                        "type": "uint256"
+                    },
+                    {
+                        "internalType": "uint256",
+                        "name": "bits",
+                        "type": "uint256"
+                    }
+                ],
+                "internalType": "struct DataAttestation.Scalars",
+                "name": "",
+                "type": "tuple"
+            }
+        ],
+        "stateMutability": "view",
+        "type": "function"
+    },
+    {
+        "inputs": [],
+        "name": "instanceOffset",
+        "outputs": [
+            {
+                "internalType": "uint8",
+                "name": "",
+                "type": "uint8"
+            }
+        ],
+        "stateMutability": "view",
+        "type": "function"
+    },
+    {
+        "inputs": [
+            {
+                "internalType": "uint256",
+                "name": "x",
+                "type": "uint256"
+            },
+            {
+                "internalType": "uint256",
+                "name": "y",
+                "type": "uint256"
+            },
+            {
+                "internalType": "uint256",
+                "name": "denominator",
+                "type": "uint256"
+            }
+        ],
+        "name": "mulDiv",
+        "outputs": [
+            {
+                "internalType": "uint256",
+                "name": "result",
+                "type": "uint256"
+            }
+        ],
+        "stateMutability": "pure",
+        "type": "function"
+    },
+    {
+        "inputs": [
+            {
+                "internalType": "int256",
+                "name": "x",
+                "type": "int256"
+            },
+            {
+                "components": [
+                    {
+                        "internalType": "uint256",
+                        "name": "decimals",
+                        "type": "uint256"
+                    },
+                    {
+                        "internalType": "uint256",
+                        "name": "bits",
+                        "type": "uint256"
+                    }
+                ],
+                "internalType": "struct DataAttestation.Scalars",
+                "name": "_scalars",
+                "type": "tuple"
+            }
+        ],
+        "name": "quantizeData",
+        "outputs": [
+            {
+                "internalType": "int256",
+                "name": "quantized_data",
+                "type": "int256"
+            }
+        ],
+        "stateMutability": "pure",
+        "type": "function"
+    },
+    {
+        "inputs": [
+            {
+                "internalType": "address",
+                "name": "target",
+                "type": "address"
+            },
+            {
+                "internalType": "bytes",
+                "name": "data",
+                "type": "bytes"
+            }
+        ],
+        "name": "staticCall",
+        "outputs": [
+            {
+                "internalType": "bytes",
+                "name": "",
+                "type": "bytes"
+            }
+        ],
+        "stateMutability": "view",
+        "type": "function"
+    },
+    {
+        "inputs": [
+            {
+                "internalType": "int256",
+                "name": "x",
+                "type": "int256"
+            }
+        ],
+        "name": "toFieldElement",
+        "outputs": [
+            {
+                "internalType": "uint256",
+                "name": "field_element",
+                "type": "uint256"
+            }
+        ],
+        "stateMutability": "pure",
+        "type": "function"
+    },
+    {
+        "inputs": [
+            {
+                "internalType": "address",
+                "name": "verifier",
+                "type": "address"
+            },
+            {
+                "internalType": "bytes",
+                "name": "encoded",
+                "type": "bytes"
+            }
+        ],
+        "name": "verifyWithDataAttestation",
+        "outputs": [
+            {
+                "internalType": "bool",
+                "name": "",
+                "type": "bool"
+            }
+        ],
+        "stateMutability": "view",
+        "type": "function"
+    }
 ]

abis/QuantizeData.json

@@ -1,4 +1,23 @@
 [
+	{
+		"inputs": [
+			{
+				"internalType": "int256[]",
+				"name": "quantized_data",
+				"type": "int256[]"
+			}
+		],
+		"name": "check_is_valid_field_element",
+		"outputs": [
+			{
+				"internalType": "uint256[]",
+				"name": "output",
+				"type": "uint256[]"
+			}
+		],
+		"stateMutability": "pure",
+		"type": "function"
+	},
 	{
 		"inputs": [
 			{
@@ -17,12 +36,41 @@
 				"type": "uint256[]"
 			}
 		],
-		"name": "quantize_data",
+		"name": "quantize_data_multi",
 		"outputs": [
 			{
-				"internalType": "int64[]",
+				"internalType": "int256[]",
 				"name": "quantized_data",
-				"type": "int64[]"
+				"type": "int256[]"
+			}
+		],
+		"stateMutability": "pure",
+		"type": "function"
+	},
+	{
+		"inputs": [
+			{
+				"internalType": "bytes",
+				"name": "data",
+				"type": "bytes"
+			},
+			{
+				"internalType": "uint256",
+				"name": "decimals",
+				"type": "uint256"
+			},
+			{
+				"internalType": "uint256[]",
+				"name": "scales",
+				"type": "uint256[]"
+			}
+		],
+		"name": "quantize_data_single",
+		"outputs": [
+			{
+				"internalType": "int256[]",
+				"name": "quantized_data",
+				"type": "int256[]"
 			}
 		],
 		"stateMutability": "pure",

benches/accum_conv.rs

@@ -73,6 +73,8 @@ impl Circuit<Fr> for MyCircuit {
                             padding: vec![(0, 0)],
                             stride: vec![1; 2],
                             group: 1,
+                            data_format: DataFormat::NCHW,
+                            kernel_format: KernelFormat::OIHW,
                         }),
                     )
                     .unwrap();

benches/accum_sumpool.rs

@@ -69,6 +69,7 @@ impl Circuit<Fr> for MyCircuit {
                             stride: vec![1, 1],
                             kernel_shape: vec![2, 2],
                             normalized: false,
+                            data_format: DataFormat::NCHW,
                         }),
                     )
                     .unwrap();

benches/poseidon.rs

@@ -23,8 +23,6 @@ use halo2curves::bn256::{Bn256, Fr};
 use rand::rngs::OsRng;
 use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 
-const L: usize = 10;
-
 #[derive(Clone, Debug)]
 struct MyCircuit {
     image: ValTensor<Fr>,
@@ -40,7 +38,7 @@ impl Circuit<Fr> for MyCircuit {
     }
 
     fn configure(cs: &mut ConstraintSystem<Fr>) -> Self::Config {
-        PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, 10>::configure(cs, ())
+        PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::configure(cs, ())
     }
 
     fn synthesize(
@@ -48,7 +46,7 @@ impl Circuit<Fr> for MyCircuit {
         config: Self::Config,
         mut layouter: impl Layouter<Fr>,
     ) -> Result<(), Error> {
-        let chip: PoseidonChip<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, L> =
+        let chip: PoseidonChip<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE> =
             PoseidonChip::new(config);
         chip.layout(&mut layouter, &[self.image.clone()], 0, &mut HashMap::new())?;
         Ok(())
@@ -59,7 +57,7 @@ fn runposeidon(c: &mut Criterion) {
     let mut group = c.benchmark_group("poseidon");
 
     for size in [64, 784, 2352, 12288].iter() {
-        let k = (PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, L>::num_rows(*size)
+        let k = (PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::num_rows(*size)
             as f32)
             .log2()
             .ceil() as u32;
@@ -67,7 +65,7 @@ fn runposeidon(c: &mut Criterion) {
 
         let message = (0..*size).map(|_| Fr::random(OsRng)).collect::<Vec<_>>();
         let _output =
-            PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, L>::run(message.to_vec())
+            PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::run(message.to_vec())
                 .unwrap();
 
         let mut image = Tensor::from(message.into_iter().map(Value::known));

benches/zero_finder.rs

@@ -0,0 +1,117 @@
+use std::thread;
+
+use criterion::{black_box, criterion_group, criterion_main, Criterion};
+use halo2curves::{bn256::Fr as F, ff::Field};
+use maybe_rayon::{
+    iter::{IndexedParallelIterator, IntoParallelRefIterator, ParallelIterator},
+    slice::ParallelSlice,
+};
+use rand::Rng;
+
+// Assuming these are your types
+#[derive(Clone)]
+#[allow(dead_code)]
+enum ValType {
+    Constant(F),
+    AssignedConstant(usize, F),
+    Other,
+}
+
+// Helper to generate test data
+fn generate_test_data(size: usize, zero_probability: f64) -> Vec<ValType> {
+    let mut rng = rand::thread_rng();
+    (0..size)
+        .map(|_i| {
+            if rng.r#gen::<f64>() < zero_probability {
+                ValType::Constant(F::ZERO)
+            } else {
+                ValType::Constant(F::ONE) // Or some other non-zero value
+            }
+        })
+        .collect()
+}
+
+fn bench_zero_finding(c: &mut Criterion) {
+    let sizes = [
+        1_000,         // 1K
+        10_000,        // 10K
+        100_000,       // 100K
+        256 * 256 * 2, // Our specific case
+        1_000_000,     // 1M
+        10_000_000,    // 10M
+    ];
+
+    let zero_probability = 0.1; // 10% zeros
+
+    let mut group = c.benchmark_group("zero_finding");
+    group.sample_size(10); // Adjust based on your needs
+
+    for &size in &sizes {
+        let data = generate_test_data(size, zero_probability);
+
+        // Benchmark sequential version
+        group.bench_function(format!("sequential_{}", size), |b| {
+            b.iter(|| {
+                let result = data
+                    .iter()
+                    .enumerate()
+                    .filter_map(|(i, e)| match e {
+                        ValType::Constant(r) | ValType::AssignedConstant(_, r) => {
+                            (*r == F::ZERO).then_some(i)
+                        }
+                        _ => None,
+                    })
+                    .collect::<Vec<_>>();
+                black_box(result)
+            })
+        });
+
+        // Benchmark parallel version
+        group.bench_function(format!("parallel_{}", size), |b| {
+            b.iter(|| {
+                let result = data
+                    .par_iter()
+                    .enumerate()
+                    .filter_map(|(i, e)| match e {
+                        ValType::Constant(r) | ValType::AssignedConstant(_, r) => {
+                            (*r == F::ZERO).then_some(i)
+                        }
+                        _ => None,
+                    })
+                    .collect::<Vec<_>>();
+                black_box(result)
+            })
+        });
+
+        // Benchmark chunked parallel version
+        group.bench_function(format!("chunked_parallel_{}", size), |b| {
+            b.iter(|| {
+                let num_cores = thread::available_parallelism()
+                    .map(|n| n.get())
+                    .unwrap_or(1);
+                let chunk_size = (size / num_cores).max(100);
+
+                let result = data
+                    .par_chunks(chunk_size)
+                    .enumerate()
+                    .flat_map(|(chunk_idx, chunk)| {
+                        chunk
+                            .par_iter() // Make sure we use par_iter() here
+                            .enumerate()
+                            .filter_map(move |(i, e)| match e {
+                                ValType::Constant(r) | ValType::AssignedConstant(_, r) => {
+                                    (*r == F::ZERO).then_some(chunk_idx * chunk_size + i)
+                                }
+                                _ => None,
+                            })
+                    })
+                    .collect::<Vec<_>>();
+                black_box(result)
+            })
+        });
+    }
+    group.finish();
+}
+
+criterion_group!(benches, bench_zero_finding);
+criterion_main!(benches);

contracts/AttestData.sol

@@ -8,21 +8,27 @@ contract LoadInstances {
      */
     function getInstancesMemory(
         bytes memory encoded
-    ) internal pure returns (uint256[] memory instances) {
+    ) public pure returns (uint256[] memory instances) {
         bytes4 funcSig;
         uint256 instances_offset;
         uint256 instances_length;
         assembly {
             // fetch function sig. Either `verifyProof(bytes,uint256[])` or `verifyProof(address,bytes,uint256[])`
             funcSig := mload(add(encoded, 0x20))
-
+        }
+        if (funcSig == 0xaf83a18d) {
+            instances_offset = 0x64;
+        } else if (funcSig == 0x1e8e1e13) {
+            instances_offset = 0x44;
+        } else {
+            revert("Invalid function signature");
+        }
+        assembly {
             // Fetch instances offset which is 4 + 32 + 32 bytes away from
             // start of encoded for `verifyProof(bytes,uint256[])`,
             // and 4 + 32 + 32 +32 away for `verifyProof(address,bytes,uint256[])`
 
-            instances_offset := mload(
-                add(encoded, add(0x44, mul(0x20, eq(funcSig, 0xaf83a18d))))
-            )
+            instances_offset := mload(add(encoded, instances_offset))
 
             instances_length := mload(add(add(encoded, 0x24), instances_offset))
         }
@@ -41,6 +47,10 @@ contract LoadInstances {
                 )
             }
         }
+        require(
+            funcSig == 0xaf83a18d || funcSig == 0x1e8e1e13,
+            "Invalid function signature"
+        );
     }
     /**
      * @dev Parse the instances array from the Halo2Verifier encoded calldata.
@@ -49,23 +59,31 @@ contract LoadInstances {
      */
     function getInstancesCalldata(
         bytes calldata encoded
-    ) internal pure returns (uint256[] memory instances) {
+    ) public pure returns (uint256[] memory instances) {
         bytes4 funcSig;
         uint256 instances_offset;
         uint256 instances_length;
         assembly {
             // fetch function sig. Either `verifyProof(bytes,uint256[])` or `verifyProof(address,bytes,uint256[])`
             funcSig := calldataload(encoded.offset)
-
+        }
+        if (funcSig == 0xaf83a18d) {
+            instances_offset = 0x44;
+        } else if (funcSig == 0x1e8e1e13) {
+            instances_offset = 0x24;
+        } else {
+            revert("Invalid function signature");
+        }
+        // We need to create a new assembly block in order for solidity
+        // to cast the funcSig to a bytes4 type. Otherwise it will load the entire first 32 bytes of the calldata
+        // within the block
+        assembly {
             // Fetch instances offset which is 4 + 32 + 32 bytes away from
             // start of encoded for `verifyProof(bytes,uint256[])`,
             // and 4 + 32 + 32 +32 away for `verifyProof(address,bytes,uint256[])`
 
             instances_offset := calldataload(
-                add(
-                    encoded.offset,
-                    add(0x24, mul(0x20, eq(funcSig, 0xaf83a18d)))
-                )
+                add(encoded.offset, instances_offset)
             )
 
             instances_length := calldataload(
@@ -96,7 +114,7 @@ contract LoadInstances {
 // The kzg commitments of a given model, all aggregated into a single bytes array.
 // At solidity generation time, the commitments are hardcoded into the contract via the COMMITMENT_KZG constant.
 // It will be used to check that the proof commitments match the expected commitments.
-bytes constant COMMITMENT_KZG = hex"";
+bytes constant COMMITMENT_KZG = hex"1234";
 
 contract SwapProofCommitments {
     /**
@@ -113,17 +131,20 @@ contract SwapProofCommitments {
         assembly {
             // fetch function sig. Either `verifyProof(bytes,uint256[])` or `verifyProof(address,bytes,uint256[])`
             funcSig := calldataload(encoded.offset)
-
+        }
+        if (funcSig == 0xaf83a18d) {
+            proof_offset = 0x24;
+        } else if (funcSig == 0x1e8e1e13) {
+            proof_offset = 0x04;
+        } else {
+            revert("Invalid function signature");
+        }
+        assembly {
             // Fetch proof offset which is 4 + 32 bytes away from
             // start of encoded for `verifyProof(bytes,uint256[])`,
             // and 4 + 32 + 32 away for `verifyProof(address,bytes,uint256[])`
 
-            proof_offset := calldataload(
-                add(
-                    encoded.offset,
-                    add(0x04, mul(0x20, eq(funcSig, 0xaf83a18d)))
-                )
-            )
+            proof_offset := calldataload(add(encoded.offset, proof_offset))
 
             proof_length := calldataload(
                 add(add(encoded.offset, 0x04), proof_offset)
@@ -154,7 +175,7 @@ contract SwapProofCommitments {
                 let wordCommitment := mload(add(commitment, i))
                 equal := eq(wordProof, wordCommitment)
                 if eq(equal, 0) {
-                    return(0, 0)
+                    break
                 }
             }
         }
@@ -163,51 +184,38 @@ contract SwapProofCommitments {
     } /// end checkKzgCommits
 }
 
-// This contract serves as a Data Attestation Verifier for the EZKL model.
-// It is designed to read and attest to instances of proofs generated from a specified circuit.
-// It is particularly constructed to read only int256 data from specified on-chain contracts' view functions.
-
-// Overview of the contract functionality:
-// 1. Initialization: Through the constructor, it sets up the contract calls that the EZKL model will read from.
-// 2. Data Quantization: Quantizes the returned data into a scaled fixed-point representation. See the `quantizeData` method for details.
-// 3. Static Calls: Makes static calls to fetch data from other contracts. See the `staticCall` method.
-// 4. Field Element Conversion: The fixed-point representation is then converted into a field element modulo P using the `toFieldElement` method.
-// 5. Data Attestation: The `attestData` method validates that the public instances match the data fetched and processed by the contract.
-// 6. Proof Verification: The `verifyWithDataAttestation` method parses the instances out of the encoded calldata and calls the `attestData` method to validate the public instances,
-// 6b. Optional KZG Commitment Verification: It also checks the KZG commitments in the proof against the expected commitments using the `checkKzgCommits` method.
-//  then calls the `verifyProof` method to verify the proof on the verifier.
-
 contract DataAttestation is LoadInstances, SwapProofCommitments {
-    /**
-     * @notice Struct used to make view only calls to accounts to fetch the data that EZKL reads from.
-     * @param the address of the account to make calls to
-     * @param the abi encoded function calls to make to the `contractAddress`
-     */
-    struct AccountCall {
-        address contractAddress;
-        mapping(uint256 => bytes) callData;
-        mapping(uint256 => uint256) decimals;
-        uint callCount;
+    // the address of the account to make calls to
+    address public immutable contractAddress;
+
+    // the abi encoded function calls to make to the `contractAddress` that returns the attested to data
+    bytes public callData;
+
+    struct Scalars {
+        // The number of base 10 decimals to scale the data by.
+        // For most ERC20 tokens this is 1e18
+        uint256 decimals;
+        // The number of fractional bits of the fixed point EZKL data points.
+        uint256 bits;
     }
-    AccountCall[] public accountCalls;
 
-    uint[] public scales;
+    Scalars[] private scalars;
 
-    address public admin;
+    function getScalars(uint256 index) public view returns (Scalars memory) {
+        return scalars[index];
+    }
 
     /**
      * @notice EZKL P value
      * @dev In order to prevent the verifier from accepting two version of the same pubInput, n and the quantity (n + P),  where n + P <= 2^256, we require that all instances are stricly less than P. a
      * @dev The reason for this is that the assmebly code of the verifier performs all arithmetic operations modulo P and as a consequence can't distinguish between n and n + P.
      */
-    uint256 constant ORDER =
+    uint256 public constant ORDER =
         uint256(
             0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001
         );
 
-    uint256 constant INPUT_CALLS = 0;
-
-    uint256 constant OUTPUT_CALLS = 0;
+    uint256 public constant HALF_ORDER = ORDER >> 1;
 
     uint8 public instanceOffset;
 
@@ -217,76 +225,29 @@ contract DataAttestation is LoadInstances, SwapProofCommitments {
      * @param _callData - The abi encoded function calls to make to the `contractAddress` that EZKL reads storage from.
      */
     constructor(
-        address[] memory _contractAddresses,
-        bytes[][] memory _callData,
-        uint256[][] memory _decimals,
-        uint[] memory _scales,
-        uint8 _instanceOffset,
-        address _admin
+        address _contractAddresses,
+        bytes memory _callData,
+        uint256[] memory _decimals,
+        uint[] memory _bits,
+        uint8 _instanceOffset
     ) {
-        admin = _admin;
-        for (uint i; i < _scales.length; i++) {
-            scales.push(1 << _scales[i]);
+        require(
+            _bits.length == _decimals.length,
+            "Invalid scalar array lengths"
+        );
+        for (uint i; i < _bits.length; i++) {
+            scalars.push(Scalars(10 ** _decimals[i], 1 << _bits[i]));
         }
-        populateAccountCalls(_contractAddresses, _callData, _decimals);
+        contractAddress = _contractAddresses;
+        callData = _callData;
         instanceOffset = _instanceOffset;
     }
 
-    function updateAdmin(address _admin) external {
-        require(msg.sender == admin, "Only admin can update admin");
-        if (_admin == address(0)) {
-            revert();
-        }
-        admin = _admin;
-    }
-
-    function updateAccountCalls(
-        address[] memory _contractAddresses,
-        bytes[][] memory _callData,
-        uint256[][] memory _decimals
-    ) external {
-        require(msg.sender == admin, "Only admin can update account calls");
-        populateAccountCalls(_contractAddresses, _callData, _decimals);
-    }
-
-    function populateAccountCalls(
-        address[] memory _contractAddresses,
-        bytes[][] memory _callData,
-        uint256[][] memory _decimals
-    ) internal {
-        require(
-            _contractAddresses.length == _callData.length &&
-                accountCalls.length == _contractAddresses.length,
-            "Invalid input length"
-        );
-        require(
-            _decimals.length == _contractAddresses.length,
-            "Invalid number of decimals"
-        );
-        // fill in the accountCalls storage array
-        uint counter = 0;
-        for (uint256 i = 0; i < _contractAddresses.length; i++) {
-            AccountCall storage accountCall = accountCalls[i];
-            accountCall.contractAddress = _contractAddresses[i];
-            accountCall.callCount = _callData[i].length;
-            for (uint256 j = 0; j < _callData[i].length; j++) {
-                accountCall.callData[j] = _callData[i][j];
-                accountCall.decimals[j] = 10 ** _decimals[i][j];
-            }
-            // count the total number of storage reads across all of the accounts
-            counter += _callData[i].length;
-        }
-        require(
-            counter == INPUT_CALLS + OUTPUT_CALLS,
-            "Invalid number of calls"
-        );
-    }
-
     function mulDiv(
         uint256 x,
         uint256 y,
         uint256 denominator
-    ) internal pure returns (uint256 result) {
+    ) public pure returns (uint256 result) {
         unchecked {
             uint256 prod0;
             uint256 prod1;
@@ -333,22 +294,29 @@ contract DataAttestation is LoadInstances, SwapProofCommitments {
     }
     /**
      * @dev Quantize the data returned from the account calls to the scale used by the EZKL model.
-     * @param data - The data returned from the account calls.
-     * @param decimals - The number of decimals the data returned from the account calls has (for floating point representation).
-     * @param scale - The scale used to convert the floating point value into a fixed point value.
+     * @param x - One of the elements of the data returned from the account calls
+     * @param _scalars - The scaling factors for the data returned from the account calls.
+     *
      */
     function quantizeData(
-        bytes memory data,
-        uint256 decimals,
-        uint256 scale
-    ) internal pure returns (int256 quantized_data) {
-        int x = abi.decode(data, (int256));
+        int x,
+        Scalars memory _scalars
+    ) public pure returns (int256 quantized_data) {
+        if (_scalars.bits == 1 && _scalars.decimals == 1) {
+            return x;
+        }
         bool neg = x < 0;
         if (neg) x = -x;
-        uint output = mulDiv(uint256(x), scale, decimals);
-        if (mulmod(uint256(x), scale, decimals) * 2 >= decimals) {
+        uint output = mulDiv(uint256(x), _scalars.bits, _scalars.decimals);
+        if (
+            mulmod(uint256(x), _scalars.bits, _scalars.decimals) * 2 >=
+            _scalars.decimals
+        ) {
             output += 1;
         }
+        if (output > HALF_ORDER) {
+            revert("Overflow field modulus");
+        }
         quantized_data = neg ? -int256(output) : int256(output);
     }
     /**
@@ -360,7 +328,7 @@ contract DataAttestation is LoadInstances, SwapProofCommitments {
     function staticCall(
         address target,
         bytes memory data
-    ) internal view returns (bytes memory) {
+    ) public view returns (bytes memory) {
         (bool success, bytes memory returndata) = target.staticcall(data);
         if (success) {
             if (returndata.length == 0) {
@@ -381,7 +349,7 @@ contract DataAttestation is LoadInstances, SwapProofCommitments {
      */
     function toFieldElement(
         int256 x
-    ) internal pure returns (uint256 field_element) {
+    ) public pure returns (uint256 field_element) {
         // The casting down to uint256 is safe because the order is about 2^254, and the value
         // of x ranges of -2^127 to 2^127, so x + int(ORDER) is always positive.
         return uint256(x + int(ORDER)) % ORDER;
@@ -391,32 +359,16 @@ contract DataAttestation is LoadInstances, SwapProofCommitments {
      * @dev Make the account calls to fetch the data that EZKL reads from and attest to the data.
      * @param instances - The public instances to the proof (the data in the proof that publicly accessible to the verifier).
      */
-    function attestData(uint256[] memory instances) internal view {
-        require(
-            instances.length >= INPUT_CALLS + OUTPUT_CALLS,
-            "Invalid public inputs length"
-        );
-        uint256 _accountCount = accountCalls.length;
-        uint counter = 0;
-        for (uint8 i = 0; i < _accountCount; ++i) {
-            address account = accountCalls[i].contractAddress;
-            for (uint8 j = 0; j < accountCalls[i].callCount; j++) {
-                bytes memory returnData = staticCall(
-                    account,
-                    accountCalls[i].callData[j]
-                );
-                uint256 scale = scales[counter];
-                int256 quantized_data = quantizeData(
-                    returnData,
-                    accountCalls[i].decimals[j],
-                    scale
-                );
-                uint256 field_element = toFieldElement(quantized_data);
-                require(
-                    field_element == instances[counter + instanceOffset],
-                    "Public input does not match"
-                );
-                counter++;
+    function attestData(uint256[] memory instances) public view {
+        bytes memory returnData = staticCall(contractAddress, callData);
+        int256[] memory x = abi.decode(returnData, (int256[]));
+        int output;
+        uint fieldElement;
+        for (uint i = 0; i < x.length; i++) {
+            output = quantizeData(x[i], scalars[i]);
+            fieldElement = toFieldElement(output);
+            if (fieldElement != instances[i]) {
+                revert("Public input does not match");
             }
         }
     }

docs/advanced_security/public_commitments.md

@@ -0,0 +1,41 @@
+## EZKL Security Note: Public Commitments and Low-Entropy Data
+
+> **Disclaimer:** this a more technical post that requires some prior knowledge of how ZK proving systems like Halo2 operate, and in particular in how these APIs are constructed. For background reading we highly recommend the [Halo2 book](https://zcash.github.io/halo2/) and [Halo2 Club](https://halo2.club/).
+
+## Overview of commitments in EZKL
+
+A common design pattern in a zero knowledge (zk) application is thus:
+- A prover has some data which is used within a circuit.
+- This data, as it may be high-dimensional or somewhat private, is pre-committed to using some hash function.
+- The zk-circuit which forms the core of the application then proves (para-phrasing) a statement of the form:
+>"I know some data D which when hashed corresponds to the pre-committed to value H + whatever else the circuit is proving over D". 
+
+From our own experience, we've implemented such patterns using snark-friendly hash functions like [Poseidon](https://www.poseidon-hash.info/), for which there is a relatively well vetted [implementation](https://docs.rs/halo2_gadgets/latest/halo2_gadgets/poseidon/index.html) in Halo2. Even then these hash functions can introduce lots of overhead and can be very expensive to generate proofs for if the dimensionality of the data D is large. 
+
+You can also implement such a pattern using Halo2's `Fixed` columns _if the privacy preservation of the pre-image is not necessary_. These are Halo2 columns (i.e in reality just polynomials) that are left unblinded (unlike the blinded `Advice` columns), and whose commitments are shared with the verifier by way of the verifying key for the application's zk-circuit. These commitments are much lower cost to generate than implementing a hashing function, such as Poseidon, within a circuit.
+
+> **Note:** Blinding is the process whereby a certain set of the final elements (i.e rows) of a Halo2 column are set to random field elements. This is the mechanism by which Halo2 achieves its zero knowledge properties for `Advice` columns. By contrast `Fixed` columns aren't zero-knowledge in that they are vulnerable to dictionary attacks in the same manner a hash function is. Given some set of known or popular data D an attacker can attempt to recover the pre-image of a hash by running D through the hash function to see if the outputs match a public commitment. These attacks aren't "possible" on blinded `Advice` columns.
+
+> **Further Note:** Note that without blinding, with access to `M` proofs, each of which contains an evaluation of the polynomial at a different point, an attacker can more easily recover a non blinded column's pre-image. This is because each proof generates a new query and evaluation of the polynomial represented by the column and as such with repetition a clearer picture can emerge of the column's pre-image. Thus unblinded columns should only be used for privacy preservation, in the manner of a hash, if the number of proofs generated against a fixed set of values is limited. More formally if M independent and _unique_ queries are generated; if M is equal to the degree + 1 of the polynomial represented by the column (i.e the unique lagrange interpolation of the values in the columns), then the column's pre-image can be recovered. As such as the logrows K increases, the more queries are required to recover the pre-image (as 2^K unique queries are required). This assumes that the entries in the column are not structured, as if they are then the number of queries required to recover the pre-image is reduced (eg. if all rows above a certain point are known to be nil).
+
+The annoyance in using `Fixed` columns comes from the fact that they require generating a new verifying key every time a new set of commitments is generated.
+
+> **Example:** Say for instance an application leverages a zero-knowledge circuit to prove the correct execution of a neural network. Every week the neural network is finetuned or retrained on new data. If the architecture remains the same then commiting to the new network parameters, along with a new proof of performance on a test set, would be an ideal setup. If we leverage `Fixed` columns to commit to the model parameters, each new commitment will require re-generating a  verifying key and sharing the new key with the verifier(s). This is not-ideal UX and can become expensive if the verifier is deployed on-chain. 
+
+An ideal commitment would thus have the low cost of a `Fixed`  column but wouldn't require regenerating a new verifying key for each new commitment.
+
+### Unblinded Advice Columns
+
+A first step in designing such a commitment is to allow for optionally unblinded `Advice` columns within the Halo2 API. These won't be included in the verifying key, AND are blinded with a constant factor `1` -- such that if someone knows the pre-image to the commitment, they can recover it by running it through the corresponding polynomial commitment scheme (in ezkl's case [KZG commitments](https://dankradfeist.de/ethereum/2020/06/16/kate-polynomial-commitments.html)). 
+
+This is implemented using the `polycommit` visibility parameter in the ezkl API.
+
+## The Vulnerability of Public Commitments
+
+
+Public commitments in EZKL (both Poseidon-hashed inputs and KZG commitments) can be vulnerable to brute-force attacks when input data has low entropy. A malicious actor could reveal committed data by searching through possible input values, compromising privacy in applications like anonymous credentials. This is particularly relevant when input data comes from known finite sets (e.g., names, dates).
+
+Example Risk: In an anonymous credential system using EZKL for ID verification, an attacker could match hashed outputs against a database of common identifying information to deanonymize users.
+
+
+

docs/advanced_security/quantization_backdoors.md

@@ -0,0 +1,54 @@
+# EZKL Security Note: Quantization-Activated Model Backdoors
+
+## Model backdoors and provenance
+
+Machine learning models inherently suffer from robustness issues, which can lead to various
+kinds of attacks, from backdoors to evasion attacks. These vulnerabilities are a direct byproductof how machine learning models learn and cannot be remediated.
+
+We say a model has a backdoor whenever a specific attacker-chosen trigger in the input leads
+to the model misbehaving. For instance, if we have an image classifier discriminating cats from dogs, the ability to turn any image of a cat into an image classified as a dog by changing a specific pixel pattern constitutes a backdoor.
+
+Backdoors can be introduced using many different vectors. An attacker can introduce a
+backdoor using traditional security vulnerabilities. For instance, they could directly alter the file containing model weights or dynamically hack the Python code of the model. In addition, backdoors can be introduced by the training data through a process known as poisoning. In this case, an attacker adds malicious data points to the dataset before the model is trained so that the model learns to associate the backdoor trigger with the intended misbehavior.
+
+All these vectors constitute a whole range of provenance challenges, as any component of an
+AI system can virtually be an entrypoint for a backdoor. Although provenance is already a
+concern with traditional code, the issue is exacerbated with AI, as retraining a model is
+cost-prohibitive. It is thus impractical to translate the “recompile it yourself” thinking to AI.
+
+## Quantization activated backdoors
+
+Backdoors are a generic concern in AI that is outside the scope of EZKL. However, EZKL may
+activate a specific subset of backdoors. Several academic papers have demonstrated the
+possibility, both in theory and in practice, of implanting undetectable and inactive backdoors in a full precision model that can be reactivated by quantization.
+
+An external attacker may trick the user of an application running EZKL into loading a model
+containing a quantization backdoor. This backdoor is active in the resulting model and circuit but not in the full-precision model supplied to EZKL, compromising the integrity of the target application and the resulting proof.
+
+### When is this a concern for me as a user?
+
+Any untrusted component in your AI stack may be a backdoor vector. In practice, the most
+sensitive parts include:
+
+- Datasets downloaded from the web or containing crowdsourced data
+- Models downloaded from the web even after finetuning
+- Untrusted software dependencies (well-known frameworks such as PyTorch can typically
+be considered trusted)
+- Any component loaded through an unsafe serialization format, such as Pickle.
+Because backdoors are inherent to ML and cannot be eliminated, reviewing the provenance of
+these sensitive components is especially important.
+
+### Responsibilities of the user and EZKL
+
+As EZKL cannot prevent backdoored models from being used, it is the responsibility of the user to review the provenance of all the components in their AI stack to ensure that no backdoor could have been implanted. EZKL shall not be held responsible for misleading prediction proofs resulting from using a backdoored model or for any harm caused to a system or its users due to a misbehaving model.
+
+### Limitations:
+
+- Attack effectiveness depends on calibration settings and internal rescaling operations.
+- Further research needed on backdoor persistence through witness/proof stages.
+- Can be mitigated by evaluating the quantized model (using `ezkl gen-witness`), rather than relying on the evaluation of the original model in pytorch or onnx-runtime as difference in evaluation could reveal a backdoor.
+
+References:
+
+1. [Quantization Backdoors to Deep Learning Commercial Frameworks (Ma et al., 2021)](https://arxiv.org/abs/2108.09187)
+2. [Planting Undetectable Backdoors in Machine Learning Models (Goldwasser et al., 2022)](https://arxiv.org/abs/2204.06974)

docs/python/requirements-docs.txt

@@ -1,4 +1,4 @@
-ezkl==15.2.0
+ezkl
 sphinx
 sphinx-rtd-theme
 sphinxcontrib-napoleon

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '15.2.0'
+release = '0.0.0'
 version = release
 
 

examples/conv2d_mnist/main.rs

@@ -32,6 +32,7 @@ use mnist::*;
 use rand::rngs::OsRng;
 use std::marker::PhantomData;
 
+
 mod params;
 
 const K: usize = 20;
@@ -208,6 +209,8 @@ where
                         padding: vec![(PADDING, PADDING); 2],
                         stride: vec![STRIDE; 2],
                         group: 1,
+                        data_format: DataFormat::NCHW,
+                        kernel_format: KernelFormat::OIHW,
                     };
                     let x = config
                         .layer_config

examples/notebooks/cat_and_dog_data.sh

@@ -0,0 +1,13 @@
+# download tess data
+# check if first argument has been set 
+if [ ! -z "$1" ]; then
+    DATA_DIR=$1
+else
+    DATA_DIR=data
+fi
+
+echo "Downloading data to $DATA_DIR"
+
+if [ ! -d "$DATA_DIR/CATDOG" ]; then
+    kaggle datasets download tongpython/cat-and-dog -p $DATA_DIR/CATDOG --unzip
+fi

examples/notebooks/data_attest.ipynb

@@ -1,601 +0,0 @@
-{
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# data-attest-ezkl\n",
-                "\n",
-                "Here's an example leveraging EZKL whereby the inputs to the model are read and attested to from an on-chain source.\n",
-                "\n",
-                "In this setup:\n",
-                "- the inputs and outputs are publicly known to the prover and verifier\n",
-                "- the on chain inputs will be fetched and then fed directly into the circuit\n",
-                "- the quantization of the on-chain inputs happens within the evm and is replicated at proving time \n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "First we import the necessary dependencies and set up logging to be as informative as possible. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# check if notebook is in colab\n",
-                "try:\n",
-                "    # install ezkl\n",
-                "    import google.colab\n",
-                "    import subprocess\n",
-                "    import sys\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
-                "\n",
-                "# rely on local installation of ezkl if the notebook is not in colab\n",
-                "except:\n",
-                "    pass\n",
-                "\n",
-                "\n",
-                "from torch import nn\n",
-                "import ezkl\n",
-                "import os\n",
-                "import json\n",
-                "import logging\n",
-                "\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"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we define our model. It is a very simple PyTorch model that has just one layer, an average pooling 2D layer. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import torch\n",
-                "# Defines the model\n",
-                "\n",
-                "class MyModel(nn.Module):\n",
-                "    def __init__(self):\n",
-                "        super(MyModel, self).__init__()\n",
-                "        self.layer = nn.AvgPool2d(2, 1, (1, 1))\n",
-                "\n",
-                "    def forward(self, x):\n",
-                "        return self.layer(x)[0]\n",
-                "\n",
-                "\n",
-                "circuit = MyModel()\n",
-                "\n",
-                "# this is where you'd train your model"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We omit training for purposes of this demonstration. We've marked where training would happen in the cell above. \n",
-                "Now we export the model to onnx and create a corresponding (randomly generated) input. This input data will eventually be stored on chain and read from according to the call_data field in the graph input.\n",
-                "\n",
-                "You can replace the random `x` with real data if you so wish. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "x = 0.1*torch.rand(1,*[3, 2, 2], requires_grad=True)\n",
-                "\n",
-                "# Flips the neural net into inference mode\n",
-                "circuit.eval()\n",
-                "\n",
-                "    # Export the model\n",
-                "torch.onnx.export(circuit,               # model being run\n",
-                "                      x,                   # model input (or a tuple for multiple inputs)\n",
-                "                      \"network.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=10,          # the ONNX version to export the model to\n",
-                "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
-                "                      input_names = ['input'],   # the model's input names\n",
-                "                      output_names = ['output'], # the model's output names\n",
-                "                      dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes\n",
-                "                                    'output' : {0 : 'batch_size'}})\n",
-                "\n",
-                "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
-                "\n",
-                "data = dict(input_data = [data_array])\n",
-                "\n",
-                "    # Serialize data into file:\n",
-                "json.dump(data, open(\"input.json\", 'w' ))\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now define a function that will create a new anvil instance which we will deploy our test contract too. This contract will contain in its storage the data that we will read from and attest to. In production you would not need to set up a local anvil instance. Instead you would replace RPC_URL with the actual RPC endpoint of the chain you are deploying your verifiers too, reading from the data on said chain."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import subprocess\n",
-                "import time\n",
-                "import threading\n",
-                "\n",
-                "# make sure anvil is running locally\n",
-                "# $ anvil -p 3030\n",
-                "\n",
-                "RPC_URL = \"http://localhost:3030\"\n",
-                "\n",
-                "# Save process globally\n",
-                "anvil_process = None\n",
-                "\n",
-                "def start_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is None:\n",
-                "        anvil_process = subprocess.Popen([\"anvil\", \"-p\", \"3030\", \"--code-size-limit=41943040\"])\n",
-                "        if anvil_process.returncode is not None:\n",
-                "            raise Exception(\"failed to start anvil process\")\n",
-                "        time.sleep(3)\n",
-                "\n",
-                "def stop_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is not None:\n",
-                "        anvil_process.terminate()\n",
-                "        anvil_process = None\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We define our `PyRunArgs` objects which contains the visibility parameters for out model. \n",
-                "- `input_visibility` defines the visibility of the model inputs\n",
-                "- `param_visibility` defines the visibility of the model weights and constants and parameters \n",
-                "- `output_visibility` defines the visibility of the model outputs\n",
-                "\n",
-                "Here we create the following setup:\n",
-                "- `input_visibility`: \"public\"\n",
-                "- `param_visibility`: \"private\"\n",
-                "- `output_visibility`: public\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import ezkl\n",
-                "\n",
-                "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",
-                "srs_path = os.path.join('kzg.srs')\n",
-                "data_path = os.path.join('input.json')\n",
-                "\n",
-                "run_args = ezkl.PyRunArgs()\n",
-                "run_args.input_visibility = \"public\"\n",
-                "run_args.param_visibility = \"private\"\n",
-                "run_args.output_visibility = \"public\"\n",
-                "run_args.num_inner_cols = 1\n",
-                "run_args.variables = [(\"batch_size\", 1)]\n",
-                "\n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a settings file. This file basically instantiates a bunch of parameters that determine their circuit shape, size etc... Because of the way we represent nonlinearities in the circuit (using Halo2's [lookup tables](https://zcash.github.io/halo2/design/proving-system/lookup.html)), it is often best to _calibrate_ this settings file as some data can fall out of range of these lookups.\n",
-                "\n",
-                "You can pass a dataset for calibration that will be representative of real inputs you might find if and when you deploy the prover. Here we create a dummy calibration dataset for demonstration purposes. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!RUST_LOG=trace\n",
-                "# TODO: Dictionary outputs\n",
-                "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# generate a bunch of dummy calibration data\n",
-                "cal_data = {\n",
-                "    \"input_data\": [(0.1*torch.rand(2, *[3, 2, 2])).flatten().tolist()],\n",
-                "}\n",
-                "\n",
-                "cal_path = os.path.join('val_data.json')\n",
-                "# save as json file\n",
-                "with open(cal_path, \"w\") as f:\n",
-                "    json.dump(cal_data, f)\n",
-                "\n",
-                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The graph input for on chain data sources is formatted completely differently compared to file based data sources.\n",
-                "\n",
-                "- For file data sources, the raw floating point values that eventually get quantized, converted into field elements and stored in `witness.json` to be consumed by the circuit are stored. The output data contains the expected floating point values returned as outputs from running your vanilla pytorch model on the given inputs.\n",
-                "- For on chain data sources, the input_data field contains all the data necessary to read and format the on chain data into something digestable by EZKL (aka field elements :-D). \n",
-                "Here is what the schema for an on-chain data source graph input file should look like:\n",
-                "    \n",
-                "```json\n",
-                "{\n",
-                "  \"input_data\": {\n",
-                "    \"rpc\": \"http://localhost:3030\", // The rpc endpoint of the chain you are deploying your verifier to\n",
-                "    \"calls\": [\n",
-                "      {\n",
-                "        \"call_data\": [\n",
-                "          [\n",
-                "            \"71e5ee5f0000000000000000000000000000000000000000000000000000000000000000\", // The abi encoded call data to a view function that returns a single on-chain data point (we only support uint256 returns for now)\n",
-                "            7 // The number of decimal places of the large uint256 value. This is our way of representing large wei values as floating points on chain, since the evm only natively supports integer values.\n",
-                "          ],\n",
-                "          [\n",
-                "            \"71e5ee5f0000000000000000000000000000000000000000000000000000000000000001\",\n",
-                "            5\n",
-                "          ],\n",
-                "          [\n",
-                "            \"71e5ee5f0000000000000000000000000000000000000000000000000000000000000002\",\n",
-                "            5\n",
-                "          ]\n",
-                "        ],\n",
-                "        \"address\": \"5fbdb2315678afecb367f032d93f642f64180aa3\" // The address of the contract that we are calling to get the data. \n",
-                "      }\n",
-                "    ]\n",
-                "  }\n",
-                "}"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "await ezkl.setup_test_evm_witness(\n",
-                "    data_path,\n",
-                "    compiled_model_path,\n",
-                "    # we write the call data to the same file as the input data\n",
-                "    data_path,\n",
-                "    input_source=ezkl.PyTestDataSource.OnChain,\n",
-                "    output_source=ezkl.PyTestDataSource.File,\n",
-                "    rpc_url=RPC_URL)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "As we use Halo2 with KZG-commitments we need an SRS string from (preferably) a multi-party trusted setup ceremony. For an overview of the procedures for such a ceremony check out [this page](https://blog.ethereum.org/2023/01/16/announcing-kzg-ceremony). The `get_srs` command retrieves a correctly sized SRS given the calibrated settings file from [here](https://github.com/han0110/halo2-kzg-srs). \n",
-                "\n",
-                "These SRS were generated with [this](https://github.com/privacy-scaling-explorations/perpetualpowersoftau) ceremony. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = await ezkl.get_srs( settings_path)\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now need to generate the circuit witness. These are the model outputs (and any hashes) that are generated when feeding the previously generated `input.json` through the circuit / model. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!export RUST_BACKTRACE=1\n",
-                "\n",
-                "witness_path = \"witness.json\"\n",
-                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Here we setup verifying and proving keys for the circuit. As the name suggests the proving key is needed for ... proving and the verifying key is needed for ... verifying. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# HERE WE SETUP THE CIRCUIT PARAMS\n",
-                "# WE GOT KEYS\n",
-                "# WE GOT CIRCUIT PARAMETERS\n",
-                "# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\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)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a full proof. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# GENERATE A PROOF\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)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "And verify it as a sanity check. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "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\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We can now create and then deploy a vanilla evm verifier."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "\n",
-                "res = await ezkl.create_evm_verifier(\n",
-                "        vk_path,\n",
-                "        \n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "    )\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import json\n",
-                "\n",
-                "addr_path_verifier = \"addr_verifier.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_evm(\n",
-                "    addr_path_verifier,\n",
-                "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
-                ")\n",
-                "\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "With the vanilla verifier deployed, we can now create the data attestation contract, which will read in the instances from the calldata to the verifier, attest to them, call the verifier and then return the result. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "\n",
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "input_path = 'input.json'\n",
-                "\n",
-                "res = await ezkl.create_evm_data_attestation(\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "    )"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we can deploy the data attest verifier contract. For security reasons, this binding will only deploy to a local anvil instance, using accounts generated by anvil. \n",
-                "So should only be used for testing purposes."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "addr_path_da = \"addr_da.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_da_evm(\n",
-                "        addr_path_da,\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        RPC_URL,\n",
-                "    )\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Call the view only verify method on the contract to verify the proof. Since it is a view function this is safe to use in production since you don't have to pass your private key."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# read the verifier address\n",
-                "addr_verifier = None\n",
-                "with open(addr_path_verifier, 'r') as f:\n",
-                "    addr = f.read()\n",
-                "#read the data attestation address\n",
-                "addr_da = None\n",
-                "with open(addr_path_da, 'r') as f:\n",
-                "    addr_da = f.read()\n",
-                "\n",
-                "res = await ezkl.verify_evm(\n",
-                "    addr,\n",
-                "    proof_path,\n",
-                "    RPC_URL,\n",
-                "    addr_da,\n",
-                ")"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "ezkl",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.12.2"
-        },
-        "orig_nbformat": 4
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}

examples/notebooks/data_attest_hashed.ipynb

@@ -1,657 +0,0 @@
-{
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# data-attest-ezkl hashed\n",
-                "\n",
-                "Here's an example leveraging EZKL whereby the hashes of the outputs to the model are read and attested to from an on-chain source.\n",
-                "\n",
-                "In this setup:\n",
-                "- the hashes of outputs are publicly known to the prover and verifier\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "First we import the necessary dependencies and set up logging to be as informative as possible. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# check if notebook is in colab\n",
-                "try:\n",
-                "    # install ezkl\n",
-                "    import google.colab\n",
-                "    import subprocess\n",
-                "    import sys\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
-                "\n",
-                "# rely on local installation of ezkl if the notebook is not in colab\n",
-                "except:\n",
-                "    pass\n",
-                "\n",
-                "\n",
-                "from torch import nn\n",
-                "import ezkl\n",
-                "import os\n",
-                "import json\n",
-                "import logging\n",
-                "\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"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we define our model. It is a very simple PyTorch model that has just one layer, an average pooling 2D layer. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import torch\n",
-                "# Defines the model\n",
-                "\n",
-                "class MyModel(nn.Module):\n",
-                "    def __init__(self):\n",
-                "        super(MyModel, self).__init__()\n",
-                "        self.layer = nn.AvgPool2d(2, 1, (1, 1))\n",
-                "\n",
-                "    def forward(self, x):\n",
-                "        return self.layer(x)[0]\n",
-                "\n",
-                "\n",
-                "circuit = MyModel()\n",
-                "\n",
-                "# this is where you'd train your model\n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We omit training for purposes of this demonstration. We've marked where training would happen in the cell above. \n",
-                "Now we export the model to onnx and create a corresponding (randomly generated) input. This input data will eventually be stored on chain and read from according to the call_data field in the graph input.\n",
-                "\n",
-                "You can replace the random `x` with real data if you so wish. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "x = 0.1*torch.rand(1,*[3, 2, 2], requires_grad=True)\n",
-                "\n",
-                "# Flips the neural net into inference mode\n",
-                "circuit.eval()\n",
-                "\n",
-                "    # Export the model\n",
-                "torch.onnx.export(circuit,               # model being run\n",
-                "                      x,                   # model input (or a tuple for multiple inputs)\n",
-                "                      \"network.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=10,          # the ONNX version to export the model to\n",
-                "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
-                "                      input_names = ['input'],   # the model's input names\n",
-                "                      output_names = ['output'], # the model's output names\n",
-                "                      dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes\n",
-                "                                    'output' : {0 : 'batch_size'}})\n",
-                "\n",
-                "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
-                "\n",
-                "data = dict(input_data = [data_array])\n",
-                "\n",
-                "    # Serialize data into file:\n",
-                "json.dump(data, open(\"input.json\", 'w' ))\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now define a function that will create a new anvil instance which we will deploy our test contract too. This contract will contain in its storage the data that we will read from and attest to. In production you would not need to set up a local anvil instance. Instead you would replace RPC_URL with the actual RPC endpoint of the chain you are deploying your verifiers too, reading from the data on said chain."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import subprocess\n",
-                "import time\n",
-                "import threading\n",
-                "\n",
-                "# make sure anvil is running locally\n",
-                "# $ anvil -p 3030\n",
-                "\n",
-                "RPC_URL = \"http://localhost:3030\"\n",
-                "\n",
-                "# Save process globally\n",
-                "anvil_process = None\n",
-                "\n",
-                "def start_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is None:\n",
-                "        anvil_process = subprocess.Popen([\"anvil\", \"-p\", \"3030\", \"--code-size-limit=41943040\"])\n",
-                "        if anvil_process.returncode is not None:\n",
-                "            raise Exception(\"failed to start anvil process\")\n",
-                "        time.sleep(3)\n",
-                "\n",
-                "def stop_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is not None:\n",
-                "        anvil_process.terminate()\n",
-                "        anvil_process = None\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We define our `PyRunArgs` objects which contains the visibility parameters for out model. \n",
-                "- `input_visibility` defines the visibility of the model inputs\n",
-                "- `param_visibility` defines the visibility of the model weights and constants and parameters \n",
-                "- `output_visibility` defines the visibility of the model outputs\n",
-                "\n",
-                "Here we create the following setup:\n",
-                "- `input_visibility`: \"private\"\n",
-                "- `param_visibility`: \"private\"\n",
-                "- `output_visibility`: hashed\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import ezkl\n",
-                "\n",
-                "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",
-                "srs_path = os.path.join('kzg.srs')\n",
-                "data_path = os.path.join('input.json')\n",
-                "\n",
-                "run_args = ezkl.PyRunArgs()\n",
-                "run_args.input_visibility = \"private\"\n",
-                "run_args.param_visibility = \"private\"\n",
-                "run_args.output_visibility = \"hashed\"\n",
-                "run_args.variables = [(\"batch_size\", 1)]\n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a settings file. This file basically instantiates a bunch of parameters that determine their circuit shape, size etc... Because of the way we represent nonlinearities in the circuit (using Halo2's [lookup tables](https://zcash.github.io/halo2/design/proving-system/lookup.html)), it is often best to _calibrate_ this settings file as some data can fall out of range of these lookups.\n",
-                "\n",
-                "You can pass a dataset for calibration that will be representative of real inputs you might find if and when you deploy the prover. Here we create a dummy calibration dataset for demonstration purposes. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!RUST_LOG=trace\n",
-                "# TODO: Dictionary outputs\n",
-                "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# generate a bunch of dummy calibration data\n",
-                "cal_data = {\n",
-                "    \"input_data\": [(0.1*torch.rand(2, *[3, 2, 2])).flatten().tolist()],\n",
-                "}\n",
-                "\n",
-                "cal_path = os.path.join('val_data.json')\n",
-                "# save as json file\n",
-                "with open(cal_path, \"w\") as f:\n",
-                "    json.dump(cal_data, f)\n",
-                "\n",
-                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "As we use Halo2 with KZG-commitments we need an SRS string from (preferably) a multi-party trusted setup ceremony. For an overview of the procedures for such a ceremony check out [this page](https://blog.ethereum.org/2023/01/16/announcing-kzg-ceremony). The `get_srs` command retrieves a correctly sized SRS given the calibrated settings file from [here](https://github.com/han0110/halo2-kzg-srs). \n",
-                "\n",
-                "These SRS were generated with [this](https://github.com/privacy-scaling-explorations/perpetualpowersoftau) ceremony. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = await ezkl.get_srs( settings_path)\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now need to generate the circuit witness. These are the model outputs (and any hashes) that are generated when feeding the previously generated `input.json` through the circuit / model. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!export RUST_BACKTRACE=1\n",
-                "\n",
-                "witness_path = \"witness.json\"\n",
-                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "print(ezkl.felt_to_big_endian(res['processed_outputs']['poseidon_hash'][0]))"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now post the hashes of the outputs to the chain. This is the data that will be read from and attested to."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from web3 import Web3, HTTPProvider\n",
-                "from solcx import compile_standard\n",
-                "from decimal import Decimal\n",
-                "import json\n",
-                "import os\n",
-                "import torch\n",
-                "\n",
-                "\n",
-                "# setup web3 instance\n",
-                "w3 = Web3(HTTPProvider(RPC_URL))\n",
-                "\n",
-                "def test_on_chain_data(res):\n",
-                "    # Step 0: Convert the tensor to a flat list\n",
-                "    data = [int(ezkl.felt_to_big_endian(res['processed_outputs']['poseidon_hash'][0]), 0)]\n",
-                "\n",
-                "    # Step 1: Prepare the data\n",
-                "    # Step 2: Prepare and compile the contract.\n",
-                "    # We are using a test contract here but in production you would\n",
-                "    # use whatever contract you are fetching data from.\n",
-                "    contract_source_code = '''\n",
-                "    // SPDX-License-Identifier: UNLICENSED\n",
-                "    pragma solidity ^0.8.17;\n",
-                "\n",
-                "    contract TestReads {\n",
-                "\n",
-                "        uint[] public arr;\n",
-                "        constructor(uint256[] memory _numbers) {\n",
-                "            for(uint256 i = 0; i < _numbers.length; i++) {\n",
-                "                arr.push(_numbers[i]);\n",
-                "            }\n",
-                "        }\n",
-                "    }\n",
-                "    '''\n",
-                "\n",
-                "    compiled_sol = compile_standard({\n",
-                "        \"language\": \"Solidity\",\n",
-                "        \"sources\": {\"testreads.sol\": {\"content\": contract_source_code}},\n",
-                "        \"settings\": {\"outputSelection\": {\"*\": {\"*\": [\"metadata\", \"evm.bytecode\", \"abi\"]}}}\n",
-                "    })\n",
-                "\n",
-                "    # Get bytecode\n",
-                "    bytecode = compiled_sol['contracts']['testreads.sol']['TestReads']['evm']['bytecode']['object']\n",
-                "\n",
-                "    # Get ABI\n",
-                "    # In production if you are reading from really large contracts you can just use\n",
-                "    # a stripped down version of the ABI of the contract you are calling, containing only the view functions you will fetch data from.\n",
-                "    abi = json.loads(compiled_sol['contracts']['testreads.sol']['TestReads']['metadata'])['output']['abi']\n",
-                "\n",
-                "    # Step 3: Deploy the contract\n",
-                "    TestReads = w3.eth.contract(abi=abi, bytecode=bytecode)\n",
-                "    tx_hash = TestReads.constructor(data).transact()\n",
-                "    tx_receipt = w3.eth.wait_for_transaction_receipt(tx_hash)\n",
-                "    # If you are deploying to production you can skip the 3 lines of code above and just instantiate the contract like this,\n",
-                "    # passing the address and abi of the contract you are fetching data from.\n",
-                "    contract = w3.eth.contract(address=tx_receipt['contractAddress'], abi=abi)\n",
-                "\n",
-                "    # Step 4: Interact with the contract\n",
-                "    calldata = []\n",
-                "    for i, _ in enumerate(data):\n",
-                "        call = contract.functions.arr(i).build_transaction()\n",
-                "        calldata.append((call['data'][2:], 0))\n",
-                "\n",
-                "    # Prepare the calls_to_account object\n",
-                "    # If you were calling view functions across multiple contracts,\n",
-                "    # you would have multiple entries in the calls_to_account array,\n",
-                "    # one for each contract.\n",
-                "    calls_to_account = [{\n",
-                "        'call_data': calldata,\n",
-                "        'address': contract.address[2:], # remove the '0x' prefix\n",
-                "    }]\n",
-                "\n",
-                "    print(f'calls_to_account: {calls_to_account}')\n",
-                "\n",
-                "    return calls_to_account\n",
-                "\n",
-                "# Now let's start the Anvil process. You don't need to do this if you are deploying to a non-local chain.\n",
-                "start_anvil()\n",
-                "\n",
-                "# Now let's call our function, passing in the same input tensor we used to export the model 2 cells above.\n",
-                "calls_to_account = test_on_chain_data(res)\n",
-                "\n",
-                "data = dict(input_data = [data_array], output_data =  {'rpc': RPC_URL, 'calls': calls_to_account })\n",
-                "\n",
-                "# Serialize on-chain data into file:\n",
-                "json.dump(data, open(\"input.json\", 'w'))\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Here we setup verifying and proving keys for the circuit. As the name suggests the proving key is needed for ... proving and the verifying key is needed for ... verifying. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# HERE WE SETUP THE CIRCUIT PARAMS\n",
-                "# WE GOT KEYS\n",
-                "# WE GOT CIRCUIT PARAMETERS\n",
-                "# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\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)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a full proof. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# GENERATE A PROOF\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)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "And verify it as a sanity check. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "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\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We can now create and then deploy a vanilla evm verifier."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "\n",
-                "res = await ezkl.create_evm_verifier(\n",
-                "        vk_path,\n",
-                "        \n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "    )\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import json\n",
-                "\n",
-                "addr_path_verifier = \"addr_verifier.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_evm(\n",
-                "    addr_path_verifier,\n",
-                "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
-                ")\n",
-                "\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "With the vanilla verifier deployed, we can now create the data attestation contract, which will read in the instances from the calldata to the verifier, attest to them, call the verifier and then return the result. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "\n",
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "input_path = 'input.json'\n",
-                "\n",
-                "res = await ezkl.create_evm_data_attestation(\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "    )"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we can deploy the data attest verifier contract. For security reasons, this binding will only deploy to a local anvil instance, using accounts generated by anvil. \n",
-                "So should only be used for testing purposes."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "addr_path_da = \"addr_da.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_da_evm(\n",
-                "        addr_path_da,\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        RPC_URL,\n",
-                "    )\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Call the view only verify method on the contract to verify the proof. Since it is a view function this is safe to use in production since you don't have to pass your private key."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# read the verifier address\n",
-                "addr_verifier = None\n",
-                "with open(addr_path_verifier, 'r') as f:\n",
-                "    addr = f.read()\n",
-                "#read the data attestation address\n",
-                "addr_da = None\n",
-                "with open(addr_path_da, 'r') as f:\n",
-                "    addr_da = f.read()\n",
-                "\n",
-                "res = await ezkl.verify_evm(\n",
-                "    addr,\n",
-                "    proof_path,\n",
-                "    RPC_URL,\n",
-                "    addr_da,\n",
-                ")"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "ezkl",
-            "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"
-        },
-        "orig_nbformat": 4
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}

examples/notebooks/data_attest_kzg_vis.ipynb

@@ -1,604 +0,0 @@
-{
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# data-attest-kzg-vis\n",
-                "\n",
-                "Here's an example leveraging EZKL whereby the inputs to the model are read and attested to from an on-chain source and the params and outputs are committed to using kzg-commitments. \n",
-                "\n",
-                "In this setup:\n",
-                "- the inputs and outputs are publicly known to the prover and verifier\n",
-                "- the on chain inputs will be fetched and then fed directly into the circuit\n",
-                "- the quantization of the on-chain inputs happens within the evm and is replicated at proving time \n",
-                "- The kzg commitment to the params and inputs will be read from the proof and checked to make sure it matches the expected commitment stored on-chain.\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "First we import the necessary dependencies and set up logging to be as informative as possible. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# check if notebook is in colab\n",
-                "try:\n",
-                "    # install ezkl\n",
-                "    import google.colab\n",
-                "    import subprocess\n",
-                "    import sys\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
-                "\n",
-                "# rely on local installation of ezkl if the notebook is not in colab\n",
-                "except:\n",
-                "    pass\n",
-                "\n",
-                "\n",
-                "from torch import nn\n",
-                "import ezkl\n",
-                "import os\n",
-                "import json\n",
-                "import logging\n",
-                "\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"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we define our model. It is a very simple PyTorch model that has just one layer, an average pooling 2D layer. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import torch\n",
-                "# Defines the model\n",
-                "\n",
-                "class MyModel(nn.Module):\n",
-                "    def __init__(self):\n",
-                "        super(MyModel, self).__init__()\n",
-                "        self.layer = nn.AvgPool2d(2, 1, (1, 1))\n",
-                "\n",
-                "    def forward(self, x):\n",
-                "        return self.layer(x)[0]\n",
-                "\n",
-                "\n",
-                "circuit = MyModel()\n",
-                "\n",
-                "# this is where you'd train your model"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We omit training for purposes of this demonstration. We've marked where training would happen in the cell above. \n",
-                "Now we export the model to onnx and create a corresponding (randomly generated) input. This input data will eventually be stored on chain and read from according to the call_data field in the graph input.\n",
-                "\n",
-                "You can replace the random `x` with real data if you so wish. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "x = 0.1*torch.rand(1,*[3, 2, 2], requires_grad=True)\n",
-                "\n",
-                "# Flips the neural net into inference mode\n",
-                "circuit.eval()\n",
-                "\n",
-                "    # Export the model\n",
-                "torch.onnx.export(circuit,               # model being run\n",
-                "                      x,                   # model input (or a tuple for multiple inputs)\n",
-                "                      \"network.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=10,          # the ONNX version to export the model to\n",
-                "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
-                "                      input_names = ['input'],   # the model's input names\n",
-                "                      output_names = ['output'], # the model's output names\n",
-                "                      dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes\n",
-                "                                    'output' : {0 : 'batch_size'}})\n",
-                "\n",
-                "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
-                "\n",
-                "data = dict(input_data = [data_array])\n",
-                "\n",
-                "    # Serialize data into file:\n",
-                "json.dump(data, open(\"input.json\", 'w' ))\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now define a function that will create a new anvil instance which we will deploy our test contract too. This contract will contain in its storage the data that we will read from and attest to. In production you would not need to set up a local anvil instance. Instead you would replace RPC_URL with the actual RPC endpoint of the chain you are deploying your verifiers too, reading from the data on said chain."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import subprocess\n",
-                "import time\n",
-                "import threading\n",
-                "\n",
-                "# make sure anvil is running locally\n",
-                "# $ anvil -p 3030\n",
-                "\n",
-                "RPC_URL = \"http://localhost:3030\"\n",
-                "\n",
-                "# Save process globally\n",
-                "anvil_process = None\n",
-                "\n",
-                "def start_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is None:\n",
-                "        anvil_process = subprocess.Popen([\"anvil\", \"-p\", \"3030\", \"--code-size-limit=41943040\"])\n",
-                "        if anvil_process.returncode is not None:\n",
-                "            raise Exception(\"failed to start anvil process\")\n",
-                "        time.sleep(3)\n",
-                "\n",
-                "def stop_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is not None:\n",
-                "        anvil_process.terminate()\n",
-                "        anvil_process = None\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We define our `PyRunArgs` objects which contains the visibility parameters for out model. \n",
-                "- `input_visibility` defines the visibility of the model inputs\n",
-                "- `param_visibility` defines the visibility of the model weights and constants and parameters \n",
-                "- `output_visibility` defines the visibility of the model outputs\n",
-                "\n",
-                "Here we create the following setup:\n",
-                "- `input_visibility`: \"public\"\n",
-                "- `param_visibility`: \"polycommitment\" \n",
-                "- `output_visibility`: \"polycommitment\"\n",
-                "\n",
-                "**Note**:\n",
-                "When we set this to polycommitment, we are saying that the model parameters are committed to using a polynomial commitment scheme. This commitment will be stored on chain as a constant stored in the DA contract, and the proof will contain the commitment to the parameters. The DA verification will then check that the commitment in the proof matches the commitment stored on chain. \n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import ezkl\n",
-                "\n",
-                "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",
-                "srs_path = os.path.join('kzg.srs')\n",
-                "data_path = os.path.join('input.json')\n",
-                "\n",
-                "run_args = ezkl.PyRunArgs()\n",
-                "run_args.input_visibility = \"public\"\n",
-                "run_args.param_visibility = \"polycommit\"\n",
-                "run_args.output_visibility = \"polycommit\"\n",
-                "run_args.num_inner_cols = 1\n",
-                "run_args.variables = [(\"batch_size\", 1)]\n",
-                "\n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a settings file. This file basically instantiates a bunch of parameters that determine their circuit shape, size etc... Because of the way we represent nonlinearities in the circuit (using Halo2's [lookup tables](https://zcash.github.io/halo2/design/proving-system/lookup.html)), it is often best to _calibrate_ this settings file as some data can fall out of range of these lookups.\n",
-                "\n",
-                "You can pass a dataset for calibration that will be representative of real inputs you might find if and when you deploy the prover. Here we create a dummy calibration dataset for demonstration purposes. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!RUST_LOG=trace\n",
-                "# TODO: Dictionary outputs\n",
-                "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# generate a bunch of dummy calibration data\n",
-                "cal_data = {\n",
-                "    \"input_data\": [(0.1*torch.rand(2, *[3, 2, 2])).flatten().tolist()],\n",
-                "}\n",
-                "\n",
-                "cal_path = os.path.join('val_data.json')\n",
-                "# save as json file\n",
-                "with open(cal_path, \"w\") as f:\n",
-                "    json.dump(cal_data, f)\n",
-                "\n",
-                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The graph input for on chain data sources is formatted completely differently compared to file based data sources.\n",
-                "\n",
-                "- For file data sources, the raw floating point values that eventually get quantized, converted into field elements and stored in `witness.json` to be consumed by the circuit are stored. The output data contains the expected floating point values returned as outputs from running your vanilla pytorch model on the given inputs.\n",
-                "- For on chain data sources, the input_data field contains all the data necessary to read and format the on chain data into something digestable by EZKL (aka field elements :-D). \n",
-                "Here is what the schema for an on-chain data source graph input file should look like:\n",
-                "    \n",
-                "```json\n",
-                "{\n",
-                "  \"input_data\": {\n",
-                "    \"rpc\": \"http://localhost:3030\", // The rpc endpoint of the chain you are deploying your verifier to\n",
-                "    \"calls\": [\n",
-                "      {\n",
-                "        \"call_data\": [\n",
-                "          [\n",
-                "            \"71e5ee5f0000000000000000000000000000000000000000000000000000000000000000\", // The abi encoded call data to a view function that returns a single on-chain data point (we only support uint256 returns for now)\n",
-                "            7 // The number of decimal places of the large uint256 value. This is our way of representing large wei values as floating points on chain, since the evm only natively supports integer values.\n",
-                "          ],\n",
-                "          [\n",
-                "            \"71e5ee5f0000000000000000000000000000000000000000000000000000000000000001\",\n",
-                "            5\n",
-                "          ],\n",
-                "          [\n",
-                "            \"71e5ee5f0000000000000000000000000000000000000000000000000000000000000002\",\n",
-                "            5\n",
-                "          ]\n",
-                "        ],\n",
-                "        \"address\": \"5fbdb2315678afecb367f032d93f642f64180aa3\" // The address of the contract that we are calling to get the data. \n",
-                "      }\n",
-                "    ]\n",
-                "  }\n",
-                "}"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "await ezkl.setup_test_evm_witness(\n",
-                "    data_path,\n",
-                "    compiled_model_path,\n",
-                "    # we write the call data to the same file as the input data\n",
-                "    data_path,\n",
-                "    input_source=ezkl.PyTestDataSource.OnChain,\n",
-                "    output_source=ezkl.PyTestDataSource.File,\n",
-                "    rpc_url=RPC_URL)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "As we use Halo2 with KZG-commitments we need an SRS string from (preferably) a multi-party trusted setup ceremony. For an overview of the procedures for such a ceremony check out [this page](https://blog.ethereum.org/2023/01/16/announcing-kzg-ceremony). The `get_srs` command retrieves a correctly sized SRS given the calibrated settings file from [here](https://github.com/han0110/halo2-kzg-srs). \n",
-                "\n",
-                "These SRS were generated with [this](https://github.com/privacy-scaling-explorations/perpetualpowersoftau) ceremony. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = await ezkl.get_srs( settings_path)\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now need to generate the circuit witness. These are the model outputs (and any hashes) that are generated when feeding the previously generated `input.json` through the circuit / model. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!export RUST_BACKTRACE=1\n",
-                "\n",
-                "witness_path = \"witness.json\"\n",
-                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Here we setup verifying and proving keys for the circuit. As the name suggests the proving key is needed for ... proving and the verifying key is needed for ... verifying. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# HERE WE SETUP THE CIRCUIT PARAMS\n",
-                "# WE GOT KEYS\n",
-                "# WE GOT CIRCUIT PARAMETERS\n",
-                "# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\n",
-                "res = ezkl.setup(\n",
-                "        compiled_model_path,\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_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a full proof. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# GENERATE A PROOF\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)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "And verify it as a sanity check. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "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\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We can now create and then deploy a vanilla evm verifier."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "\n",
-                "res = await ezkl.create_evm_verifier(\n",
-                "        vk_path,\n",
-                "        \n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "    )\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "\n",
-                "addr_path_verifier = \"addr_verifier.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_evm(\n",
-                "    addr_path_verifier,\n",
-                "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
-                ")\n",
-                "\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "When deploying a DA with kzg commitments, we need to make sure to also pass a witness file that contains the commitments to the parameters and inputs. This is because the verifier will need to check that the commitments in the proof match the commitments stored on chain."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "\n",
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "input_path = 'input.json'\n",
-                "\n",
-                "res = await ezkl.create_evm_data_attestation(\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "        witness_path = witness_path,\n",
-                "    )"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we can deploy the data attest verifier contract. For security reasons, this binding will only deploy to a local anvil instance, using accounts generated by anvil. \n",
-                "So should only be used for testing purposes."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "addr_path_da = \"addr_da.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_da_evm(\n",
-                "        addr_path_da,\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        RPC_URL,\n",
-                "    )\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Call the view only verify method on the contract to verify the proof. Since it is a view function this is safe to use in production since you don't have to pass your private key."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# read the verifier address\n",
-                "addr_verifier = None\n",
-                "with open(addr_path_verifier, 'r') as f:\n",
-                "    addr = f.read()\n",
-                "#read the data attestation address\n",
-                "addr_da = None\n",
-                "with open(addr_path_da, 'r') as f:\n",
-                "    addr_da = f.read()\n",
-                "\n",
-                "res = await ezkl.verify_evm(\n",
-                "    addr,\n",
-                "    proof_path,\n",
-                "    RPC_URL,\n",
-                "    addr_da,\n",
-                ")"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "ezkl",
-            "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": "3.9.13"
-        },
-        "orig_nbformat": 4
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}

examples/notebooks/generalized_inverse.ipynb

@@ -77,6 +77,7 @@
       "outputs": [],
       "source": [
         "gip_run_args = ezkl.PyRunArgs()\n",
+        "gip_run_args.ignore_range_check_inputs_outputs = True\n",
         "gip_run_args.input_visibility = \"polycommit\"  # matrix and generalized inverse commitments\n",
         "gip_run_args.output_visibility = \"fixed\"   # no parameters used\n",
         "gip_run_args.param_visibility = \"fixed\" # should be Tensor(True)"
@@ -335,9 +336,9 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.15"
+      "version": "3.9.13"
     }
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

examples/notebooks/hashed_vis.ipynb

@@ -453,8 +453,8 @@
                 "\n",
                 "res = await ezkl.deploy_evm(\n",
                 "    address_path,\n",
+                "    'http://127.0.0.1:3030',\n",
                 "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
                 ")\n",
                 "\n",
                 "assert res == True\n",
@@ -474,8 +474,8 @@
                 "\n",
                 "res = await ezkl.verify_evm(\n",
                 "    addr,\n",
+                "    \"http://127.0.0.1:3030\",\n",
                 "    proof_path,\n",
-                "    \"http://127.0.0.1:3030\"\n",
                 ")\n",
                 "assert res == True"
             ]
@@ -510,4 +510,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 2
-}
+}

examples/notebooks/kzg_vis.ipynb

@@ -462,8 +462,8 @@
                 "\n",
                 "res = await ezkl.deploy_evm(\n",
                 "    address_path,\n",
+                "    'http://127.0.0.1:3030',\n",
                 "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
                 ")\n",
                 "\n",
                 "assert res == True\n",
@@ -483,8 +483,8 @@
                 "\n",
                 "res = await ezkl.verify_evm(\n",
                 "    addr,\n",
+                "    \"http://127.0.0.1:3030\",\n",
                 "    proof_path,\n",
-                "    \"http://127.0.0.1:3030\"\n",
                 ")\n",
                 "assert res == True"
             ]
@@ -512,4 +512,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 2
-}
+}

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/logistic_regression.ipynb

@@ -271,7 +271,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.12.2"
+            "version": "3.12.7"
         }
     },
     "nbformat": 4,

examples/notebooks/mean_postgres.ipynb

@@ -1,456 +0,0 @@
-{
-  "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"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

examples/notebooks/mnist_classifier.ipynb

@@ -504,8 +504,8 @@
         "\n",
         "res = await ezkl.deploy_evm(\n",
         "    address_path,\n",
+        "    'http://127.0.0.1:3030',\n",
         "    sol_code_path,\n",
-        "    'http://127.0.0.1:3030'\n",
         ")\n",
         "\n",
         "assert res == True\n",
@@ -527,8 +527,8 @@
         "\n",
         "res = await ezkl.verify_evm(\n",
         "    addr,\n",
-        "    proof_path,\n",
-        "    \"http://127.0.0.1:3030\"\n",
+        "    \"http://127.0.0.1:3030\",\n",
+        "    proof_path\n",
         ")\n",
         "assert res == True"
       ]
@@ -558,4 +558,4 @@
   },
   "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
     },

examples/notebooks/neural_bow.ipynb

@@ -0,0 +1,766 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "This is a zk version of the tutorial found [here](https://github.com/bentrevett/pytorch-sentiment-analysis/blob/main/1%20-%20Neural%20Bag%20of%20Words.ipynb). The original tutorial is part of the PyTorch Sentiment Analysis series by Ben Trevett.\n",
+    "\n",
+    "1 - NBoW\n",
+    "\n",
+    "In this series we'll be building a machine learning model to perform sentiment analysis -- a subset of text classification where the task is to detect if a given sentence is positive or negative -- using PyTorch and torchtext. The dataset used will be movie reviews from the IMDb dataset, which we'll obtain using the datasets library.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Preparing Data\n",
+    "\n",
+    "Before we can implement our NBoW model, we first have to perform quite a few steps to get our data ready to use. NLP usually requires quite a lot of data wrangling beforehand, though libraries such as datasets and torchtext handle most of this for us.\n",
+    "\n",
+    "The steps to take are:\n",
+    "\n",
+    "    1. importing modules\n",
+    "    2. loading data\n",
+    "    3. tokenizing data\n",
+    "    4. creating data splits\n",
+    "    5. creating a vocabulary\n",
+    "    6. numericalizing data\n",
+    "    7. creating the data loaders\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install torchtex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections\n",
+    "\n",
+    "import datasets\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import torchtext\n",
+    "import tqdm\n",
+    "\n",
+    "# It is usually good practice to run your experiments multiple times with different random seeds -- both to measure the variance of your model and also to avoid having results only calculated with either \"good\" or \"bad\" seeds, i.e. being very lucky or unlucky with the randomness in the training process.\n",
+    "\n",
+    "seed = 1234\n",
+    "\n",
+    "np.random.seed(seed)\n",
+    "torch.manual_seed(seed)\n",
+    "torch.cuda.manual_seed(seed)\n",
+    "torch.backends.cudnn.deterministic = True\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data, test_data = datasets.load_dataset(\"imdb\", split=[\"train\", \"test\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can check the features attribute of a split to get more information about the features. We can see that text is a Value of dtype=string -- in other words, it's a string -- and that label is a ClassLabel. A ClassLabel means the feature is an integer representation of which class the example belongs to. num_classes=2 means that our labels are one of two values, 0 or 1, and names=['neg', 'pos'] gives us the human-readable versions of those values. Thus, a label of 0 means the example is a negative review and a label of 1 means the example is a positive review."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data.features\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the first things we need to do to our data is tokenize it. Machine learning models aren't designed to handle strings, they're design to handle numbers. So what we need to do is break down our string into individual tokens, and then convert these tokens to numbers. We'll get to the conversion later, but first we'll look at tokenization.\n",
+    "\n",
+    "Tokenization involves using a tokenizer to process the strings in our dataset. A tokenizer is a function that goes from a string to a list of strings. There are many types of tokenizers available, but we're going to use a relatively simple one provided by torchtext called the basic_english tokenizer. We load our tokenizer as such:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer = torchtext.data.utils.get_tokenizer(\"basic_english\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tokenize_example(example, tokenizer, max_length):\n",
+    "    tokens = tokenizer(example[\"text\"])[:max_length]\n",
+    "    return {\"tokens\": tokens}\n",
+    "\n",
+    "\n",
+    "max_length = 256\n",
+    "\n",
+    "train_data = train_data.map(\n",
+    "    tokenize_example, fn_kwargs={\"tokenizer\": tokenizer, \"max_length\": max_length}\n",
+    ")\n",
+    "test_data = test_data.map(\n",
+    "    tokenize_example, fn_kwargs={\"tokenizer\": tokenizer, \"max_length\": max_length}\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# create validation data \n",
+    "# Why have both a validation set and a test set? Your test set respresents the real world data that you'd see if you actually deployed this model. You won't be able to see what data your model will be fed once deployed, and your test set is supposed to reflect that. Every time we tune our model hyperparameters or training set-up to make it do a bit better on the test set, we are leak information from the test set into the training process. If we do this too often then we begin to overfit on the test set. Hence, we need some data which can act as a \"proxy\" test set which we can look at more frequently in order to evaluate how well our model actually does on unseen data -- this is the validation set.\n",
+    "\n",
+    "test_size = 0.25\n",
+    "\n",
+    "train_valid_data = train_data.train_test_split(test_size=test_size)\n",
+    "train_data = train_valid_data[\"train\"]\n",
+    "valid_data = train_valid_data[\"test\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we have to build a vocabulary. This is look-up table where every unique token in your dataset has a corresponding index (an integer).\n",
+    "\n",
+    "We do this as machine learning models cannot operate on strings, only numerical vaslues. Each index is used to construct a one-hot vector for each token. A one-hot vector is a vector where all the elements are 0, except one, which is 1, and the dimensionality is the total number of unique tokens in your vocabulary, commonly denoted by V."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "min_freq = 5\n",
+    "special_tokens = [\"<unk>\", \"<pad>\"]\n",
+    "\n",
+    "vocab = torchtext.vocab.build_vocab_from_iterator(\n",
+    "    train_data[\"tokens\"],\n",
+    "    min_freq=min_freq,\n",
+    "    specials=special_tokens,\n",
+    ")\n",
+    "\n",
+    "# We store the indices of the unknown and padding tokens (zero and one, respectively) in variables, as we'll use these further on in this notebook.\n",
+    "\n",
+    "unk_index = vocab[\"<unk>\"]\n",
+    "pad_index = vocab[\"<pad>\"]\n",
+    "\n",
+    "\n",
+    "vocab.set_default_index(unk_index)\n",
+    "\n",
+    "# To look-up a list of tokens, we can use the vocabulary's lookup_indices method.\n",
+    "vocab.lookup_indices([\"hello\", \"world\", \"some_token\", \"<pad>\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we have our vocabulary, we can numericalize our data. This involves converting the tokens within our dataset into indices. Similar to how we tokenized our data using the Dataset.map method, we'll define a function that takes an example and our vocabulary, gets the index for each token in each example and then creates an ids field which containes the numericalized tokens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def numericalize_example(example, vocab):\n",
+    "    ids = vocab.lookup_indices(example[\"tokens\"])\n",
+    "    return {\"ids\": ids}\n",
+    "\n",
+    "train_data = train_data.map(numericalize_example, fn_kwargs={\"vocab\": vocab})\n",
+    "valid_data = valid_data.map(numericalize_example, fn_kwargs={\"vocab\": vocab})\n",
+    "test_data = test_data.map(numericalize_example, fn_kwargs={\"vocab\": vocab})\n",
+    "\n",
+    "train_data = train_data.with_format(type=\"torch\", columns=[\"ids\", \"label\"])\n",
+    "valid_data = valid_data.with_format(type=\"torch\", columns=[\"ids\", \"label\"])\n",
+    "test_data = test_data.with_format(type=\"torch\", columns=[\"ids\", \"label\"])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final step of preparing the data is creating the data loaders. We can iterate over a data loader to retrieve batches of examples. This is also where we will perform any padding that is necessary.\n",
+    "\n",
+    "We first need to define a function to collate a batch, consisting of a list of examples, into what we want our data loader to output.\n",
+    "\n",
+    "Here, our desired output from the data loader is a dictionary with keys of \"ids\" and \"label\".\n",
+    "\n",
+    "The value of batch[\"ids\"] should be a tensor of shape [batch size, length], where length is the length of the longest sentence (in terms of tokens) within the batch, and all sentences shorter than this should be padded to that length.\n",
+    "\n",
+    "The value of batch[\"label\"] should be a tensor of shape [batch size] consisting of the label for each sentence in the batch.\n",
+    "\n",
+    "We define a function, get_collate_fn, which is passed the pad token index and returns the actual collate function. Within the actual collate function, collate_fn, we get a list of \"ids\" tensors for each example in the batch, and then use the pad_sequence function, which converts the list of tensors into the desired [batch size, length] shaped tensor and performs padding using the specified pad_index. By default, pad_sequence will return a [length, batch size] shaped tensor, but by setting batch_first=True, these two dimensions are switched. We get a list of \"label\" tensors and convert the list of tensors into a single [batch size] shaped tensor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_collate_fn(pad_index):\n",
+    "    def collate_fn(batch):\n",
+    "        batch_ids = [i[\"ids\"] for i in batch]\n",
+    "        batch_ids = nn.utils.rnn.pad_sequence(\n",
+    "            batch_ids, padding_value=pad_index, batch_first=True\n",
+    "        )\n",
+    "        batch_label = [i[\"label\"] for i in batch]\n",
+    "        batch_label = torch.stack(batch_label)\n",
+    "        batch = {\"ids\": batch_ids, \"label\": batch_label}\n",
+    "        return batch\n",
+    "\n",
+    "    return collate_fn\n",
+    "\n",
+    "def get_data_loader(dataset, batch_size, pad_index, shuffle=False):\n",
+    "    collate_fn = get_collate_fn(pad_index)\n",
+    "    data_loader = torch.utils.data.DataLoader(\n",
+    "        dataset=dataset,\n",
+    "        batch_size=batch_size,\n",
+    "        collate_fn=collate_fn,\n",
+    "        shuffle=shuffle,\n",
+    "    )\n",
+    "    return data_loader\n",
+    "\n",
+    "\n",
+    "batch_size = 512\n",
+    "\n",
+    "train_data_loader = get_data_loader(train_data, batch_size, pad_index, shuffle=True)\n",
+    "valid_data_loader = get_data_loader(valid_data, batch_size, pad_index)\n",
+    "test_data_loader = get_data_loader(test_data, batch_size, pad_index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "class NBoW(nn.Module):\n",
+    "    def __init__(self, vocab_size, embedding_dim, output_dim, pad_index):\n",
+    "        super().__init__()\n",
+    "        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_index)\n",
+    "        self.fc = nn.Linear(embedding_dim, output_dim)\n",
+    "\n",
+    "    def forward(self, ids):\n",
+    "        # ids = [batch size, seq len]\n",
+    "        embedded = self.embedding(ids)\n",
+    "        # embedded = [batch size, seq len, embedding dim]\n",
+    "        pooled = embedded.mean(dim=1)\n",
+    "        # pooled = [batch size, embedding dim]\n",
+    "        prediction = self.fc(pooled)\n",
+    "        # prediction = [batch size, output dim]\n",
+    "        return prediction\n",
+    "\n",
+    "\n",
+    "vocab_size = len(vocab)\n",
+    "embedding_dim = 300\n",
+    "output_dim = len(train_data.unique(\"label\"))\n",
+    "\n",
+    "model = NBoW(vocab_size, embedding_dim, output_dim, pad_index)\n",
+    "\n",
+    "def count_parameters(model):\n",
+    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
+    "\n",
+    "\n",
+    "print(f\"The model has {count_parameters(model):,} trainable parameters\")\n",
+    "\n",
+    "vectors = torchtext.vocab.GloVe()\n",
+    "\n",
+    "pretrained_embedding = vectors.get_vecs_by_tokens(vocab.get_itos())\n",
+    "\n",
+    "optimizer = optim.Adam(model.parameters())\n",
+    "\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "model = model.to(device)\n",
+    "criterion = criterion.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(data_loader, model, criterion, optimizer, device):\n",
+    "    model.train()\n",
+    "    epoch_losses = []\n",
+    "    epoch_accs = []\n",
+    "    for batch in tqdm.tqdm(data_loader, desc=\"training...\"):\n",
+    "        ids = batch[\"ids\"].to(device)\n",
+    "        label = batch[\"label\"].to(device)\n",
+    "        prediction = model(ids)\n",
+    "        loss = criterion(prediction, label)\n",
+    "        accuracy = get_accuracy(prediction, label)\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        epoch_losses.append(loss.item())\n",
+    "        epoch_accs.append(accuracy.item())\n",
+    "    return np.mean(epoch_losses), np.mean(epoch_accs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate(data_loader, model, criterion, device):\n",
+    "    model.eval()\n",
+    "    epoch_losses = []\n",
+    "    epoch_accs = []\n",
+    "    with torch.no_grad():\n",
+    "        for batch in tqdm.tqdm(data_loader, desc=\"evaluating...\"):\n",
+    "            ids = batch[\"ids\"].to(device)\n",
+    "            label = batch[\"label\"].to(device)\n",
+    "            prediction = model(ids)\n",
+    "            loss = criterion(prediction, label)\n",
+    "            accuracy = get_accuracy(prediction, label)\n",
+    "            epoch_losses.append(loss.item())\n",
+    "            epoch_accs.append(accuracy.item())\n",
+    "    return np.mean(epoch_losses), np.mean(epoch_accs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_accuracy(prediction, label):\n",
+    "    batch_size, _ = prediction.shape\n",
+    "    predicted_classes = prediction.argmax(dim=-1)\n",
+    "    correct_predictions = predicted_classes.eq(label).sum()\n",
+    "    accuracy = correct_predictions / batch_size\n",
+    "    return accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_epochs = 10\n",
+    "best_valid_loss = float(\"inf\")\n",
+    "\n",
+    "metrics = collections.defaultdict(list)\n",
+    "\n",
+    "for epoch in range(n_epochs):\n",
+    "    train_loss, train_acc = train(\n",
+    "        train_data_loader, model, criterion, optimizer, device\n",
+    "    )\n",
+    "    valid_loss, valid_acc = evaluate(valid_data_loader, model, criterion, device)\n",
+    "    metrics[\"train_losses\"].append(train_loss)\n",
+    "    metrics[\"train_accs\"].append(train_acc)\n",
+    "    metrics[\"valid_losses\"].append(valid_loss)\n",
+    "    metrics[\"valid_accs\"].append(valid_acc)\n",
+    "    if valid_loss < best_valid_loss:\n",
+    "        best_valid_loss = valid_loss\n",
+    "        torch.save(model.state_dict(), \"nbow.pt\")\n",
+    "    print(f\"epoch: {epoch}\")\n",
+    "    print(f\"train_loss: {train_loss:.3f}, train_acc: {train_acc:.3f}\")\n",
+    "    print(f\"valid_loss: {valid_loss:.3f}, valid_acc: {valid_acc:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize=(10, 6))\n",
+    "ax = fig.add_subplot(1, 1, 1)\n",
+    "ax.plot(metrics[\"train_losses\"], label=\"train loss\")\n",
+    "ax.plot(metrics[\"valid_losses\"], label=\"valid loss\")\n",
+    "ax.set_xlabel(\"epoch\")\n",
+    "ax.set_ylabel(\"loss\")\n",
+    "ax.set_xticks(range(n_epochs))\n",
+    "ax.legend()\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize=(10, 6))\n",
+    "ax = fig.add_subplot(1, 1, 1)\n",
+    "ax.plot(metrics[\"train_accs\"], label=\"train accuracy\")\n",
+    "ax.plot(metrics[\"valid_accs\"], label=\"valid accuracy\")\n",
+    "ax.set_xlabel(\"epoch\")\n",
+    "ax.set_ylabel(\"loss\")\n",
+    "ax.set_xticks(range(n_epochs))\n",
+    "ax.legend()\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.load_state_dict(torch.load(\"nbow.pt\"))\n",
+    "\n",
+    "test_loss, test_acc = evaluate(test_data_loader, model, criterion, device)\n",
+    "\n",
+    "print(f\"test_loss: {test_loss:.3f}, test_acc: {test_acc:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict_sentiment(text, model, tokenizer, vocab, device):\n",
+    "    tokens = tokenizer(text)\n",
+    "    ids = vocab.lookup_indices(tokens)\n",
+    "    tensor = torch.LongTensor(ids).unsqueeze(dim=0).to(device)\n",
+    "    prediction = model(tensor).squeeze(dim=0)\n",
+    "    probability = torch.softmax(prediction, dim=-1)\n",
+    "    predicted_class = prediction.argmax(dim=-1).item()\n",
+    "    predicted_probability = probability[predicted_class].item()\n",
+    "    return predicted_class, predicted_probability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This film is terrible!\"\n",
+    "\n",
+    "predict_sentiment(text, model, tokenizer, vocab, device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This film is great!\"\n",
+    "\n",
+    "predict_sentiment(text, model, tokenizer, vocab, device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This film is not terrible, it's great!\"\n",
+    "\n",
+    "predict_sentiment(text, model, tokenizer, vocab, device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This film is not great, it's terrible!\"\n",
+    "\n",
+    "predict_sentiment(text, model, tokenizer, vocab, device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def text_to_tensor(text, tokenizer, vocab, device):\n",
+    "    tokens = tokenizer(text)\n",
+    "    ids = vocab.lookup_indices(tokens)\n",
+    "    tensor = torch.LongTensor(ids).unsqueeze(dim=0).to(device)\n",
+    "    return tensor\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we do onnx stuff to get the data ready for the zk-circuit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import json\n",
+    "\n",
+    "text = \"This film is terrible!\"\n",
+    "x = text_to_tensor(text, tokenizer, vocab, device)\n",
+    "\n",
+    "# Flips the neural net into inference mode\n",
+    "model.eval()\n",
+    "model.to('cpu')\n",
+    "\n",
+    "model_path = \"network.onnx\"\n",
+    "data_path = \"input.json\"\n",
+    "\n",
+    "    # Export the model\n",
+    "torch.onnx.export(model,               # model being run\n",
+    "                      x,                   # model input (or a tuple for multiple inputs)\n",
+    "                      model_path,            # where to save the model (can be a file or file-like object)\n",
+    "                      export_params=True,        # store the trained parameter weights inside the model file\n",
+    "                      opset_version=10,          # the ONNX version to export the model to\n",
+    "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
+    "                      input_names = ['input'],   # the model's input names\n",
+    "                      output_names = ['output'], # the model's output names\n",
+    "                      dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes\n",
+    "                                    'output' : {0 : 'batch_size'}})\n",
+    "\n",
+    "\n",
+    "\n",
+    "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
+    "\n",
+    "data_json = dict(input_data = [data_array])\n",
+    "\n",
+    "print(data_json)\n",
+    "\n",
+    "    # Serialize data into file:\n",
+    "json.dump(data_json, open(data_path, 'w'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ezkl\n",
+    "\n",
+    "run_args = ezkl.PyRunArgs()\n",
+    "run_args.logrows = 23\n",
+    "run_args.scale_rebase_multiplier = 10\n",
+    "# inputs should be auditable by all\n",
+    "run_args.input_visibility = \"public\"\n",
+    "# same with outputs\n",
+    "run_args.output_visibility = \"public\"\n",
+    "# for simplicity, we'll just use the fixed model visibility: i.e it is public and can't be changed by the prover\n",
+    "run_args.param_visibility = \"fixed\"\n",
+    "\n",
+    "\n",
+    "# TODO: Dictionary outputs\n",
+    "res = ezkl.gen_settings(py_run_args=run_args)\n",
+    "assert res == True\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = ezkl.compile_circuit()\n",
+    "assert res == True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# srs path\n",
+    "res = await ezkl.get_srs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# now generate the witness file\n",
+    "res = await ezkl.gen_witness()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = ezkl.mock()\n",
+    "assert res == True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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",
+    "res = ezkl.setup()\n",
+    "\n",
+    "assert res == True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# GENERATE A PROOF\n",
+    "res = ezkl.prove(proof_path=\"proof.json\")\n",
+    "\n",
+    "print(res)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# VERIFY IT\n",
+    "res = ezkl.verify()\n",
+    "\n",
+    "assert res == True\n",
+    "print(\"verified\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also verify it on chain by creating an onchain verifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check if notebook is in colab\n",
+    "try:\n",
+    "    import google.colab\n",
+    "    import subprocess\n",
+    "    import sys\n",
+    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"solc-select\"])\n",
+    "    !solc-select install 0.8.20\n",
+    "    !solc-select use 0.8.20\n",
+    "    !solc --version\n",
+    "    import os\n",
+    "\n",
+    "# rely on local installation if the notebook is not in colab\n",
+    "except:\n",
+    "    import os\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = await ezkl.create_evm_verifier()\n",
+    "assert res == True\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should see a `Verifier.sol`. Right-click and save it locally.\n",
+    "\n",
+    "Now go to [https://remix.ethereum.org](https://remix.ethereum.org).\n",
+    "\n",
+    "Create a new file within remix and copy the verifier code over.\n",
+    "\n",
+    "Finally, compile the code and deploy. For the demo you can deploy to the test environment within remix.\n",
+    "\n",
+    "If everything works, you would have deployed your verifer onchain! Copy the values in the cell above to the respective fields to test if the verifier is working."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/notebooks/random_forest.ipynb

@@ -152,9 +152,11 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "!RUST_LOG=trace\n",
-                "# TODO: Dictionary outputs\n",
-                "res = ezkl.gen_settings(model_path, settings_path)\n",
+                "run_args = ezkl.PyRunArgs()\n",
+                "# logrows\n",
+                "run_args.logrows = 20\n",
+                "\n",
+                "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
                 "assert res == True\n"
             ]
         },
@@ -302,7 +304,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.12.2"
+            "version": "3.9.13"
         }
     },
     "nbformat": 4,

examples/notebooks/reusable_verifier.ipynb

@@ -125,7 +125,7 @@
     "\n",
     "    witness_path = os.path.join(name, \"witness.json\")\n",
     "    sol_code_path = os.path.join(name, 'test.sol')\n",
-    "    sol_key_code_path = os.path.join(name, 'test_key.sol')\n",
+    "    vka_path = os.path.join(name, 'vka.bytes')\n",
     "    abi_path = os.path.join(name, 'test.abi')\n",
     "    proof_path = os.path.join(name, \"proof.json\")\n",
     "\n",
@@ -177,7 +177,7 @@
     "    res = await ezkl.create_evm_verifier(vk_path, settings_path, sol_code_path, abi_path, reusable=True)\n",
     "    assert res == True\n",
     "\n",
-    "    res = await ezkl.create_evm_vka(vk_path, settings_path, sol_key_code_path, abi_path)\n",
+    "    res = await ezkl.create_evm_vka(vk_path, settings_path, vka_path)\n",
     "    assert res == True\n"
    ]
   },
@@ -220,15 +220,6 @@
     "Check that the generated verifiers are identical for all models."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_anvil()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -270,8 +261,8 @@
     "\n",
     "res = await ezkl.deploy_evm(\n",
     "    addr_path_verifier,\n",
-    "    sol_code_path,\n",
     "    'http://127.0.0.1:3030',\n",
+    "    sol_code_path,\n",
     "    \"verifier/reusable\"\n",
     ")\n",
     "\n",
@@ -296,20 +287,21 @@
    "source": [
     "for name in names:\n",
     "    addr_path_vk = \"addr_vk.txt\"\n",
-    "    sol_key_code_path = os.path.join(name, 'test_key.sol')\n",
-    "    res = await ezkl.deploy_evm(addr_path_vk, sol_key_code_path, 'http://127.0.0.1:3030', \"vka\")\n",
+    "    vka_path = os.path.join(name, 'vka.bytes')\n",
+    "    res = await ezkl.register_vka(\n",
+    "        addr,\n",
+    "        'http://127.0.0.1:3030',\n",
+    "        vka_path=vka_path,\n",
+    "    )\n",
     "    assert res == True\n",
-    "\n",
-    "    with open(addr_path_vk, 'r') as file:\n",
-    "        addr_vk = file.read().rstrip()\n",
     "        \n",
     "    proof_path = os.path.join(name, \"proof.json\")\n",
     "    sol_code_path = os.path.join(name, 'vk.sol')\n",
     "    res = await ezkl.verify_evm(\n",
     "        addr,\n",
-    "        proof_path,\n",
     "        \"http://127.0.0.1:3030\",\n",
-    "        addr_vk = addr_vk\n",
+    "        proof_path,\n",
+    "        vka_path = vka_path\n",
     "    )\n",
     "    assert res == True"
    ]

examples/notebooks/set_membership.ipynb

@@ -167,6 +167,8 @@
         "run_args = ezkl.PyRunArgs()\n",
         "# \"hashed/private\" means that the output of the hashing is not visible to the verifier and is instead fed into the computational graph\n",
         "run_args.input_visibility = \"hashed/private/0\"\n",
+        "# as the inputs are felts we turn off input range checks\n",
+        "run_args.ignore_range_check_inputs_outputs = True\n",
         "# we set it to fix the set we want to check membership for\n",
         "run_args.param_visibility = \"fixed\"\n",
         "# the output is public -- set membership fails if it is not = 0\n",
@@ -519,4 +521,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

examples/notebooks/simple_demo_aggregated_proofs.ipynb

@@ -171,7 +171,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -328,7 +328,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 26,
             "id": "171702d3",
             "metadata": {},
             "outputs": [],
@@ -348,7 +348,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 27,
             "id": "671dfdd5",
             "metadata": {},
             "outputs": [],
@@ -364,7 +364,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 28,
             "id": "50eba2f4",
             "metadata": {},
             "outputs": [],
@@ -399,9 +399,9 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.7"
         }
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}

examples/notebooks/solvency.ipynb

@@ -204,6 +204,7 @@
         "run_args = ezkl.PyRunArgs()\n",
         "# \"polycommit\" means that the output of the hashing is not visible to the verifier and is instead fed into the computational graph\n",
         "run_args.input_visibility = \"polycommit\"\n",
+        "run_args.ignore_range_check_inputs_outputs = True\n",
         "# the parameters are public\n",
         "run_args.param_visibility = \"fixed\"\n",
         "# the output is public (this is the inequality test)\n",
@@ -514,4 +515,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

examples/notebooks/variance.ipynb

@@ -666,7 +666,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": null,
       "metadata": {},
       "outputs": [
         {
@@ -689,8 +689,8 @@
         "# await\n",
         "res = await ezkl.deploy_evm(\n",
         "    address_path,\n",
+        "    'http://127.0.0.1:3030',\n",
         "    sol_code_path,\n",
-        "    'http://127.0.0.1:3030'\n",
         ")\n",
         "\n",
         "assert res == True\n",
@@ -701,7 +701,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": null,
       "metadata": {},
       "outputs": [
         {
@@ -722,8 +722,8 @@
         "\n",
         "res = await ezkl.verify_evm(\n",
         "    addr,\n",
+        "    \"http://127.0.0.1:3030\",\n",
         "    proof_path,\n",
-        "    \"http://127.0.0.1:3030\"\n",
         ")\n",
         "assert res == True"
       ]
@@ -743,7 +743,8 @@
       "provenance": []
     },
     "kernelspec": {
-      "display_name": "Python 3",
+      "display_name": ".env",
+      "language": "python",
       "name": "python3"
     },
     "language_info": {
@@ -756,7 +757,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.2"
+      "version": "3.12.9"
     }
   },
   "nbformat": 4,

examples/notebooks/voice_judge.ipynb

@@ -849,8 +849,8 @@
                 "\n",
                 "res = await ezkl.deploy_evm(\n",
                 "    address_path,\n",
+                "    'http://127.0.0.1:3030',\n",
                 "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
                 ")\n",
                 "\n",
                 "assert res == True\n",
@@ -870,8 +870,8 @@
                 "\n",
                 "res = await ezkl.verify_evm(\n",
                 "    addr,\n",
-                "    proof_path,\n",
-                "    \"http://127.0.0.1:3030\"\n",
+                "    \"http://127.0.0.1:3030\",\n",
+                "    proof_path\n",
                 ")\n",
                 "assert res == True"
             ]
@@ -905,4 +905,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 2
-}
+}

examples/notebooks/world_rotation.ipynb

@@ -1,547 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
-   "metadata": {},
-   "source": [
-    "## World rotation\n",
-    "\n",
-    "Here we demonstrate how to use the EZKL package to rotate an on-chain world. \n",
-    "\n",
-    "![zk-gaming-diagram-transformed](https://hackmd.io/_uploads/HkApuQGV6.png)\n",
-    "> **A typical ZK application flow**. For the shape rotators out there — this is an easily digestible example. A user computes a ZK-proof that they have calculated a valid rotation of a world. They submit this proof to a verifier contract which governs an on-chain world, along with a new set of coordinates, and the world rotation updates. Observe that it’s possible for one player to initiate a *global* change.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "95613ee9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check if notebook is in colab\n",
-    "try:\n",
-    "    # install ezkl\n",
-    "    import google.colab\n",
-    "    import subprocess\n",
-    "    import sys\n",
-    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
-    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
-    "\n",
-    "# rely on local installation of ezkl if the notebook is not in colab\n",
-    "except:\n",
-    "    pass\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch import nn\n",
-    "import ezkl\n",
-    "import os\n",
-    "import json\n",
-    "import torch\n",
-    "import math\n",
-    "\n",
-    "# these are constants for the rotation\n",
-    "phi = torch.tensor(5 * math.pi / 180)\n",
-    "s = torch.sin(phi)\n",
-    "c = torch.cos(phi)\n",
-    "\n",
-    "\n",
-    "class RotateStuff(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super(RotateStuff, self).__init__()\n",
-    "\n",
-    "        # create a rotation matrix -- the matrix is constant and is transposed for convenience\n",
-    "        self.rot = torch.stack([torch.stack([c, -s]),\n",
-    "                   torch.stack([s, c])]).t()\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x_rot = x @ self.rot   # same as x_rot = (rot @ x.t()).t() due to rot in O(n) (SO(n) even)\n",
-    "        return x_rot\n",
-    "\n",
-    "\n",
-    "circuit = RotateStuff()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This will showcase the principle directions of rotation by plotting the rotation of a single unit vector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot\n",
-    "pyplot.figure(figsize=(3, 3))\n",
-    "pyplot.arrow(0, 0, 1, 0, width=0.02, alpha=0.5)\n",
-    "pyplot.arrow(0, 0, 0, 1, width=0.02, alpha=0.5)\n",
-    "pyplot.arrow(0, 0, circuit.rot[0, 0].item(), circuit.rot[0, 1].item(), width=0.02)\n",
-    "pyplot.arrow(0, 0, circuit.rot[1, 0].item(), circuit.rot[1, 1].item(), width=0.02)\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",
-    "srs_path = os.path.join('kzg.srs')\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",
-    "# initial principle vectors for the rotation are as in the plot above\n",
-    "x = torch.tensor([[1, 0], [0, 1]], dtype=torch.float32)\n",
-    "\n",
-    "# Flips the neural net into inference mode\n",
-    "circuit.eval()\n",
-    "\n",
-    "    # Export the model\n",
-    "torch.onnx.export(circuit,               # model being run\n",
-    "                      x,                   # model input (or a tuple for multiple inputs)\n",
-    "                      model_path,            # where to save the model (can be a file or file-like object)\n",
-    "                      export_params=True,        # store the trained parameter weights inside the model file\n",
-    "                      opset_version=10,          # the ONNX version to export the model to\n",
-    "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
-    "                      input_names = ['input'],   # the model's input names\n",
-    "                      output_names = ['output'], # the model's output names\n",
-    "                      )\n",
-    "\n",
-    "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
-    "\n",
-    "data = dict(input_data = [data_array])\n",
-    "\n",
-    "    # Serialize data into file:\n",
-    "json.dump( data, open(data_path, 'w' ))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### World rotation in 2D on-chain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For demo purposes we deploy these coordinates to a contract running locally using Anvil. This creates our on-chain world. We then rotate the world using the EZKL package and submit the proof to the contract. The contract then updates the world rotation. For demo purposes we do this repeatedly, rotating the world by 1 transform each time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import subprocess\n",
-    "import time\n",
-    "import threading\n",
-    "\n",
-    "# make sure anvil is running locally\n",
-    "# $ anvil -p 3030\n",
-    "\n",
-    "RPC_URL = \"http://localhost:3030\"\n",
-    "\n",
-    "# Save process globally\n",
-    "anvil_process = None\n",
-    "\n",
-    "def start_anvil():\n",
-    "    global anvil_process\n",
-    "    if anvil_process is None:\n",
-    "        anvil_process = subprocess.Popen([\"anvil\", \"-p\", \"3030\", \"--code-size-limit=41943040\"])\n",
-    "        if anvil_process.returncode is not None:\n",
-    "            raise Exception(\"failed to start anvil process\")\n",
-    "        time.sleep(3)\n",
-    "\n",
-    "def stop_anvil():\n",
-    "    global anvil_process\n",
-    "    if anvil_process is not None:\n",
-    "        anvil_process.terminate()\n",
-    "        anvil_process = None\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We define our `PyRunArgs` objects which contains the visibility parameters for out model. \n",
-    "- `input_visibility` defines the visibility of the model inputs\n",
-    "- `param_visibility` defines the visibility of the model weights and constants and parameters \n",
-    "- `output_visibility` defines the visibility of the model outputs\n",
-    "\n",
-    "Here we create the following setup:\n",
-    "- `input_visibility`: \"public\"\n",
-    "- `param_visibility`: \"fixed\"\n",
-    "- `output_visibility`: public"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d5e374a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "py_run_args = ezkl.PyRunArgs()\n",
-    "py_run_args.input_visibility = \"public\"\n",
-    "py_run_args.output_visibility = \"public\"\n",
-    "py_run_args.param_visibility = \"private\" # private by default\n",
-    "py_run_args.scale_rebase_multiplier = 10\n",
-    "\n",
-    "res = ezkl.gen_settings(model_path, settings_path, py_run_args=py_run_args)"
-   ]
-  },
-  {
-   "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": "markdown",
-   "metadata": {},
-   "source": [
-    "We also define a contract that holds out test data. This contract will contain in its storage the data that we will read from and attest to. In production you would not need to set up a local anvil instance. Instead you would replace RPC_URL with the actual RPC endpoint of the chain you are deploying your verifiers too, reading from the data on said chain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2007dc77",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ezkl.setup_test_evm_witness(\n",
-    "    data_path,\n",
-    "    compiled_model_path,\n",
-    "    # we write the call data to the same file as the input data\n",
-    "    data_path,\n",
-    "    input_source=ezkl.PyTestDataSource.OnChain,\n",
-    "    output_source=ezkl.PyTestDataSource.File,\n",
-    "    rpc_url=RPC_URL)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ab993958",
-   "metadata": {},
-   "source": [
-    "As we use Halo2 with KZG-commitments we need an SRS string from (preferably) a multi-party trusted setup ceremony. For an overview of the procedures for such a ceremony check out [this page](https://blog.ethereum.org/2023/01/16/announcing-kzg-ceremony). The `get_srs` command retrieves a correctly sized SRS given the calibrated settings file from [here](https://github.com/han0110/halo2-kzg-srs). \n",
-    "\n",
-    "These SRS were generated with [this](https://github.com/privacy-scaling-explorations/perpetualpowersoftau) ceremony. "
-   ]
-  },
-  {
-   "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",
-    "witness = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
-    "assert os.path.isfile(witness_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad58432e",
-   "metadata": {},
-   "source": [
-    "Here we setup verifying and proving keys for the circuit. As the name suggests the proving key is needed for ... proving and the verifying key is needed for ... verifying. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b1c561a8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "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": "markdown",
-   "id": "1746c8d1",
-   "metadata": {},
-   "source": [
-    "We can now create an EVM verifier contract from our circuit. This contract will be deployed to the chain we are using. In this case we are using a local anvil instance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d1920c0f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abi_path = 'test.abi'\n",
-    "sol_code_path = 'test.sol'\n",
-    "\n",
-    "res = await ezkl.create_evm_verifier(\n",
-    "        vk_path,\n",
-    "        settings_path,\n",
-    "        sol_code_path,\n",
-    "        abi_path,\n",
-    "    )\n",
-    "assert res == True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0fd7f22b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "addr_path_verifier = \"addr_verifier.txt\"\n",
-    "\n",
-    "res = await ezkl.deploy_evm(\n",
-    "    addr_path_verifier,\n",
-    "    sol_code_path,\n",
-    "    'http://127.0.0.1:3030'\n",
-    ")\n",
-    "\n",
-    "assert res == True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9c0dffab",
-   "metadata": {},
-   "source": [
-    "With the vanilla verifier deployed, we can now create the data attestation contract, which will read in the instances from the calldata to the verifier, attest to them, call the verifier and then return the result. \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cc888848",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c2db14d7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abi_path = 'test.abi'\n",
-    "sol_code_path = 'test.sol'\n",
-    "input_path = 'input.json'\n",
-    "\n",
-    "res = await ezkl.create_evm_data_attestation(\n",
-    "        input_path,\n",
-    "        settings_path,\n",
-    "        sol_code_path,\n",
-    "        abi_path,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a018ba6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "addr_path_da = \"addr_da.txt\"\n",
-    "\n",
-    "res = await ezkl.deploy_da_evm(\n",
-    "        addr_path_da,\n",
-    "        input_path,\n",
-    "        settings_path,\n",
-    "        sol_code_path,\n",
-    "        RPC_URL,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2adad845",
-   "metadata": {},
-   "source": [
-    "Now we can pull in the data from the contract and calculate a new set of coordinates. We then rotate the world by 1 transform and submit the proof to the contract. The contract could then update the world rotation (logic not inserted here). For demo purposes we do this repeatedly, rotating the world by 1 transform. "
-   ]
-  },
-  {
-   "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": "markdown",
-   "id": "90eda56e",
-   "metadata": {},
-   "source": [
-    "Call the view only verify method on the contract to verify the proof. Since it is a view function this is safe to use in production since you don't have to pass your private key."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "76f00d41",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# read the verifier address\n",
-    "addr_verifier = None\n",
-    "with open(addr_path_verifier, 'r') as f:\n",
-    "    addr = f.read()\n",
-    "#read the data attestation address\n",
-    "addr_da = None\n",
-    "with open(addr_path_da, 'r') as f:\n",
-    "    addr_da = f.read()\n",
-    "\n",
-    "res = ezkl.verify_evm(\n",
-    "    addr,\n",
-    "    proof_path,\n",
-    "    RPC_URL,\n",
-    "    addr_da,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a sanity check lets plot the rotations of the unit vectors. We can see that the unit vectors rotate as expected by the output of the circuit. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "witness['outputs'][0][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings = json.load(open(settings_path, 'r'))\n",
-    "out_scale = settings[\"model_output_scales\"][0]\n",
-    "\n",
-    "from matplotlib import pyplot\n",
-    "pyplot.figure(figsize=(3, 3))\n",
-    "pyplot.arrow(0, 0, 1, 0, width=0.02, alpha=0.5)\n",
-    "pyplot.arrow(0, 0, 0, 1, width=0.02, alpha=0.5)\n",
-    "\n",
-    "arrow_x = ezkl.felt_to_float(witness['outputs'][0][0], out_scale)\n",
-    "arrow_y = ezkl.felt_to_float(witness['outputs'][0][1], out_scale)\n",
-    "pyplot.arrow(0, 0, arrow_x, arrow_y, width=0.02)\n",
-    "arrow_x = ezkl.felt_to_float(witness['outputs'][0][2], out_scale)\n",
-    "arrow_y = ezkl.felt_to_float(witness['outputs'][0][3], out_scale)\n",
-    "pyplot.arrow(0, 0, arrow_x, arrow_y, width=0.02)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/onnx/1d_conv/input.json

@@ -0,0 +1,106 @@
+{
+  "input_data": [
+    [
+      8761,
+      7654,
+      8501,
+      2404,
+      6929,
+      8858,
+      5946,
+      3673,
+      4131,
+      3854,
+      8137,
+      8239,
+      9038,
+      6299,
+      1118,
+      9737,
+      208,
+      7954,
+      3691,
+      610,
+      3468,
+      3314,
+      8658,
+      8366,
+      2850,
+      477,
+      6114,
+      232,
+      4601,
+      7420,
+      5713,
+      2936,
+      6061,
+      2870,
+      8421,
+      177,
+      7107,
+      7382,
+      6115,
+      5487,
+      8502,
+      2559,
+      1875,
+      129,
+      8533,
+      8201,
+      8414,
+      4775,
+      9817,
+      3127,
+      8761,
+      7654,
+      8501,
+      2404,
+      6929,
+      8858,
+      5946,
+      3673,
+      4131,
+      3854,
+      8137,
+      8239,
+      9038,
+      6299,
+      1118,
+      9737,
+      208,
+      7954,
+      3691,
+      610,
+      3468,
+      3314,
+      8658,
+      8366,
+      2850,
+      477,
+      6114,
+      232,
+      4601,
+      7420,
+      5713,
+      2936,
+      6061,
+      2870,
+      8421,
+      177,
+      7107,
+      7382,
+      6115,
+      5487,
+      8502,
+      2559,
+      1875,
+      129,
+      8533,
+      8201,
+      8414,
+      4775,
+      9817,
+      3127
+    ]
+  ]
+}

examples/onnx/1l_div/settings.json

@@ -0,0 +1 @@
+{"run_args":{"input_scale":7,"param_scale":7,"scale_rebase_multiplier":1,"lookup_range":[-32768,32768],"logrows":17,"num_inner_cols":2,"variables":[["batch_size",1]],"input_visibility":"Private","output_visibility":"Public","param_visibility":"Private","rebase_frac_zero_constants":false,"check_mode":"UNSAFE","commitment":"KZG","decomp_base":16384,"decomp_legs":2,"bounded_log_lookup":false,"ignore_range_check_inputs_outputs":false},"num_rows":54,"total_assignments":109,"total_const_size":4,"total_dynamic_col_size":0,"max_dynamic_input_len":0,"num_dynamic_lookups":0,"num_shuffles":0,"total_shuffle_col_size":0,"model_instance_shapes":[[1,1]],"model_output_scales":[7],"model_input_scales":[7],"module_sizes":{"polycommit":[],"poseidon":[0,[0]]},"required_lookups":[],"required_range_checks":[[-1,1],[0,16383]],"check_mode":"UNSAFE","version":"0.0.0","num_blinding_factors":null,"timestamp":1739396322131,"input_types":["F32"],"output_types":["F32"]}

examples/onnx/exp/gen.py

@@ -0,0 +1,42 @@
+from torch import nn
+import torch
+import json
+import numpy as np
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+
+    def forward(self, x):          
+        m = torch.exp(x)
+        
+        return m 
+
+
+circuit = MyModel()
+
+x = torch.empty(1, 8).uniform_(0, 1)
+
+out = circuit(x)
+
+print(out)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))

examples/onnx/exp/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.5801457762718201, 0.6019012331962585, 0.8695418238639832, 0.17170941829681396, 0.500616729259491, 0.353726327419281, 0.6726185083389282, 0.5936906337738037]]}

examples/onnx/exp/network.onnx

@@ -0,0 +1,14 @@
+pytorch2.2.2:o
+
+inputoutput/Exp"Exp
+main_graphZ!
+input
+

+
+batch_size
+b"
+output
+

+
+batch_size
+B

examples/onnx/general_exp/gen.py

@@ -0,0 +1,41 @@
+from torch import nn
+import torch
+import json
+import numpy as np
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+
+    def forward(self, x):          
+        m = 10**x
+        return m 
+
+
+circuit = MyModel()
+
+x = torch.empty(1, 8).uniform_(0, 1)
+
+out = circuit(x)
+
+print(out)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))

examples/onnx/general_exp/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.9837989807128906, 0.026381194591522217, 0.3403851389884949, 0.14531707763671875, 0.24652725458145142, 0.7945117354393005, 0.4076554775238037, 0.23064672946929932]]}

examples/onnx/integer_div/gen.py

@@ -0,0 +1,42 @@
+from torch import nn
+import torch
+import json
+import numpy as np
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+
+    def forward(self, x):          
+        return x // 3
+
+
+circuit = MyModel()
+
+x = torch.randint(0, 10, (1, 2, 2, 8)) 
+
+out = circuit(x)
+
+print(x)
+print(out)
+print(x/3)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))

examples/onnx/integer_div/input.json

@@ -0,0 +1 @@
+{"input_data": [[3, 4, 0, 9, 2, 6, 2, 5, 1, 5, 3, 5, 5, 7, 0, 2, 6, 1, 4, 4, 1, 9, 7, 7, 5, 8, 2, 0, 1, 5, 9, 8]]}

examples/onnx/log/gen.py

@@ -0,0 +1,42 @@
+from torch import nn
+import torch
+import json
+import numpy as np
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+
+    def forward(self, x):          
+        m = torch.log(x)
+        
+        return m 
+
+
+circuit = MyModel()
+
+x = torch.empty(1, 8).uniform_(0, 3)
+
+out = circuit(x)
+
+print(out)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))

examples/onnx/log/input.json

@@ -0,0 +1 @@
+{"input_data": [[1.9252371788024902, 1.8418371677398682, 0.8400403261184692, 2.083845853805542, 0.9760497808456421, 0.6940176486968994, 0.015579521656036377, 2.2689192295074463]]}

examples/onnx/log/network.onnx

@@ -0,0 +1,14 @@
+pytorch2.2.2:o
+
+inputoutput/Log"Log
+main_graphZ!
+input
+

+
+batch_size
+b"
+output
+

+
+batch_size
+B

examples/onnx/rounding_ops/input.json

@@ -1 +1,148 @@
-{"input_shapes": [[3, 2, 3], [3, 2, 3], [3, 2, 3], [3, 2, 3]], "input_data": [[0.6261028051376343, 0.49872446060180664, -0.04514765739440918, 0.5936200618743896, 0.9271858930587769, 0.6688600778579712, -0.20331168174743652, -0.7016235589981079, 0.025863051414489746, -0.19426143169403076, 0.9827852249145508, 0.4897397756576538, 0.2992602586746216, 0.7011144161224365, 0.9278832674026489, 0.5943725109100342, -0.573331356048584, 0.3675816059112549], [0.7803324460983276, -0.9616303443908691, 0.6070173978805542, -0.028337717056274414, -0.5080242156982422, -0.9280107021331787, 0.6150380373001099, 0.3865993022918701, -0.43668973445892334, 0.17152702808380127, 0.5144252777099609, -0.28881049156188965, 0.8932310342788696, 0.059034109115600586, 0.6865451335906982, 0.009820222854614258, 0.23011493682861328, -0.9492779970169067], [-0.21352827548980713, -0.16015326976776123, -0.38964390754699707, 0.13464701175689697, -0.8814496994018555, 0.5037975311279297, -0.804405927658081, 0.9858957529067993, 0.19567716121673584, 0.9777265787124634, 0.6151977777481079, 0.568595290184021, 0.10584986209869385, -0.8975653648376465, 0.6235959529876709, -0.547879695892334, 0.9289869070053101, 0.7567293643951416]], "output_data": [[1.0, 0.0, -0.0, 1.0, 1.0, 1.0, -0.0, -1.0, 0.0, -0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, -1.0, 0.0], [0.0, -1.0, 0.0, -1.0, -1.0, -1.0, 0.0, 0.0, -1.0, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0], [-0.0, -0.0, -0.0, 1.0, -0.0, 1.0, -0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.0, 1.0, -0.0, 1.0, 1.0]]}
+{
+    "input_shapes": [
+        [
+            3,
+            2,
+            3
+        ],
+        [
+            3,
+            2,
+            3
+        ],
+        [
+            3,
+            2,
+            3
+        ],
+        [
+            3,
+            2,
+            3
+        ]
+    ],
+    "input_data": [
+        [
+            0.5,
+            1.5,
+            -0.04514765739440918,
+            0.5936200618743896,
+            0.9271858930587769,
+            0.6688600778579712,
+            -0.20331168174743652,
+            -0.7016235589981079,
+            0.025863051414489746,
+            -0.19426143169403076,
+            0.9827852249145508,
+            0.4897397756576538,
+            -1.5,
+            -0.5,
+            0.9278832674026489,
+            0.5943725109100342,
+            -0.573331356048584,
+            0.3675816059112549
+        ],
+        [
+            0.7803324460983276,
+            -0.9616303443908691,
+            0.6070173978805542,
+            -0.028337717056274414,
+            -0.5080242156982422,
+            -0.9280107021331787,
+            0.6150380373001099,
+            0.3865993022918701,
+            -0.43668973445892334,
+            0.17152702808380127,
+            0.5144252777099609,
+            -0.28881049156188965,
+            0.8932310342788696,
+            0.059034109115600586,
+            0.6865451335906982,
+            0.009820222854614258,
+            0.23011493682861328,
+            -0.9492779970169067
+        ],
+        [
+            -0.21352827548980713,
+            -0.16015326976776123,
+            -0.38964390754699707,
+            0.13464701175689697,
+            -0.8814496994018555,
+            0.5037975311279297,
+            -0.804405927658081,
+            0.9858957529067993,
+            0.19567716121673584,
+            0.9777265787124634,
+            0.6151977777481079,
+            0.568595290184021,
+            0.10584986209869385,
+            -0.8975653648376465,
+            0.6235959529876709,
+            -0.547879695892334,
+            0.9289869070053101,
+            0.7567293643951416
+        ]
+    ],
+    "output_data": [
+        [
+            1.0,
+            0.0,
+            -0.0,
+            1.0,
+            1.0,
+            1.0,
+            -0.0,
+            -1.0,
+            0.0,
+            -0.0,
+            1.0,
+            0.0,
+            0.0,
+            1.0,
+            1.0,
+            1.0,
+            -1.0,
+            0.0
+        ],
+        [
+            0.0,
+            -1.0,
+            0.0,
+            -1.0,
+            -1.0,
+            -1.0,
+            0.0,
+            0.0,
+            -1.0,
+            0.0,
+            0.0,
+            -1.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            -1.0
+        ],
+        [
+            -0.0,
+            -0.0,
+            -0.0,
+            1.0,
+            -0.0,
+            1.0,
+            -0.0,
+            1.0,
+            1.0,
+            1.0,
+            1.0,
+            1.0,
+            1.0,
+            -0.0,
+            1.0,
+            -0.0,
+            1.0,
+            1.0
+        ]
+    ]
+}

examples/onnx/scatter_nd/gen.py

@@ -1,75 +1,52 @@
-import torch
-import torch.nn as nn
-import sys
+from torch import nn
 import json
-
-sys.path.append("..")
-
-class Model(nn.Module):
-    """
-    Just one Linear layer
-    """
-    def __init__(self, configs):
-        super(Model, self).__init__()
-        self.seq_len = configs.seq_len
-        self.pred_len = configs.pred_len
-
-        # Use this line if you want to visualize the weights
-        # self.Linear.weight = nn.Parameter((1/self.seq_len)*torch.ones([self.pred_len,self.seq_len]))
-        self.channels = configs.enc_in
-        self.individual = configs.individual
-        if self.individual:
-            self.Linear = nn.ModuleList()
-            for i in range(self.channels):
-                self.Linear.append(nn.Linear(self.seq_len,self.pred_len))
-        else:
-            self.Linear = nn.Linear(self.seq_len, self.pred_len)
-
-    def forward(self, x):
-        # x: [Batch, Input length, Channel]
-        if self.individual:
-            output = torch.zeros([x.size(0),self.pred_len,x.size(2)],dtype=x.dtype).to(x.device)
-            for i in range(self.channels):
-                output[:,:,i] = self.Linear[i](x[:,:,i])
-            x = output
-        else:
-            x = self.Linear(x.permute(0,2,1)).permute(0,2,1)
-        return x # [Batch, Output length, Channel]
-
-class Configs:
-    def __init__(self, seq_len, pred_len, enc_in=321, individual=True):
-      self.seq_len = seq_len
-      self.pred_len = pred_len
-      self.enc_in = enc_in
-      self.individual = individual
-
-model = 'Linear'
-seq_len = 10
-pred_len = 4
-enc_in = 3
-
-configs = Configs(seq_len, pred_len, enc_in, True)
-circuit = Model(configs)
-
-x = torch.randn(1, seq_len, pred_len)
+import numpy as np
+import tf2onnx
 
 
-torch.onnx.export(circuit, x, "network.onnx",
-                  export_params=True,        # store the trained parameter weights inside the model file
-                  opset_version=15,          # the ONNX version to export the model to
-                  do_constant_folding=True,  # whether to execute constant folding for optimization
-                  # the model's input names
-                  input_names=['input'],
-                  output_names=['output'],  # the model's output names
-                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
-                                'output': {0: 'batch_size'}})
+import tensorflow as tf
+from tensorflow.keras.layers import *
+from tensorflow.keras.models import Model
 
 
-d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+# gather_nd in tf then export to onnx
+x = in1 = Input((4, 1), dtype=tf.int32)
+w = in2 = Input((4, ), dtype=tf.int32)
+
+class MyLayer(Layer):
+    def call(self, x, w):
+        shape = tf.constant([8])
+        return tf.scatter_nd(x, w, shape)
+
+x = MyLayer()(x, w)
+
+
+
+tm = Model((in1, in2), x)
+tm.summary()
+tm.compile(optimizer='adam', loss='mse')
+
+shape = [1, 4, 1]
+index_shape = [1, 4]
+# After training, export to onnx (network.onnx) and create a data file (input.json)
+x = np.random.randint(0, 4, shape)
+# w = random int tensor
+w = np.random.randint(0, 4, index_shape)
+
+spec = tf.TensorSpec(shape, tf.int32, name='input_0')
+index_spec = tf.TensorSpec(index_shape, tf.int32, name='input_1')
+
+model_path = "network.onnx"
+
+tf2onnx.convert.from_keras(tm, input_signature=[spec, index_spec], inputs_as_nchw=['input_0', 'input_1'], opset=12, output_path=model_path)
+
+
+d = x.reshape([-1]).tolist()
+d1 = w.reshape([-1]).tolist()
 
 
 data = dict(
-    input_data=[d1],
+    input_data=[d, d1],
 )
 
 # Serialize data into file:

examples/onnx/scatter_nd/input.json

@@ -1 +1,16 @@
-{"input_data": [[0.1874287724494934, 1.0498261451721191, 0.22384068369865417, 1.048445224761963, -0.5670360326766968, -0.38653188943862915, 0.12878702580928802, -2.3675858974456787, 0.5800458192825317, -0.43653929233551025, -0.2511898875236511, 0.3324051797389984, 0.27960312366485596, 0.4763695001602173, 0.3796705901622772, 1.1334782838821411, -0.87981778383255, -1.2451434135437012, 0.7672272324562073, -0.24404007196426392, -0.6875824928283691, 0.3619358539581299, -0.10131897777318954, 0.7169521450996399, 1.6585893630981445, -0.5451845526695251, 0.429487019777298, 0.7426952123641968, -0.2543637454509735, 0.06546942889690399, 0.7939824461936951, 0.1579471379518509, -0.043604474514722824, -0.8621711730957031, -0.5344759821891785, -0.05880478024482727, -0.17351101338863373, 0.5095029473304749, -0.7864817976951599, -0.449171245098114]]}
+{
+    "input_data": [
+        [
+            0,
+            1,
+            2,
+            3
+        ],
+        [
+            1,
+            0,
+            2,
+            1
+        ]
+    ]
+}

ezkl.pyi

@@ -0,0 +1,789 @@
+# This file is automatically generated by pyo3_stub_gen
+# ruff: noqa: E501, F401
+
+import os
+import pathlib
+import typing
+from enum import Enum, auto
+
+class PyG1:
+    r"""
+    pyclass containing the struct used for G1, this is mostly a helper class
+    """
+    ...
+
+class PyG1Affine:
+    r"""
+    pyclass containing the struct used for G1
+    """
+    ...
+
+class PyRunArgs:
+    r"""
+    Python class containing the struct used for run_args
+    
+    Returns
+    -------
+    PyRunArgs
+    """
+    ...
+
+class PyCommitments(Enum):
+    r"""
+    pyclass representing an enum, denoting the type of commitment
+    """
+    KZG = auto()
+    IPA = auto()
+
+class PyInputType(Enum):
+    Bool = auto()
+    F16 = auto()
+    F32 = auto()
+    F64 = auto()
+    Int = auto()
+    TDim = auto()
+
+class PyTestDataSource(Enum):
+    r"""
+    pyclass representing an enum
+    """
+    File = auto()
+    OnChain = auto()
+
+def aggregate(aggregation_snarks:typing.Sequence[str | os.PathLike | pathlib.Path],proof_path:str | os.PathLike | pathlib.Path,vk_path:str | os.PathLike | pathlib.Path,transcript:str,logrows:int,check_mode:str,split_proofs:bool,srs_path:typing.Optional[str | os.PathLike | pathlib.Path],commitment:PyCommitments) -> bool:
+    r"""
+    Creates an aggregated proof
+    
+    Arguments
+    ---------
+    aggregation_snarks: list[str]
+        List of paths to the various proofs
+    
+    proof_path: str
+        Path to output the aggregated proof
+    
+    vk_path: str
+        Path to the VK file
+    
+    transcript:
+        Proof transcript type to be used. `evm` used by default. `poseidon` is also supported
+    
+    logrows:
+        Logrows used for aggregation circuit
+    
+    check_mode: str
+        Run sanity checks during calculations. Accepts `safe` or `unsafe`
+    
+    split-proofs: bool
+         Whether the accumulated proofs are segments of a larger circuit
+    
+    srs_path: str
+        Path to the SRS used
+    
+    commitment: str
+        Accepts "kzg" or "ipa"
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def buffer_to_felts(buffer:typing.Sequence[int]) -> list[str]:
+    r"""
+    Converts a buffer to vector of field elements
+    
+    Arguments
+    -------
+    buffer: list[int]
+        List of integers representing a buffer
+    
+    Returns
+    -------
+    list[str]
+        List of field elements represented as strings
+    """
+    ...
+
+def calibrate_settings(data:str | os.PathLike | pathlib.Path,model:str | os.PathLike | pathlib.Path,settings:str | os.PathLike | pathlib.Path,target:str,lookup_safety_margin:float,scales:typing.Optional[typing.Sequence[int]],scale_rebase_multiplier:typing.Sequence[int],max_logrows:typing.Optional[int]) -> typing.Any:
+    r"""
+    Calibrates the circuit settings
+    
+    Arguments
+    ---------
+    data: str
+        Path to the calibration data
+    
+    model: str
+        Path to the onnx file
+    
+    settings: str
+        Path to the settings file
+    
+    lookup_safety_margin: int
+         the lookup safety margin to use for calibration. if the max lookup is 2^k, then the max lookup will be 2^k * lookup_safety_margin. larger = safer but slower
+    
+    scales: list[int]
+        Optional scales to specifically try for calibration
+    
+    scale_rebase_multiplier: list[int]
+        Optional scale rebase multipliers to specifically try for calibration. This is the multiplier at which we divide to return to the input scale.
+    
+    max_logrows: int
+        Optional max logrows to use for calibration
+    
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def compile_circuit(model:str | os.PathLike | pathlib.Path,compiled_circuit:str | os.PathLike | pathlib.Path,settings_path:str | os.PathLike | pathlib.Path) -> bool:
+    r"""
+    Compiles the circuit for use in other steps
+    
+    Arguments
+    ---------
+    model: str
+        Path to the onnx model file
+    
+    compiled_circuit: str
+        Path to output the compiled circuit
+    
+    settings_path: str
+        Path to the settings files
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def create_evm_verifier(vk_path:str | os.PathLike | pathlib.Path,settings_path:str | os.PathLike | pathlib.Path,sol_code_path:str | os.PathLike | pathlib.Path,abi_path:str | os.PathLike | pathlib.Path,srs_path:typing.Optional[str | os.PathLike | pathlib.Path],reusable:bool) -> typing.Any:
+    r"""
+    Creates an EVM compatible verifier, you will need solc installed in your environment to run this
+    
+    Arguments
+    ---------
+    vk_path: str
+        The path to the verification key file
+    
+    settings_path: str
+        The path to the settings file
+    
+    sol_code_path: str
+        The path to the create the solidity verifier
+    
+    abi_path: str
+        The path to create the ABI for the solidity verifier
+    
+    srs_path: str
+        The path to the SRS file
+    
+    reusable: bool
+        Whether the verifier should be rendered as a reusable contract. If so, then you will need to deploy the VK artifact separately which you can generate using the create_evm_vka command
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def create_evm_verifier_aggr(aggregation_settings:typing.Sequence[str | os.PathLike | pathlib.Path],vk_path:str | os.PathLike | pathlib.Path,sol_code_path:str | os.PathLike | pathlib.Path,abi_path:str | os.PathLike | pathlib.Path,logrows:int,srs_path:typing.Optional[str | os.PathLike | pathlib.Path],reusable:bool) -> typing.Any:
+    r"""
+    Creates an evm compatible aggregate verifier, you will need solc installed in your environment to run this
+    
+    Arguments
+    ---------
+    aggregation_settings: str
+        path to the settings file
+    
+    vk_path: str
+        The path to load the desired verification key file
+    
+    sol_code_path: str
+        The path to the Solidity code
+    
+    abi_path: str
+        The path to output the Solidity verifier ABI
+    
+    logrows: int
+        Number of logrows used during aggregated setup
+    
+    srs_path: str
+        The path to the SRS file
+    
+    reusable: bool
+        Whether the verifier should be rendered as a reusable contract. If so, then you will need to deploy the VK artifact separately which you can generate using the create_evm_vka command
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def create_evm_vka(vk_path:str | os.PathLike | pathlib.Path,settings_path:str | os.PathLike | pathlib.Path,vka_path:str | os.PathLike | pathlib.Path,srs_path:typing.Optional[str | os.PathLike | pathlib.Path]) -> typing.Any:
+    r"""
+    Creates an Evm VK artifact. This command generated a VK with circuit specific meta data encoding in memory for use by the reusable H2 verifier.
+    This is useful for deploying verifier that were otherwise too big to fit on chain and required aggregation.
+    
+    Arguments
+    ---------
+    vk_path: str
+        The path to the verification key file
+    
+    settings_path: str
+        The path to the settings file
+    
+    vka_path: str
+        The path to the create the vka calldata.
+    
+    abi_path: str
+        The path to create the ABI for the solidity verifier
+    
+    srs_path: str
+        The path to the SRS file
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def deploy_evm(addr_path:str | os.PathLike | pathlib.Path,sol_code_path:str | os.PathLike | pathlib.Path,rpc_url:typing.Optional[str],contract_type:str,optimizer_runs:int,private_key:typing.Optional[str]) -> typing.Any:
+    r"""
+    deploys the solidity verifier
+    """
+    ...
+
+def encode_evm_calldata(proof:str | os.PathLike | pathlib.Path,calldata:str | os.PathLike | pathlib.Path,addr_vk:typing.Optional[str]) -> list[int]:
+    r"""
+    Creates encoded evm calldata from a proof file
+    
+    Arguments
+    ---------
+    proof: str
+        Path to the proof file
+    
+    calldata: str
+       Path to the calldata file to save
+    
+    addr_vk: str
+       The address of the verification key contract (if the verifier key is to be rendered as a separate contract)
+    
+    Returns
+    -------
+    vec[u8]
+       The encoded calldata
+    """
+    ...
+
+def felt_to_big_endian(felt:str) -> str:
+    r"""
+    Converts a field element hex string to big endian
+    
+    Arguments
+    -------
+    felt: str
+        The field element represented as a string
+    
+    
+    Returns
+    -------
+    str
+        field element represented as a string
+    """
+    ...
+
+def felt_to_float(felt:str,scale:int) -> float:
+    r"""
+    Converts a field element hex string to a floating point number
+    
+    Arguments
+    -------
+    felt: str
+       The field element represented as a string
+    
+    scale: float
+        The scaling factor used to convert the field element into a floating point representation
+    
+    Returns
+    -------
+    float
+    """
+    ...
+
+def felt_to_int(felt:str) -> int:
+    r"""
+    Converts a field element hex string to an integer
+    
+    Arguments
+    -------
+    felt: str
+        The field element represented as a string
+    
+    Returns
+    -------
+    int
+    """
+    ...
+
+def float_to_felt(input:float,scale:int,input_type:PyInputType) -> str:
+    r"""
+    Converts a floating point element to a field element hex string
+    
+    Arguments
+    -------
+    input: float
+       The field element represented as a string
+    
+    scale: float
+        The scaling factor used to quantize the float into a field element
+    
+    input_type: PyInputType
+        The type of the input
+    
+    Returns
+    -------
+    str
+        The field element represented as a string
+    """
+    ...
+
+def gen_settings(model:str | os.PathLike | pathlib.Path,output:str | os.PathLike | pathlib.Path,py_run_args:typing.Optional[PyRunArgs]) -> bool:
+    r"""
+    Generates the circuit settings
+    
+    Arguments
+    ---------
+    model: str
+        Path to the onnx file
+    
+    output: str
+        Path to create the settings file
+    
+    py_run_args: PyRunArgs
+        PyRunArgs object to initialize the settings
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def gen_srs(srs_path:str | os.PathLike | pathlib.Path,logrows:int) -> None:
+    r"""
+    Generates the Structured Reference String (SRS), use this only for testing purposes
+    
+    Arguments
+    ---------
+    srs_path: str
+        Path to the create the SRS file
+    
+    logrows: int
+        The number of logrows for the SRS file
+    """
+    ...
+
+def gen_vk_from_pk_aggr(path_to_pk:str | os.PathLike | pathlib.Path,vk_output_path:str | os.PathLike | pathlib.Path) -> bool:
+    r"""
+    Generates a vk from a pk for an aggregate circuit and saves it to a file
+    
+    Arguments
+    -------
+    path_to_pk: str
+        Path to the proving key
+    
+    vk_output_path: str
+        Path to create the vk file
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def gen_vk_from_pk_single(path_to_pk:str | os.PathLike | pathlib.Path,circuit_settings_path:str | os.PathLike | pathlib.Path,vk_output_path:str | os.PathLike | pathlib.Path) -> bool:
+    r"""
+    Generates a vk from a pk for a model circuit and saves it to a file
+    
+    Arguments
+    -------
+    path_to_pk: str
+        Path to the proving key
+    
+    circuit_settings_path: str
+        Path to the witness file
+    
+    vk_output_path: str
+        Path to create the vk file
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def gen_witness(data:str | os.PathLike | pathlib.Path,model:str | os.PathLike | pathlib.Path,output:typing.Optional[str | os.PathLike | pathlib.Path],vk_path:typing.Optional[str | os.PathLike | pathlib.Path],srs_path:typing.Optional[str | os.PathLike | pathlib.Path]) -> typing.Any:
+    r"""
+    Runs the forward pass operation to generate a witness
+    
+    Arguments
+    ---------
+    data: str
+        Path to the data file
+    
+    model: str
+        Path to the compiled model file
+    
+    output: str
+        Path to create the witness file
+    
+    vk_path: str
+        Path to the verification key
+    
+    srs_path: str
+        Path to the SRS file
+    
+    Returns
+    -------
+    dict
+        Python object containing the witness values
+    """
+    ...
+
+def get_srs(settings_path:typing.Optional[str | os.PathLike | pathlib.Path],logrows:typing.Optional[int],srs_path:typing.Optional[str | os.PathLike | pathlib.Path],commitment:typing.Optional[PyCommitments]) -> typing.Any:
+    r"""
+    Gets a public srs
+    
+    Arguments
+    ---------
+    settings_path: str
+        Path to the settings file
+    
+    logrows: int
+        The number of logrows for the SRS file
+    
+    srs_path: str
+        Path to the create the SRS file
+    
+    commitment: str
+        Specify the commitment used ("kzg", "ipa")
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def ipa_commit(message:typing.Sequence[str],vk_path:str | os.PathLike | pathlib.Path,settings_path:str | os.PathLike | pathlib.Path,srs_path:typing.Optional[str | os.PathLike | pathlib.Path]) -> list[PyG1Affine]:
+    r"""
+    Generate an ipa commitment.
+    
+    Arguments
+    -------
+    message: list[str]
+        List of field elements represnted as strings
+    
+    vk_path: str
+        Path to the verification key
+    
+    settings_path: str
+        Path to the settings file
+    
+    srs_path: str
+        Path to the Structure Reference String (SRS) file
+    
+    Returns
+    -------
+    list[PyG1Affine]
+    """
+    ...
+
+def kzg_commit(message:typing.Sequence[str],vk_path:str | os.PathLike | pathlib.Path,settings_path:str | os.PathLike | pathlib.Path,srs_path:typing.Optional[str | os.PathLike | pathlib.Path]) -> list[PyG1Affine]:
+    r"""
+    Generate a kzg commitment.
+    
+    Arguments
+    -------
+    message: list[str]
+        List of field elements represnted as strings
+    
+    vk_path: str
+        Path to the verification key
+    
+    settings_path: str
+        Path to the settings file
+    
+    srs_path: str
+        Path to the Structure Reference String (SRS) file
+    
+    Returns
+    -------
+    list[PyG1Affine]
+    """
+    ...
+
+def mock(witness:str | os.PathLike | pathlib.Path,model:str | os.PathLike | pathlib.Path) -> bool:
+    r"""
+    Mocks the prover
+    
+    Arguments
+    ---------
+    witness: str
+        Path to the witness file
+    
+    model: str
+        Path to the compiled model file
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def mock_aggregate(aggregation_snarks:typing.Sequence[str | os.PathLike | pathlib.Path],logrows:int,split_proofs:bool) -> bool:
+    r"""
+    Mocks the aggregate prover
+    
+    Arguments
+    ---------
+    aggregation_snarks: list[str]
+        List of paths to the relevant proof files
+    
+    logrows: int
+        Number of logrows to use for the aggregation circuit
+    
+    split_proofs: bool
+        Indicates whether the accumulated are segments of a larger proof
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def poseidon_hash(message:typing.Sequence[str]) -> list[str]:
+    r"""
+    Generate a poseidon hash.
+    
+    Arguments
+    -------
+    message: list[str]
+        List of field elements represented as strings
+    
+    Returns
+    -------
+    list[str]
+        List of field elements represented as strings
+    """
+    ...
+
+def prove(witness:str | os.PathLike | pathlib.Path,model:str | os.PathLike | pathlib.Path,pk_path:str | os.PathLike | pathlib.Path,proof_path:typing.Optional[str | os.PathLike | pathlib.Path],proof_type:str,srs_path:typing.Optional[str | os.PathLike | pathlib.Path]) -> typing.Any:
+    r"""
+    Runs the prover on a set of inputs
+    
+    Arguments
+    ---------
+    witness: str
+        Path to the witness file
+    
+    model: str
+        Path to the compiled model file
+    
+    pk_path: str
+        Path to the proving key file
+    
+    proof_path: str
+        Path to create the proof file
+    
+    proof_type: str
+        Accepts `single`, `for-aggr`
+    
+    srs_path: str
+        Path to the SRS file
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def setup(model:str | os.PathLike | pathlib.Path,vk_path:str | os.PathLike | pathlib.Path,pk_path:str | os.PathLike | pathlib.Path,srs_path:typing.Optional[str | os.PathLike | pathlib.Path],witness_path:typing.Optional[str | os.PathLike | pathlib.Path],disable_selector_compression:bool) -> bool:
+    r"""
+    Runs the setup process
+    
+    Arguments
+    ---------
+    model: str
+        Path to the compiled model file
+    
+    vk_path: str
+        Path to create the verification key file
+    
+    pk_path: str
+        Path to create the proving key file
+    
+    srs_path: str
+        Path to the SRS file
+    
+    witness_path: str
+        Path to the witness file
+    
+    disable_selector_compression: bool
+        Whether to compress the selectors or not
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def setup_aggregate(sample_snarks:typing.Sequence[str | os.PathLike | pathlib.Path],vk_path:str | os.PathLike | pathlib.Path,pk_path:str | os.PathLike | pathlib.Path,logrows:int,split_proofs:bool,srs_path:typing.Optional[str | os.PathLike | pathlib.Path],disable_selector_compression:bool,commitment:PyCommitments) -> bool:
+    r"""
+     Runs the setup process for an aggregate setup
+    
+    Arguments
+    ---------
+    sample_snarks: list[str]
+        List of paths to the various proofs
+    
+    vk_path: str
+        Path to create the aggregated VK
+    
+    pk_path: str
+        Path to create the aggregated PK
+    
+    logrows: int
+        Number of logrows to use
+    
+    split_proofs: bool
+        Whether the accumulated are segments of a larger proof
+    
+    srs_path: str
+        Path to the SRS file
+    
+    disable_selector_compression: bool
+        Whether to compress selectors
+    
+    commitment: str
+        Accepts `kzg`, `ipa`
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+
+def swap_proof_commitments(proof_path:str | os.PathLike | pathlib.Path,witness_path:str | os.PathLike | pathlib.Path) -> None:
+    r"""
+    Swap the commitments in a proof
+    
+    Arguments
+    -------
+    proof_path: str
+        Path to the proof file
+    
+    witness_path: str
+        Path to the witness file
+    """
+    ...
+
+def table(model:str | os.PathLike | pathlib.Path,py_run_args:typing.Optional[PyRunArgs]) -> str:
+    r"""
+    Displays the table as a string in python
+    
+    Arguments
+    ---------
+    model: str
+        Path to the onnx file
+    
+    Returns
+    ---------
+    str
+        Table of the nodes in the onnx file
+    """
+    ...
+
+def verify(proof_path:str | os.PathLike | pathlib.Path,settings_path:str | os.PathLike | pathlib.Path,vk_path:str | os.PathLike | pathlib.Path,srs_path:typing.Optional[str | os.PathLike | pathlib.Path],reduced_srs:bool) -> bool:
+    r"""
+    Verifies a given proof
+    
+    Arguments
+    ---------
+    proof_path: str
+        Path to create the proof file
+    
+    settings_path: str
+        Path to the settings file
+    
+    vk_path: str
+        Path to the verification key file
+    
+    srs_path: str
+        Path to the SRS file
+    
+    non_reduced_srs: bool
+        Whether to reduce the number of SRS logrows to the number of instances rather than the number of logrows used for proofs (only works if the srs were generated in the same ceremony)
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def verify_aggr(proof_path:str | os.PathLike | pathlib.Path,vk_path:str | os.PathLike | pathlib.Path,logrows:int,commitment:PyCommitments,reduced_srs:bool,srs_path:typing.Optional[str | os.PathLike | pathlib.Path]) -> bool:
+    r"""
+    Verifies and aggregate proof
+    
+    Arguments
+    ---------
+    proof_path: str
+         The path to the proof file
+    
+    vk_path: str
+        The path to the verification key file
+    
+    logrows: int
+        logrows used for aggregation circuit
+    
+    commitment: str
+        Accepts "kzg" or "ipa"
+    
+    reduced_srs: bool
+        Whether to reduce the number of SRS logrows to the number of instances rather than the number of logrows used for proofs (only works if the srs were generated in the same ceremony)
+    
+    srs_path: str
+        The path to the SRS file
+    
+    Returns
+    -------
+    bool
+    """
+    ...
+
+def verify_evm(addr_verifier:str,proof_path:str | os.PathLike | pathlib.Path,rpc_url:typing.Optional[str],vka_path:typing.Optional[str]) -> typing.Any:
+    r"""
+    verifies an evm compatible proof, you will need solc installed in your environment to run this
+    
+    Arguments
+    ---------
+    addr_verifier: str
+        The verifier contract's address as a hex string
+    
+    proof_path: str
+        The path to the proof file (generated using the prove command)
+    
+    rpc_url: str
+        RPC URL for an Ethereum node, if None will use Anvil but WON'T persist state
+    
+    vka_path: str
+        The path to the VKA calldata bytes file (generated using the create_evm_vka command)
+    Returns
+    -------
+    bool
+    """
+    ...
+

pyproject.toml

@@ -12,6 +12,7 @@ asyncio_mode = "auto"
 
 [project]
 name = "ezkl"
+version = "0.0.0"
 requires-python = ">=3.7"
 classifiers = [
     "Programming Language :: Rust",

rust-toolchain

@@ -1,3 +1,3 @@
 [toolchain]
-channel = "nightly-2024-07-18"
+channel = "nightly-2025-02-17"
 components = ["rustfmt", "clippy"]

src/bin/ezkl.rs

@@ -1,7 +1,11 @@
 // ignore file if compiling for wasm
+
+#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
+use mimalloc::MiMalloc;
+
 #[global_allocator]
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-static GLOBAL: mimalloc::MiMalloc = mimalloc::MiMalloc;
+static GLOBAL: MiMalloc = MiMalloc;
 
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use clap::{CommandFactory, Parser};
@@ -24,6 +28,8 @@ use std::env;
 #[tokio::main(flavor = "current_thread")]
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 pub async fn main() {
+    use log::debug;
+
     let args = Cli::parse();
 
     if let Some(generator) = args.generator {
@@ -38,7 +44,7 @@ pub async fn main() {
         } else {
             info!("Running with CPU");
         }
-        info!(
+        debug!(
             "command: \n {}",
             &command.as_json().to_colored_json_auto().unwrap()
         );

src/bin/py_stub_gen.rs

@@ -0,0 +1,9 @@
+use pyo3_stub_gen::Result;
+
+fn main() -> Result<()> {
+    // `stub_info` is a function defined by `define_stub_info_gatherer!` macro.
+    env_logger::Builder::from_env(env_logger::Env::default().filter_or("RUST_LOG", "info")).init();
+    let stub = ezkl::bindings::python::stub_info()?;
+    stub.generate()?;
+    Ok(())
+}

src/bindings/universal.rs

@@ -1,6 +1,7 @@
 use halo2_proofs::{
     plonk::*,
     poly::{
+        VerificationStrategy,
         commitment::{CommitmentScheme, ParamsProver},
         ipa::{
             commitment::{IPACommitmentScheme, ParamsIPA},
@@ -12,7 +13,6 @@ use halo2_proofs::{
             multiopen::{ProverSHPLONK, VerifierSHPLONK},
             strategy::SingleStrategy as KZGSingleStrategy,
         },
-        VerificationStrategy,
     },
 };
 use std::fmt::Display;
@@ -20,15 +20,15 @@ use std::io::BufReader;
 use std::str::FromStr;
 
 use crate::{
+    CheckMode, Commitments, EZKLError as InnerEZKLError,
     circuit::region::RegionSettings,
     graph::GraphSettings,
     pfsys::{
-        create_proof_circuit,
+        TranscriptType, create_proof_circuit,
         evm::aggregation_kzg::{AggregationCircuit, PoseidonTranscript},
-        verify_proof_circuit, TranscriptType,
+        verify_proof_circuit,
     },
     tensor::TensorType,
-    CheckMode, Commitments, EZKLError as InnerEZKLError,
 };
 
 use crate::graph::{GraphCircuit, GraphWitness};
@@ -66,26 +66,24 @@ impl From<InnerEZKLError> for EZKLError {
 pub(crate) fn encode_verifier_calldata(
     // TODO - shuold it be pub(crate) or pub or pub(super)?
     proof: Vec<u8>,
-    vk_address: Option<Vec<u8>>,
+    vka: Option<Vec<u8>>,
 ) -> Result<Vec<u8>, EZKLError> {
     let snark: crate::pfsys::Snark<Fr, G1Affine> =
         serde_json::from_slice(&proof[..]).map_err(InnerEZKLError::from)?;
 
-    let vk_address: Option<[u8; 20]> = if let Some(vk_address) = vk_address {
-        let array: [u8; 20] =
-            serde_json::from_slice(&vk_address[..]).map_err(InnerEZKLError::from)?;
+    let vka_buf: Option<Vec<[u8; 32]>> = if let Some(vka) = vka {
+        let array: Vec<[u8; 32]> =
+            serde_json::from_slice(&vka[..]).map_err(InnerEZKLError::from)?;
         Some(array)
     } else {
         None
     };
 
+    let vka: Option<&[[u8; 32]]> = vka_buf.as_deref();
+
     let flattened_instances = snark.instances.into_iter().flatten();
 
-    let encoded = encode_calldata(
-        vk_address,
-        &snark.proof,
-        &flattened_instances.collect::<Vec<_>>(),
-    );
+    let encoded = encode_calldata(vka, &snark.proof, &flattened_instances.collect::<Vec<_>>());
 
     Ok(encoded)
 }
@@ -141,10 +139,11 @@ pub(crate) fn gen_vk(
     .map_err(|e| EZKLError::InternalError(format!("Failed to create verifying key: {}", e)))?;
 
     let mut serialized_vk = Vec::new();
-    vk.write(&mut serialized_vk, halo2_proofs::SerdeFormat::RawBytes)
-        .map_err(|e| {
-            EZKLError::InternalError(format!("Failed to serialize verifying key: {}", e))
-        })?;
+    vk.write(
+        &mut serialized_vk,
+        halo2_proofs::SerdeFormat::RawBytesUnchecked,
+    )
+    .map_err(|e| EZKLError::InternalError(format!("Failed to serialize verifying key: {}", e)))?;
 
     Ok(serialized_vk)
 }
@@ -165,7 +164,7 @@ pub(crate) fn gen_pk(
     let mut reader = BufReader::new(&vk[..]);
     let vk = VerifyingKey::<G1Affine>::read::<_, GraphCircuit>(
         &mut reader,
-        halo2_proofs::SerdeFormat::RawBytes,
+        halo2_proofs::SerdeFormat::RawBytesUnchecked,
         circuit.settings().clone(),
     )
     .map_err(|e| EZKLError::InternalError(format!("Failed to deserialize verifying key: {}", e)))?;
@@ -197,7 +196,7 @@ pub(crate) fn verify(
     let mut reader = BufReader::new(&vk[..]);
     let vk = VerifyingKey::<G1Affine>::read::<_, GraphCircuit>(
         &mut reader,
-        halo2_proofs::SerdeFormat::RawBytes,
+        halo2_proofs::SerdeFormat::RawBytesUnchecked,
         circuit_settings.clone(),
     )
     .map_err(|e| EZKLError::InternalError(format!("Failed to deserialize vk: {}", e)))?;
@@ -277,7 +276,7 @@ pub(crate) fn verify_aggr(
     let mut reader = BufReader::new(&vk[..]);
     let vk = VerifyingKey::<G1Affine>::read::<_, AggregationCircuit>(
         &mut reader,
-        halo2_proofs::SerdeFormat::RawBytes,
+        halo2_proofs::SerdeFormat::RawBytesUnchecked,
         (),
     )
     .map_err(|e| EZKLError::InternalError(format!("Failed to deserialize vk: {}", e)))?;
@@ -365,7 +364,7 @@ pub(crate) fn prove(
     let mut reader = BufReader::new(&pk[..]);
     let pk = ProvingKey::<G1Affine>::read::<_, GraphCircuit>(
         &mut reader,
-        halo2_proofs::SerdeFormat::RawBytes,
+        halo2_proofs::SerdeFormat::RawBytesUnchecked,
         circuit.settings().clone(),
     )
     .map_err(|e| EZKLError::InternalError(format!("Failed to deserialize proving key: {}", e)))?;
@@ -487,7 +486,7 @@ pub(crate) fn vk_validation(vk: Vec<u8>, settings: Vec<u8>) -> Result<bool, EZKL
     let mut reader = BufReader::new(&vk[..]);
     let _ = VerifyingKey::<G1Affine>::read::<_, GraphCircuit>(
         &mut reader,
-        halo2_proofs::SerdeFormat::RawBytes,
+        halo2_proofs::SerdeFormat::RawBytesUnchecked,
         circuit_settings,
     )
     .map_err(|e| EZKLError::InternalError(format!("Failed to deserialize verifying key: {}", e)))?;
@@ -504,7 +503,7 @@ pub(crate) fn pk_validation(pk: Vec<u8>, settings: Vec<u8>) -> Result<bool, EZKL
     let mut reader = BufReader::new(&pk[..]);
     let _ = ProvingKey::<G1Affine>::read::<_, GraphCircuit>(
         &mut reader,
-        halo2_proofs::SerdeFormat::RawBytes,
+        halo2_proofs::SerdeFormat::RawBytesUnchecked,
         circuit_settings,
     )
     .map_err(|e| EZKLError::InternalError(format!("Failed to deserialize proving key: {}", e)))?;

src/bindings/wasm.rs

@@ -8,10 +8,7 @@ use crate::{
         Module,
     },
     fieldutils::{felt_to_integer_rep, integer_rep_to_felt},
-    graph::{
-        modules::POSEIDON_LEN_GRAPH, quantize_float, scale_to_multiplier, GraphCircuit,
-        GraphSettings,
-    },
+    graph::{quantize_float, scale_to_multiplier, GraphCircuit, GraphSettings},
 };
 use console_error_panic_hook;
 use halo2_proofs::{
@@ -22,6 +19,7 @@ use halo2curves::{
     bn256::{Bn256, Fr, G1Affine},
     ff::PrimeField,
 };
+use std::str::FromStr;
 use wasm_bindgen::prelude::*;
 use wasm_bindgen_console_logger::DEFAULT_LOGGER;
 
@@ -113,9 +111,15 @@ pub fn feltToFloat(
 #[wasm_bindgen]
 #[allow(non_snake_case)]
 pub fn floatToFelt(
-    input: f64,
+    mut input: f64,
     scale: crate::Scale,
+    input_type: &str,
 ) -> Result<wasm_bindgen::Clamped<Vec<u8>>, JsError> {
+    crate::circuit::InputType::roundtrip(
+        &crate::circuit::InputType::from_str(input_type)
+            .map_err(|e| JsError::new(&format!("{}", e)))?,
+        &mut input,
+    );
     let int_rep =
         quantize_float(&input, 0.0, scale).map_err(|e| JsError::new(&format!("{}", e)))?;
     let felt = integer_rep_to_felt(int_rep);
@@ -224,10 +228,7 @@ pub fn poseidonHash(
     let message: Vec<Fr> = serde_json::from_slice(&message[..])
         .map_err(|e| JsError::new(&format!("Failed to deserialize message: {}", e)))?;
 
-    let output =
-        PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, POSEIDON_LEN_GRAPH>::run(
-            message.clone(),
-        )
+    let output = PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::run(message.clone())
         .map_err(|e| JsError::new(&format!("{}", e)))?;
 
     Ok(wasm_bindgen::Clamped(serde_json::to_vec(&output).map_err(

src/circuit/modules/polycommit.rs

@@ -1,7 +1,7 @@
 /*
 An easy-to-use implementation of the Poseidon Hash in the form of a Halo2 Chip. While the Poseidon Hash function
 is already implemented in halo2_gadgets, there is no wrapper chip that makes it easy to use in other circuits.
-Thanks to https://github.com/summa-dev/summa-solvency/blob/master/src/chips/poseidon/hash.rs for the inspiration (and also helping us understand how to use this).
+Thanks to https://github.com/summa-dev/summa-solvency/blob/master/zk_prover/src/chips/poseidon/hash.rs for the inspiration (and also helping us understand how to use this).
 */
 
 use std::collections::HashMap;

src/circuit/modules/poseidon.rs

@@ -1,20 +1,18 @@
 /*
 An easy-to-use implementation of the Poseidon Hash in the form of a Halo2 Chip. While the Poseidon Hash function
 is already implemented in halo2_gadgets, there is no wrapper chip that makes it easy to use in other circuits.
-Thanks to https://github.com/summa-dev/summa-solvency/blob/master/src/chips/poseidon/hash.rs for the inspiration (and also helping us understand how to use this).
+Thanks to https://github.com/summa-dev/summa-solvency/blob/master/zk_prover/src/chips/poseidon/hash.rs for the inspiration (and also helping us understand how to use this).
 */
 
 pub mod poseidon_params;
 pub mod spec;
 
 // This chip adds a set of advice columns to the gadget Chip to store the inputs of the hash
-use halo2_gadgets::poseidon::{primitives::*, Hash, Pow5Chip, Pow5Config};
-use halo2_proofs::arithmetic::Field;
+use halo2_gadgets::poseidon::{
+    primitives::VariableLength, primitives::*, Hash, Pow5Chip, Pow5Config,
+};
 use halo2_proofs::halo2curves::bn256::Fr as Fp;
 use halo2_proofs::{circuit::*, plonk::*};
-// use maybe_rayon::prelude::{IndexedParallelIterator, IntoParallelRefIterator};
-use maybe_rayon::prelude::ParallelIterator;
-use maybe_rayon::slice::ParallelSlice;
 
 use std::marker::PhantomData;
 
@@ -40,22 +38,17 @@ pub struct PoseidonConfig<const WIDTH: usize, const RATE: usize> {
     pub pow5_config: Pow5Config<Fp, WIDTH, RATE>,
 }
 
-type InputAssignments = (Vec<AssignedCell<Fp, Fp>>, AssignedCell<Fp, Fp>);
+type InputAssignments = Vec<AssignedCell<Fp, Fp>>;
 
 /// PoseidonChip is a wrapper around the Pow5Chip that adds a set of advice columns to the gadget Chip to store the inputs of the hash
 #[derive(Debug, Clone)]
-pub struct PoseidonChip<
-    S: Spec<Fp, WIDTH, RATE> + Sync,
-    const WIDTH: usize,
-    const RATE: usize,
-    const L: usize,
-> {
+pub struct PoseidonChip<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize> {
     config: PoseidonConfig<WIDTH, RATE>,
     _marker: PhantomData<S>,
 }
 
-impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, const L: usize>
-    PoseidonChip<S, WIDTH, RATE, L>
+impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize>
+    PoseidonChip<S, WIDTH, RATE>
 {
     /// Creates a new PoseidonChip
     pub fn configure_with_cols(
@@ -82,8 +75,8 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
     }
 }
 
-impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, const L: usize>
-    PoseidonChip<S, WIDTH, RATE, L>
+impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize>
+    PoseidonChip<S, WIDTH, RATE>
 {
     /// Configuration of the PoseidonChip
     pub fn configure_with_optional_instance(
@@ -100,9 +93,6 @@ 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(
@@ -116,8 +106,8 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
     }
 }
 
-impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, const L: usize>
-    Module<Fp> for PoseidonChip<S, WIDTH, RATE, L>
+impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize> Module<Fp>
+    for PoseidonChip<S, WIDTH, RATE>
 {
     type Config = PoseidonConfig<WIDTH, RATE>;
     type InputAssignments = InputAssignments;
@@ -152,9 +142,6 @@ 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();
@@ -176,7 +163,10 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
         message: &[ValTensor<Fp>],
         constants: &mut ConstantsMap<Fp>,
     ) -> Result<Self::InputAssignments, ModuleError> {
-        assert_eq!(message.len(), 1);
+        if message.len() != 1 {
+            return Err(ModuleError::InputWrongLength(message.len()));
+        }
+
         let message = message[0].clone();
 
         let start_time = instant::Instant::now();
@@ -186,95 +176,81 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
         let res = layouter.assign_region(
             || "load message",
             |mut region| {
-                let assigned_message: Result<Vec<AssignedCell<Fp, Fp>>, ModuleError> =
-                    match &message {
-                        ValTensor::Value { inner: v, .. } => {
-                            v.iter()
-                                .enumerate()
-                                .map(|(i, value)| {
-                                    let x = i % WIDTH;
-                                    let y = i / WIDTH;
+                let assigned_message: Result<Vec<AssignedCell<Fp, Fp>>, _> = match &message {
+                    ValTensor::Value { inner: v, .. } => v
+                        .iter()
+                        .enumerate()
+                        .map(|(i, value)| {
+                            let x = i % WIDTH;
+                            let y = i / WIDTH;
 
-                                    match value {
-                                        ValType::Value(v) => region
-                                            .assign_advice(
-                                                || format!("load message_{}", i),
-                                                self.config.hash_inputs[x],
-                                                y,
-                                                || *v,
-                                            )
-                                            .map_err(|e| e.into()),
-                                        ValType::PrevAssigned(v)
-                                        | ValType::AssignedConstant(v, ..) => Ok(v.clone()),
-                                        ValType::Constant(f) => {
-                                            if local_constants.contains_key(f) {
-                                                Ok(constants
-                                                    .get(f)
-                                                    .unwrap()
-                                                    .assigned_cell()
-                                                    .ok_or(ModuleError::ConstantNotAssigned)?)
-                                            } else {
-                                                let res = region.assign_advice_from_constant(
-                                                    || format!("load message_{}", i),
-                                                    self.config.hash_inputs[x],
-                                                    y,
-                                                    *f,
-                                                )?;
-
-                                                constants.insert(
-                                                    *f,
-                                                    ValType::AssignedConstant(res.clone(), *f),
-                                                );
-
-                                                Ok(res)
-                                            }
-                                        }
-                                        e => Err(ModuleError::WrongInputType(
-                                            format!("{:?}", e),
-                                            "PrevAssigned".to_string(),
-                                        )),
-                                    }
-                                })
-                                .collect()
-                        }
-                        ValTensor::Instance {
-                            dims,
-                            inner: col,
-                            idx,
-                            initial_offset,
-                            ..
-                        } => {
-                            // this should never ever fail
-                            let num_elems = dims[*idx].iter().product::<usize>();
-                            (0..num_elems)
-                                .map(|i| {
-                                    let x = i % WIDTH;
-                                    let y = i / WIDTH;
-                                    region.assign_advice_from_instance(
-                                        || "pub input anchor",
-                                        *col,
-                                        initial_offset + i,
+                            match value {
+                                ValType::Value(v) => region
+                                    .assign_advice(
+                                        || format!("load message_{}", i),
                                         self.config.hash_inputs[x],
                                         y,
+                                        || *v,
                                     )
-                                })
-                                .collect::<Result<Vec<_>, _>>()
-                                .map_err(|e| e.into())
-                        }
-                    };
+                                    .map_err(|e| e.into()),
+                                ValType::PrevAssigned(v) | ValType::AssignedConstant(v, ..) => {
+                                    Ok(v.clone())
+                                }
+                                ValType::Constant(f) => {
+                                    if local_constants.contains_key(f) {
+                                        Ok(constants
+                                            .get(f)
+                                            .unwrap()
+                                            .assigned_cell()
+                                            .ok_or(ModuleError::ConstantNotAssigned)?)
+                                    } else {
+                                        let res = region.assign_advice_from_constant(
+                                            || format!("load message_{}", i),
+                                            self.config.hash_inputs[x],
+                                            y,
+                                            *f,
+                                        )?;
 
-                let offset = message.len() / WIDTH + 1;
+                                        constants
+                                            .insert(*f, ValType::AssignedConstant(res.clone(), *f));
 
-                let zero_val = region
-                    .assign_advice_from_constant(
-                        || "",
-                        self.config.hash_inputs[0],
-                        offset,
-                        Fp::ZERO,
-                    )
-                    .unwrap();
+                                        Ok(res)
+                                    }
+                                }
+                                e => Err(ModuleError::WrongInputType(
+                                    format!("{:?}", e),
+                                    "AssignedValue".to_string(),
+                                )),
+                            }
+                        })
+                        .collect(),
+                    ValTensor::Instance {
+                        dims,
+                        inner: col,
+                        idx,
+                        initial_offset,
+                        ..
+                    } => {
+                        // this should never ever fail
+                        let num_elems = dims[*idx].iter().product::<usize>();
+                        (0..num_elems)
+                            .map(|i| {
+                                let x = i % WIDTH;
+                                let y = i / WIDTH;
+                                region.assign_advice_from_instance(
+                                    || "pub input anchor",
+                                    *col,
+                                    initial_offset + i,
+                                    self.config.hash_inputs[x],
+                                    y,
+                                )
+                            })
+                            .collect::<Result<Vec<_>, _>>()
+                            .map_err(|e| e.into())
+                    }
+                };
 
-                Ok((assigned_message?, zero_val))
+                Ok(assigned_message?)
             },
         );
         log::trace!(
@@ -295,7 +271,13 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
         row_offset: usize,
         constants: &mut ConstantsMap<Fp>,
     ) -> Result<ValTensor<Fp>, ModuleError> {
-        let (mut input_cells, zero_val) = self.layout_inputs(layouter, input, constants)?;
+        let input_cells = self.layout_inputs(layouter, input, constants)?;
+
+        // empty hash case
+        if input_cells.is_empty() {
+            return Ok(input[0].clone());
+        }
+
         // extract the values from the input cells
         let mut assigned_input: Tensor<ValType<Fp>> =
             input_cells.iter().map(|e| ValType::from(e.clone())).into();
@@ -303,52 +285,25 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
 
         let start_time = instant::Instant::now();
 
-        let mut one_iter = false;
-        // do the Tree dance baby
-        while input_cells.len() > 1 || !one_iter {
-            let hashes: Result<Vec<AssignedCell<Fp, Fp>>, ModuleError> = input_cells
-                .chunks(L)
-                .enumerate()
-                .map(|(i, block)| {
-                    let _start_time = instant::Instant::now();
+        let pow5_chip = Pow5Chip::construct(self.config.pow5_config.clone());
+        // initialize the hasher
+        let hasher = Hash::<_, _, S, VariableLength, WIDTH, RATE>::init(
+            pow5_chip,
+            layouter.namespace(|| "block_hasher"),
+        )?;
 
-                    let mut block = block.to_vec();
-                    let remainder = block.len() % L;
-
-                    if remainder != 0 {
-                        block.extend(vec![zero_val.clone(); L - remainder]);
-                    }
-
-                    let pow5_chip = Pow5Chip::construct(self.config.pow5_config.clone());
-                    // initialize the hasher
-                    let hasher = Hash::<_, _, S, ConstantLength<L>, WIDTH, RATE>::init(
-                        pow5_chip,
-                        layouter.namespace(|| "block_hasher"),
-                    )?;
-
-                    let hash = hasher.hash(
-                        layouter.namespace(|| "hash"),
-                        block.to_vec().try_into().map_err(|_| Error::Synthesis)?,
-                    );
-
-                    if i == 0 {
-                        log::trace!("block (L={:?}) took: {:?}", L, _start_time.elapsed());
-                    }
-
-                    hash
-                })
-                .collect::<Result<Vec<_>, _>>()
-                .map_err(|e| e.into());
-
-            log::trace!("hashes (N={:?}) took: {:?}", len, start_time.elapsed());
-            one_iter = true;
-            input_cells = hashes?;
-        }
+        let hash: AssignedCell<Fp, Fp> = hasher.hash(
+            layouter.namespace(|| "hash"),
+            input_cells
+                .to_vec()
+                .try_into()
+                .map_err(|_| Error::Synthesis)?,
+        )?;
 
         let duration = start_time.elapsed();
         log::trace!("layout (N={:?}) took: {:?}", len, duration);
 
-        let result = Tensor::from(input_cells.iter().map(|e| ValType::from(e.clone())));
+        let result = Tensor::from(vec![ValType::from(hash.clone())].into_iter());
 
         let output = match result[0].clone() {
             ValType::PrevAssigned(v) => v,
@@ -387,69 +342,59 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
 
     ///
     fn run(message: Vec<Fp>) -> Result<Vec<Vec<Fp>>, ModuleError> {
-        let mut hash_inputs = message;
-
-        let len = hash_inputs.len();
+        let len = message.len();
+        if len == 0 {
+            return Ok(vec![vec![]]);
+        }
 
         let start_time = instant::Instant::now();
 
-        let mut one_iter = false;
-        // do the Tree dance baby
-        while hash_inputs.len() > 1 || !one_iter {
-            let hashes: Vec<Fp> = hash_inputs
-                .par_chunks(L)
-                .map(|block| {
-                    let mut block = block.to_vec();
-                    let remainder = block.len() % L;
-
-                    if remainder != 0 {
-                        block.extend(vec![Fp::ZERO; L - remainder].iter());
-                    }
-
-                    let block_len = block.len();
-
-                    let message = block
-                        .try_into()
-                        .map_err(|_| ModuleError::InputWrongLength(block_len))?;
-
-                    Ok(halo2_gadgets::poseidon::primitives::Hash::<
-                        _,
-                        S,
-                        ConstantLength<L>,
-                        { WIDTH },
-                        { RATE },
-                    >::init()
-                    .hash(message))
-                })
-                .collect::<Result<Vec<_>, ModuleError>>()?;
-            one_iter = true;
-            hash_inputs = hashes;
-        }
+        let hash = halo2_gadgets::poseidon::primitives::Hash::<
+            _,
+            S,
+            VariableLength,
+            { WIDTH },
+            { RATE },
+        >::init()
+        .hash(message);
 
         let duration = start_time.elapsed();
         log::trace!("run (N={:?}) took: {:?}", len, duration);
 
-        Ok(vec![hash_inputs])
+        Ok(vec![vec![hash]])
     }
 
-    fn num_rows(mut input_len: usize) -> usize {
+    fn num_rows(input_len: usize) -> usize {
         // this was determined by running the circuit and looking at the number of constraints
         // in the test called hash_for_a_range_of_input_sizes, then regressing in python to find the slope
-        let fixed_cost: usize = 41 * L;
+        // import numpy as np
+        // from scipy import stats
 
-        let mut num_rows = 0;
+        // x = np.array([32, 64, 96, 128, 160, 192])
+        // y = np.array([1298, 2594, 3890, 5186, 6482, 7778])
 
-        loop {
-            // the number of times the input_len is divisible by L
-            let num_chunks = input_len / L + 1;
-            num_rows += num_chunks * fixed_cost;
-            if num_chunks == 1 {
-                break;
-            }
-            input_len = num_chunks;
-        }
+        // slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
 
-        num_rows
+        // print(f"slope: {slope}")
+        // print(f"intercept: {intercept}")
+        // print(f"R^2: {r_value**2}")
+
+        // # Predict for any x
+        // def predict(x):
+        // return slope * x + intercept
+
+        // # Test prediction
+        // test_x = 256
+        // print(f"Predicted value for x={test_x}: {predict(test_x)}")
+        // our output:
+        // slope: 40.5
+        // intercept: 2.0
+        // R^2: 1.0
+        // Predicted value for x=256: 10370.0
+        let fixed_cost: usize = 41 * input_len;
+
+        // the cost of the hash function is linear with the number of inputs
+        fixed_cost + 2
     }
 }
 
@@ -476,12 +421,12 @@ mod tests {
     const RATE: usize = POSEIDON_RATE;
     const R: usize = 240;
 
-    struct HashCircuit<S: Spec<Fp, WIDTH, RATE>, const L: usize> {
+    struct HashCircuit<S: Spec<Fp, WIDTH, RATE>> {
         message: ValTensor<Fp>,
         _spec: PhantomData<S>,
     }
 
-    impl<S: Spec<Fp, WIDTH, RATE>, const L: usize> Circuit<Fp> for HashCircuit<S, L> {
+    impl<S: Spec<Fp, WIDTH, RATE>> Circuit<Fp> for HashCircuit<S> {
         type Config = PoseidonConfig<WIDTH, RATE>;
         type FloorPlanner = ModulePlanner;
         type Params = ();
@@ -497,7 +442,7 @@ mod tests {
         }
 
         fn configure(meta: &mut ConstraintSystem<Fp>) -> PoseidonConfig<WIDTH, RATE> {
-            PoseidonChip::<PoseidonSpec, WIDTH, RATE, L>::configure(meta, ())
+            PoseidonChip::<PoseidonSpec, WIDTH, RATE>::configure(meta, ())
         }
 
         fn synthesize(
@@ -505,7 +450,7 @@ mod tests {
             config: PoseidonConfig<WIDTH, RATE>,
             mut layouter: impl Layouter<Fp>,
         ) -> Result<(), Error> {
-            let chip: PoseidonChip<PoseidonSpec, WIDTH, RATE, L> = PoseidonChip::new(config);
+            let chip: PoseidonChip<PoseidonSpec, WIDTH, RATE> = PoseidonChip::new(config);
             chip.layout(
                 &mut layouter,
                 &[self.message.clone()],
@@ -517,18 +462,33 @@ mod tests {
         }
     }
 
+    #[test]
+    fn poseidon_hash_empty() {
+        let message = [];
+        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.to_vec()).unwrap();
+        let mut message: Tensor<ValType<Fp>> =
+            message.into_iter().map(|m| Value::known(m).into()).into();
+        let k = 9;
+        let circuit = HashCircuit::<PoseidonSpec> {
+            message: message.into(),
+            _spec: PhantomData,
+        };
+        let prover = halo2_proofs::dev::MockProver::run(k, &circuit, vec![vec![]]).unwrap();
+        assert_eq!(prover.verify(), Ok(()))
+    }
+
     #[test]
     fn poseidon_hash() {
         let rng = rand::rngs::OsRng;
 
         let message = [Fp::random(rng), Fp::random(rng)];
-        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE, 2>::run(message.to_vec()).unwrap();
+        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.to_vec()).unwrap();
 
         let mut message: Tensor<ValType<Fp>> =
             message.into_iter().map(|m| Value::known(m).into()).into();
 
         let k = 9;
-        let circuit = HashCircuit::<PoseidonSpec, 2> {
+        let circuit = HashCircuit::<PoseidonSpec> {
             message: message.into(),
             _spec: PhantomData,
         };
@@ -541,13 +501,13 @@ mod tests {
         let rng = rand::rngs::OsRng;
 
         let message = [Fp::random(rng), Fp::random(rng), Fp::random(rng)];
-        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE, 3>::run(message.to_vec()).unwrap();
+        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.to_vec()).unwrap();
 
         let mut message: Tensor<ValType<Fp>> =
             message.into_iter().map(|m| Value::known(m).into()).into();
 
         let k = 9;
-        let circuit = HashCircuit::<PoseidonSpec, 3> {
+        let circuit = HashCircuit::<PoseidonSpec> {
             message: message.into(),
             _spec: PhantomData,
         };
@@ -563,23 +523,21 @@ mod tests {
         #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
         env_logger::init();
 
-        {
-            let i = 32;
+        for i in (32..128).step_by(32) {
             // print a bunch of new lines
-            println!(
+            log::info!(
                 "i is {} -------------------------------------------------",
                 i
             );
 
             let message: Vec<Fp> = (0..i).map(|_| Fp::random(rng)).collect::<Vec<_>>();
-            let output =
-                PoseidonChip::<PoseidonSpec, WIDTH, RATE, 32>::run(message.clone()).unwrap();
+            let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.clone()).unwrap();
 
             let mut message: Tensor<ValType<Fp>> =
                 message.into_iter().map(|m| Value::known(m).into()).into();
 
             let k = 17;
-            let circuit = HashCircuit::<PoseidonSpec, 32> {
+            let circuit = HashCircuit::<PoseidonSpec> {
                 message: message.into(),
                 _spec: PhantomData,
             };
@@ -596,13 +554,13 @@ mod tests {
 
         let mut message: Vec<Fp> = (0..2048).map(|_| Fp::random(rng)).collect::<Vec<_>>();
 
-        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE, 25>::run(message.clone()).unwrap();
+        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.clone()).unwrap();
 
         let mut message: Tensor<ValType<Fp>> =
             message.into_iter().map(|m| Value::known(m).into()).into();
 
         let k = 17;
-        let circuit = HashCircuit::<PoseidonSpec, 25> {
+        let circuit = HashCircuit::<PoseidonSpec> {
             message: message.into(),
             _spec: PhantomData,
         };

src/circuit/ops/base.rs

@@ -17,12 +17,14 @@ pub enum BaseOp {
     Sub,
     SumInit,
     Sum,
-    IsBoolean,
 }
 
 /// Matches a [BaseOp] to an operation over inputs
 impl BaseOp {
-    /// forward func
+    /// forward func for non-accumulating operations
+    /// # Panics
+    /// Panics if called on an accumulating operation
+    /// # Examples
     pub fn nonaccum_f<
         T: TensorType + Add<Output = T> + Sub<Output = T> + Mul<Output = T> + Neg<Output = T>,
     >(
@@ -34,12 +36,13 @@ impl BaseOp {
             BaseOp::Add => a + b,
             BaseOp::Sub => a - b,
             BaseOp::Mult => a * b,
-            BaseOp::IsBoolean => b,
             _ => panic!("nonaccum_f called on accumulating operation"),
         }
     }
 
-    /// forward func
+    /// forward func for accumulating operations
+    /// # Panics
+    /// Panics if called on a non-accumulating operation
     pub fn accum_f<
         T: TensorType + Add<Output = T> + Sub<Output = T> + Mul<Output = T> + Neg<Output = T>,
     >(
@@ -74,7 +77,6 @@ impl BaseOp {
             BaseOp::Mult => "MULT",
             BaseOp::Sum => "SUM",
             BaseOp::SumInit => "SUMINIT",
-            BaseOp::IsBoolean => "ISBOOLEAN",
         }
     }
 
@@ -90,7 +92,6 @@ impl BaseOp {
             BaseOp::Mult => (0, 1),
             BaseOp::Sum => (-1, 2),
             BaseOp::SumInit => (0, 1),
-            BaseOp::IsBoolean => (0, 1),
         }
     }
 
@@ -106,7 +107,6 @@ impl BaseOp {
             BaseOp::Mult => 2,
             BaseOp::Sum => 1,
             BaseOp::SumInit => 1,
-            BaseOp::IsBoolean => 0,
         }
     }
 
@@ -122,7 +122,6 @@ impl BaseOp {
             BaseOp::SumInit => 0,
             BaseOp::CumProd => 1,
             BaseOp::CumProdInit => 0,
-            BaseOp::IsBoolean => 0,
         }
     }
 }

src/circuit/ops/chip.rs

@@ -2,16 +2,15 @@ use std::str::FromStr;
 
 use halo2_proofs::{
     circuit::Layouter,
-    plonk::{ConstraintSystem, Constraints, Expression, Selector},
+    plonk::{ConstraintSystem, Constraints, Expression, Selector, TableColumn},
     poly::Rotation,
 };
 use log::debug;
 #[cfg(feature = "python-bindings")]
 use pyo3::{
-    conversion::{FromPyObject, PyTryFrom},
+    conversion::{FromPyObject, IntoPy},
     exceptions::PyValueError,
     prelude::*,
-    types::PyString,
 };
 use serde::{Deserialize, Serialize};
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
@@ -21,7 +20,6 @@ use crate::{
     circuit::{
         ops::base::BaseOp,
         table::{Range, RangeCheck, Table},
-        utils,
     },
     tensor::{Tensor, TensorType, ValTensor, VarTensor},
 };
@@ -76,51 +74,12 @@ impl FromStr for CheckMode {
     }
 }
 
-#[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)]
-pub struct Tolerance {
-    pub val: f32,
-    pub scale: utils::F32,
-}
-
-impl std::fmt::Display for Tolerance {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        write!(f, "{:.2}", self.val)
-    }
-}
-
-#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-impl ToFlags for Tolerance {
-    /// Convert the struct to a subcommand string
-    fn to_flags(&self) -> Vec<String> {
-        vec![format!("{}", self)]
-    }
-}
-
-impl FromStr for Tolerance {
-    type Err = String;
-
-    fn from_str(s: &str) -> Result<Self, Self::Err> {
-        if let Ok(val) = s.parse::<f32>() {
-            Ok(Tolerance {
-                val,
-                scale: utils::F32(1.0),
-            })
-        } else {
-            Err(
-                "Invalid tolerance value provided. It should expressed as a percentage (f32)."
-                    .to_string(),
-            )
-        }
-    }
-}
-
-impl From<f32> for Tolerance {
-    fn from(value: f32) -> Self {
-        Tolerance {
-            val: value,
-            scale: utils::F32(1.0),
+impl CheckMode {
+    /// Returns the value of the check mode
+    pub fn is_safe(&self) -> bool {
+        match self {
+            CheckMode::SAFE => true,
+            CheckMode::UNSAFE => false,
         }
     }
 }
@@ -139,10 +98,9 @@ impl IntoPy<PyObject> for CheckMode {
 #[cfg(feature = "python-bindings")]
 /// Obtains CheckMode from PyObject (Required for CheckMode to be compatible with Python)
 impl<'source> FromPyObject<'source> for CheckMode {
-    fn extract(ob: &'source PyAny) -> PyResult<Self> {
-        let trystr = <PyString as PyTryFrom>::try_from(ob)?;
-        let strval = trystr.to_string();
-        match strval.to_lowercase().as_str() {
+    fn extract_bound(ob: &pyo3::Bound<'source, pyo3::PyAny>) -> PyResult<Self> {
+        let trystr = String::extract_bound(ob)?;
+        match trystr.to_lowercase().as_str() {
             "safe" => Ok(CheckMode::SAFE),
             "unsafe" => Ok(CheckMode::UNSAFE),
             _ => Err(PyValueError::new_err("Invalid value for CheckMode")),
@@ -150,29 +108,6 @@ impl<'source> FromPyObject<'source> for CheckMode {
     }
 }
 
-#[cfg(feature = "python-bindings")]
-/// Converts Tolerance into a PyObject (Required for Tolerance to be compatible with Python)
-impl IntoPy<PyObject> for Tolerance {
-    fn into_py(self, py: Python) -> PyObject {
-        (self.val, self.scale.0).to_object(py)
-    }
-}
-
-#[cfg(feature = "python-bindings")]
-/// Obtains Tolerance from PyObject (Required for Tolerance to be compatible with Python)
-impl<'source> FromPyObject<'source> for Tolerance {
-    fn extract(ob: &'source PyAny) -> PyResult<Self> {
-        if let Ok((val, scale)) = ob.extract::<(f32, f32)>() {
-            Ok(Tolerance {
-                val,
-                scale: utils::F32(scale),
-            })
-        } else {
-            Err(PyValueError::new_err("Invalid tolerance value provided. "))
-        }
-    }
-}
-
 /// A struct representing the selectors for the dynamic lookup tables
 #[derive(Clone, Debug, Default)]
 pub struct DynamicLookups {
@@ -207,15 +142,16 @@ 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 {
@@ -226,9 +162,13 @@ 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(),
+            ],
         }
     }
 }
@@ -328,6 +268,8 @@ pub struct BaseConfig<F: PrimeField + TensorType + PartialOrd> {
     /// Activate sanity checks
     pub check_mode: CheckMode,
     _marker: PhantomData<F>,
+    /// shared table inputs
+    pub shared_table_inputs: Vec<TableColumn>,
 }
 
 impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
@@ -340,6 +282,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
             shuffles: Shuffles::dummy(col_size, num_inner_cols),
             range_checks: RangeChecks::dummy(col_size, num_inner_cols),
             check_mode: CheckMode::SAFE,
+            shared_table_inputs: vec![],
             _marker: PhantomData,
         }
     }
@@ -366,13 +309,18 @@ 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() {
                 nonaccum_selectors.insert((BaseOp::Add, i, j), meta.selector());
                 nonaccum_selectors.insert((BaseOp::Sub, i, j), meta.selector());
                 nonaccum_selectors.insert((BaseOp::Mult, i, j), meta.selector());
-                nonaccum_selectors.insert((BaseOp::IsBoolean, i, j), meta.selector());
             }
         }
 
@@ -406,24 +354,13 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
                 // Get output expressions for each input channel
                 let (rotation_offset, rng) = base_op.query_offset_rng();
 
-                let constraints = match base_op {
-                    BaseOp::IsBoolean => {
-                        let expected_output: Tensor<Expression<F>> = output
-                            .query_rng(meta, *block_idx, *inner_col_idx, 0, 1)
-                            .expect("non accum: output query failed");
+                let constraints = {
+                    let expected_output: Tensor<Expression<F>> = output
+                        .query_rng(meta, *block_idx, *inner_col_idx, rotation_offset, rng)
+                        .expect("non accum: output query failed");
 
-                        let output = expected_output[base_op.constraint_idx()].clone();
-
-                        vec![(output.clone()) * (output.clone() - Expression::Constant(F::from(1)))]
-                    }
-                    _ => {
-                        let expected_output: Tensor<Expression<F>> = output
-                            .query_rng(meta, *block_idx, *inner_col_idx, rotation_offset, rng)
-                            .expect("non accum: output query failed");
-
-                        let res = base_op.nonaccum_f((qis[0].clone(), qis[1].clone()));
-                        vec![expected_output[base_op.constraint_idx()].clone() - res]
-                    }
+                    let res = base_op.nonaccum_f((qis[0].clone(), qis[1].clone()));
+                    vec![expected_output[base_op.constraint_idx()].clone() - res]
                 };
 
                 Constraints::with_selector(selector, constraints)
@@ -478,6 +415,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
             dynamic_lookups: DynamicLookups::default(),
             shuffles: Shuffles::default(),
             range_checks: RangeChecks::default(),
+            shared_table_inputs: vec![],
             check_mode,
             _marker: PhantomData,
         }
@@ -508,21 +446,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
             return Err(CircuitError::WrongColumnType(output.name().to_string()));
         }
 
-        // we borrow mutably twice so we need to do this dance
-
         let table = if !self.static_lookups.tables.contains_key(nl) {
-            // as all tables have the same input we see if there's another table who's input we can reuse
-            let table = if let Some(table) = self.static_lookups.tables.values().next() {
-                Table::<F>::configure(
-                    cs,
-                    lookup_range,
-                    logrows,
-                    nl,
-                    Some(table.table_inputs.clone()),
-                )
-            } else {
-                Table::<F>::configure(cs, lookup_range, logrows, nl, None)
-            };
+            let table =
+                Table::<F>::configure(cs, lookup_range, logrows, nl, &mut self.shared_table_inputs);
             self.static_lookups.tables.insert(nl.clone(), table.clone());
             table
         } else {
@@ -573,9 +499,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
                         // this is 0 if the index is the same as the column index (starting from 1)
 
                         let col_expr = sel.clone()
-                            * table
+                            * (table
                                 .selector_constructor
-                                .get_expr_at_idx(col_idx, synthetic_sel);
+                                .get_expr_at_idx(col_idx, synthetic_sel));
 
                         let multiplier =
                             table.selector_constructor.get_selector_val_at_idx(col_idx);
@@ -607,6 +533,40 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
                         res
                     });
                 }
+
+                // add a degree-k custom constraint of the following form to the range check and
+                // static lookup configuration.
+                // 𝑚𝑢𝑙𝑡𝑖𝑠𝑒𝑙 · ∏ (𝑠𝑒𝑙 − 𝑖) = 0 where 𝑠𝑒𝑙 is the synthetic_sel, and the product is over the set of overflowed columns
+                // and 𝑚𝑢𝑙𝑡𝑖𝑠𝑒𝑙 is the selector value at the column index
+                cs.create_gate("range_check_on_sel", |cs| {
+                    let synthetic_sel = match len {
+                        1 => Expression::Constant(F::from(1)),
+                        _ => match index {
+                            VarTensor::Advice { inner: advices, .. } => {
+                                cs.query_advice(advices[x][y], Rotation(0))
+                            }
+                            _ => unreachable!(),
+                        },
+                    };
+
+                    let range_check_on_synthetic_sel = match len {
+                        1 => Expression::Constant(F::from(0)),
+                        _ => {
+                            let mut initial_expr = Expression::Constant(F::from(1));
+                            for i in 0..len {
+                                initial_expr = initial_expr
+                                    * (synthetic_sel.clone()
+                                        - Expression::Constant(F::from(i as u64)))
+                            }
+                            initial_expr
+                        }
+                    };
+
+                    let sel = cs.query_selector(multi_col_selector);
+
+                    Constraints::with_selector(sel, vec![range_check_on_synthetic_sel])
+                });
+
                 self.static_lookups
                     .selectors
                     .insert((nl.clone(), x, y), multi_col_selector);
@@ -732,8 +692,8 @@ 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,
@@ -744,14 +704,14 @@ 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<_>>()
@@ -759,23 +719,23 @@ 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 {
@@ -787,9 +747,9 @@ 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))
                                 }
@@ -798,7 +758,7 @@ 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
@@ -809,13 +769,13 @@ 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");
@@ -847,7 +807,6 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
         let range_check = if let std::collections::btree_map::Entry::Vacant(e) =
             self.range_checks.ranges.entry(range)
         {
-            // as all tables have the same input we see if there's another table who's input we can reuse
             let range_check = RangeCheck::<F>::configure(cs, range, logrows);
             e.insert(range_check.clone());
             range_check
@@ -885,9 +844,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
                         let default_x = range_check.get_first_element(col_idx);
 
                         let col_expr = sel.clone()
-                            * range_check
+                            * (range_check
                                 .selector_constructor
-                                .get_expr_at_idx(col_idx, synthetic_sel);
+                                .get_expr_at_idx(col_idx, synthetic_sel));
 
                         let multiplier = range_check
                             .selector_constructor
@@ -910,6 +869,40 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
                         res
                     });
                 }
+
+                // add a degree-k custom constraint of the following form to the range check and
+                // static lookup configuration.
+                // 𝑚𝑢𝑙𝑡𝑖𝑠𝑒𝑙 · ∏ (𝑠𝑒𝑙 − 𝑖) = 0 where 𝑠𝑒𝑙 is the synthetic_sel, and the product is over the set of overflowed columns
+                // and 𝑚𝑢𝑙𝑡𝑖𝑠𝑒𝑙 is the selector value at the column index
+                cs.create_gate("range_check_on_sel", |cs| {
+                    let synthetic_sel = match len {
+                        1 => Expression::Constant(F::from(1)),
+                        _ => match index {
+                            VarTensor::Advice { inner: advices, .. } => {
+                                cs.query_advice(advices[x][y], Rotation(0))
+                            }
+                            _ => unreachable!(),
+                        },
+                    };
+
+                    let range_check_on_synthetic_sel = match len {
+                        1 => Expression::Constant(F::from(0)),
+                        _ => {
+                            let mut initial_expr = Expression::Constant(F::from(1));
+                            for i in 0..len {
+                                initial_expr = initial_expr
+                                    * (synthetic_sel.clone()
+                                        - Expression::Constant(F::from(i as u64)))
+                            }
+                            initial_expr
+                        }
+                    };
+
+                    let sel = cs.query_selector(multi_col_selector);
+
+                    Constraints::with_selector(sel, vec![range_check_on_synthetic_sel])
+                });
+
                 self.range_checks
                     .selectors
                     .insert((range, x, y), multi_col_selector);

src/circuit/ops/errors.rs

@@ -25,7 +25,7 @@ pub enum CircuitError {
     /// This operation is unsupported
     #[error("unsupported operation in graph")]
     UnsupportedOp,
-    ///
+    /// Invalid einsum expression
     #[error("invalid einsum expression")]
     InvalidEinsum,
     /// Flush error
@@ -97,4 +97,16 @@ pub enum CircuitError {
     /// Invalid scale
     #[error("negative scale for an op that requires positive inputs {0}")]
     NegativeScale(String),
+    #[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,
+    /// Visibility has not been set
+    #[error("visibility has not been set")]
+    UnsetVisibility,
+    /// A decomposition base overflowed
+    #[error("decomposition base overflowed")]
+    DecompositionBaseOverflow,
 }

src/circuit/ops/hybrid.rs

@@ -1,9 +1,9 @@
 use super::*;
 use crate::{
-    circuit::{layouts, utils, Tolerance},
-    fieldutils::integer_rep_to_felt,
+    circuit::{layouts, utils},
+    fieldutils::{IntegerRep, integer_rep_to_felt},
     graph::multiplier_to_scale,
-    tensor::{self, Tensor, TensorType, ValTensor},
+    tensor::{self, DataFormat, Tensor, TensorType, ValTensor},
 };
 use halo2curves::ff::PrimeField;
 use serde::{Deserialize, Serialize};
@@ -13,6 +13,22 @@ use serde::{Deserialize, Serialize};
 /// An enum representing the operations that consist of both lookups and arithmetic operations.
 #[derive(Clone, Debug, Serialize, Deserialize)]
 pub enum HybridOp {
+    Ln {
+        scale: utils::F32,
+        eps: f64,
+    },
+    Rsqrt {
+        input_scale: utils::F32,
+        output_scale: utils::F32,
+        eps: f64,
+    },
+    Sqrt {
+        scale: utils::F32,
+    },
+    RoundHalfToEven {
+        scale: utils::F32,
+        legs: usize,
+    },
     Ceil {
         scale: utils::F32,
         legs: usize,
@@ -28,10 +44,10 @@ pub enum HybridOp {
     Recip {
         input_scale: utils::F32,
         output_scale: utils::F32,
+        eps: f64,
     },
     Div {
         denom: utils::F32,
-        use_range_check_for_int: bool,
     },
     ReduceMax {
         axes: Vec<usize>,
@@ -44,11 +60,13 @@ pub enum HybridOp {
         stride: Vec<usize>,
         kernel_shape: Vec<usize>,
         normalized: bool,
+        data_format: DataFormat,
     },
     MaxPool {
         padding: Vec<(usize, usize)>,
         stride: Vec<usize>,
         pool_dims: Vec<usize>,
+        data_format: DataFormat,
     },
     ReduceMin {
         axes: Vec<usize>,
@@ -62,8 +80,11 @@ pub enum HybridOp {
         input_scale: utils::F32,
         output_scale: utils::F32,
         axes: Vec<usize>,
+        eps: f64,
+    },
+    Output {
+        decomp: bool,
     },
-    RangeCheck(Tolerance),
     Greater,
     GreaterEqual,
     Less,
@@ -108,33 +129,43 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
 
     fn as_string(&self) -> String {
         match self {
+            HybridOp::Rsqrt {
+                input_scale,
+                output_scale,
+                eps,
+            } => format!(
+                "RSQRT (input_scale={}, output_scale={}, eps={})",
+                input_scale, output_scale, eps
+            ),
+            HybridOp::Sqrt { scale } => format!("SQRT(scale={})", scale),
+            HybridOp::Ln { scale, eps } => format!("LN(scale={}, eps={})", scale, eps),
+            HybridOp::RoundHalfToEven { scale, legs } => {
+                format!("ROUND_HALF_TO_EVEN(scale={}, legs={})", scale, legs)
+            }
             HybridOp::Ceil { scale, legs } => format!("CEIL(scale={}, legs={})", scale, legs),
             HybridOp::Floor { scale, legs } => format!("FLOOR(scale={}, legs={})", scale, legs),
             HybridOp::Round { scale, legs } => format!("ROUND(scale={}, legs={})", scale, legs),
-            HybridOp::Max => format!("MAX"),
-            HybridOp::Min => format!("MIN"),
+
+            HybridOp::Max => "MAX".to_string(),
+            HybridOp::Min => "MIN".to_string(),
             HybridOp::Recip {
                 input_scale,
                 output_scale,
+                eps,
             } => format!(
-                "RECIP (input_scale={}, output_scale={})",
-                input_scale, output_scale
-            ),
-            HybridOp::Div {
-                denom,
-                use_range_check_for_int,
-            } => format!(
-                "DIV (denom={}, use_range_check_for_int={})",
-                denom, use_range_check_for_int
+                "RECIP (input_scale={}, output_scale={}, eps={})",
+                input_scale, output_scale, eps
             ),
+            HybridOp::Div { denom } => format!("DIV (denom={})", denom),
             HybridOp::SumPool {
                 padding,
                 stride,
                 kernel_shape,
                 normalized,
+                data_format,
             } => format!(
-                "SUMPOOL (padding={:?}, stride={:?}, kernel_shape={:?}, normalized={})",
-                padding, stride, kernel_shape, normalized
+                "SUMPOOL (padding={:?}, stride={:?}, kernel_shape={:?}, normalized={}, data_format={:?})",
+                padding, stride, kernel_shape, normalized, data_format
             ),
             HybridOp::ReduceMax { axes } => format!("REDUCEMAX (axes={:?})", axes),
             HybridOp::ReduceArgMax { dim } => format!("REDUCEARGMAX (dim={})", dim),
@@ -142,9 +173,10 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                 padding,
                 stride,
                 pool_dims,
+                data_format,
             } => format!(
-                "MaxPool (padding={:?}, stride={:?}, pool_dims={:?})",
-                padding, stride, pool_dims
+                "MaxPool (padding={:?}, stride={:?}, pool_dims={:?}, data_format={:?})",
+                padding, stride, pool_dims, data_format
             ),
             HybridOp::ReduceMin { axes } => format!("REDUCEMIN (axes={:?})", axes),
             HybridOp::ReduceArgMin { dim } => format!("REDUCEARGMIN (dim={})", dim),
@@ -152,13 +184,16 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                 input_scale,
                 output_scale,
                 axes,
+                eps,
             } => {
                 format!(
-                    "SOFTMAX (input_scale={}, output_scale={}, axes={:?})",
-                    input_scale, output_scale, axes
+                    "SOFTMAX (input_scale={}, output_scale={}, axes={:?}, eps={})",
+                    input_scale, output_scale, axes, eps
                 )
             }
-            HybridOp::RangeCheck(p) => format!("RANGECHECK (tol={:?})", p),
+            HybridOp::Output { decomp } => {
+                format!("OUTPUT (decomp={})", decomp)
+            }
             HybridOp::Greater => "GREATER".to_string(),
             HybridOp::GreaterEqual => "GREATEREQUAL".to_string(),
             HybridOp::Less => "LESS".to_string(),
@@ -181,6 +216,27 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
         values: &[ValTensor<F>],
     ) -> Result<Option<ValTensor<F>>, CircuitError> {
         Ok(Some(match self {
+            HybridOp::Rsqrt {
+                input_scale,
+                output_scale,
+                eps,
+            } => layouts::rsqrt(
+                config,
+                region,
+                values[..].try_into()?,
+                *input_scale,
+                *output_scale,
+                *eps,
+            )?,
+            HybridOp::Sqrt { scale } => {
+                layouts::sqrt(config, region, values[..].try_into()?, *scale)?
+            }
+            HybridOp::Ln { scale, eps } => {
+                layouts::ln(config, region, values[..].try_into()?, *scale, *eps)?
+            }
+            HybridOp::RoundHalfToEven { scale, legs } => {
+                layouts::round_half_to_even(config, region, values[..].try_into()?, *scale, *legs)?
+            }
             HybridOp::Ceil { scale, legs } => {
                 layouts::ceil(config, region, values[..].try_into()?, *scale, *legs)?
             }
@@ -197,6 +253,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                 stride,
                 kernel_shape,
                 normalized,
+                data_format,
             } => layouts::sumpool(
                 config,
                 region,
@@ -205,28 +262,27 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                 stride,
                 kernel_shape,
                 *normalized,
+                *data_format,
             )?,
             HybridOp::Recip {
                 input_scale,
                 output_scale,
+                eps,
             } => layouts::recip(
                 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),
+                *eps,
             )?,
-            HybridOp::Div {
-                denom,
-                use_range_check_for_int,
-                ..
-            } => {
-                if denom.0.fract() == 0.0 && *use_range_check_for_int {
-                    layouts::loop_div(
+            HybridOp::Div { denom, .. } => {
+                if denom.0.fract() == 0.0 {
+                    layouts::div(
                         config,
                         region,
                         values[..].try_into()?,
-                        integer_rep_to_felt(denom.0 as i128),
+                        integer_rep_to_felt(denom.0 as IntegerRep),
                     )?
                 } else {
                     layouts::nonlinearity(
@@ -249,6 +305,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                 padding,
                 stride,
                 pool_dims,
+                data_format,
             } => layouts::max_pool(
                 config,
                 region,
@@ -256,6 +313,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                 padding,
                 stride,
                 pool_dims,
+                *data_format,
             )?,
             HybridOp::ReduceMax { axes } => {
                 layouts::max_axes(config, region, values[..].try_into()?, axes)?
@@ -273,6 +331,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                 input_scale,
                 output_scale,
                 axes,
+                eps,
             } => layouts::softmax_axes(
                 config,
                 region,
@@ -280,14 +339,11 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                 *input_scale,
                 *output_scale,
                 axes,
+                *eps,
             )?,
-            HybridOp::RangeCheck(tol) => layouts::range_check_percent(
-                config,
-                region,
-                values[..].try_into()?,
-                tol.scale,
-                tol.val,
-            )?,
+            HybridOp::Output { decomp } => {
+                layouts::output(config, region, values[..].try_into()?, *decomp)?
+            }
             HybridOp::Greater => layouts::greater(config, region, values[..].try_into()?)?,
             HybridOp::GreaterEqual => {
                 layouts::greater_equal(config, region, values[..].try_into()?)?
@@ -313,9 +369,19 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
             | HybridOp::ReduceArgMax { .. }
             | HybridOp::OneHot { .. }
             | HybridOp::ReduceArgMin { .. } => 0,
-            HybridOp::Softmax { output_scale, .. } | HybridOp::Recip { output_scale, .. } => {
+
+            HybridOp::Recip { output_scale, .. } | HybridOp::Rsqrt { output_scale, .. } => {
                 multiplier_to_scale(output_scale.0 as f64)
             }
+            HybridOp::Softmax {
+                output_scale,
+                input_scale,
+                ..
+            } => multiplier_to_scale((output_scale.0 * input_scale.0) as f64),
+            HybridOp::Ln {
+                scale: output_scale,
+                eps: _,
+            } => 4 * multiplier_to_scale(output_scale.0 as f64),
             _ => in_scales[0],
         };
         Ok(scale)

src/circuit/ops/lookup.rs

@@ -4,7 +4,6 @@ use serde::{Deserialize, Serialize};
 use crate::{
     circuit::{layouts, table::Range, utils},
     fieldutils::{felt_to_integer_rep, integer_rep_to_felt, IntegerRep},
-    graph::multiplier_to_scale,
     tensor::{self, Tensor, TensorError, TensorType},
 };
 
@@ -16,13 +15,11 @@ use halo2curves::ff::PrimeField;
 #[derive(Clone, Debug, PartialEq, Eq, Hash, PartialOrd, Ord, Deserialize, Serialize)]
 pub enum LookupOp {
     Div { denom: utils::F32 },
-    Cast { scale: utils::F32 },
-    RoundHalfToEven { scale: utils::F32 },
-    Sqrt { scale: utils::F32 },
-    Rsqrt { scale: utils::F32 },
-    Sigmoid { scale: utils::F32 },
+    IsOdd,
+    PowersOfTwo { scale: utils::F32 },
     Ln { scale: utils::F32 },
-    Exp { scale: utils::F32 },
+    Sigmoid { scale: utils::F32 },
+    Exp { scale: utils::F32, base: utils::F32 },
     Cos { scale: utils::F32 },
     ACos { scale: utils::F32 },
     Cosh { scale: utils::F32 },
@@ -51,16 +48,14 @@ impl LookupOp {
     /// as path
     pub fn as_path(&self) -> String {
         match self {
-            LookupOp::RoundHalfToEven { scale } => format!("round_half_to_even_{}", scale),
             LookupOp::Pow { scale, a } => format!("pow_{}_{}", scale, a),
-            LookupOp::Div { denom } => format!("div_{}", denom),
-            LookupOp::Cast { scale } => format!("cast_{}", scale),
-            LookupOp::Sigmoid { scale } => format!("sigmoid_{}", scale),
-            LookupOp::Sqrt { scale } => format!("sqrt_{}", scale),
-            LookupOp::Rsqrt { scale } => format!("rsqrt_{}", scale),
-            LookupOp::Erf { scale } => format!("erf_{}", scale),
-            LookupOp::Exp { scale } => format!("exp_{}", scale),
             LookupOp::Ln { scale } => format!("ln_{}", scale),
+            LookupOp::PowersOfTwo { scale } => format!("pow2_{}", scale),
+            LookupOp::IsOdd => "is_odd".to_string(),
+            LookupOp::Div { denom } => format!("div_{}", denom),
+            LookupOp::Sigmoid { scale } => format!("sigmoid_{}", scale),
+            LookupOp::Erf { scale } => format!("erf_{}", scale),
+            LookupOp::Exp { scale, base } => format!("exp_{}_{}", scale, base),
             LookupOp::Cos { scale } => format!("cos_{}", scale),
             LookupOp::ACos { scale } => format!("acos_{}", scale),
             LookupOp::Cosh { scale } => format!("cosh_{}", scale),
@@ -85,36 +80,28 @@ impl LookupOp {
         let x = x[0].clone().map(|x| felt_to_integer_rep(x));
         let res =
             match &self {
-                LookupOp::RoundHalfToEven { scale } => Ok::<_, TensorError>(
-                    tensor::ops::nonlinearities::round_half_to_even(&x, scale.into()),
-                ),
+                LookupOp::Ln { scale } => {
+                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ln(&x, scale.into()))
+                }
+                LookupOp::PowersOfTwo { scale } => {
+                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ipow2(&x, scale.0.into()))
+                }
+                LookupOp::IsOdd => Ok::<_, TensorError>(tensor::ops::nonlinearities::is_odd(&x)),
                 LookupOp::Pow { scale, a } => Ok::<_, TensorError>(
                     tensor::ops::nonlinearities::pow(&x, scale.0.into(), a.0.into()),
                 ),
                 LookupOp::Div { denom } => Ok::<_, TensorError>(
                     tensor::ops::nonlinearities::const_div(&x, f32::from(*denom).into()),
                 ),
-                LookupOp::Cast { scale } => Ok::<_, TensorError>(
-                    tensor::ops::nonlinearities::const_div(&x, f32::from(*scale).into()),
-                ),
                 LookupOp::Sigmoid { scale } => {
                     Ok::<_, TensorError>(tensor::ops::nonlinearities::sigmoid(&x, scale.into()))
                 }
-                LookupOp::Sqrt { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::sqrt(&x, scale.into()))
-                }
-                LookupOp::Rsqrt { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::rsqrt(&x, scale.into()))
-                }
                 LookupOp::Erf { scale } => {
                     Ok::<_, TensorError>(tensor::ops::nonlinearities::erffunc(&x, scale.into()))
                 }
-                LookupOp::Exp { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::exp(&x, scale.into()))
-                }
-                LookupOp::Ln { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ln(&x, scale.into()))
-                }
+                LookupOp::Exp { scale, base } => Ok::<_, TensorError>(
+                    tensor::ops::nonlinearities::exp(&x, scale.into(), base.into()),
+                ),
                 LookupOp::Cos { scale } => {
                     Ok::<_, TensorError>(tensor::ops::nonlinearities::cos(&x, scale.into()))
                 }
@@ -171,16 +158,14 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Lookup
     /// Returns the name of the operation
     fn as_string(&self) -> String {
         match self {
-            LookupOp::RoundHalfToEven { scale } => format!("ROUND_HALF_TO_EVEN(scale={})", scale),
+            LookupOp::Ln { scale } => format!("LN(scale={})", scale),
+            LookupOp::PowersOfTwo { scale } => format!("POWERS_OF_TWO(scale={})", scale),
+            LookupOp::IsOdd => "IS_ODD".to_string(),
             LookupOp::Pow { a, scale } => format!("POW(scale={}, exponent={})", scale, a),
             LookupOp::Div { denom, .. } => format!("DIV(denom={})", denom),
-            LookupOp::Cast { scale } => format!("CAST(scale={})", scale),
-            LookupOp::Ln { scale } => format!("LN(scale={})", scale),
             LookupOp::Sigmoid { scale } => format!("SIGMOID(scale={})", scale),
-            LookupOp::Sqrt { scale } => format!("SQRT(scale={})", scale),
             LookupOp::Erf { scale } => format!("ERF(scale={})", scale),
-            LookupOp::Rsqrt { scale } => format!("RSQRT(scale={})", scale),
-            LookupOp::Exp { scale } => format!("EXP(scale={})", scale),
+            LookupOp::Exp { scale, base } => format!("EXP(scale={}, base={})", scale, base),
             LookupOp::Tan { scale } => format!("TAN(scale={})", scale),
             LookupOp::ATan { scale } => format!("ATAN(scale={})", scale),
             LookupOp::Tanh { scale } => format!("TANH(scale={})", scale),
@@ -213,13 +198,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Lookup
 
     /// Returns the scale of the output of the operation.
     fn out_scale(&self, inputs_scale: Vec<crate::Scale>) -> Result<crate::Scale, CircuitError> {
-        let scale = match self {
-            LookupOp::Cast { scale } => {
-                let in_scale = inputs_scale[0];
-                in_scale + multiplier_to_scale(1. / scale.0 as f64)
-            }
-            _ => inputs_scale[0],
-        };
+        let scale = inputs_scale[0];
         Ok(scale)
     }
 

src/circuit/ops/mod.rs

@@ -1,6 +1,8 @@
 use std::any::Any;
 
 use serde::{Deserialize, Serialize};
+#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
+use tract_onnx::prelude::DatumType;
 
 use crate::{
     graph::quantize_tensor,
@@ -96,6 +98,8 @@ pub enum InputType {
     Int,
     ///
     TDim,
+    ///
+    Unknown,
 }
 
 impl InputType {
@@ -105,7 +109,10 @@ impl InputType {
     }
 
     ///
-    pub fn roundtrip<T: num::ToPrimitive + num::FromPrimitive + Clone>(&self, input: &mut T) {
+    pub fn roundtrip<T: num::ToPrimitive + num::FromPrimitive + Clone + std::fmt::Debug>(
+        &self,
+        input: &mut T,
+    ) {
         match self {
             InputType::Bool => {
                 let boolean_input = input.clone().to_i64().unwrap();
@@ -118,7 +125,7 @@ impl InputType {
                 *input = T::from_f32(f32_input).unwrap();
             }
             InputType::F32 => {
-                let f32_input = input.clone().to_f32().unwrap();
+                let f32_input: f32 = input.clone().to_f32().unwrap();
                 *input = T::from_f32(f32_input).unwrap();
             }
             InputType::F64 => {
@@ -129,6 +136,47 @@ impl InputType {
                 let int_input = input.clone().to_i64().unwrap();
                 *input = T::from_i64(int_input).unwrap();
             }
+            InputType::Unknown => {}
+        }
+    }
+}
+
+impl std::str::FromStr for InputType {
+    type Err = CircuitError;
+
+    fn from_str(s: &str) -> Result<Self, Self::Err> {
+        match s {
+            "bool" => Ok(InputType::Bool),
+            "f16" => Ok(InputType::F16),
+            "f32" => Ok(InputType::F32),
+            "f64" => Ok(InputType::F64),
+            "int" => Ok(InputType::Int),
+            "tdim" => Ok(InputType::TDim),
+            e => Err(CircuitError::InvalidInputType(e.to_string())),
+        }
+    }
+}
+
+#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
+impl From<DatumType> for InputType {
+    /// # Panics
+    /// Panics if the datum type is not supported
+    fn from(datum_type: DatumType) -> Self {
+        match datum_type {
+            DatumType::Bool => InputType::Bool,
+            DatumType::F16 => InputType::F16,
+            DatumType::F32 => InputType::F32,
+            DatumType::F64 => InputType::F64,
+            DatumType::I8 => InputType::Int,
+            DatumType::I16 => InputType::Int,
+            DatumType::I32 => InputType::Int,
+            DatumType::I64 => InputType::Int,
+            DatumType::U8 => InputType::Int,
+            DatumType::U16 => InputType::Int,
+            DatumType::U32 => InputType::Int,
+            DatumType::U64 => InputType::Int,
+            DatumType::TDim => InputType::TDim,
+            _ => unimplemented!(),
         }
     }
 }
@@ -140,6 +188,8 @@ pub struct Input {
     pub scale: crate::Scale,
     ///
     pub datum_type: InputType,
+    /// decomp check
+    pub decomp: bool,
 }
 
 impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Input {
@@ -177,6 +227,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Input
                     config,
                     region,
                     values[..].try_into()?,
+                    self.decomp,
                 )?)),
             }
         } else {
@@ -232,20 +283,26 @@ pub struct Constant<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> {
     ///
     #[serde(skip)]
     pub pre_assigned_val: Option<ValTensor<F>>,
+    ///
+    pub decomp: bool,
 }
 
 impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Constant<F> {
     ///
-    pub fn new(quantized_values: Tensor<F>, raw_values: Tensor<f32>) -> Self {
+    pub fn new(quantized_values: Tensor<F>, raw_values: Tensor<f32>, decomp: bool) -> Self {
         Self {
             quantized_values,
             raw_values,
             pre_assigned_val: None,
+            decomp,
         }
     }
     /// Rebase the scale of the constant
     pub fn rebase_scale(&mut self, new_scale: crate::Scale) -> Result<(), CircuitError> {
-        let visibility = self.quantized_values.visibility().unwrap();
+        let visibility = match self.quantized_values.visibility() {
+            Some(v) => v,
+            None => return Err(CircuitError::UnsetVisibility),
+        };
         self.quantized_values = quantize_tensor(self.raw_values.clone(), new_scale, &visibility)?;
         Ok(())
     }
@@ -262,13 +319,8 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Constant<F> {
 }
 
 impl<
-        F: PrimeField
-            + TensorType
-            + PartialOrd
-            + std::hash::Hash
-            + Serialize
-            + for<'de> Deserialize<'de>,
-    > Op<F> for Constant<F>
+    F: PrimeField + TensorType + PartialOrd + std::hash::Hash + Serialize + for<'de> Deserialize<'de>,
+> Op<F> for Constant<F>
 {
     fn as_any(&self) -> &dyn Any {
         self
@@ -289,7 +341,12 @@ impl<
             self.quantized_values.clone().try_into()?
         };
         // we gotta constrain it once if its used multiple times
-        Ok(Some(layouts::identity(config, region, &[value])?))
+        Ok(Some(layouts::identity(
+            config,
+            region,
+            &[value],
+            self.decomp,
+        )?))
     }
 
     fn clone_dyn(&self) -> Box<dyn Op<F>> {

src/circuit/ops/poly.rs

@@ -4,6 +4,7 @@ use crate::{
         utils::{self, F32},
     },
     tensor::{self, Tensor, TensorError},
+    tensor::{DataFormat, KernelFormat},
 };
 
 use super::{base::BaseOp, *};
@@ -43,10 +44,12 @@ pub enum PolyOp {
         padding: Vec<(usize, usize)>,
         stride: Vec<usize>,
         group: usize,
+        data_format: DataFormat,
+        kernel_format: KernelFormat,
     },
     Downsample {
         axis: usize,
-        stride: usize,
+        stride: isize,
         modulo: usize,
     },
     DeConv {
@@ -54,6 +57,8 @@ pub enum PolyOp {
         output_padding: Vec<usize>,
         stride: Vec<usize>,
         group: usize,
+        data_format: DataFormat,
+        kernel_format: KernelFormat,
     },
     Add,
     Sub,
@@ -103,13 +108,8 @@ pub enum PolyOp {
 }
 
 impl<
-        F: PrimeField
-            + TensorType
-            + PartialOrd
-            + std::hash::Hash
-            + Serialize
-            + for<'de> Deserialize<'de>,
-    > Op<F> for PolyOp
+    F: PrimeField + TensorType + PartialOrd + std::hash::Hash + Serialize + for<'de> Deserialize<'de>,
+> Op<F> for PolyOp
 {
     /// Returns a reference to the Any trait.
     fn as_any(&self) -> &dyn Any {
@@ -165,10 +165,12 @@ impl<
                 stride,
                 padding,
                 group,
+                data_format,
+                kernel_format,
             } => {
                 format!(
-                    "CONV (stride={:?}, padding={:?}, group={})",
-                    stride, padding, group
+                    "CONV (stride={:?}, padding={:?}, group={}, data_format={:?}, kernel_format={:?})",
+                    stride, padding, group, data_format, kernel_format
                 )
             }
             PolyOp::DeConv {
@@ -176,10 +178,12 @@ impl<
                 padding,
                 output_padding,
                 group,
+                data_format,
+                kernel_format,
             } => {
                 format!(
-                    "DECONV (stride={:?}, padding={:?}, output_padding={:?}, group={})",
-                    stride, padding, output_padding, group
+                    "DECONV (stride={:?}, padding={:?}, output_padding={:?}, group={}, data_format={:?}, kernel_format={:?})",
+                    stride, padding, output_padding, group, data_format, kernel_format
                 )
             }
             PolyOp::Concat { axis } => format!("CONCAT (axis={})", axis),
@@ -242,6 +246,8 @@ impl<
                 padding,
                 stride,
                 group,
+                data_format,
+                kernel_format,
             } => layouts::conv(
                 config,
                 region,
@@ -249,9 +255,17 @@ impl<
                 padding,
                 stride,
                 *group,
+                *data_format,
+                *kernel_format,
             )?,
             PolyOp::GatherElements { dim, constant_idx } => {
                 if let Some(idx) = constant_idx {
+                    if values.len() != 1 {
+                        return Err(TensorError::DimError(
+                            "GatherElements only accepts single inputs".to_string(),
+                        )
+                        .into());
+                    }
                     tensor::ops::gather_elements(values[0].get_inner_tensor()?, idx, *dim)?.into()
                 } else {
                     layouts::gather_elements(config, region, values[..].try_into()?, *dim)?.0
@@ -269,6 +283,12 @@ impl<
             }
             PolyOp::ScatterElements { dim, constant_idx } => {
                 if let Some(idx) = constant_idx {
+                    if values.len() != 2 {
+                        return Err(TensorError::DimError(
+                            "ScatterElements requires two inputs".to_string(),
+                        )
+                        .into());
+                    }
                     tensor::ops::scatter(
                         values[0].get_inner_tensor()?,
                         idx,
@@ -297,6 +317,8 @@ impl<
                 output_padding,
                 stride,
                 group,
+                data_format,
+                kernel_format,
             } => layouts::deconv(
                 config,
                 region,
@@ -305,13 +327,17 @@ impl<
                 output_padding,
                 stride,
                 *group,
+                *data_format,
+                *kernel_format,
             )?,
             PolyOp::Add => layouts::pairwise(config, region, values[..].try_into()?, BaseOp::Add)?,
             PolyOp::Sub => layouts::pairwise(config, region, values[..].try_into()?, BaseOp::Sub)?,
             PolyOp::Mult => {
                 layouts::pairwise(config, region, values[..].try_into()?, BaseOp::Mult)?
             }
-            PolyOp::Identity { .. } => layouts::identity(config, region, values[..].try_into()?)?,
+            PolyOp::Identity { .. } => {
+                layouts::identity(config, region, values[..].try_into()?, false)?
+            }
             PolyOp::Reshape(d) | PolyOp::Flatten(d) => layouts::reshape(values[..].try_into()?, d)?,
             PolyOp::Pad(p) => {
                 if values.len() != 1 {

src/circuit/ops/region.rs

@@ -211,7 +211,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
             self.min_lookup_inputs().to_string().green(),
             self.max_range_size().to_string().green(),
             self.dynamic_lookup_col_coord().to_string().green(),
-            self.shuffle_col_coord().to_string().green(), 
+            self.shuffle_col_coord().to_string().green(),
             self.max_dynamic_input_len().to_string().green()
         );
     }
@@ -474,6 +474,17 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         Ok(())
     }
 
+    /// Update the max and min forcefully
+    pub fn update_max_min_lookup_inputs_force(
+        &mut self,
+        min: IntegerRep,
+        max: IntegerRep,
+    ) -> Result<(), CircuitError> {
+        self.statistics.max_lookup_inputs = self.statistics.max_lookup_inputs.max(max);
+        self.statistics.min_lookup_inputs = self.statistics.min_lookup_inputs.min(min);
+        Ok(())
+    }
+
     /// Update the max and min from inputs
     pub fn update_max_min_lookup_range(&mut self, range: Range) -> Result<(), CircuitError> {
         if range.0 > range.1 {
@@ -600,7 +611,6 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         var: &VarTensor,
         values: &ValTensor<F>,
     ) -> Result<(ValTensor<F>, usize), CircuitError> {
-
         self.update_max_dynamic_input_len(values.len());
 
         if let Some(region) = &self.region {
@@ -661,22 +671,17 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
     }
 
     /// Assign a valtensor to a vartensor with duplication
-    pub fn assign_with_duplication(
+    pub fn assign_with_duplication_unconstrained(
         &mut self,
         var: &VarTensor,
         values: &ValTensor<F>,
-        check_mode: &crate::circuit::CheckMode,
-        single_inner_col: bool,
     ) -> Result<(ValTensor<F>, usize), Error> {
         if let Some(region) = &self.region {
             // duplicates every nth element to adjust for column overflow
-            let (res, len) = var.assign_with_duplication(
+            let (res, len) = var.assign_with_duplication_unconstrained(
                 &mut region.borrow_mut(),
-                self.row,
                 self.linear_coord,
                 values,
-                check_mode,
-                single_inner_col,
                 &mut self.assigned_constants,
             )?;
             Ok((res, len))
@@ -685,7 +690,37 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
                 self.row,
                 self.linear_coord,
                 values,
-                single_inner_col,
+                false,
+                &mut self.assigned_constants,
+            )?;
+            Ok((values.clone(), len))
+        }
+    }
+
+    /// Assign a valtensor to a vartensor with duplication
+    pub fn assign_with_duplication_constrained(
+        &mut self,
+        var: &VarTensor,
+        values: &ValTensor<F>,
+        check_mode: &crate::circuit::CheckMode,
+    ) -> Result<(ValTensor<F>, usize), Error> {
+        if let Some(region) = &self.region {
+            // duplicates every nth element to adjust for column overflow
+            let (res, len) = var.assign_with_duplication_constrained(
+                &mut region.borrow_mut(),
+                self.row,
+                self.linear_coord,
+                values,
+                check_mode,
+                &mut self.assigned_constants,
+            )?;
+            Ok((res, len))
+        } else {
+            let (_, len) = var.dummy_assign_with_duplication(
+                self.row,
+                self.linear_coord,
+                values,
+                true,
                 &mut self.assigned_constants,
             )?;
             Ok((values.clone(), len))

src/circuit/table.rs

@@ -132,30 +132,29 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
         (first_element, op_f.output[0])
     }
 
-    ///
+    /// calculates the column size given the number of rows and reserved blinding rows
     pub fn cal_col_size(logrows: usize, reserved_blinding_rows: usize) -> usize {
         2usize.pow(logrows as u32) - reserved_blinding_rows
     }
-
-    ///
-    pub fn cal_bit_range(bits: usize, reserved_blinding_rows: usize) -> usize {
-        2usize.pow(bits as u32) - reserved_blinding_rows
-    }
 }
 
 ///
 pub fn num_cols_required(range_len: IntegerRep, col_size: usize) -> usize {
     // number of cols needed to store the range
-    (range_len / (col_size as IntegerRep)) as usize + 1
+    (range_len / col_size as IntegerRep) as usize + 1
 }
 
 impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
+    /// get largest element represented by the range
+    pub fn largest(&self) -> IntegerRep {
+        self.range.0 + (self.col_size * self.table_inputs.len() - 1) as IntegerRep
+    }
     fn name(&self) -> String {
         format!(
             "{}_{}_{}",
             self.nonlinearity.as_path(),
             self.range.0,
-            self.range.1
+            self.largest()
         )
     }
     /// Configures the table.
@@ -164,7 +163,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
         range: Range,
         logrows: usize,
         nonlinearity: &LookupOp,
-        preexisting_inputs: Option<Vec<TableColumn>>,
+        preexisting_inputs: &mut Vec<TableColumn>,
     ) -> Table<F> {
         let factors = cs.blinding_factors() + RESERVED_BLINDING_ROWS_PAD;
         let col_size = Self::cal_col_size(logrows, factors);
@@ -173,28 +172,28 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
 
         debug!("table range: {:?}", range);
 
-        let table_inputs = preexisting_inputs.unwrap_or_else(|| {
-            let mut cols = vec![];
-            for _ in 0..num_cols {
-                cols.push(cs.lookup_table_column());
+        // validate enough columns are provided to store the range
+        if preexisting_inputs.len() < num_cols {
+            // add columns to match the required number of columns
+            let diff = num_cols - preexisting_inputs.len();
+            for _ in 0..diff {
+                preexisting_inputs.push(cs.lookup_table_column());
             }
-            cols
-        });
-
-        let num_cols = table_inputs.len();
+        }
 
+        let num_cols = preexisting_inputs.len();
         if num_cols > 1 {
             warn!("Using {} columns for non-linearity table.", num_cols);
         }
 
-        let table_outputs = table_inputs
+        let table_outputs = preexisting_inputs
             .iter()
             .map(|_| cs.lookup_table_column())
             .collect::<Vec<_>>();
 
         Table {
             nonlinearity: nonlinearity.clone(),
-            table_inputs,
+            table_inputs: preexisting_inputs.clone(),
             table_outputs,
             is_assigned: false,
             selector_constructor: SelectorConstructor::new(num_cols),
@@ -222,7 +221,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
         }
 
         let smallest = self.range.0;
-        let largest = self.range.1;
+        let largest = self.largest();
 
         let gen_table = || -> Result<(Tensor<F>, Tensor<F>), crate::tensor::TensorError> {
             let inputs = Tensor::from(smallest..=largest)
@@ -291,6 +290,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
 
                                 row_offset += chunk_idx * self.col_size;
                                 let (x, y) = self.cartesian_coord(row_offset);
+
                                 if !preassigned_input {
                                     table.assign_cell(
                                         || format!("nl_i_col row {}", row_offset),
@@ -350,16 +350,11 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RangeCheck<F> {
         integer_rep_to_felt(chunk * (self.col_size as IntegerRep) + self.range.0)
     }
 
-    ///
+    /// calculates the column size
     pub fn cal_col_size(logrows: usize, reserved_blinding_rows: usize) -> usize {
         2usize.pow(logrows as u32) - reserved_blinding_rows
     }
 
-    ///
-    pub fn cal_bit_range(bits: usize, reserved_blinding_rows: usize) -> usize {
-        2usize.pow(bits as u32) - reserved_blinding_rows
-    }
-
     /// get column index given input
     pub fn get_col_index(&self, input: F) -> F {
         //    range is split up into chunks of size col_size, find the chunk that input is in

src/circuit/tests.rs

@@ -1,5 +1,6 @@
 use crate::circuit::ops::poly::PolyOp;
 use crate::circuit::*;
+use crate::tensor::{DataFormat, KernelFormat};
 use crate::tensor::{Tensor, TensorType, ValTensor, VarTensor};
 use halo2_proofs::{
     circuit::{Layouter, SimpleFloorPlanner, Value},
@@ -1040,6 +1041,10 @@ 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)
         }
 
@@ -1061,6 +1066,8 @@ mod conv {
                                     padding: vec![(1, 1); 2],
                                     stride: vec![2; 2],
                                     group: 1,
+                                    data_format: DataFormat::default(),
+                                    kernel_format: KernelFormat::default(),
                                 }),
                             )
                             .map_err(|_| Error::Synthesis)
@@ -1171,7 +1178,7 @@ mod conv_col_ultra_overflow {
 
     use super::*;
 
-    const K: usize = 4;
+    const K: usize = 6;
     const LEN: usize = 10;
 
     #[derive(Clone)]
@@ -1191,9 +1198,10 @@ 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)
         }
 
@@ -1215,6 +1223,8 @@ mod conv_col_ultra_overflow {
                                     padding: vec![(1, 1); 2],
                                     stride: vec![2; 2],
                                     group: 1,
+                                    data_format: DataFormat::default(),
+                                    kernel_format: KernelFormat::default(),
                                 }),
                             )
                             .map_err(|_| Error::Synthesis)
@@ -1372,6 +1382,8 @@ mod conv_relu_col_ultra_overflow {
                                     padding: vec![(1, 1); 2],
                                     stride: vec![2; 2],
                                     group: 1,
+                                    data_format: DataFormat::default(),
+                                    kernel_format: KernelFormat::default(),
                                 }),
                             )
                             .map_err(|_| Error::Synthesis);
@@ -1776,13 +1788,18 @@ 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
         }
@@ -1803,6 +1820,7 @@ mod shuffle {
                                 &mut region,
                                 &self.inputs[i],
                                 &self.references[i],
+                                layouts::SortCollisionMode::Unsorted,
                             )
                             .map_err(|_| Error::Synthesis)?;
                         }
@@ -1988,7 +2006,7 @@ mod add_with_overflow_and_poseidon {
             let base = BaseConfig::configure(cs, &[a, b], &output, CheckMode::SAFE);
             VarTensor::constant_cols(cs, K, 2, false);
 
-            let poseidon = PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::configure(cs, ());
+            let poseidon = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::configure(cs, ());
 
             MyCircuitConfig { base, poseidon }
         }
@@ -1998,7 +2016,7 @@ mod add_with_overflow_and_poseidon {
             mut config: Self::Config,
             mut layouter: impl Layouter<Fr>,
         ) -> Result<(), Error> {
-            let poseidon_chip: PoseidonChip<PoseidonSpec, WIDTH, RATE, WIDTH> =
+            let poseidon_chip: PoseidonChip<PoseidonSpec, WIDTH, RATE> =
                 PoseidonChip::new(config.poseidon.clone());
 
             let assigned_inputs_a =
@@ -2033,11 +2051,9 @@ mod add_with_overflow_and_poseidon {
         let b = (0..LEN)
             .map(|i| halo2curves::bn256::Fr::from(i as u64 + 1))
             .collect::<Vec<_>>();
-        let commitment_a =
-            PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::run(a.clone()).unwrap()[0][0];
+        let commitment_a = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(a.clone()).unwrap()[0][0];
 
-        let commitment_b =
-            PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::run(b.clone()).unwrap()[0][0];
+        let commitment_b = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(b.clone()).unwrap()[0][0];
 
         // parameters
         let a = Tensor::from(a.into_iter().map(Value::known));
@@ -2059,13 +2075,11 @@ mod add_with_overflow_and_poseidon {
         let b = (0..LEN)
             .map(|i| halo2curves::bn256::Fr::from(i as u64 + 1))
             .collect::<Vec<_>>();
-        let commitment_a = PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::run(a.clone())
-            .unwrap()[0][0]
-            + Fr::one();
+        let commitment_a =
+            PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(a.clone()).unwrap()[0][0] + Fr::one();
 
-        let commitment_b = PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::run(b.clone())
-            .unwrap()[0][0]
-            + Fr::one();
+        let commitment_b =
+            PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(b.clone()).unwrap()[0][0] + Fr::one();
 
         // parameters
         let a = Tensor::from(a.into_iter().map(Value::known));

src/commands.rs

@@ -1,13 +1,9 @@
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 use alloy::primitives::Address as H160;
 use clap::{Command, Parser, Subcommand};
 use clap_complete::{generate, Generator, Shell};
 #[cfg(feature = "python-bindings")]
-use pyo3::{
-    conversion::{FromPyObject, PyTryFrom},
-    exceptions::PyValueError,
-    prelude::*,
-    types::PyString,
-};
+use pyo3::{conversion::FromPyObject, exceptions::PyValueError, prelude::*};
 use serde::{Deserialize, Serialize};
 use std::path::PathBuf;
 use std::str::FromStr;
@@ -16,7 +12,6 @@ use tosubcommand::{ToFlags, ToSubcommand};
 use crate::{pfsys::ProofType, Commitments, RunArgs};
 
 use crate::circuit::CheckMode;
-use crate::graph::TestDataSource;
 use crate::pfsys::TranscriptType;
 
 /// The default path to the .json data file
@@ -47,20 +42,14 @@ pub const DEFAULT_SPLIT: &str = "false";
 pub const DEFAULT_VERIFIER_ABI: &str = "verifier_abi.json";
 /// Default verifier abi for aggregated proofs
 pub const DEFAULT_VERIFIER_AGGREGATED_ABI: &str = "verifier_aggr_abi.json";
-/// Default verifier abi for data attestation
-pub const DEFAULT_VERIFIER_DA_ABI: &str = "verifier_da_abi.json";
 /// Default solidity code
 pub const DEFAULT_SOL_CODE: &str = "evm_deploy.sol";
 /// Default calldata path
 pub const DEFAULT_CALLDATA: &str = "calldata.bytes";
 /// Default solidity code for aggregated proofs
 pub const DEFAULT_SOL_CODE_AGGREGATED: &str = "evm_deploy_aggr.sol";
-/// Default solidity code for data attestation
-pub const DEFAULT_SOL_CODE_DA: &str = "evm_deploy_da.sol";
 /// Default contract address
 pub const DEFAULT_CONTRACT_ADDRESS: &str = "contract.address";
-/// Default contract address for data attestation
-pub const DEFAULT_CONTRACT_ADDRESS_DA: &str = "contract_da.address";
 /// Default contract address for vk
 pub const DEFAULT_CONTRACT_ADDRESS_VK: &str = "contract_vk.address";
 /// Default check mode
@@ -83,18 +72,24 @@ pub const DEFAULT_DISABLE_SELECTOR_COMPRESSION: &str = "false";
 pub const DEFAULT_RENDER_REUSABLE: &str = "false";
 /// Default contract deployment type
 pub const DEFAULT_CONTRACT_DEPLOYMENT_TYPE: &str = "verifier";
-/// Default VK sol path
-pub const DEFAULT_VK_SOL: &str = "vk.sol";
+/// Default VKA calldata path
+pub const DEFAULT_VKA: &str = "vka.bytes";
 /// 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";
+/// Default number of decimals for instances rescaling on-chain.
+pub const DEFAULT_DECIMALS: &str = "18";
+/// Default path for the vka digest file
+pub const DEFAULT_VKA_DIGEST: &str = "vka.digest";
 
 #[cfg(feature = "python-bindings")]
 /// Converts TranscriptType into a PyObject (Required for TranscriptType to be compatible with Python)
@@ -109,8 +104,8 @@ impl IntoPy<PyObject> for TranscriptType {
 #[cfg(feature = "python-bindings")]
 /// Obtains TranscriptType from PyObject (Required for TranscriptType to be compatible with Python)
 impl<'source> FromPyObject<'source> for TranscriptType {
-    fn extract(ob: &'source PyAny) -> PyResult<Self> {
-        let trystr = <PyString as PyTryFrom>::try_from(ob)?;
+    fn extract_bound(ob: &pyo3::Bound<'source, pyo3::PyAny>) -> PyResult<Self> {
+        let trystr = String::extract_bound(ob)?;
         let strval = trystr.to_string();
         match strval.to_lowercase().as_str() {
             "poseidon" => Ok(TranscriptType::Poseidon),
@@ -190,15 +185,11 @@ pub enum ContractType {
         /// Can also be used as an alternative to aggregation for verifiers that are otherwise too large to fit on-chain.
         reusable: bool,
     },
-    /// Deploys a verifying key artifact that the reusable verifier loads into memory during runtime. Encodes the circuit specific data that was otherwise hardcoded onto the stack.
-    VerifyingKeyArtifact,
 }
 
 impl Default for ContractType {
     fn default() -> Self {
-        ContractType::Verifier {
-            reusable: false,
-        }
+        ContractType::Verifier { reusable: false }
     }
 }
 
@@ -210,11 +201,8 @@ impl std::fmt::Display for ContractType {
             match self {
                 ContractType::Verifier { reusable: true } => {
                     "verifier/reusable".to_string()
-                },
-                ContractType::Verifier {
-                    reusable: false,
-                } => "verifier".to_string(),
-                ContractType::VerifyingKeyArtifact => "vka".to_string(),
+                }
+                ContractType::Verifier { reusable: false } => "verifier".to_string(),
             }
         )
     }
@@ -231,7 +219,6 @@ impl From<&str> for ContractType {
         match s {
             "verifier" => ContractType::Verifier { reusable: false },
             "verifier/reusable" => ContractType::Verifier { reusable: true },
-            "vka" => ContractType::VerifyingKeyArtifact,
             _ => {
                 log::error!("Invalid value for ContractType");
                 log::warn!("Defaulting to verifier");
@@ -241,25 +228,25 @@ impl From<&str> for ContractType {
     }
 }
 
-
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 #[derive(Debug, Copy, Clone, Serialize, Deserialize, PartialEq, PartialOrd)]
 /// wrapper for H160 to make it easy to parse into flag vals
 pub struct H160Flag {
     inner: H160,
 }
-
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 impl From<H160Flag> for H160 {
     fn from(val: H160Flag) -> H160 {
         val.inner
     }
 }
-
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 impl ToFlags for H160Flag {
     fn to_flags(&self) -> Vec<String> {
         vec![format!("{:#x}", self.inner)]
     }
 }
-
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 impl From<&str> for H160Flag {
     fn from(s: &str) -> Self {
         Self {
@@ -287,9 +274,8 @@ impl IntoPy<PyObject> for CalibrationTarget {
 #[cfg(feature = "python-bindings")]
 /// Obtains CalibrationTarget from PyObject (Required for CalibrationTarget to be compatible with Python)
 impl<'source> FromPyObject<'source> for CalibrationTarget {
-    fn extract(ob: &'source PyAny) -> PyResult<Self> {
-        let trystr = <PyString as PyTryFrom>::try_from(ob)?;
-        let strval = trystr.to_string();
+    fn extract_bound(ob: &pyo3::Bound<'source, pyo3::PyAny>) -> PyResult<Self> {
+        let strval = String::extract_bound(ob)?;
         match strval.to_lowercase().as_str() {
             "resources" => Ok(CalibrationTarget::Resources {
                 col_overflow: false,
@@ -306,13 +292,8 @@ impl<'source> FromPyObject<'source> for CalibrationTarget {
 impl IntoPy<PyObject> for ContractType {
     fn into_py(self, py: Python) -> PyObject {
         match self {
-            ContractType::Verifier { reusable: true } => {
-                "verifier/reusable".to_object(py)
-            }
-            ContractType::Verifier {
-                reusable: false,
-            } => "verifier".to_object(py),
-            ContractType::VerifyingKeyArtifact => "vka".to_object(py),
+            ContractType::Verifier { reusable: true } => "verifier/reusable".to_object(py),
+            ContractType::Verifier { reusable: false } => "verifier".to_object(py),
         }
     }
 }
@@ -320,31 +301,15 @@ impl IntoPy<PyObject> for ContractType {
 #[cfg(feature = "python-bindings")]
 /// Obtains ContractType from PyObject (Required for ContractType to be compatible with Python)
 impl<'source> FromPyObject<'source> for ContractType {
-    fn extract(ob: &'source PyAny) -> PyResult<Self> {
-        let trystr = <PyString as PyTryFrom>::try_from(ob)?;
-        let strval = trystr.to_string();
+    fn extract_bound(ob: &pyo3::Bound<'source, pyo3::PyAny>) -> PyResult<Self> {
+        let strval = String::extract_bound(ob)?;
         match strval.to_lowercase().as_str() {
-            "verifier" => Ok(ContractType::Verifier {
-                reusable: false,
-            }),
+            "verifier" => Ok(ContractType::Verifier { reusable: false }),
             "verifier/reusable" => Ok(ContractType::Verifier { reusable: true }),
-            "vka" => Ok(ContractType::VerifyingKeyArtifact),
             _ => Err(PyValueError::new_err("Invalid value for ContractType")),
         }
     }
 }
-// not wasm
-use lazy_static::lazy_static;
-
-// if CARGO VERSION is 0.0.0 replace with "source - no compatibility guaranteed"
-lazy_static! {
-    /// The version of the ezkl library
-    pub static ref VERSION: &'static str =  if env!("CARGO_PKG_VERSION") == "0.0.0" {
-       "source - no compatibility guaranteed"
-    } else {
-        env!("CARGO_PKG_VERSION")
-    };
-}
 
 /// Get the styles for the CLI
 pub fn get_styles() -> clap::builder::Styles {
@@ -353,49 +318,54 @@ pub fn get_styles() -> clap::builder::Styles {
             clap::builder::styling::Style::new()
                 .bold()
                 .underline()
-                .fg_color(Some(clap::builder::styling::Color::Ansi(clap::builder::styling::AnsiColor::Cyan))),
+                .fg_color(Some(clap::builder::styling::Color::Ansi(
+                    clap::builder::styling::AnsiColor::Cyan,
+                ))),
         )
         .header(
             clap::builder::styling::Style::new()
                 .bold()
                 .underline()
-                .fg_color(Some(clap::builder::styling::Color::Ansi(clap::builder::styling::AnsiColor::Cyan))),
-        )
-        .literal(
-            clap::builder::styling::Style::new().fg_color(Some(clap::builder::styling::Color::Ansi(clap::builder::styling::AnsiColor::Magenta))),
-        )
-        .invalid(
-            clap::builder::styling::Style::new()
-                .bold()
-                .fg_color(Some(clap::builder::styling::Color::Ansi(clap::builder::styling::AnsiColor::Red))),
-        )
-        .error(
-            clap::builder::styling::Style::new()
-                .bold()
-                .fg_color(Some(clap::builder::styling::Color::Ansi(clap::builder::styling::AnsiColor::Red))),
+                .fg_color(Some(clap::builder::styling::Color::Ansi(
+                    clap::builder::styling::AnsiColor::Cyan,
+                ))),
         )
+        .literal(clap::builder::styling::Style::new().fg_color(Some(
+            clap::builder::styling::Color::Ansi(clap::builder::styling::AnsiColor::Magenta),
+        )))
+        .invalid(clap::builder::styling::Style::new().bold().fg_color(Some(
+            clap::builder::styling::Color::Ansi(clap::builder::styling::AnsiColor::Red),
+        )))
+        .error(clap::builder::styling::Style::new().bold().fg_color(Some(
+            clap::builder::styling::Color::Ansi(clap::builder::styling::AnsiColor::Red),
+        )))
         .valid(
             clap::builder::styling::Style::new()
                 .bold()
                 .underline()
-                .fg_color(Some(clap::builder::styling::Color::Ansi(clap::builder::styling::AnsiColor::Green))),
-        )
-        .placeholder(
-            clap::builder::styling::Style::new().fg_color(Some(clap::builder::styling::Color::Ansi(clap::builder::styling::AnsiColor::White))),
+                .fg_color(Some(clap::builder::styling::Color::Ansi(
+                    clap::builder::styling::AnsiColor::Green,
+                ))),
         )
+        .placeholder(clap::builder::styling::Style::new().fg_color(Some(
+            clap::builder::styling::Color::Ansi(clap::builder::styling::AnsiColor::White),
+        )))
 }
 
-
 /// Print completions for the given generator
-pub fn print_completions<G: Generator>(gen: G, cmd: &mut Command) {
-    generate(gen, cmd, cmd.get_name().to_string(), &mut std::io::stdout());
+pub fn print_completions<G: Generator>(r#gen: G, cmd: &mut Command) {
+    generate(
+        r#gen,
+        cmd,
+        cmd.get_name().to_string(),
+        &mut std::io::stdout(),
+    );
 }
 
-
 #[allow(missing_docs)]
 #[derive(Parser, Debug, Clone)]
 #[command(author, about, long_about = None)]
-#[clap(version = *VERSION, styles = get_styles(), trailing_var_arg = true)]
+#[clap(version = crate::version(), styles = get_styles(), trailing_var_arg = true)]
 pub struct Cli {
     /// If provided, outputs the completion file for given shell
     #[clap(long = "generate", value_parser)]
@@ -405,6 +375,43 @@ pub struct Cli {
     pub command: Option<Commands>,
 }
 
+/// Custom parser for data field that handles both direct JSON strings and file paths with '@' prefix
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, PartialOrd)]
+pub struct DataField(pub String);
+
+impl FromStr for DataField {
+    type Err = String;
+
+    fn from_str(s: &str) -> Result<Self, Self::Err> {
+        // Check if the input starts with '@'
+        if s.starts_with('@') {
+            // Extract the file path (remove the '@' prefix)
+            let file_path = &s[1..];
+
+            // Read the file content
+            let content = std::fs::read_to_string(file_path)
+                .map_err(|e| format!("Failed to read data file '{}': {}", file_path, e))?;
+
+            // Return the file content as the data field value
+            Ok(DataField(content))
+        } else {
+            // Use the input string directly
+            Ok(DataField(s.to_string()))
+        }
+    }
+}
+
+impl ToFlags for DataField {
+    fn to_flags(&self) -> Vec<String> {
+        vec![self.0.clone()]
+    }
+}
+
+impl std::fmt::Display for DataField {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "{}", self.0)
+    }
+}
 
 #[allow(missing_docs)]
 #[derive(Debug, Subcommand, Clone, Deserialize, Serialize, PartialEq, PartialOrd, ToSubcommand)]
@@ -424,9 +431,9 @@ pub enum Commands {
 
     /// Generates the witness from an input file.
     GenWitness {
-        /// The path to the .json data file
-        #[arg(short = 'D', long, default_value = DEFAULT_DATA, value_hint = clap::ValueHint::FilePath)]
-        data: Option<PathBuf>,
+        /// The path to the .json data file (with @ prefix) or a raw data string of the form '{"input_data": [[1, 2, 3]]}'
+        #[arg(short = 'D', long, default_value = DEFAULT_DATA, value_parser = DataField::from_str)]
+        data: Option<DataField>,
         /// The path to the compiled model file (generated using the compile-circuit command)
         #[arg(short = 'M', long, default_value = DEFAULT_COMPILED_CIRCUIT, value_hint = clap::ValueHint::FilePath)]
         compiled_circuit: Option<PathBuf>,
@@ -453,12 +460,32 @@ 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,
+        /// min value for random data
+        #[arg(long, value_hint = clap::ValueHint::Other)]
+        min: Option<f32>,
+        /// max value for random data
+        #[arg(long, value_hint = clap::ValueHint::Other)]
+        max: Option<f32>,
+    },
     /// Calibrates the proving scale, lookup bits and logrows from a circuit settings file.
-        CalibrateSettings {
+    CalibrateSettings {
         /// The path to the .json calibration data file.
         #[arg(short = 'D', long, default_value = DEFAULT_CALIBRATION_FILE, value_hint = clap::ValueHint::FilePath)]
-        data: Option<PathBuf>,
+        data: Option<String>,
         /// The path to the .onnx model file
         #[arg(short = 'M', long, default_value = DEFAULT_MODEL, value_hint = clap::ValueHint::FilePath)]
         model: Option<PathBuf>,
@@ -486,9 +513,6 @@ pub enum Commands {
         /// max logrows to use for calibration, 26 is the max public SRS size
         #[arg(long, value_hint = clap::ValueHint::Other)]
         max_logrows: Option<u32>,
-        // whether to only range check rebases (instead of trying both range check and lookup)
-        #[arg(long, default_value = DEFAULT_ONLY_RANGE_CHECK_REBASE, action = clap::ArgAction::SetTrue)]
-        only_range_check_rebase: Option<bool>,
     },
 
     /// Generates a dummy SRS
@@ -505,7 +529,7 @@ pub enum Commands {
         commitment: Option<Commitments>,
     },
 
-        /// Gets an SRS from a circuit settings file.
+    /// Gets an SRS from a circuit settings file.
     #[command(name = "get-srs")]
     GetSrs {
         /// The path to output the desired srs file, if set to None will save to ~/.ezkl/srs
@@ -590,7 +614,7 @@ pub enum Commands {
             require_equals = true,
             num_args = 0..=1,
             default_value_t = TranscriptType::default(),
-            value_enum, 
+            value_enum,
             value_hint = clap::ValueHint::Other
         )]
         transcript: TranscriptType,
@@ -640,44 +664,7 @@ pub enum Commands {
         #[arg(long, default_value = DEFAULT_DISABLE_SELECTOR_COMPRESSION, action = clap::ArgAction::SetTrue)]
         disable_selector_compression: Option<bool>,
     },
-        /// Deploys a test contact that the data attester reads from and creates a data attestation formatted input.json file that contains call data information
-    #[command(arg_required_else_help = true)]
-    SetupTestEvmData {
-        /// The path to the .json data file, which should include both the network input (possibly private) and the network output (public input to the proof)
-        #[arg(short = 'D', long, value_hint = clap::ValueHint::FilePath)]
-        data: Option<PathBuf>,
-        /// The path to the compiled model file (generated using the compile-circuit command)
-        #[arg(short = 'M', long, value_hint = clap::ValueHint::FilePath)]
-        compiled_circuit: Option<PathBuf>,
-        /// For testing purposes only. The optional path to the .json data file that will be generated that contains the OnChain data storage information
-        /// derived from the file information in the data .json file.
-        /// Should include both the network input (possibly private) and the network output (public input to the proof)
-        #[arg(short = 'T', long, value_hint = clap::ValueHint::FilePath)]
-        test_data: PathBuf,
-        /// RPC URL for an Ethereum node, if None will use Anvil but WON'T persist state
-        #[arg(short = 'U', long, value_hint = clap::ValueHint::Url)]
-        rpc_url: Option<String>,
-        /// where the input data come from
-        #[arg(long, default_value = "on-chain", value_hint = clap::ValueHint::Other)]
-        input_source: TestDataSource,
-        /// where the output data come from
-        #[arg(long, default_value = "on-chain", value_hint = clap::ValueHint::Other)]
-        output_source: TestDataSource,
-    },
-        /// The Data Attestation Verifier contract stores the account calls to fetch data to feed into ezkl. This call data can be updated by an admin account. This tests that admin account is able to update this call data.
-    #[command(arg_required_else_help = true)]
-    TestUpdateAccountCalls {
-        /// The path to the verifier contract's address
-        #[arg(long, value_hint = clap::ValueHint::Other)]
-        addr: H160Flag,
-        /// The path to the .json data file.
-        #[arg(short = 'D', long, value_hint = clap::ValueHint::FilePath)]
-        data: Option<PathBuf>,
-        /// RPC URL for an Ethereum node, if None will use Anvil but WON'T persist state
-        #[arg(short = 'U', long, value_hint = clap::ValueHint::Url)]
-        rpc_url: Option<String>,
-    },
-        /// Swaps the positions in the transcript that correspond to commitments
+    /// Swaps the positions in the transcript that correspond to commitments
     SwapProofCommitments {
         /// The path to the proof file
         #[arg(short = 'P', long, default_value = DEFAULT_PROOF, value_hint = clap::ValueHint::FilePath)]
@@ -687,7 +674,7 @@ pub enum Commands {
         witness_path: Option<PathBuf>,
     },
 
-        /// Loads model, data, and creates proof
+    /// Loads model, data, and creates proof
     Prove {
         /// The path to the .json witness file (generated using the gen-witness command)
         #[arg(short = 'W', long, default_value = DEFAULT_WITNESS, value_hint = clap::ValueHint::FilePath)]
@@ -709,7 +696,7 @@ pub enum Commands {
             require_equals = true,
             num_args = 0..=1,
             default_value_t = ProofType::Single,
-            value_enum, 
+            value_enum,
             value_hint = clap::ValueHint::Other
         )]
         proof_type: ProofType,
@@ -717,8 +704,9 @@ pub enum Commands {
         #[arg(long, default_value = DEFAULT_CHECKMODE, value_hint = clap::ValueHint::Other)]
         check_mode: Option<CheckMode>,
     },
-        /// Encodes a proof into evm calldata
+    /// Encodes a proof into evm calldata
     #[command(name = "encode-evm-calldata")]
+    #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
     EncodeEvmCalldata {
         /// The path to the proof file (generated using the prove command)
         #[arg(long, default_value = DEFAULT_PROOF, value_hint = clap::ValueHint::FilePath)]
@@ -726,12 +714,13 @@ pub enum Commands {
         /// The path to save the calldata to
         #[arg(long, default_value = DEFAULT_CALLDATA, value_hint = clap::ValueHint::FilePath)]
         calldata_path: Option<PathBuf>,
-        /// The path to the verification key address (only used if the vk is rendered as a separate contract)
+        /// The path to the serialized VKA file
         #[arg(long, value_hint = clap::ValueHint::Other)]
-        addr_vk: Option<H160Flag>,
+        vka_path: Option<PathBuf>,
     },
-        /// Creates an Evm verifier for a single proof
+    /// Creates an Evm verifier for a single proof
     #[command(name = "create-evm-verifier")]
+    #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
     CreateEvmVerifier {
         /// The path to SRS, if None will use ~/.ezkl/srs/kzg{logrows}.srs
         #[arg(long, value_hint = clap::ValueHint::FilePath)]
@@ -752,9 +741,10 @@ pub enum Commands {
         #[arg(long, default_value = DEFAULT_RENDER_REUSABLE, action = clap::ArgAction::SetTrue)]
         reusable: Option<bool>,
     },
-        /// Creates an Evm verifier artifact for a single proof to be used by the reusable verifier
+    /// Creates an evm verifier artifact to be used by the reusable verifier
     #[command(name = "create-evm-vka")]
-    CreateEvmVKArtifact {
+    #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
+    CreateEvmVka {
         /// The path to SRS, if None will use ~/.ezkl/srs/kzg{logrows}.srs
         #[arg(long, value_hint = clap::ValueHint::FilePath)]
         srs_path: Option<PathBuf>,
@@ -764,39 +754,18 @@ pub enum Commands {
         /// The path to load the desired verification key file
         #[arg(long, default_value = DEFAULT_VK, value_hint = clap::ValueHint::FilePath)]
         vk_path: Option<PathBuf>,
-        /// The path to output the Solidity code
-        #[arg(long, default_value = DEFAULT_VK_SOL, value_hint = clap::ValueHint::FilePath)]
-        sol_code_path: Option<PathBuf>,
-        /// The path to output the Solidity verifier ABI
-        #[arg(long, default_value = DEFAULT_VK_ABI, value_hint = clap::ValueHint::FilePath)]
-        abi_path: Option<PathBuf>,
-    },
-        /// Creates an Evm verifier that attests to on-chain inputs for a single proof
-    #[command(name = "create-evm-da")]
-    CreateEvmDataAttestation {
-        /// The path to load circuit settings .json file from (generated using the gen-settings command)
-        #[arg(short = 'S', long, default_value = DEFAULT_SETTINGS, value_hint = clap::ValueHint::FilePath)]
-        settings_path: Option<PathBuf>,
-        /// The path to output the Solidity code
-        #[arg(long, default_value = DEFAULT_SOL_CODE_DA, value_hint = clap::ValueHint::FilePath)]
-        sol_code_path: Option<PathBuf>,
-        /// The path to output the Solidity verifier ABI
-        #[arg(long, default_value = DEFAULT_VERIFIER_DA_ABI, value_hint = clap::ValueHint::FilePath)]
-        abi_path: Option<PathBuf>,
-        /// The path to the .json data file, which should
-        /// contain the necessary calldata and account addresses
-        /// needed to read from all the on-chain
-        /// view functions that return the data that the network
-        /// ingests as inputs.
-        #[arg(short = 'D', long, default_value = DEFAULT_DATA, value_hint = clap::ValueHint::FilePath)]
-        data: Option<PathBuf>,
-        /// The path to the witness file. This is needed for proof swapping for kzg commitments.
-        #[arg(short = 'W', long, default_value = DEFAULT_WITNESS, value_hint = clap::ValueHint::FilePath)]
-        witness: Option<PathBuf>,
+        /// The path to output the vka calldata
+        #[arg(long, default_value = DEFAULT_VKA, value_hint = clap::ValueHint::FilePath)]
+        vka_path: Option<PathBuf>,
+        /// The number of decimals we want to use for the rescaling of the instances into on-chain floats
+        /// Default is 18, which is the number of decimals used by most ERC20 tokens
+        #[arg(long, default_value = DEFAULT_DECIMALS, value_hint = clap::ValueHint::Other)]
+        decimals: Option<usize>,
     },
 
-        /// Creates an Evm verifier for an aggregate proof
+    /// Creates an Evm verifier for an aggregate proof
     #[command(name = "create-evm-verifier-aggr")]
+    #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
     CreateEvmVerifierAggr {
         /// The path to SRS, if None will use ~/.ezkl/srs/kzg{logrows}.srs
         #[arg(long, value_hint = clap::ValueHint::FilePath)]
@@ -859,14 +828,15 @@ pub enum Commands {
         #[arg(long, default_value = DEFAULT_COMMITMENT, value_hint = clap::ValueHint::Other)]
         commitment: Option<Commitments>,
     },
-        /// Deploys an evm contract (verifier, reusable verifier, or vk artifact) that is generated by ezkl
+    /// Deploys an evm contract (verifier, reusable verifier, or vk artifact) that is generated by ezkl
+    #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
     DeployEvm {
         /// The path to the Solidity code (generated using the create-evm-verifier command)
         #[arg(long, default_value = DEFAULT_SOL_CODE, value_hint = clap::ValueHint::FilePath)]
         sol_code_path: Option<PathBuf>,
-        /// RPC URL for an Ethereum node, if None will use Anvil but WON'T persist state
-        #[arg(short = 'U', long, value_hint = clap::ValueHint::Url)]
-        rpc_url: Option<String>,
+        /// RPC URL for an Ethereum node
+        #[arg(short = 'U', long,  default_value = DEFAULT_CONTRACT_ADDRESS, value_hint = clap::ValueHint::Url)]
+        rpc_url: String,
         #[arg(long, default_value = DEFAULT_CONTRACT_ADDRESS, value_hint = clap::ValueHint::Other)]
         /// The path to output the contract address
         addr_path: Option<PathBuf>,
@@ -880,33 +850,9 @@ pub enum Commands {
         #[arg(long = "contract-type", short = 'C', default_value = DEFAULT_CONTRACT_DEPLOYMENT_TYPE, value_hint = clap::ValueHint::Other)]
         contract: ContractType,
     },
-        /// Deploys an evm verifier that allows for data attestation
-    #[command(name = "deploy-evm-da")]
-    DeployEvmDataAttestation {
-        /// The path to the .json data file, which should include both the network input (possibly private) and the network output (public input to the proof)
-        #[arg(short = 'D', long, default_value = DEFAULT_DATA, value_hint = clap::ValueHint::FilePath)]
-        data: Option<PathBuf>,
-        /// The path to load circuit settings .json file from (generated using the gen-settings command)
-        #[arg(long, default_value = DEFAULT_SETTINGS, value_hint = clap::ValueHint::FilePath)]
-        settings_path: Option<PathBuf>,
-        /// The path to the Solidity code
-        #[arg(long, default_value = DEFAULT_SOL_CODE_DA, value_hint = clap::ValueHint::FilePath)]
-        sol_code_path: Option<PathBuf>,
-        /// RPC URL for an Ethereum node, if None will use Anvil but WON'T persist state
-        #[arg(short = 'U', long, value_hint = clap::ValueHint::Url)]
-        rpc_url: Option<String>,
-        #[arg(long, default_value = DEFAULT_CONTRACT_ADDRESS_DA, value_hint = clap::ValueHint::FilePath)]
-        /// The path to output the contract address
-        addr_path: Option<PathBuf>,
-        /// The optimizer runs to set on the verifier. (Lower values optimize for deployment, while higher values optimize for execution)
-        #[arg(long, default_value = DEFAULT_OPTIMIZER_RUNS, value_hint = clap::ValueHint::Other)]
-        optimizer_runs: usize,
-        /// Private secp256K1 key in hex format, 64 chars, no 0x prefix, of the account signing transactions. If None the private key will be generated by Anvil
-        #[arg(short = 'P', long, value_hint = clap::ValueHint::Other)]
-        private_key: Option<String>,
-    },
-        /// Verifies a proof using a local Evm executor, returning accept or reject
+    /// Verifies a proof using a local Evm executor, returning accept or reject
     #[command(name = "verify-evm")]
+    #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
     VerifyEvm {
         /// The path to the proof file (generated using the prove command)
         #[arg(long, default_value = DEFAULT_PROOF, value_hint = clap::ValueHint::FilePath)]
@@ -914,15 +860,32 @@ pub enum Commands {
         /// The path to verifier contract's address
         #[arg(long, default_value = DEFAULT_CONTRACT_ADDRESS, value_hint = clap::ValueHint::Other)]
         addr_verifier: H160Flag,
+        /// RPC URL for an Ethereum node
+        #[arg(short = 'U', long, value_hint = clap::ValueHint::Url)]
+        rpc_url: String,
+        /// The path to the serialized vka file
+        #[arg(long, default_value = DEFAULT_VKA, value_hint = clap::ValueHint::FilePath)]
+        vka_path: Option<PathBuf>,
+    },
+    /// Registers a VKA, returning the its digest used to identify it on-chain.
+    #[command(name = "register-vka")]
+    #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
+    RegisterVka {
         /// RPC URL for an Ethereum node, if None will use Anvil but WON'T persist state
         #[arg(short = 'U', long, value_hint = clap::ValueHint::Url)]
-        rpc_url: Option<String>,
-        /// does the verifier use data attestation ?
-        #[arg(long, value_hint = clap::ValueHint::Other)]
-        addr_da: Option<H160Flag>,
-        // is the vk rendered seperately, if so specify an address
-        #[arg(long, value_hint = clap::ValueHint::Other)]
-        addr_vk: Option<H160Flag>,
+        rpc_url: String,
+        /// The path to the reusable verifier contract's address
+        #[arg(long, default_value = DEFAULT_CONTRACT_ADDRESS, value_hint = clap::ValueHint::Other)]
+        addr_verifier: H160Flag,
+        /// The path to the serialized VKA file
+        #[arg(long, default_value = DEFAULT_VKA, value_hint = clap::ValueHint::FilePath)]
+        vka_path: Option<PathBuf>,
+        /// The path to output the VKA digest to
+        #[arg(long, default_value = DEFAULT_VKA_DIGEST, value_hint = clap::ValueHint::FilePath)]
+        vka_digest_path: Option<PathBuf>,
+        /// Private secp256K1 key in hex format, 64 chars, no 0x prefix, of the account signing transactions. If None the private key will be generated by Anvil
+        #[arg(short = 'P', long, value_hint = clap::ValueHint::Other)]
+        private_key: Option<String>,
     },
     #[cfg(not(feature = "no-update"))]
     /// Updates ezkl binary to version specified (or latest if not specified)
@@ -933,7 +896,6 @@ pub enum Commands {
     },
 }
 
-
 impl Commands {
     /// Converts the commands to a json string
     pub fn as_json(&self) -> String {
@@ -944,4 +906,4 @@ impl Commands {
     pub fn from_json(json: &str) -> Self {
         serde_json::from_str(json).unwrap()
     }
-}
+}

src/execute.rs

@@ -1,12 +1,13 @@
 use crate::circuit::region::RegionSettings;
 use crate::circuit::CheckMode;
 use crate::commands::CalibrationTarget;
-use crate::eth::{deploy_contract_via_solidity, deploy_da_verifier_via_solidity};
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
+use crate::eth::{deploy_contract_via_solidity, register_vka_via_rv};
 #[allow(unused_imports)]
-use crate::eth::{fix_da_sol, get_contract_artifacts, verify_proof_via_solidity};
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
+use crate::eth::{get_contract_artifacts, verify_proof_via_solidity};
 use crate::graph::input::GraphData;
 use crate::graph::{GraphCircuit, GraphSettings, GraphWitness, Model};
-use crate::graph::{TestDataSource, TestSources};
 use crate::pfsys::evm::aggregation_kzg::{AggregationCircuit, PoseidonTranscript};
 use crate::pfsys::{
     create_keys, load_pk, load_vk, save_params, save_pk, Snark, StrategyType, TranscriptType,
@@ -38,6 +39,7 @@ use halo2_proofs::poly::kzg::{
 };
 use halo2_proofs::poly::VerificationStrategy;
 use halo2_proofs::transcript::{EncodedChallenge, TranscriptReadBuffer};
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 use halo2_solidity_verifier;
 use halo2curves::bn256::{Bn256, Fr, G1Affine};
 use halo2curves::ff::{FromUniformBytes, WithSmallOrderMulGroup};
@@ -45,6 +47,7 @@ use halo2curves::serde::SerdeObject;
 use indicatif::{ProgressBar, ProgressStyle};
 use instant::Instant;
 use itertools::Itertools;
+use lazy_static::lazy_static;
 use log::debug;
 use log::{info, trace, warn};
 use serde::de::DeserializeOwned;
@@ -53,17 +56,20 @@ use snark_verifier::loader::native::NativeLoader;
 use snark_verifier::system::halo2::compile;
 use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use snark_verifier::system::halo2::Config;
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 use std::fs::File;
 use std::io::BufWriter;
-use std::io::{Cursor, Write};
+use std::io::Cursor;
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
+use std::io::Write;
 use std::path::Path;
 use std::path::PathBuf;
 use std::str::FromStr;
 use std::time::Duration;
 use tabled::Tabled;
 use thiserror::Error;
-
-use lazy_static::lazy_static;
+use tract_onnx::prelude::IntoTensor;
+use tract_onnx::prelude::Tensor as TractTensor;
 
 lazy_static! {
     #[derive(Debug)]
@@ -113,7 +119,7 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
         } => gen_srs_cmd(
             srs_path,
             logrows as u32,
-            commitment.unwrap_or(Commitments::from_str(DEFAULT_COMMITMENT).unwrap()),
+            commitment.unwrap_or_else(|| Commitments::from_str(DEFAULT_COMMITMENT).unwrap()),
         ),
         Commands::GetSrs {
             srs_path,
@@ -131,6 +137,21 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
             settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
             args,
         ),
+        Commands::GenRandomData {
+            model,
+            data,
+            variables,
+            seed,
+            min,
+            max,
+        } => gen_random_data(
+            model.unwrap_or(DEFAULT_MODEL.into()),
+            data.unwrap_or(DEFAULT_DATA.into()),
+            variables,
+            seed,
+            min,
+            max,
+        ),
         Commands::CalibrateSettings {
             model,
             settings_path,
@@ -140,7 +161,6 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
             scales,
             scale_rebase_multiplier,
             max_logrows,
-            only_range_check_rebase,
         } => calibrate(
             model.unwrap_or(DEFAULT_MODEL.into()),
             data.unwrap_or(DEFAULT_DATA.into()),
@@ -149,10 +169,8 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
             lookup_safety_margin,
             scales,
             scale_rebase_multiplier,
-            only_range_check_rebase.unwrap_or(DEFAULT_ONLY_RANGE_CHECK_REBASE.parse().unwrap()),
             max_logrows,
         )
-        .await
         .map(|e| serde_json::to_string(&e).unwrap()),
         Commands::GenWitness {
             data,
@@ -162,17 +180,17 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
             srs_path,
         } => gen_witness(
             compiled_circuit.unwrap_or(DEFAULT_COMPILED_CIRCUIT.into()),
-            data.unwrap_or(DEFAULT_DATA.into()),
+            data.unwrap_or(DataField(DEFAULT_DATA.into())).to_string(),
             Some(output.unwrap_or(DEFAULT_WITNESS.into())),
             vk_path,
             srs_path,
         )
-        .await
         .map(|e| serde_json::to_string(&e).unwrap()),
         Commands::Mock { model, witness } => mock(
             model.unwrap_or(DEFAULT_MODEL.into()),
             witness.unwrap_or(DEFAULT_WITNESS.into()),
         ),
+        #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
         Commands::CreateEvmVerifier {
             vk_path,
             srs_path,
@@ -191,49 +209,35 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
             )
             .await
         }
+        #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
         Commands::EncodeEvmCalldata {
             proof_path,
             calldata_path,
-            addr_vk,
+            vka_path,
         } => encode_evm_calldata(
             proof_path.unwrap_or(DEFAULT_PROOF.into()),
             calldata_path.unwrap_or(DEFAULT_CALLDATA.into()),
-            addr_vk,
+            vka_path,
         )
         .map(|e| serde_json::to_string(&e).unwrap()),
-
-        Commands::CreateEvmVKArtifact {
+        #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
+        Commands::CreateEvmVka {
             vk_path,
             srs_path,
             settings_path,
-            sol_code_path,
-            abi_path,
+            vka_path,
+            decimals,
         } => {
             create_evm_vka(
                 vk_path.unwrap_or(DEFAULT_VK.into()),
                 srs_path,
                 settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
-                sol_code_path.unwrap_or(DEFAULT_VK_SOL.into()),
-                abi_path.unwrap_or(DEFAULT_VK_ABI.into()),
-            )
-            .await
-        }
-        Commands::CreateEvmDataAttestation {
-            settings_path,
-            sol_code_path,
-            abi_path,
-            data,
-            witness,
-        } => {
-            create_evm_data_attestation(
-                settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
-                sol_code_path.unwrap_or(DEFAULT_SOL_CODE_DA.into()),
-                abi_path.unwrap_or(DEFAULT_VERIFIER_DA_ABI.into()),
-                data.unwrap_or(DEFAULT_DATA.into()),
-                witness,
+                vka_path.unwrap_or(DEFAULT_VKA.into()),
+                decimals.unwrap_or(DEFAULT_DECIMALS.parse().unwrap()),
             )
             .await
         }
+        #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
         Commands::CreateEvmVerifierAggr {
             vk_path,
             srs_path,
@@ -279,29 +283,6 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
             disable_selector_compression
                 .unwrap_or(DEFAULT_DISABLE_SELECTOR_COMPRESSION.parse().unwrap()),
         ),
-        Commands::SetupTestEvmData {
-            data,
-            compiled_circuit,
-            test_data,
-            rpc_url,
-            input_source,
-            output_source,
-        } => {
-            setup_test_evm_witness(
-                data.unwrap_or(DEFAULT_DATA.into()),
-                compiled_circuit.unwrap_or(DEFAULT_COMPILED_CIRCUIT.into()),
-                test_data,
-                rpc_url,
-                input_source,
-                output_source,
-            )
-            .await
-        }
-        Commands::TestUpdateAccountCalls {
-            addr,
-            data,
-            rpc_url,
-        } => test_update_account_calls(addr, data.unwrap_or(DEFAULT_DATA.into()), rpc_url).await,
         Commands::SwapProofCommitments {
             proof_path,
             witness_path,
@@ -410,6 +391,7 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
             commitment.into(),
         )
         .map(|e| serde_json::to_string(&e).unwrap()),
+        #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
         Commands::DeployEvm {
             sol_code_path,
             rpc_url,
@@ -428,39 +410,35 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
             )
             .await
         }
-        Commands::DeployEvmDataAttestation {
-            data,
-            settings_path,
-            sol_code_path,
-            rpc_url,
-            addr_path,
-            optimizer_runs,
-            private_key,
-        } => {
-            deploy_da_evm(
-                data.unwrap_or(DEFAULT_DATA.into()),
-                settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
-                sol_code_path.unwrap_or(DEFAULT_SOL_CODE_DA.into()),
-                rpc_url,
-                addr_path.unwrap_or(DEFAULT_CONTRACT_ADDRESS_DA.into()),
-                optimizer_runs,
-                private_key,
-            )
-            .await
-        }
+        #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
         Commands::VerifyEvm {
             proof_path,
             addr_verifier,
             rpc_url,
-            addr_da,
-            addr_vk,
+            vka_path,
         } => {
             verify_evm(
                 proof_path.unwrap_or(DEFAULT_PROOF.into()),
                 addr_verifier,
                 rpc_url,
-                addr_da,
-                addr_vk,
+                vka_path,
+            )
+            .await
+        }
+        #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
+        Commands::RegisterVka {
+            addr_verifier,
+            vka_path,
+            rpc_url,
+            vka_digest_path,
+            private_key,
+        } => {
+            register_vka(
+                rpc_url,
+                addr_verifier,
+                vka_path.unwrap_or(DEFAULT_VKA.into()),
+                vka_digest_path.unwrap_or(DEFAULT_VKA_DIGEST.into()),
+                private_key,
             )
             .await
         }
@@ -502,7 +480,9 @@ fn update_ezkl_binary(version: &Option<String>) -> Result<String, EZKLError> {
         .status()
         .is_err()
     {
-        log::warn!("bash is not installed on this system, trying to run the install script with sh (may fail)");
+        log::warn!(
+            "bash is not installed on this system, trying to run the install script with sh (may fail)"
+        );
         "sh"
     } else {
         "bash"
@@ -671,10 +651,10 @@ pub(crate) async fn get_srs_cmd(
             let srs_uri = format!("{}{}", PUBLIC_SRS_URL, k);
             let mut reader = Cursor::new(fetch_srs(&srs_uri).await?);
             // check the SRS
-                let pb = init_spinner();
-                pb.set_message("Validating SRS (this may take a while) ...");
+            let pb = init_spinner();
+            pb.set_message("Validating SRS (this may take a while) ...");
             let params = ParamsKZG::<Bn256>::read(&mut reader)?;
-                pb.finish_with_message("SRS validated.");
+            pb.finish_with_message("SRS validated.");
 
             info!("Saving SRS to disk...");
             let computed_srs_path = get_srs_path(k, srs_path.clone(), commitment);
@@ -682,7 +662,10 @@ pub(crate) async fn get_srs_cmd(
             let mut buffer = BufWriter::with_capacity(*EZKL_BUF_CAPACITY, &mut file);
             params.write(&mut buffer)?;
 
-            info!("Saved SRS to {}.", computed_srs_path.as_os_str().to_str().unwrap_or("disk"));
+            info!(
+                "Saved SRS to {}.",
+                computed_srs_path.as_os_str().to_str().unwrap_or("disk")
+            );
 
             info!("SRS downloaded");
         } else {
@@ -706,9 +689,9 @@ pub(crate) fn table(model: PathBuf, run_args: RunArgs) -> Result<String, EZKLErr
     Ok(String::new())
 }
 
-pub(crate) async fn gen_witness(
+pub(crate) fn gen_witness(
     compiled_circuit_path: PathBuf,
-    data: PathBuf,
+    data: String,
     output: Option<PathBuf>,
     vk_path: Option<PathBuf>,
     srs_path: Option<PathBuf>,
@@ -716,7 +699,7 @@ pub(crate) async fn gen_witness(
     // these aren't real values so the sanity checks are mostly meaningless
 
     let mut circuit = GraphCircuit::load(compiled_circuit_path)?;
-    let data: GraphData = GraphData::from_path(data)?;
+    let data = GraphData::from_str(&data)?;
     let settings = circuit.settings().clone();
 
     let vk = if let Some(vk) = vk_path {
@@ -728,7 +711,7 @@ pub(crate) async fn gen_witness(
         None
     };
 
-        let mut input = circuit.load_graph_input(&data).await?;
+    let mut input = circuit.load_graph_input(&data)?;
     #[cfg(any(not(feature = "ezkl"), target_arch = "wasm32"))]
     let mut input = circuit.load_graph_input(&data)?;
 
@@ -824,6 +807,85 @@ 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,
+    min: Option<f32>,
+    max: Option<f32>,
+) -> 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()))?;
+
+    let min = min.unwrap_or(0.0);
+    let max = max.unwrap_or(1.0);
+
+    /// Generates a random tensor of a given size and type.
+    fn random(
+        sizes: &[usize],
+        datum_type: tract_onnx::prelude::DatumType,
+        seed: u64,
+        min: f32,
+        max: f32,
+    ) -> 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_range(min..max));
+        tensor.cast_to_dt(datum_type).unwrap().into_owned()
+    }
+
+    fn tensor_for_fact(
+        fact: &tract_onnx::prelude::TypedFact,
+        seed: u64,
+        min: f32,
+        max: f32,
+    ) -> 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,
+            min,
+            max,
+        )
+    }
+
+    let generated = input_facts
+        .iter()
+        .map(|v| tensor_for_fact(v, seed, min, max))
+        .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();
@@ -960,15 +1022,14 @@ impl AccuracyResults {
 /// Calibrate the circuit parameters to a given a dataset
 #[allow(trivial_casts)]
 #[allow(clippy::too_many_arguments)]
-pub(crate) async fn calibrate(
+pub(crate) fn calibrate(
     model_path: PathBuf,
-    data: PathBuf,
+    data: String,
     settings_path: PathBuf,
     target: CalibrationTarget,
     lookup_safety_margin: f64,
     scales: Option<Vec<crate::Scale>>,
     scale_rebase_multiplier: Vec<u32>,
-    only_range_check_rebase: bool,
     max_logrows: Option<u32>,
 ) -> Result<GraphSettings, EZKLError> {
     use log::error;
@@ -977,7 +1038,7 @@ pub(crate) async fn calibrate(
 
     use crate::fieldutils::IntegerRep;
 
-    let data = GraphData::from_path(data)?;
+    let data = GraphData::from_str(&data)?;
     // load the pre-generated settings
     let settings = GraphSettings::load(&settings_path)?;
     // now retrieve the run args
@@ -987,7 +1048,7 @@ pub(crate) async fn calibrate(
 
     let input_shapes = model.graph.input_shapes()?;
 
-    let chunks = data.split_into_batches(input_shapes).await?;
+    let chunks = data.split_into_batches(input_shapes)?;
     info!("num calibration batches: {}", chunks.len());
 
     debug!("running onnx predictions...");
@@ -1004,12 +1065,6 @@ pub(crate) async fn calibrate(
         (11..14).collect::<Vec<crate::Scale>>()
     };
 
-    let div_rebasing = if only_range_check_rebase {
-        vec![false]
-    } else {
-        vec![true, false]
-    };
-
     let mut found_params: Vec<GraphSettings> = vec![];
 
     // 2 x 2 grid
@@ -1047,12 +1102,6 @@ pub(crate) async fn calibrate(
         .map(|(a, b)| (*a, *b))
         .collect::<Vec<((crate::Scale, crate::Scale), u32)>>();
 
-    let range_grid = range_grid
-        .iter()
-        .cartesian_product(div_rebasing.iter())
-        .map(|(a, b)| (*a, *b))
-        .collect::<Vec<(((crate::Scale, crate::Scale), u32), bool)>>();
-
     let mut forward_pass_res = HashMap::new();
 
     let pb = init_bar(range_grid.len() as u64);
@@ -1061,30 +1110,23 @@ pub(crate) async fn calibrate(
     let mut num_failed = 0;
     let mut num_passed = 0;
 
-    for (((input_scale, param_scale), scale_rebase_multiplier), div_rebasing) in range_grid {
+    for ((input_scale, param_scale), scale_rebase_multiplier) in range_grid {
         pb.set_message(format!(
-            "i-scale: {}, p-scale: {}, rebase-(x): {}, div-rebase: {}, fail: {}, pass: {}",
+            "i-scale: {}, p-scale: {}, rebase-(x): {}, fail: {}, pass: {}",
             input_scale.to_string().blue(),
             param_scale.to_string().blue(),
-            scale_rebase_multiplier.to_string().blue(),
-            div_rebasing.to_string().yellow(),
+            scale_rebase_multiplier.to_string().yellow(),
             num_failed.to_string().red(),
             num_passed.to_string().green()
         ));
 
-        let key = (
-            input_scale,
-            param_scale,
-            scale_rebase_multiplier,
-            div_rebasing,
-        );
+        let key = (input_scale, param_scale, scale_rebase_multiplier);
         forward_pass_res.insert(key, vec![]);
 
         let local_run_args = RunArgs {
             input_scale,
             param_scale,
             scale_rebase_multiplier,
-            div_rebasing,
             lookup_range: (IntegerRep::MIN, IntegerRep::MAX),
             ..settings.run_args.clone()
         };
@@ -1117,7 +1159,7 @@ pub(crate) async fn calibrate(
                 let chunk = chunk.clone();
 
                 let data = circuit
-                    .load_graph_from_file_exclusively(&chunk)
+                    .load_graph_input(&chunk)
                     .map_err(|e| format!("failed to load circuit inputs: {}", e))?;
 
                 let forward_res = circuit
@@ -1188,7 +1230,6 @@ pub(crate) async fn calibrate(
             let found_run_args = RunArgs {
                 input_scale: new_settings.run_args.input_scale,
                 param_scale: new_settings.run_args.param_scale,
-                div_rebasing: new_settings.run_args.div_rebasing,
                 lookup_range: new_settings.run_args.lookup_range,
                 logrows: new_settings.run_args.logrows,
                 scale_rebase_multiplier: new_settings.run_args.scale_rebase_multiplier,
@@ -1296,7 +1337,6 @@ pub(crate) async fn calibrate(
             best_params.run_args.input_scale,
             best_params.run_args.param_scale,
             best_params.run_args.scale_rebase_multiplier,
-            best_params.run_args.div_rebasing,
         ))
         .ok_or("no params found")?
         .iter()
@@ -1374,6 +1414,7 @@ pub(crate) fn mock(
     Ok(String::new())
 }
 
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 pub(crate) async fn create_evm_verifier(
     vk_path: PathBuf,
     srs_path: Option<PathBuf>,
@@ -1391,7 +1432,9 @@ pub(crate) async fn create_evm_verifier(
     )?;
 
     let num_instance = settings.total_instances();
-    let num_instance: usize = num_instance.iter().sum::<usize>();
+    // create a scales array that is the same length as the number of instances, all populated with 0
+    let scales = vec![0; num_instance.len()];
+    // let poseidon_instance = settings.module_sizes.num_instances().iter().sum::<usize>();
 
     let vk = load_vk::<KZGCommitmentScheme<Bn256>, GraphCircuit>(vk_path, settings)?;
     trace!("params computed");
@@ -1400,7 +1443,10 @@ pub(crate) async fn create_evm_verifier(
         &params,
         &vk,
         halo2_solidity_verifier::BatchOpenScheme::Bdfg21,
-        num_instance,
+        &num_instance,
+        &scales,
+        0,
+        0,
     );
     let (verifier_solidity, name) = if reusable {
         (generator.render_separately()?.0, "Halo2VerifierReusable") // ignore the rendered vk artifact for now and generate it in create_evm_vka
@@ -1418,12 +1464,13 @@ pub(crate) async fn create_evm_verifier(
     Ok(String::new())
 }
 
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 pub(crate) async fn create_evm_vka(
     vk_path: PathBuf,
     srs_path: Option<PathBuf>,
     settings_path: PathBuf,
-    sol_code_path: PathBuf,
-    abi_path: PathBuf,
+    vka_path: PathBuf,
+    decimals: usize,
 ) -> Result<String, EZKLError> {
     let settings = GraphSettings::load(&settings_path)?;
     let commitment: Commitments = settings.run_args.commitment.into();
@@ -1433,136 +1480,55 @@ pub(crate) async fn create_evm_vka(
         commitment,
     )?;
 
-    let num_instance = settings.total_instances();
-    let num_instance: usize = num_instance.iter().sum::<usize>();
+    let num_poseidon_instance = settings.module_sizes.num_instances().iter().sum::<usize>();
+    let num_fixed_point_instance = settings
+        .model_instance_shapes
+        .iter()
+        .map(|x| x.iter().product::<usize>())
+        .collect_vec();
 
+    let scales = settings.get_model_instance_scales();
     let vk = load_vk::<KZGCommitmentScheme<Bn256>, GraphCircuit>(vk_path, settings)?;
     trace!("params computed");
+    // assert that the decimals must be less than or equal to 38 to prevent overflow
+    if decimals > 38 {
+        return Err("decimals must be less than or equal to 38".into());
+    }
 
     let generator = halo2_solidity_verifier::SolidityGenerator::new(
         &params,
         &vk,
         halo2_solidity_verifier::BatchOpenScheme::Bdfg21,
-        num_instance,
+        &num_fixed_point_instance,
+        &scales,
+        decimals,
+        num_poseidon_instance,
     );
 
-    let vk_solidity = generator.render_separately()?.1;
+    let vka_words: Vec<[u8; 32]> = generator.render_separately_vka_words()?.1;
+    let serialized_vka_words = bincode::serialize(&vka_words).or_else(|e| {
+        Err(EZKLError::from(format!(
+            "Failed to serialize vka words: {}",
+            e
+        )))
+    })?;
 
-    File::create(sol_code_path.clone())?.write_all(vk_solidity.as_bytes())?;
+    File::create(vka_path.clone())?.write_all(&serialized_vka_words)?;
 
-    // fetch abi of the contract
-    let (abi, _, _) = get_contract_artifacts(sol_code_path, "Halo2VerifyingArtifact", 0).await?;
-    // save abi to file
-    serde_json::to_writer(std::fs::File::create(abi_path)?, &abi)?;
+    // Load in the vka words and deserialize them and check that they match the original
+    let bytes = std::fs::read(vka_path)?;
+    let vka_buf: Vec<[u8; 32]> = bincode::deserialize(&bytes)
+        .map_err(|e| EZKLError::from(format!("Failed to deserialize vka words: {e}")))?;
+    if vka_buf != vka_words {
+        return Err("vka words do not match".into());
+    };
 
     Ok(String::new())
 }
-
-pub(crate) async fn create_evm_data_attestation(
-    settings_path: PathBuf,
-    sol_code_path: PathBuf,
-    abi_path: PathBuf,
-    input: PathBuf,
-    witness: Option<PathBuf>,
-) -> Result<String, EZKLError> {
-    #[allow(unused_imports)]
-    use crate::graph::{DataSource, VarVisibility};
-    use crate::{graph::Visibility, pfsys::get_proof_commitments};
-
-    let settings = GraphSettings::load(&settings_path)?;
-
-    let visibility = VarVisibility::from_args(&settings.run_args)?;
-    trace!("params computed");
-
-    // if input is not provided, we just instantiate dummy input data
-    let data = GraphData::from_path(input).unwrap_or(GraphData::new(DataSource::File(vec![])));
-
-    let output_data = if let Some(DataSource::OnChain(source)) = data.output_data {
-        if visibility.output.is_private() {
-            return Err("private output data on chain is not supported on chain".into());
-        }
-        let mut on_chain_output_data = vec![];
-        for call in source.calls {
-            on_chain_output_data.push(call);
-        }
-        Some(on_chain_output_data)
-    } else {
-        None
-    };
-
-    let input_data = if let DataSource::OnChain(source) = data.input_data {
-        if visibility.input.is_private() {
-            return Err("private input data on chain is not supported on chain".into());
-        }
-        let mut on_chain_input_data = vec![];
-        for call in source.calls {
-            on_chain_input_data.push(call);
-        }
-        Some(on_chain_input_data)
-    } else {
-        None
-    };
-
-    // Read the settings file. Look if either the run_ars.input_visibility, run_args.output_visibility or run_args.param_visibility is KZGCommit
-    // if so, then we need to load the witness
-
-    let commitment_bytes = if settings.run_args.input_visibility == Visibility::KZGCommit
-        || settings.run_args.output_visibility == Visibility::KZGCommit
-        || settings.run_args.param_visibility == Visibility::KZGCommit
-    {
-        let witness = GraphWitness::from_path(witness.unwrap_or(DEFAULT_WITNESS.into()))?;
-        let commitments = witness.get_polycommitments();
-        let proof_first_bytes = get_proof_commitments::<
-            KZGCommitmentScheme<Bn256>,
-            _,
-            EvmTranscript<G1Affine, _, _, _>,
-        >(&commitments);
-
-        Some(proof_first_bytes.unwrap())
-    } else {
-        None
-    };
-
-    let output = fix_da_sol(input_data, output_data, commitment_bytes)?;
-    let mut f = File::create(sol_code_path.clone())?;
-    let _ = f.write(output.as_bytes());
-    // fetch abi of the contract
-    let (abi, _, _) = get_contract_artifacts(sol_code_path, "DataAttestation", 0).await?;
-    // save abi to file
-    serde_json::to_writer(std::fs::File::create(abi_path)?, &abi)?;
-
-    Ok(String::new())
-}
-
-pub(crate) async fn deploy_da_evm(
-    data: PathBuf,
-    settings_path: PathBuf,
-    sol_code_path: PathBuf,
-    rpc_url: Option<String>,
-    addr_path: PathBuf,
-    runs: usize,
-    private_key: Option<String>,
-) -> Result<String, EZKLError> {
-    let contract_address = deploy_da_verifier_via_solidity(
-        settings_path,
-        data,
-        sol_code_path,
-        rpc_url.as_deref(),
-        runs,
-        private_key.as_deref(),
-    )
-    .await?;
-    info!("Contract deployed at: {}", contract_address);
-
-    let mut f = File::create(addr_path)?;
-    write!(f, "{:#?}", contract_address)?;
-
-    Ok(String::new())
-}
-
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 pub(crate) async fn deploy_evm(
     sol_code_path: PathBuf,
-    rpc_url: Option<String>,
+    rpc_url: String,
     addr_path: PathBuf,
     runs: usize,
     private_key: Option<String>,
@@ -1571,11 +1537,10 @@ pub(crate) async fn deploy_evm(
     let contract_name = match contract {
         ContractType::Verifier { reusable: false } => "Halo2Verifier",
         ContractType::Verifier { reusable: true } => "Halo2VerifierReusable",
-        ContractType::VerifyingKeyArtifact => "Halo2VerifyingArtifact",
     };
     let contract_address = deploy_contract_via_solidity(
         sol_code_path,
-        rpc_url.as_deref(),
+        &rpc_url,
         runs,
         private_key.as_deref(),
         contract_name,
@@ -1589,21 +1554,61 @@ pub(crate) async fn deploy_evm(
     Ok(String::new())
 }
 
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
+pub(crate) async fn register_vka(
+    rpc_url: String,
+    rv_addr: H160Flag,
+    vka_path: PathBuf,
+    vka_digest_path: PathBuf,
+    private_key: Option<String>,
+) -> Result<String, EZKLError> {
+    // Load the vka, which is bincode serialized, from the vka_path
+    let bytes = std::fs::read(vka_path)?;
+    let vka_buf: Vec<[u8; 32]> = bincode::deserialize(&bytes)
+        .map_err(|e| EZKLError::from(format!("Failed to deserialize vka words: {e}")))?;
+    let vka_digest = register_vka_via_rv(
+        rpc_url.as_ref(),
+        private_key.as_deref(),
+        rv_addr.into(),
+        &vka_buf,
+    )
+    .await?;
+
+    info!("VKA digest: {:#?}", vka_digest);
+
+    let mut f = File::create(vka_digest_path)?;
+    write!(f, "{:#?}", vka_digest)?;
+    Ok(String::new())
+}
+
 /// Encodes the calldata for the EVM verifier (both aggregated and single proof)
+/// TODO: Add a "RV address param" which will query the "RegisteredVKA" events to fetch the
+/// VKA from the vka_digest.
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 pub(crate) fn encode_evm_calldata(
     proof_path: PathBuf,
     calldata_path: PathBuf,
-    addr_vk: Option<H160Flag>,
+    vka_path: Option<PathBuf>,
 ) -> Result<Vec<u8>, EZKLError> {
     let snark = Snark::load::<IPACommitmentScheme<G1Affine>>(&proof_path)?;
 
     let flattened_instances = snark.instances.into_iter().flatten();
 
+    // Load the vka, which is bincode serialized, from the vka_path
+    let vka_buf: Option<Vec<[u8; 32]>> =
+        match vka_path {
+            Some(path) => {
+                let bytes = std::fs::read(path)?;
+                Some(bincode::deserialize(&bytes).map_err(|e| {
+                    EZKLError::from(format!("Failed to deserialize vka words: {e}"))
+                })?)
+            }
+            None => None,
+        };
+
+    let vka: Option<&[[u8; 32]]> = vka_buf.as_deref();
     let encoded = halo2_solidity_verifier::encode_calldata(
-        addr_vk
-            .as_ref()
-            .map(|x| alloy::primitives::Address::from(*x).0)
-            .map(|x| x.0),
+        vka,
         &snark.proof,
         &flattened_instances.collect::<Vec<_>>(),
     );
@@ -1615,35 +1620,24 @@ pub(crate) fn encode_evm_calldata(
     Ok(encoded)
 }
 
+/// TODO: Add an optional vka_digest param that will allow use to fetch the assocaited VKA
+/// from the RegisteredVKA events on the RV.
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 pub(crate) async fn verify_evm(
     proof_path: PathBuf,
     addr_verifier: H160Flag,
-    rpc_url: Option<String>,
-    addr_da: Option<H160Flag>,
-    addr_vk: Option<H160Flag>,
+    rpc_url: String,
+    vka_path: Option<PathBuf>,
 ) -> Result<String, EZKLError> {
-    use crate::eth::verify_proof_with_data_attestation;
-
     let proof = Snark::load::<KZGCommitmentScheme<Bn256>>(&proof_path)?;
 
-    let result = if let Some(addr_da) = addr_da {
-        verify_proof_with_data_attestation(
-            proof.clone(),
-            addr_verifier.into(),
-            addr_da.into(),
-            addr_vk.map(|s| s.into()),
-            rpc_url.as_deref(),
-        )
-        .await?
-    } else {
-        verify_proof_via_solidity(
-            proof.clone(),
-            addr_verifier.into(),
-            addr_vk.map(|s| s.into()),
-            rpc_url.as_deref(),
-        )
-        .await?
-    };
+    let result = verify_proof_via_solidity(
+        proof.clone(),
+        addr_verifier.into(),
+        vka_path.map(|s| s.into()),
+        rpc_url.as_ref(),
+    )
+    .await?;
 
     info!("Solidity verification result: {}", result);
 
@@ -1654,6 +1648,7 @@ pub(crate) async fn verify_evm(
     Ok(String::new())
 }
 
+#[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
 pub(crate) async fn create_evm_aggregate_verifier(
     vk_path: PathBuf,
     srs_path: Option<PathBuf>,
@@ -1679,8 +1674,8 @@ pub(crate) async fn create_evm_aggregate_verifier(
         .sum();
 
     let num_instance = AggregationCircuit::num_instance(num_instance);
+    let scales = vec![0; num_instance.len()];
     assert_eq!(num_instance.len(), 1);
-    let num_instance = num_instance[0];
 
     let agg_vk = load_vk::<KZGCommitmentScheme<Bn256>, AggregationCircuit>(vk_path, ())?;
 
@@ -1688,7 +1683,10 @@ pub(crate) async fn create_evm_aggregate_verifier(
         &params,
         &agg_vk,
         halo2_solidity_verifier::BatchOpenScheme::Bdfg21,
-        num_instance,
+        &num_instance,
+        &scales,
+        0,
+        0,
     );
 
     let acc_encoding = halo2_solidity_verifier::AccumulatorEncoding::new(
@@ -1777,53 +1775,7 @@ pub(crate) fn setup(
     Ok(String::new())
 }
 
-pub(crate) async fn setup_test_evm_witness(
-    data_path: PathBuf,
-    compiled_circuit_path: PathBuf,
-    test_data: PathBuf,
-    rpc_url: Option<String>,
-    input_source: TestDataSource,
-    output_source: TestDataSource,
-) -> Result<String, EZKLError> {
-    use crate::graph::TestOnChainData;
-
-    let mut data = GraphData::from_path(data_path)?;
-    let mut circuit = GraphCircuit::load(compiled_circuit_path)?;
-
-    // if both input and output are from files fail
-    if matches!(input_source, TestDataSource::File) && matches!(output_source, TestDataSource::File)
-    {
-        return Err("Both input and output cannot be from files".into());
-    }
-
-    let test_on_chain_data = TestOnChainData {
-        data: test_data.clone(),
-        rpc: rpc_url,
-        data_sources: TestSources {
-            input: input_source,
-            output: output_source,
-        },
-    };
-
-    circuit
-        .populate_on_chain_test_data(&mut data, test_on_chain_data)
-        .await?;
-
-    Ok(String::new())
-}
-
 use crate::pfsys::ProofType;
-pub(crate) async fn test_update_account_calls(
-    addr: H160Flag,
-    data: PathBuf,
-    rpc_url: Option<String>,
-) -> Result<String, EZKLError> {
-    use crate::eth::update_account_calls;
-
-    update_account_calls(addr.into(), data, rpc_url.as_deref()).await?;
-
-    Ok(String::new())
-}
 
 #[allow(clippy::too_many_arguments)]
 pub(crate) fn prove(
@@ -2022,7 +1974,7 @@ pub(crate) fn mock_aggregate(
         }
     }
     // proof aggregation
-        let pb = {
+    let pb = {
         let pb = init_spinner();
         pb.set_message("Aggregating (may take a while)...");
         pb
@@ -2033,10 +1985,11 @@ pub(crate) fn mock_aggregate(
     let prover = halo2_proofs::dev::MockProver::run(logrows, &circuit, vec![circuit.instances()])
         .map_err(|e| ExecutionError::MockProverError(e.to_string()))?;
     prover.verify().map_err(ExecutionError::VerifyError)?;
-        pb.finish_with_message("Done.");
+    pb.finish_with_message("Done.");
     Ok(String::new())
 }
 
+#[allow(clippy::too_many_arguments)]
 pub(crate) fn setup_aggregate(
     sample_snarks: Vec<PathBuf>,
     vk_path: PathBuf,
@@ -2127,7 +2080,7 @@ pub(crate) fn aggregate(
     }
 
     // proof aggregation
-        let pb = {
+    let pb = {
         let pb = init_spinner();
         pb.set_message("Aggregating (may take a while)...");
         pb
@@ -2276,7 +2229,7 @@ pub(crate) fn aggregate(
     );
     snark.save(&proof_path)?;
 
-        pb.finish_with_message("Done.");
+    pb.finish_with_message("Done.");
 
     Ok(snark)
 }

src/fieldutils.rs

@@ -5,10 +5,12 @@ 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)
+    } else if x == IntegerRep::MIN {
+        -F::from_u128(x.saturating_neg() as u128) - F::ONE
     } else {
         -F::from_u128(x.saturating_neg() as u128)
     }
@@ -32,6 +34,9 @@ pub fn felt_to_f64<F: PrimeField + PartialOrd + Field>(x: F) -> f64 {
 /// Converts a PrimeField element to an i64.
 pub fn felt_to_integer_rep<F: PrimeField + PartialOrd + Field>(x: F) -> IntegerRep {
     if x > F::from_u128(IntegerRep::MAX as u128) {
+        if x == -F::from_u128(IntegerRep::MAX as u128) - F::ONE {
+            return IntegerRep::MIN;
+        }
         let rep = (-x).to_repr();
         let negtmp: &[u8] = rep.as_ref();
         let lower_128: u128 = u128::from_le_bytes(negtmp[..16].try_into().unwrap());
@@ -51,7 +56,7 @@ mod test {
     use halo2curves::pasta::Fp as F;
 
     #[test]
-    fn test_conv() {
+    fn integerreptofelt() {
         let res: F = integer_rep_to_felt(-15);
         assert_eq!(res, -F::from(15));
 
@@ -69,8 +74,24 @@ 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);
         }
     }
+
+    #[test]
+    fn felttointegerrepmin() {
+        let x = IntegerRep::MIN;
+        let fieldx: F = integer_rep_to_felt::<F>(x);
+        let xf: IntegerRep = felt_to_integer_rep::<F>(fieldx);
+        assert_eq!(x, xf);
+    }
+
+    #[test]
+    fn felttointegerrepmax() {
+        let x = IntegerRep::MAX;
+        let fieldx: F = integer_rep_to_felt::<F>(x);
+        let xf: IntegerRep = felt_to_integer_rep::<F>(fieldx);
+        assert_eq!(x, xf);
+    }
 }

src/graph/errors.rs

@@ -11,6 +11,12 @@ pub enum GraphError {
     /// Shape mismatch in circuit construction
     #[error("invalid dimensions used for node {0} ({1})")]
     InvalidDims(usize, String),
+    /// Non scalar power
+    #[error("we only support scalar powers")]
+    NonScalarPower,
+    /// Non scalar base for exponentiation
+    #[error("we only support scalar bases for exponentiation")]
+    NonScalarBase,
     /// Wrong method was called to configure an op
     #[error("wrong method was called to configure node {0} ({1})")]
     WrongMethod(usize, String),
@@ -27,7 +33,7 @@ pub enum GraphError {
     #[error("a node is missing required params: {0}")]
     MissingParams(String),
     /// A node has missing parameters
-    #[error("a node is has misformed params: {0}")]
+    #[error("a node has misformed params: {0}")]
     MisformedParams(String),
     /// Error in the configuration of the visibility of variables
     #[error("there should be at least one set of public variables")]
@@ -92,14 +98,13 @@ pub enum GraphError {
         feature = "ezkl",
         not(all(target_arch = "wasm32", target_os = "unknown"))
     ))]
-    #[error("[tokio postgres] {0}")]
-    TokioPostgresError(#[from] tokio_postgres::Error),
     /// Eth error
     #[cfg(all(
         feature = "ezkl",
         not(all(target_arch = "wasm32", target_os = "unknown"))
     ))]
     #[error("[eth] {0}")]
+    #[cfg(all(feature = "eth", not(target_arch = "wasm32")))]
     EthError(#[from] crate::eth::EthError),
     /// Json error
     #[error("[json] {0}")]
@@ -113,13 +118,13 @@ pub enum GraphError {
     /// Missing input for a node
     #[error("missing input for node {0}")]
     MissingInput(usize),
-    ///
+    /// Ranges can only be constant
     #[error("range only supports constant inputs in a zk circuit")]
     NonConstantRange,
-    ///
+    /// Trilu diagonal must be constant
     #[error("trilu only supports constant diagonals in a zk circuit")]
     NonConstantTrilu,
-    ///
+    /// The witness was too short
     #[error("insufficient witness values to generate a fixed output")]
     InsufficientWitnessValues,
     /// Missing scale
@@ -135,7 +140,9 @@ pub enum GraphError {
     #[error("range check {0} is too large")]
     RangeCheckTooLarge(usize),
     ///Cannot use on-chain data source as private data
-    #[error("cannot use on-chain data source as 1) output for on-chain test 2) as private data 3) as input when using wasm.")]
+    #[error(
+        "cannot use on-chain data source as 1) output for on-chain test 2) as private data 3) as input when using wasm."
+    )]
     OnChainDataSource,
     /// Missing data source
     #[error("missing data source")]
@@ -143,4 +150,13 @@ pub enum GraphError {
     /// Invalid RunArg
     #[error("invalid RunArgs: {0}")]
     InvalidRunArgs(String),
+    /// Only nearest neighbor interpolation is supported
+    #[error("only nearest neighbor interpolation is supported")]
+    InvalidInterpolation,
+    /// Node has a missing output
+    #[error("node {0} has a missing output")]
+    MissingOutput(usize),
+    /// Inssuficient advice columns
+    #[error("insuficcient advice columns (need {0} at least)")]
+    InsufficientAdviceColumns(usize),
 }