Merge branch 'main' into ac/artifact-version-warning

chore: version mismatch warnings for artifacts
2026-01-13 16:27:59 -05:00 · 2025-01-06 15:49:58 +00:00 · 2025-01-06 15:35:37 +00:00
85 changed files with 3716 additions and 8057 deletions
--- a/.github/workflows/benchmarks.yml
+++ b/.github/workflows/benchmarks.yml
@@ -6,16 +6,23 @@ on:
        description: "Test scenario tags"

 jobs:
-
-  bench_poseidon:
-    permissions:
-      contents: read
+  bench_elgamal:
    runs-on: self-hosted
    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+          toolchain: nightly-2023-06-27
+          override: true
+          components: rustfmt, clippy
+      - name: Bench elgamal
+        run: cargo bench --verbose --bench elgamal
+
+  bench_poseidon:
+    runs-on: self-hosted
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -24,15 +31,11 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -41,15 +44,11 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -58,15 +57,11 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -75,15 +70,11 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -92,15 +83,11 @@ jobs:
        run: cargo bench --verbose --bench relu

  bench_accum_dot:
-    permissions:
-      contents: read
    runs-on: self-hosted
    needs: [bench_poseidon]
    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -109,15 +96,11 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -126,15 +109,11 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -143,15 +122,11 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -160,15 +135,11 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -177,15 +148,11 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly-2023-06-27
          override: true
--- a/.github/workflows/engine.yml
+++ b/.github/workflows/engine.yml
@@ -15,24 +15,17 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
-        with:
-          toolchain: nightly-2025-02-17
+          toolchain: nightly-2024-07-18
          override: true
          components: rustfmt, clippy
-      - uses: jetli/wasm-pack-action@0d096b08b4e5a7de8c28de67e11e945404e9eefa  #v0.4.0
+      - uses: jetli/wasm-pack-action@v0.4.0
        with:
          # Pin to version 0.12.1
          version: 'v0.12.1'
@@ -40,7 +33,7 @@ jobs:
        run: rustup target add wasm32-unknown-unknown

      - name: Add rust-src
-        run: rustup component add rust-src --toolchain nightly-2025-02-17-x86_64-unknown-linux-gnu
+        run: rustup component add rust-src --toolchain nightly-2024-07-18-x86_64-unknown-linux-gnu
      - name: Install binaryen
        run: |
          set -e
@@ -49,41 +42,41 @@ 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: ${{ github.ref_name }}
        run: |
-            cat > pkg/package.json << EOF
-            {
-              "name": "@ezkljs/engine",
-              "version": "$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
+          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

      - name: Replace memory definition in nodejs
        run: |
@@ -176,7 +169,7 @@ 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@1a4442cacd436585916779262731d5b162bc6ec7  #v3.8.2
+        uses: actions/setup-node@v3
        with:
          node-version: "18.12.1"
          registry-url: "https://registry.npmjs.org"
@@ -191,26 +184,21 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
+      - uses: actions/checkout@v4
      - name: Update version in package.json
        shell: bash
+        env:
+          RELEASE_TAG: ${{ github.ref_name }}
        run: |
-          sed -i "s|\"version\": \".*\"|\"version\": \"$RELEASE_TAG\"|" in-browser-evm-verifier/package.json
+          sed -i "s|\"version\": \".*\"|\"version\": \"${{ github.ref_name }}\"|" in-browser-evm-verifier/package.json
      - name: Prepare tag and fetch package integrity
        run: |
-          CLEANED_TAG=${RELEASE_TAG} # Get the tag from ref_name
+          CLEANED_TAG=${{ github.ref_name }} # Get the tag from ref_name
          CLEANED_TAG="${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)
@@ -230,13 +218,13 @@ 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@eae0cfeb286e66ffb5155f1a79b90583a127a68b  #v2.4.1
+        uses: pnpm/action-setup@v2
        with:
          version: 8
      - name: Set up Node.js
-        uses: actions/setup-node@1a4442cacd436585916779262731d5b162bc6ec7  #v3.8.2
+        uses: actions/setup-node@v3
        with:
          node-version: "18.12.1"
          registry-url: "https://registry.npmjs.org"
@@ -247,4 +235,4 @@ jobs:
          pnpm run build
          pnpm publish --no-git-checks
        env:
-          NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
+          NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}  
--- a/.github/workflows/large-tests.yml
+++ b/.github/workflows/large-tests.yml
@@ -6,16 +6,12 @@ on:
        description: "Test scenario tags"
 jobs:
  large-tests:
-    permissions:
-      contents: read
    runs-on: kaiju
    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+      - uses: actions/checkout@v4
+      - uses: actions-rs/toolchain@v1
        with:
-          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
-        with:
-          toolchain: nightly-2025-02-17
+          toolchain: nightly-2024-07-18
          override: true
          components: rustfmt, clippy
      - name: nanoGPT Mock
--- a/.github/workflows/pypi-gpu.yml
+++ b/.github/workflows/pypi-gpu.yml
@@ -18,46 +18,39 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions/setup-python@b64ffcaf5b410884ad320a9cfac8866006a109aa  #v4.8.0
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v4
        with:
          python-version: 3.12
          architecture: x64

-      - name: Set pyproject.toml version to match github tag and rename ezkl to ezkl-gpu
+      - name: Set pyproject.toml version to match github tag
        shell: bash
        run: |
            mv pyproject.toml pyproject.toml.orig
-            sed "s/ezkl/ezkl-gpu/" pyproject.toml.orig > pyproject.toml.tmp
-            sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" pyproject.toml.tmp > pyproject.toml
+            sed "s/ezkl/ezkl-gpu/" pyproject.toml.orig >pyproject.toml
+            sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" pyproject.toml.orig >pyproject.toml

-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly-2023-06-27
          override: true
          components: rustfmt, clippy

-      - name: Set Cargo.toml version to match github tag and rename ezkl to ezkl-gpu
+      - name: Set Cargo.toml version to match github tag
        shell: bash
-        # the ezkl substitution here looks for the first instance of name = "ezkl" and changes it to "ezkl-gpu"
+        env:
+          RELEASE_TAG: ${{ github.ref_name }}
        run: |
          mv Cargo.toml Cargo.toml.orig
-          sed "0,/name = \"ezkl\"/s/name = \"ezkl\"/name = \"ezkl-gpu\"/" Cargo.toml.orig > Cargo.toml.tmp
-          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.toml.tmp > Cargo.toml
+          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
+          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.lock.orig >Cargo.lock

      - name: Install required libraries
        shell: bash
@@ -65,7 +58,7 @@ jobs:
          sudo apt-get update && sudo apt-get install -y openssl pkg-config libssl-dev

      - name: Build wheels
-        uses: PyO3/maturin-action@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
+        uses: PyO3/maturin-action@v1
        with:
          target: ${{ matrix.target }}
          manylinux: auto
@@ -78,7 +71,7 @@ jobs:
          pip install ezkl-gpu --no-index --find-links dist --force-reinstall

      - name: Upload wheels
-        uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08  #v4.6.0
+        uses: actions/upload-artifact@v3
        with:
          name: wheels
          path: dist
@@ -94,7 +87,7 @@ jobs:
    # needs: [ macos, windows, linux, linux-cross, musllinux, musllinux-cross ]
    needs: [linux]
    steps:
-      - uses: actions/download-artifact@fa0a91b85d4f404e444e00e005971372dc801d16  #v4.1.8
+      - uses: actions/download-artifact@v3
        with:
          name: wheels
      - name: List Files
@@ -106,14 +99,14 @@ jobs:
      # publishes to PyPI
      - name: Publish package distributions to PyPI
        continue-on-error: true
-        uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc  #v1.12.4
+        uses: pypa/gh-action-pypi-publish@unstable/v1
        with:
-          packages-dir: ./wheels
+          packages-dir: ./

      # publishes to TestPyPI
      - name: Publish package distribution to TestPyPI
        continue-on-error: true
-        uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc  #v1.12.4
+        uses: pypa/gh-action-pypi-publish@unstable/v1
        with:
          repository-url: https://test.pypi.org/legacy/
-          packages-dir: ./wheels
+          packages-dir: ./
--- a/.github/workflows/pypi.yml
+++ b/.github/workflows/pypi.yml
@@ -16,53 +16,36 @@ 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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions/setup-python@b64ffcaf5b410884ad320a9cfac8866006a109aa  #v4.8.0
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v4
        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@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rs/toolchain@v1
        with:
-          toolchain: nightly-2025-02-17
+          toolchain: nightly-2024-07-18
          override: true
          components: rustfmt, clippy

      - name: Build wheels
-        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
+        uses: PyO3/maturin-action@v1
        with:
          target: ${{ matrix.target }}
          args: --release --out dist --features python-bindings
@@ -73,36 +56,24 @@ jobs:
          python -c "import ezkl"

      - name: Upload wheels
-        uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08  #v4.6.0
+        uses: actions/upload-artifact@v3
        with:
-          name: dist-macos-${{ matrix.target }}
+          name: wheels
          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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions/setup-python@b64ffcaf5b410884ad320a9cfac8866006a109aa  #v4.8.0
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v4
        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:
@@ -113,14 +84,14 @@ jobs:
          mv Cargo.lock Cargo.lock.orig
          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.lock.orig >Cargo.lock

-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rs/toolchain@v1
        with:
-          toolchain: nightly-2025-02-17
+          toolchain: nightly-2024-07-18
          override: true
          components: rustfmt, clippy

      - name: Build wheels
-        uses: PyO3/maturin-action@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
+        uses: PyO3/maturin-action@v1
        with:
          target: ${{ matrix.target }}
          args: --release --out dist --features python-bindings
@@ -130,36 +101,24 @@ jobs:
          python -c "import ezkl"

      - name: Upload wheels
-        uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08  #v4.6.0 #v4.6.0
+        uses: actions/upload-artifact@v3
        with:
-          name: dist-windows-${{ matrix.target }}
+          name: wheels
          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@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions/setup-python@b64ffcaf5b410884ad320a9cfac8866006a109aa  #v4.8.0
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v4
        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:
@@ -170,13 +129,14 @@ 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@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
+        uses: PyO3/maturin-action@v1
        with:
          target: ${{ matrix.target }}
          manylinux: auto
@@ -203,14 +163,63 @@ jobs:
          python -c "import ezkl"

      - name: Upload wheels
-        uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08  #v4.6.0
+        uses: actions/upload-artifact@v3
        with:
-          name: dist-linux-${{ matrix.target }}
+          name: wheels
          path: dist

+  #  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: ${{ github.token }}
+  #       install: |
+  #         apt-get update
+  #         apt-get install -y --no-install-recommends python3 python3-pip
+  #         pip3 install -U pip
+  #       run: |
+  #         pip3 install ezkl --no-index --find-links dist/ --force-reinstall
+  #         python3 -c "import ezkl"
+
+  #   - name: Upload wheels
+  #     uses: actions/upload-artifact@v3
+  #     with:
+  #       name: wheels
+  #       path: dist
+
  musllinux:
-    permissions:
-      contents: read
    runs-on: ubuntu-latest
    if: startsWith(github.ref, 'refs/tags/')
    strategy:
@@ -218,10 +227,8 @@ jobs:
        target:
          - x86_64-unknown-linux-musl
    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions/setup-python@b64ffcaf5b410884ad320a9cfac8866006a109aa  #v4.8.0
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v4
        with:
          python-version: 3.12
          architecture: x64
@@ -243,14 +250,13 @@ jobs:
          sed "s/0\\.0\\.0/${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
-
      - name: Install required libraries
        shell: bash
        run: |
          sudo apt-get update && sudo apt-get install -y pkg-config libssl-dev

      - name: Build wheels
-        uses: PyO3/maturin-action@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
+        uses: PyO3/maturin-action@v1
        with:
          target: ${{ matrix.target }}
          manylinux: musllinux_1_2
@@ -271,14 +277,12 @@ jobs:
            python3 -c "import ezkl"

      - name: Upload wheels
-        uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08  #v4.6.0
+        uses: actions/upload-artifact@v3
        with:
-          name: dist-musllinux-${{ matrix.target }}
+          name: wheels
          path: dist

  musllinux-cross:
-    permissions:
-      contents: read
    runs-on: ubuntu-latest
    if: startsWith(github.ref, 'refs/tags/')
    strategy:
@@ -287,21 +291,11 @@ jobs:
          - target: aarch64-unknown-linux-musl
            arch: aarch64
    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - uses: actions/setup-python@b64ffcaf5b410884ad320a9cfac8866006a109aa  #v4.8.0
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v4
        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:
@@ -313,13 +307,13 @@ jobs:
          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.lock.orig >Cargo.lock

      - name: Build wheels
-        uses: PyO3/maturin-action@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
+        uses: PyO3/maturin-action@v1
        with:
          target: ${{ matrix.platform.target }}
          manylinux: musllinux_1_2
          args: --release --out dist --features python-bindings

-      - uses: uraimo/run-on-arch-action@5397f9e30a9b62422f302092631c99ae1effcd9e  #v2.8.1
+      - uses: uraimo/run-on-arch-action@v2.8.1
        name: Install built wheel
        with:
          arch: ${{ matrix.platform.arch }}
@@ -334,9 +328,9 @@ jobs:
            python3 -c "import ezkl"

      - name: Upload wheels
-        uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08  #v4.6.0
+        uses: actions/upload-artifact@v3
        with:
-          name: dist-musllinux-${{ matrix.platform.target }}
+          name: wheels
          path: dist

  pypi-publish:
@@ -345,43 +339,44 @@ 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@fa0a91b85d4f404e444e00e005971372dc801d16  #v4.1.8
+      - uses: actions/download-artifact@v3
        with:
-          pattern: dist-*
-          merge-multiple: true
-          path: wheels
+          name: wheels
      - name: List Files
        run: ls -R

-      # # 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: ./
+      # 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 PyPI
      - name: Publish package distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc  #v1.12.4
+        continue-on-error: true
+        uses: pypa/gh-action-pypi-publish@unstable/v1
        with:
-          packages-dir: ./wheels
+          packages-dir: ./

+      # publishes to TestPyPI
+      - name: Publish package distribution to TestPyPI
+        continue-on-error: true
+        uses: pypa/gh-action-pypi-publish@unstable/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
        with:
          webhook_url: ${{ secrets.RTDS_WEBHOOK_URL }}
          webhook_token: ${{ secrets.RTDS_WEBHOOK_TOKEN }}
-          commit_ref: ${{ github.ref_name }}
+          commit_ref: ${{ github.ref_name }}
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -10,9 +10,6 @@ on:
      - "*"
 jobs:
  create-release:
-    permissions:
-      contents: read
-      packages: write
    name: create-release
    runs-on: ubuntu-22.04
    if: startsWith(github.ref, 'refs/tags/')
@@ -30,15 +27,12 @@ jobs:

      - name: Create Github Release
        id: create-release
-        uses: softprops/action-gh-release@c95fe1489396fe8a9eb87c0abf8aa5b2ef267fda  #v2.2.1
+        uses: softprops/action-gh-release@v1
        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
@@ -49,16 +43,13 @@ jobs:
      RUST_BACKTRACE: 1
      PCRE2_SYS_STATIC: 1
    steps:
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rs/toolchain@v1
        with:
-          toolchain: nightly-2025-02-17
+          toolchain: nightly-2024-07-18
          override: true
          components: rustfmt, clippy
      - name: Checkout repo
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-
+        uses: actions/checkout@v4

      - name: Get release version from tag
        shell: bash
@@ -90,7 +81,7 @@ jobs:
          echo "ASSET=build-artifacts/ezkl-linux-gpu.tar.gz" >> $GITHUB_ENV

      - name: Upload release archive
-        uses: actions/upload-release-asset@e8f9f06c4b078e705bd2ea027f0926603fc9b4d5  #v1.0.2
+        uses: actions/upload-release-asset@v1.0.2
        env:
          GITHUB_TOKEN: ${{ secrets.RELEASE_TOKEN }}
        with:
@@ -100,10 +91,6 @@ 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 }}
@@ -119,34 +106,32 @@ jobs:
        include:
          - build: windows-msvc
            os: windows-latest
-            rust: nightly-2025-02-17
+            rust: nightly-2024-07-18
            target: x86_64-pc-windows-msvc
          - build: macos
            os: macos-13
-            rust: nightly-2025-02-17
+            rust: nightly-2024-07-18
            target: x86_64-apple-darwin
          - build: macos-aarch64
            os: macos-13
-            rust: nightly-2025-02-17
+            rust: nightly-2024-07-18
            target: aarch64-apple-darwin
          - build: linux-musl
            os: ubuntu-22.04
-            rust: nightly-2025-02-17
+            rust: nightly-2024-07-18
            target: x86_64-unknown-linux-musl
          - build: linux-gnu
            os: ubuntu-22.04
-            rust: nightly-2025-02-17
+            rust: nightly-2024-07-18
            target: x86_64-unknown-linux-gnu
          - build: linux-aarch64
            os: ubuntu-22.04
-            rust: nightly-2025-02-17
+            rust: nightly-2024-07-18
            target: aarch64-unknown-linux-gnu

    steps:
      - name: Checkout repo
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
+        uses: actions/checkout@v4

      - name: Get release version from tag
        shell: bash
@@ -170,7 +155,7 @@ jobs:
          fi

      - name: Install Rust
-        uses: dtolnay/rust-toolchain@4f94fbe7e03939b0e674bcc9ca609a16088f63ff  #nightly branch, TODO: update when required
+        uses: dtolnay/rust-toolchain@nightly
        with:
          target: ${{ matrix.target }}

@@ -196,18 +181,14 @@ jobs:
          echo "target flag is: ${{ env.TARGET_FLAGS }}"
          echo "target dir is: ${{ env.TARGET_DIR }}"

-      - name: Build release binary (no asm or metal)
-        if: matrix.build != 'linux-gnu' && matrix.build != 'macos-aarch64'
+      - name: Build release binary (no asm)
+        if: matrix.build != 'linux-gnu'
        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"
@@ -233,7 +214,7 @@ jobs:
          echo "ASSET=build-artifacts/ezkl-win.zip" >> $GITHUB_ENV

      - name: Upload release archive
-        uses: actions/upload-release-asset@e8f9f06c4b078e705bd2ea027f0926603fc9b4d5  #v1.0.2
+        uses: actions/upload-release-asset@v1.0.2
        env:
          GITHUB_TOKEN: ${{ secrets.RELEASE_TOKEN }}
        with:
--- a/.github/workflows/rust.yml
+++ b/.github/workflows/rust.yml
--- a/.github/workflows/static-analysis.yml
+++ b/.github/workflows/static-analysis.yml
@@ -1,32 +0,0 @@
-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 .
-
-
--- a/.github/workflows/swift-pm.yml
+++ b/.github/workflows/swift-pm.yml
@@ -9,24 +9,18 @@ on:

 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
+        uses: actions/checkout@v3

      - name: Extract TAG from github.ref_name
        run: |
          # github.ref_name is provided by GitHub Actions and contains the tag name directly.
-          TAG="${RELEASE_TAG}"
+          TAG="${{ github.ref_name }}"
          echo "Original TAG: $TAG"
          # Remove leading 'v' if present to match the Swift Package Manager version format.
          NEW_TAG=${TAG#v}
@@ -34,7 +28,7 @@ jobs:
          echo "TAG=$NEW_TAG" >> $GITHUB_ENV

      - name: Install Rust (nightly)
-        uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+        uses: actions-rs/toolchain@v1
        with:
          toolchain: nightly
          override: true
@@ -53,8 +47,7 @@ jobs:

      - name: Copy Test Files
        run: |
-          rm -rf ezkl-swift-package/Tests/EzklAssets/
-          mkdir -p ezkl-swift-package/Tests/EzklAssets/
+          rm -rf 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
@@ -112,6 +105,7 @@ jobs:
          cd ezkl-swift-package
          git add Sources/EzklCoreBindings Tests/EzklAssets
          git commit -m "Automatically updated EzklCoreBindings for EZKL"
+          
          if ! git push origin; then
            echo "::error::Failed to push changes to ${{ env.EZKL_SWIFT_PACKAGE_REPO }}. Please ensure that EZKL_PORTER_TOKEN has the correct permissions."
            exit 1
@@ -121,6 +115,7 @@ jobs:
        run: |
          cd ezkl-swift-package
          source $GITHUB_ENV
+          
          # Tag the latest commit on the current branch
          if git rev-parse "$TAG" >/dev/null 2>&1; then
            echo "Tag $TAG already exists locally. Skipping tag creation."
--- a/.github/workflows/tagging.yml
+++ b/.github/workflows/tagging.yml
@@ -11,12 +11,10 @@ jobs:
      contents: write

    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
+      - uses: actions/checkout@v4
      - name: Bump version and push tag
        id: tag_version
-        uses: mathieudutour/github-tag-action@a22cf08638b34d5badda920f9daf6e72c477b07b  #v6.2
+        uses: mathieudutour/github-tag-action@v6.2
        with:
          github_token: ${{ secrets.GITHUB_TOKEN }}

@@ -46,7 +44,7 @@ jobs:
          git tag $RELEASE_TAG

      - name: Push changes
-        uses: ad-m/github-push-action@77c5b412c50b723d2a4fbc6d71fb5723bcd439aa  #master
+        uses: ad-m/github-push-action@master
        env:
          RELEASE_TAG: ${{ steps.tag_version.outputs.new_tag }}
        with:
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -1,6 +1,6 @@
 # This file is automatically @generated by Cargo.
 # It is not intended for manual editing.
-version = 4
+version = 3

 [[package]]
 name = "addr2line"
@@ -944,7 +944,7 @@ dependencies = [
 "bitflags 2.5.0",
 "cexpr",
 "clang-sys",
- "itertools 0.11.0",
+ "itertools 0.12.1",
 "lazy_static",
 "lazycell",
 "log",
@@ -1760,7 +1760,7 @@ checksum = "a650a461c6a8ff1ef205ed9a2ad56579309853fecefc2423f73dced342f92258"
 [[package]]
 name = "ecc"
 version = "0.1.0"
-source = "git+https://github.com/zkonduit/halo2wrong?branch=ac%2Fchunked-mv-lookup#b43ebe30e84825d0d004fa27803d99c4187d419f"
+source = "git+https://github.com/zkonduit/halo2wrong?branch=ac/chunked-mv-lookup#b43ebe30e84825d0d004fa27803d99c4187d419f"
 dependencies = [
 "integer",
 "num-bigint",
@@ -1835,16 +1835,6 @@ dependencies = [
 "syn 2.0.90",
 ]

-[[package]]
-name = "env_filter"
-version = "0.1.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "186e05a59d4c50738528153b83b0b0194d3a29507dfec16eccd4b342903397d0"
-dependencies = [
- "log",
- "regex",
-]
-
 [[package]]
 name = "env_logger"
 version = "0.10.2"
@@ -1858,19 +1848,6 @@ dependencies = [
 "termcolor",
 ]

-[[package]]
-name = "env_logger"
-version = "0.11.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "dcaee3d8e3cfc3fd92428d477bc97fc29ec8716d180c0d74c643bb26166660e0"
-dependencies = [
- "anstream",
- "anstyle",
- "env_filter",
- "humantime",
- "log",
-]
-
 [[package]]
 name = "equivalent"
 version = "1.0.1"
@@ -1946,7 +1923,7 @@ dependencies = [
 "console_error_panic_hook",
 "criterion 0.5.1",
 "ecc",
- "env_logger 0.10.2",
+ "env_logger",
 "ethabi",
 "foundry-compilers",
 "gag",
@@ -1954,7 +1931,7 @@ dependencies = [
 "halo2_gadgets",
 "halo2_proofs",
 "halo2_solidity_verifier",
- "halo2curves 0.7.0 (git+https://github.com/privacy-scaling-explorations/halo2curves?rev=b753a832e92d5c86c5c997327a9cf9de86a18851)",
+ "halo2curves 0.7.0",
 "hex",
 "indicatif",
 "instant",
@@ -1962,17 +1939,20 @@ dependencies = [
 "lazy_static",
 "log",
 "maybe-rayon",
+ "metal",
 "mimalloc",
 "mnist",
 "num",
 "objc",
 "openssl",
 "pg_bigdecimal",
+ "portable-atomic",
 "pyo3",
 "pyo3-async-runtimes",
 "pyo3-log",
 "pyo3-stub-gen",
 "rand 0.8.5",
+ "regex",
 "reqwest",
 "semver 1.0.22",
 "seq-macro",
@@ -2397,7 +2377,7 @@ dependencies = [
 [[package]]
 name = "halo2_gadgets"
 version = "0.2.0"
-source = "git+https://github.com/zkonduit/halo2#f441c920be45f8f05d2c06a173d82e8885a5ed4d"
+source = "git+https://github.com/zkonduit/halo2#6d72498928cdb69ce0de9f2230d2873ca2cf5324"
 dependencies = [
 "arrayvec 0.7.4",
 "bitvec",
@@ -2414,14 +2394,14 @@ dependencies = [
 [[package]]
 name = "halo2_proofs"
 version = "0.3.0"
-source = "git+https://github.com/zkonduit/halo2#f441c920be45f8f05d2c06a173d82e8885a5ed4d#f441c920be45f8f05d2c06a173d82e8885a5ed4d"
+source = "git+https://github.com/zkonduit/halo2#6d72498928cdb69ce0de9f2230d2873ca2cf5324#6d72498928cdb69ce0de9f2230d2873ca2cf5324"
 dependencies = [
 "bincode",
 "blake2b_simd",
- "env_logger 0.10.2",
+ "env_logger",
 "ff",
 "group",
- "halo2curves 0.7.0 (git+https://github.com/privacy-scaling-explorations/halo2curves?rev=b753a832e92d5c86c5c997327a9cf9de86a18851)",
+ "halo2curves 0.7.0",
 "icicle-bn254",
 "icicle-core",
 "icicle-cuda-runtime",
@@ -2429,7 +2409,6 @@ dependencies = [
 "lazy_static",
 "log",
 "maybe-rayon",
- "mopro-msm",
 "rand_chacha",
 "rand_core 0.6.4",
 "rustc-hash 2.0.0",
@@ -2515,36 +2494,6 @@ dependencies = [
 "subtle",
 ]

-[[package]]
-name = "halo2curves"
-version = "0.7.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d380afeef3f1d4d3245b76895172018cfb087d9976a7cabcd5597775b2933e07"
-dependencies = [
- "blake2",
- "digest 0.10.7",
- "ff",
- "group",
- "halo2derive 0.1.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "hex",
- "lazy_static",
- "num-bigint",
- "num-integer",
- "num-traits",
- "pairing",
- "pasta_curves",
- "paste",
- "rand 0.8.5",
- "rand_core 0.6.4",
- "rayon",
- "serde",
- "serde_arrays",
- "sha2",
- "static_assertions",
- "subtle",
- "unroll",
-]
-
 [[package]]
 name = "halo2curves"
 version = "0.7.0"
@@ -2554,7 +2503,7 @@ dependencies = [
 "digest 0.10.7",
 "ff",
 "group",
- "halo2derive 0.1.0 (git+https://github.com/privacy-scaling-explorations/halo2curves?rev=b753a832e92d5c86c5c997327a9cf9de86a18851)",
+ "halo2derive",
 "hex",
 "lazy_static",
 "num-bigint",
@@ -2574,20 +2523,6 @@ dependencies = [
 "unroll",
 ]

-[[package]]
-name = "halo2derive"
-version = "0.1.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bdb99e7492b4f5ff469d238db464131b86c2eaac814a78715acba369f64d2c76"
-dependencies = [
- "num-bigint",
- "num-integer",
- "num-traits",
- "proc-macro2",
- "quote",
- "syn 1.0.109",
-]
-
 [[package]]
 name = "halo2derive"
 version = "0.1.0"
@@ -2604,7 +2539,7 @@ dependencies = [
 [[package]]
 name = "halo2wrong"
 version = "0.1.0"
-source = "git+https://github.com/zkonduit/halo2wrong?branch=ac%2Fchunked-mv-lookup#b43ebe30e84825d0d004fa27803d99c4187d419f"
+source = "git+https://github.com/zkonduit/halo2wrong?branch=ac/chunked-mv-lookup#b43ebe30e84825d0d004fa27803d99c4187d419f"
 dependencies = [
 "halo2_proofs",
 "num-bigint",
@@ -2955,7 +2890,7 @@ dependencies = [
 [[package]]
 name = "integer"
 version = "0.1.0"
-source = "git+https://github.com/zkonduit/halo2wrong?branch=ac%2Fchunked-mv-lookup#b43ebe30e84825d0d004fa27803d99c4187d419f"
+source = "git+https://github.com/zkonduit/halo2wrong?branch=ac/chunked-mv-lookup#b43ebe30e84825d0d004fa27803d99c4187d419f"
 dependencies = [
 "maingate",
 "num-bigint",
@@ -3266,7 +3201,7 @@ dependencies = [
 [[package]]
 name = "maingate"
 version = "0.1.0"
-source = "git+https://github.com/zkonduit/halo2wrong?branch=ac%2Fchunked-mv-lookup#b43ebe30e84825d0d004fa27803d99c4187d419f"
+source = "git+https://github.com/zkonduit/halo2wrong?branch=ac/chunked-mv-lookup#b43ebe30e84825d0d004fa27803d99c4187d419f"
 dependencies = [
 "halo2wrong",
 "num-bigint",
@@ -3348,8 +3283,7 @@ dependencies = [
 [[package]]
 name = "metal"
 version = "0.29.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7ecfd3296f8c56b7c1f6fbac3c71cefa9d78ce009850c45000015f206dc7fa21"
+source = "git+https://github.com/gfx-rs/metal-rs#0e1918b34689c4b8cd13a43372f9898680547ee9"
 dependencies = [
 "bitflags 2.5.0",
 "block",
@@ -3420,28 +3354,6 @@ dependencies = [
 "byteorder",
 ]

-[[package]]
-name = "mopro-msm"
-version = "0.1.0"
-source = "git+https://github.com/zkonduit/metal-msm-gpu-acceleration.git#be5f647b1a6c1a6ea9024390744a2b4d87f5d002"
-dependencies = [
- "bincode",
- "env_logger 0.11.6",
- "halo2curves 0.7.0 (registry+https://github.com/rust-lang/crates.io-index)",
- "instant",
- "itertools 0.13.0",
- "lazy_static",
- "log",
- "metal",
- "objc",
- "once_cell",
- "rand 0.8.5",
- "rayon",
- "serde",
- "thiserror",
- "walkdir",
-]
-
 [[package]]
 name = "native-tls"
 version = "0.2.11"
@@ -3675,9 +3587,9 @@ checksum = "ff011a302c396a5197692431fc1948019154afc178baf7d8e37367442a4601cf"

 [[package]]
 name = "openssl-src"
-version = "300.4.1+3.4.0"
+version = "300.2.3+3.2.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "faa4eac4138c62414b5622d1b31c5c304f34b406b013c079c2bbc652fdd6678c"
+checksum = "5cff92b6f71555b61bb9315f7c64da3ca43d87531622120fea0195fc761b4843"
 dependencies = [
 "cc",
 ]
@@ -5230,7 +5142,7 @@ checksum = "b7c388c1b5e93756d0c740965c41e8822f866621d41acbdf6336a6a168f8840c"
 [[package]]
 name = "snark-verifier"
 version = "0.1.1"
-source = "git+https://github.com/zkonduit/snark-verifier?branch=ac%2Fchunked-mv-lookup#8762701ab8fa04e7d243a346030afd85633ec970"
+source = "git+https://github.com/zkonduit/snark-verifier?branch=ac/chunked-mv-lookup#8762701ab8fa04e7d243a346030afd85633ec970"
 dependencies = [
 "ecc",
 "halo2_proofs",
@@ -6234,7 +6146,7 @@ dependencies = [
 [[package]]
 name = "uniffi_testing"
 version = "0.28.0"
-source = "git+https://github.com/ElusAegis/uniffi-rs?branch=feat%2Ftesting-feature-build-fix#4684b9e7da2d9c964c2b3a73883947aab7370a06"
+source = "git+https://github.com/ElusAegis/uniffi-rs?branch=feat/testing-feature-build-fix#4684b9e7da2d9c964c2b3a73883947aab7370a06"
 dependencies = [
 "anyhow",
 "camino",
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -3,7 +3,7 @@ cargo-features = ["profile-rustflags"]
 [package]
 name = "ezkl"
 version = "0.0.0"
-edition = "2024"
+edition = "2021"
 default-run = "ezkl"

 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
@@ -40,6 +40,7 @@ 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 }

@@ -73,6 +74,7 @@ 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",
@@ -89,6 +91,7 @@ pyo3-async-runtimes = { git = "https://github.com/PyO3/pyo3-async-runtimes", ver
 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 }
@@ -242,14 +245,16 @@ ezkl = [
    "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",
@@ -272,15 +277,13 @@ icicle = ["halo2_proofs/icicle_gpu"]
 empty-cmd = []
 no-banner = []
 no-update = []
-macos-metal = ["halo2_proofs/macos"]
-ios-metal = ["halo2_proofs/ios"]
+

 [patch.'https://github.com/zkonduit/halo2']
-halo2_proofs = { git = "https://github.com/zkonduit/halo2#f441c920be45f8f05d2c06a173d82e8885a5ed4d", package = "halo2_proofs" }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2#6d72498928cdb69ce0de9f2230d2873ca2cf5324", package = "halo2_proofs" }

 [patch.'https://github.com/zkonduit/halo2#0654e92bdf725fd44d849bfef3643870a8c7d50b']
-halo2_proofs = { git = "https://github.com/zkonduit/halo2#f441c920be45f8f05d2c06a173d82e8885a5ed4d", package = "halo2_proofs" }
-
+halo2_proofs = { git = "https://github.com/zkonduit/halo2#6d72498928cdb69ce0de9f2230d2873ca2cf5324", package = "halo2_proofs" }

 [patch.crates-io]
 uniffi_testing = { git = "https://github.com/ElusAegis/uniffi-rs", branch = "feat/testing-feature-build-fix" }
@@ -289,13 +292,9 @@ uniffi_testing = { git = "https://github.com/ElusAegis/uniffi-rs", branch = "fea
 rustflags = ["-C", "relocation-model=pic"]
 lto = "fat"
 codegen-units = 1
-#panic = "abort"
+# panic = "abort"


-[profile.test-runs]
-inherits = "dev"
-opt-level = 3
-
 [package.metadata.wasm-pack.profile.release]
 wasm-opt = [
    "-O4",
--- a/README.md
+++ b/README.md
@@ -150,13 +150,6 @@ Ezkl is unaudited, beta software undergoing rapid development. There may be bugs

 > 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. 

-
-### Advanced security topics 
-
-Check out `docs/advanced_security` for more advanced information on potential threat vectors. 
-
-
-
 ### no warranty

 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,
--- a/benches/accum_conv.rs
+++ b/benches/accum_conv.rs
@@ -73,8 +73,6 @@ impl Circuit<Fr> for MyCircuit {
                            padding: vec![(0, 0)],
                            stride: vec![1; 2],
                            group: 1,
-                            data_format: DataFormat::NCHW,
-                            kernel_format: KernelFormat::OIHW,
                        }),
                    )
                    .unwrap();
--- a/benches/accum_sumpool.rs
+++ b/benches/accum_sumpool.rs
@@ -69,7 +69,6 @@ impl Circuit<Fr> for MyCircuit {
                            stride: vec![1, 1],
                            kernel_shape: vec![2, 2],
                            normalized: false,
-                            data_format: DataFormat::NCHW,
                        }),
                    )
                    .unwrap();
--- a/benches/poseidon.rs
+++ b/benches/poseidon.rs
@@ -23,6 +23,8 @@ 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>,
@@ -38,7 +40,7 @@ impl Circuit<Fr> for MyCircuit {
    }

    fn configure(cs: &mut ConstraintSystem<Fr>) -> Self::Config {
-        PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::configure(cs, ())
+        PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, 10>::configure(cs, ())
    }

    fn synthesize(
@@ -46,7 +48,7 @@ impl Circuit<Fr> for MyCircuit {
        config: Self::Config,
        mut layouter: impl Layouter<Fr>,
    ) -> Result<(), Error> {
-        let chip: PoseidonChip<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE> =
+        let chip: PoseidonChip<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, L> =
            PoseidonChip::new(config);
        chip.layout(&mut layouter, &[self.image.clone()], 0, &mut HashMap::new())?;
        Ok(())
@@ -57,7 +59,7 @@ 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>::num_rows(*size)
+        let k = (PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, L>::num_rows(*size)
            as f32)
            .log2()
            .ceil() as u32;
@@ -65,7 +67,7 @@ fn runposeidon(c: &mut Criterion) {

        let message = (0..*size).map(|_| Fr::random(OsRng)).collect::<Vec<_>>();
        let _output =
-            PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::run(message.to_vec())
+            PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, L>::run(message.to_vec())
                .unwrap();

        let mut image = Tensor::from(message.into_iter().map(Value::known));
--- a/benches/zero_finder.rs
+++ b/benches/zero_finder.rs
@@ -10,7 +10,6 @@ use rand::Rng;

 // Assuming these are your types
 #[derive(Clone)]
-#[allow(dead_code)]
 enum ValType {
    Constant(F),
    AssignedConstant(usize, F),
@@ -22,7 +21,7 @@ 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 {
+            if rng.gen::<f64>() < zero_probability {
                ValType::Constant(F::ZERO)
            } else {
                ValType::Constant(F::ONE) // Or some other non-zero value
--- a/docs/advanced_security/public_commitments.md
+++ b/docs/advanced_security/public_commitments.md
@@ -1,41 +0,0 @@
-## 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.
-
-
-
--- a/docs/advanced_security/quantization_backdoors.md
+++ b/docs/advanced_security/quantization_backdoors.md
@@ -1,54 +0,0 @@
-# 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)
--- a/examples/conv2d_mnist/main.rs
+++ b/examples/conv2d_mnist/main.rs
@@ -32,7 +32,6 @@ use mnist::*;
 use rand::rngs::OsRng;
 use std::marker::PhantomData;

-
 mod params;

 const K: usize = 20;
@@ -209,8 +208,6 @@ 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
--- a/examples/notebooks/cat_and_dog.ipynb
+++ b/examples/notebooks/cat_and_dog.ipynb
--- a/examples/notebooks/cat_and_dog_data.sh
+++ b/examples/notebooks/cat_and_dog_data.sh
@@ -1,13 +0,0 @@
-# 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
--- a/examples/notebooks/felt_conversion_test.ipynb
+++ b/examples/notebooks/felt_conversion_test.ipynb
@@ -2,7 +2,7 @@
 "cells": [
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
@@ -54,7 +54,7 @@
    "    gip_run_args.param_scale = 19\n",
    "    gip_run_args.logrows = 8\n",
    "    run_args = ezkl.gen_settings(py_run_args=gip_run_args)\n",
-    "    await ezkl.get_srs(commitment=ezkl.PyCommitments.KZG)\n",
+    "    ezkl.get_srs(commitment=ezkl.PyCommitments.KZG)\n",
    "    ezkl.compile_circuit()\n",
    "    res = await ezkl.gen_witness()\n",
    "    print(res)\n",
--- a/examples/notebooks/generalized_inverse.ipynb
+++ b/examples/notebooks/generalized_inverse.ipynb
@@ -77,7 +77,6 @@
      "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)"
@@ -336,9 +335,9 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.9.15"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
--- a/examples/notebooks/linear_regression.ipynb
+++ b/examples/notebooks/linear_regression.ipynb
@@ -1,284 +1,279 @@
 {
- "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
-}
+    "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
+}
--- a/examples/notebooks/mean_postgres.ipynb
+++ b/examples/notebooks/mean_postgres.ipynb
@@ -1,462 +1,456 @@
 {
- "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/"
+  "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"
+      ]
    },
-    "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/"
+    {
+      "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"
+      ]
    },
-    "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",
-    "run_args = ezkl.PyRunArgs()\n",
-    "run_args.decomp_legs = 4\n",
-    "\n",
-    "# Generate settings using ezkl\n",
-    "res = ezkl.gen_settings(onnx_filename, settings_filename, py_run_args=run_args)\n",
-    "\n",
-    "assert res == True\n",
-    "\n",
-    "res = await ezkl.calibrate_settings(input_filename, onnx_filename, settings_filename, \"resources\")\n",
-    "\n",
-    "assert res == True\n",
-    "\n",
-    "await ezkl.get_srs(settings_filename)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "ezkl.compile_circuit(onnx_filename, compiled_filename, settings_filename)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2wIAHwqH2_mo"
+      },
+      "source": [
+        "**Import Dependencies**"
+      ]
    },
-    "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/"
+    {
+      "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__)"
+      ]
    },
-    "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": []
+    {
+      "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"
+    }
  },
-  "kernelspec": {
-   "display_name": ".env",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
--- a/examples/notebooks/mnist_gan_proof_splitting.ipynb
+++ b/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=26, split_proofs = True)"
+                "ezkl.mock_aggregate(proofs, logrows=23, split_proofs = True)"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
-            "display_name": ".env",
+            "display_name": "ezkl",
            "language": "python",
            "name": "python3"
        },
@@ -478,7 +478,7 @@
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
-            "version": "3.12.7"
+            "version": "3.12.5"
        },
        "orig_nbformat": 4
    },
--- a/examples/notebooks/random_forest.ipynb
+++ b/examples/notebooks/random_forest.ipynb
@@ -152,11 +152,9 @@
            "metadata": {},
            "outputs": [],
            "source": [
-                "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",
+                "!RUST_LOG=trace\n",
+                "# TODO: Dictionary outputs\n",
+                "res = ezkl.gen_settings(model_path, settings_path)\n",
                "assert res == True\n"
            ]
        },
@@ -304,7 +302,7 @@
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
-            "version": "3.9.13"
+            "version": "3.12.2"
        }
    },
    "nbformat": 4,
--- a/examples/notebooks/set_membership.ipynb
+++ b/examples/notebooks/set_membership.ipynb
@@ -167,8 +167,6 @@
        "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",
@@ -521,4 +519,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
--- a/examples/notebooks/solvency.ipynb
+++ b/examples/notebooks/solvency.ipynb
@@ -204,7 +204,6 @@
        "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",
@@ -515,4 +514,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
--- a/examples/notebooks/univ3-da.ipynb
+++ b/examples/notebooks/univ3-da.ipynb
@@ -20,7 +20,7 @@
        },
        {
            "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": [
@@ -60,7 +60,7 @@
        },
        {
            "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": [
@@ -94,7 +94,7 @@
        },
        {
            "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": [
@@ -134,7 +134,7 @@
        },
        {
            "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 44,
            "metadata": {},
            "outputs": [],
            "source": [
@@ -183,7 +183,7 @@
        },
        {
            "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 6,
            "metadata": {},
            "outputs": [],
            "source": [
@@ -201,7 +201,6 @@
                "run_args.input_visibility = \"public\"\n",
                "run_args.param_visibility = \"private\"\n",
                "run_args.output_visibility = \"public\"\n",
-                "run_args.decomp_legs=6\n",
                "run_args.num_inner_cols = 1\n",
                "run_args.variables = [(\"batch_size\", 1)]"
            ]
--- a/examples/onnx/1d_conv/input.json
+++ b/examples/onnx/1d_conv/input.json
@@ -1,106 +0,0 @@
-{
-  "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
-    ]
-  ]
-}
--- a/examples/onnx/1d_conv/network.onnx
+++ b/examples/onnx/1d_conv/network.onnx
--- a/examples/onnx/1l_div/settings.json
+++ b/examples/onnx/1l_div/settings.json
@@ -1 +0,0 @@
-{"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"]}
--- a/examples/onnx/fr_age/input.json
+++ b/examples/onnx/fr_age/input.json
--- a/examples/onnx/fr_age/network.onnx
+++ b/examples/onnx/fr_age/network.onnx
--- a/examples/onnx/hierarchical_risk/input.json
+++ b/examples/onnx/hierarchical_risk/input.json
--- a/examples/onnx/hierarchical_risk/network.onnx
+++ b/examples/onnx/hierarchical_risk/network.onnx
--- a/examples/onnx/integer_div/gen.py
+++ b/examples/onnx/integer_div/gen.py
@@ -1,42 +0,0 @@
-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'))
--- a/examples/onnx/integer_div/input.json
+++ b/examples/onnx/integer_div/input.json
@@ -1 +0,0 @@
-{"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]]}
--- a/examples/onnx/integer_div/network.onnx
+++ b/examples/onnx/integer_div/network.onnx
--- a/2
+++ b/2
@@ -1,3 +1,3 @@
 [toolchain]
-channel = "nightly-2025-02-17"
+channel = "nightly-2024-07-18"
 components = ["rustfmt", "clippy"]
--- a/src/bin/ezkl.rs
+++ b/src/bin/ezkl.rs
@@ -1,11 +1,7 @@
 // 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;
+static GLOBAL: mimalloc::MiMalloc = mimalloc::MiMalloc;

 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use clap::{CommandFactory, Parser};
@@ -28,8 +24,6 @@ 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 {
@@ -44,7 +38,7 @@ pub async fn main() {
        } else {
            info!("Running with CPU");
        }
-        debug!(
+        info!(
            "command: \n {}",
            &command.as_json().to_colored_json_auto().unwrap()
        );
--- a/src/bindings/python.rs
+++ b/src/bindings/python.rs
@@ -4,10 +4,11 @@ use crate::circuit::modules::poseidon::{
    PoseidonChip,
 };
 use crate::circuit::modules::Module;
-use crate::circuit::CheckMode;
 use crate::circuit::InputType;
+use crate::circuit::{CheckMode, Tolerance};
 use crate::commands::*;
 use crate::fieldutils::{felt_to_integer_rep, integer_rep_to_felt, IntegerRep};
+use crate::graph::modules::POSEIDON_LEN_GRAPH;
 use crate::graph::TestDataSource;
 use crate::graph::{
    quantize_float, scale_to_multiplier, GraphCircuit, GraphSettings, Model, Visibility,
@@ -155,6 +156,9 @@ impl pyo3::ToPyObject for PyG1Affine {
 #[derive(Clone)]
 #[gen_stub_pyclass]
 struct PyRunArgs {
+    #[pyo3(get, set)]
+    /// float: The tolerance for error on model outputs
+    pub tolerance: f32,
    #[pyo3(get, set)]
    /// int: The denominator in the fixed point representation used when quantizing inputs
    pub input_scale: crate::Scale,
@@ -203,9 +207,6 @@ struct PyRunArgs {
    /// bool: Should the circuit use unbounded lookups for log
    #[pyo3(get, set)]
    pub bounded_log_lookup: bool,
-    /// bool: Should the circuit use range checks for inputs and outputs (set to false if the input is a felt)
-    #[pyo3(get, set)]
-    pub ignore_range_check_inputs_outputs: bool,
 }

 /// default instantiation of PyRunArgs
@@ -222,6 +223,7 @@ impl From<PyRunArgs> for RunArgs {
    fn from(py_run_args: PyRunArgs) -> Self {
        RunArgs {
            bounded_log_lookup: py_run_args.bounded_log_lookup,
+            tolerance: Tolerance::from(py_run_args.tolerance),
            input_scale: py_run_args.input_scale,
            param_scale: py_run_args.param_scale,
            num_inner_cols: py_run_args.num_inner_cols,
@@ -237,7 +239,6 @@ impl From<PyRunArgs> for RunArgs {
            commitment: Some(py_run_args.commitment.into()),
            decomp_base: py_run_args.decomp_base,
            decomp_legs: py_run_args.decomp_legs,
-            ignore_range_check_inputs_outputs: py_run_args.ignore_range_check_inputs_outputs,
        }
    }
 }
@@ -246,6 +247,7 @@ impl Into<PyRunArgs> for RunArgs {
    fn into(self) -> PyRunArgs {
        PyRunArgs {
            bounded_log_lookup: self.bounded_log_lookup,
+            tolerance: self.tolerance.val,
            input_scale: self.input_scale,
            param_scale: self.param_scale,
            num_inner_cols: self.num_inner_cols,
@@ -261,7 +263,6 @@ impl Into<PyRunArgs> for RunArgs {
            commitment: self.commitment.into(),
            decomp_base: self.decomp_base,
            decomp_legs: self.decomp_legs,
-            ignore_range_check_inputs_outputs: self.ignore_range_check_inputs_outputs,
        }
    }
 }
@@ -332,8 +333,6 @@ enum PyInputType {
    Int,
    ///
    TDim,
-    ///
-    Unknown,
 }

 impl From<InputType> for PyInputType {
@@ -345,7 +344,6 @@ impl From<InputType> for PyInputType {
            InputType::F64 => PyInputType::F64,
            InputType::Int => PyInputType::Int,
            InputType::TDim => PyInputType::TDim,
-            InputType::Unknown => PyInputType::Unknown,
        }
    }
 }
@@ -359,7 +357,6 @@ impl From<PyInputType> for InputType {
            PyInputType::F64 => InputType::F64,
            PyInputType::Int => InputType::Int,
            PyInputType::TDim => InputType::TDim,
-            PyInputType::Unknown => InputType::Unknown,
        }
    }
 }
@@ -374,7 +371,6 @@ impl FromStr for PyInputType {
            "f64" => Ok(PyInputType::F64),
            "int" => Ok(PyInputType::Int),
            "tdim" => Ok(PyInputType::TDim),
-            "unknown" => Ok(PyInputType::Unknown),
            _ => Err("Invalid value for InputType".to_string()),
        }
    }
@@ -577,7 +573,10 @@ fn poseidon_hash(message: Vec<PyFelt>) -> PyResult<Vec<PyFelt>> {
        .map(crate::pfsys::string_to_field::<Fr>)
        .collect::<Vec<_>>();

-    let output = PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::run(message.clone())
+    let output =
+        PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, POSEIDON_LEN_GRAPH>::run(
+            message.clone(),
+        )
        .map_err(|_| PyIOError::new_err("Failed to run poseidon"))?;

    let hash = output[0]
@@ -592,7 +591,7 @@ fn poseidon_hash(message: Vec<PyFelt>) -> PyResult<Vec<PyFelt>> {
 /// Arguments
 /// -------
 /// message: list[str]
-///     List of field elements represented as strings
+///     List of field elements represnted as strings
 ///
 /// vk_path: str
 ///     Path to the verification key
@@ -651,7 +650,7 @@ fn kzg_commit(
 /// Arguments
 /// -------
 /// message: list[str]
-///     List of field elements represented as strings
+///     List of field elements represnted as strings
 ///
 /// vk_path: str
 ///     Path to the verification key
@@ -939,45 +938,6 @@ fn gen_settings(
    Ok(true)
 }

-/// Generates random data for the model
-///
-/// Arguments
-/// ---------
-/// model: str
-///     Path to the onnx file
-///
-/// output: str
-///     Path to create the data file
-///
-/// seed: int
-///     Random seed to use for generated data
-///
-/// variables
-/// Returns
-/// -------
-/// bool
-///
-#[pyfunction(signature = (
-    model=PathBuf::from(DEFAULT_MODEL),
-    output=PathBuf::from(DEFAULT_SETTINGS),
-    variables=Vec::from([("batch_size".to_string(), 1)]),
-    seed=DEFAULT_SEED.parse().unwrap(),
-))]
-#[gen_stub_pyfunction]
-fn gen_random_data(
-    model: PathBuf,
-    output: PathBuf,
-    variables: Vec<(String, usize)>,
-    seed: u64,
-) -> Result<bool, PyErr> {
-    crate::execute::gen_random_data(model, output, variables, seed).map_err(|e| {
-        let err_str = format!("Failed to generate settings: {}", e);
-        PyRuntimeError::new_err(err_str)
-    })?;
-
-    Ok(true)
-}
-
 /// Calibrates the circuit settings
 ///
 /// Arguments
@@ -1009,7 +969,7 @@ fn gen_random_data(
 /// bool
 ///
 #[pyfunction(signature = (
-    data = String::from(DEFAULT_CALIBRATION_FILE),
+    data = PathBuf::from(DEFAULT_CALIBRATION_FILE),
    model = PathBuf::from(DEFAULT_MODEL),
    settings = PathBuf::from(DEFAULT_SETTINGS),
    target = CalibrationTarget::default(), // default is "resources
@@ -1021,7 +981,7 @@ fn gen_random_data(
 #[gen_stub_pyfunction]
 fn calibrate_settings(
    py: Python,
-    data: String,
+    data: PathBuf,
    model: PathBuf,
    settings: PathBuf,
    target: CalibrationTarget,
@@ -1076,7 +1036,7 @@ fn calibrate_settings(
 ///     Python object containing the witness values
 ///
 #[pyfunction(signature = (
-    data=String::from(DEFAULT_DATA),
+    data=PathBuf::from(DEFAULT_DATA),
    model=PathBuf::from(DEFAULT_COMPILED_CIRCUIT),
    output=PathBuf::from(DEFAULT_WITNESS),
    vk_path=None,
@@ -1085,7 +1045,7 @@ fn calibrate_settings(
 #[gen_stub_pyfunction]
 fn gen_witness(
    py: Python,
-    data: String,
+    data: PathBuf,
    model: PathBuf,
    output: Option<PathBuf>,
    vk_path: Option<PathBuf>,
@@ -1754,7 +1714,7 @@ fn create_evm_vka(
 /// bool
 ///
 #[pyfunction(signature = (
-    input_data=String::from(DEFAULT_DATA),
+    input_data=PathBuf::from(DEFAULT_DATA),
    settings_path=PathBuf::from(DEFAULT_SETTINGS),
    sol_code_path=PathBuf::from(DEFAULT_SOL_CODE_DA),
    abi_path=PathBuf::from(DEFAULT_VERIFIER_DA_ABI),
@@ -1763,7 +1723,7 @@ fn create_evm_vka(
 #[gen_stub_pyfunction]
 fn create_evm_data_attestation(
    py: Python,
-    input_data: String,
+    input_data: PathBuf,
    settings_path: PathBuf,
    sol_code_path: PathBuf,
    abi_path: PathBuf,
@@ -1824,7 +1784,7 @@ fn create_evm_data_attestation(
 #[gen_stub_pyfunction]
 fn setup_test_evm_witness(
    py: Python,
-    data_path: String,
+    data_path: PathBuf,
    compiled_circuit_path: PathBuf,
    test_data: PathBuf,
    input_source: PyTestDataSource,
@@ -1902,7 +1862,7 @@ fn deploy_evm(
 fn deploy_da_evm(
    py: Python,
    addr_path: PathBuf,
-    input_data: String,
+    input_data: PathBuf,
    settings_path: PathBuf,
    sol_code_path: PathBuf,
    rpc_url: Option<String>,
@@ -1945,7 +1905,7 @@ fn deploy_da_evm(
 ///     does the verifier use data attestation ?
 ///
 /// addr_vk: str
-///     The address of the separate VK contract (if the verifier key is rendered as a separate contract)
+///     The addess of the separate VK contract (if the verifier key is rendered as a separate contract)
 /// Returns
 /// -------
 /// bool
@@ -2095,7 +2055,6 @@ fn ezkl(m: &Bound<'_, PyModule>) -> PyResult<()> {
    m.add_function(wrap_pyfunction!(get_srs, m)?)?;
    m.add_function(wrap_pyfunction!(gen_witness, m)?)?;
    m.add_function(wrap_pyfunction!(gen_settings, m)?)?;
-    m.add_function(wrap_pyfunction!(gen_random_data, m)?)?;
    m.add_function(wrap_pyfunction!(calibrate_settings, m)?)?;
    m.add_function(wrap_pyfunction!(aggregate, m)?)?;
    m.add_function(wrap_pyfunction!(mock_aggregate, m)?)?;
--- a/src/bindings/wasm.rs
+++ b/src/bindings/wasm.rs
@@ -8,7 +8,10 @@ use crate::{
        Module,
    },
    fieldutils::{felt_to_integer_rep, integer_rep_to_felt},
-    graph::{quantize_float, scale_to_multiplier, GraphCircuit, GraphSettings},
+    graph::{
+        modules::POSEIDON_LEN_GRAPH, quantize_float, scale_to_multiplier, GraphCircuit,
+        GraphSettings,
+    },
 };
 use console_error_panic_hook;
 use halo2_proofs::{
@@ -228,7 +231,10 @@ 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>::run(message.clone())
+    let output =
+        PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, POSEIDON_LEN_GRAPH>::run(
+            message.clone(),
+        )
        .map_err(|e| JsError::new(&format!("{}", e)))?;

    Ok(wasm_bindgen::Clamped(serde_json::to_vec(&output).map_err(
--- a/src/circuit/modules/polycommit.rs
+++ b/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/zk_prover/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/src/chips/poseidon/hash.rs for the inspiration (and also helping us understand how to use this).
 */

 use std::collections::HashMap;
--- a/src/circuit/modules/poseidon.rs
+++ b/src/circuit/modules/poseidon.rs
@@ -1,18 +1,20 @@
 /*
 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/zk_prover/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/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::VariableLength, primitives::*, Hash, Pow5Chip, Pow5Config,
-};
+use halo2_gadgets::poseidon::{primitives::*, Hash, Pow5Chip, Pow5Config};
+use halo2_proofs::arithmetic::Field;
 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;

@@ -38,17 +40,22 @@ pub struct PoseidonConfig<const WIDTH: usize, const RATE: usize> {
    pub pow5_config: Pow5Config<Fp, WIDTH, RATE>,
 }

-type InputAssignments = Vec<AssignedCell<Fp, Fp>>;
+type InputAssignments = (Vec<AssignedCell<Fp, Fp>>, 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> {
+pub struct PoseidonChip<
+    S: Spec<Fp, WIDTH, RATE> + Sync,
+    const WIDTH: usize,
+    const RATE: usize,
+    const L: usize,
+> {
    config: PoseidonConfig<WIDTH, RATE>,
    _marker: PhantomData<S>,
 }

-impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize>
-    PoseidonChip<S, WIDTH, RATE>
+impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, const L: usize>
+    PoseidonChip<S, WIDTH, RATE, L>
 {
    /// Creates a new PoseidonChip
    pub fn configure_with_cols(
@@ -75,8 +82,8 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize>
    }
 }

-impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize>
-    PoseidonChip<S, WIDTH, RATE>
+impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, const L: usize>
+    PoseidonChip<S, WIDTH, RATE, L>
 {
    /// Configuration of the PoseidonChip
    pub fn configure_with_optional_instance(
@@ -93,6 +100,9 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize>
        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(
@@ -106,8 +116,8 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize>
    }
 }

-impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize> Module<Fp>
-    for PoseidonChip<S, WIDTH, RATE>
+impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, const L: usize>
+    Module<Fp> for PoseidonChip<S, WIDTH, RATE, L>
 {
    type Config = PoseidonConfig<WIDTH, RATE>;
    type InputAssignments = InputAssignments;
@@ -142,6 +152,9 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize> Mod
        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();
@@ -163,10 +176,7 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize> Mod
        message: &[ValTensor<Fp>],
        constants: &mut ConstantsMap<Fp>,
    ) -> Result<Self::InputAssignments, ModuleError> {
-        if message.len() != 1 {
-            return Err(ModuleError::InputWrongLength(message.len()));
-        }
-
+        assert_eq!(message.len(), 1);
        let message = message[0].clone();

        let start_time = instant::Instant::now();
@@ -176,81 +186,95 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize> Mod
        let res = layouter.assign_region(
            || "load message",
            |mut region| {
-                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;
+                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;

-                            match value {
-                                ValType::Value(v) => region
-                                    .assign_advice(
-                                        || format!("load message_{}", i),
+                                    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,
                                        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,
-                                        )?;
+                                })
+                                .collect::<Result<Vec<_>, _>>()
+                                .map_err(|e| e.into())
+                        }
+                    };

-                                        constants
-                                            .insert(*f, ValType::AssignedConstant(res.clone(), *f));
+                let offset = message.len() / WIDTH + 1;

-                                        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())
-                    }
-                };
+                let zero_val = region
+                    .assign_advice_from_constant(
+                        || "",
+                        self.config.hash_inputs[0],
+                        offset,
+                        Fp::ZERO,
+                    )
+                    .unwrap();

-                Ok(assigned_message?)
+                Ok((assigned_message?, zero_val))
            },
        );
        log::trace!(
@@ -271,13 +295,7 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize> Mod
        row_offset: usize,
        constants: &mut ConstantsMap<Fp>,
    ) -> Result<ValTensor<Fp>, ModuleError> {
-        let input_cells = self.layout_inputs(layouter, input, constants)?;
-
-        // empty hash case
-        if input_cells.is_empty() {
-            return Ok(input[0].clone());
-        }
-
+        let (mut input_cells, zero_val) = self.layout_inputs(layouter, input, constants)?;
        // extract the values from the input cells
        let mut assigned_input: Tensor<ValType<Fp>> =
            input_cells.iter().map(|e| ValType::from(e.clone())).into();
@@ -285,25 +303,52 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize> Mod

        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 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 hash: AssignedCell<Fp, Fp> = hasher.hash(
-            layouter.namespace(|| "hash"),
-            input_cells
-                .to_vec()
-                .try_into()
-                .map_err(|_| Error::Synthesis)?,
-        )?;
+                    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 duration = start_time.elapsed();
        log::trace!("layout (N={:?}) took: {:?}", len, duration);

-        let result = Tensor::from(vec![ValType::from(hash.clone())].into_iter());
+        let result = Tensor::from(input_cells.iter().map(|e| ValType::from(e.clone())));

        let output = match result[0].clone() {
            ValType::PrevAssigned(v) => v,
@@ -342,59 +387,69 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize> Mod

    ///
    fn run(message: Vec<Fp>) -> Result<Vec<Vec<Fp>>, ModuleError> {
-        let len = message.len();
-        if len == 0 {
-            return Ok(vec![vec![]]);
-        }
+        let mut hash_inputs = message;
+
+        let len = hash_inputs.len();

        let start_time = instant::Instant::now();

-        let hash = halo2_gadgets::poseidon::primitives::Hash::<
-            _,
-            S,
-            VariableLength,
-            { WIDTH },
-            { RATE },
-        >::init()
-        .hash(message);
+        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 duration = start_time.elapsed();
        log::trace!("run (N={:?}) took: {:?}", len, duration);

-        Ok(vec![vec![hash]])
+        Ok(vec![hash_inputs])
    }

-    fn num_rows(input_len: usize) -> usize {
+    fn num_rows(mut 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
-        // import numpy as np
-        // from scipy import stats
+        let fixed_cost: usize = 41 * L;

-        // x = np.array([32, 64, 96, 128, 160, 192])
-        // y = np.array([1298, 2594, 3890, 5186, 6482, 7778])
+        let mut num_rows = 0;

-        // slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
+        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;
+        }

-        // 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
+        num_rows
    }
 }

@@ -421,12 +476,12 @@ mod tests {
    const RATE: usize = POSEIDON_RATE;
    const R: usize = 240;

-    struct HashCircuit<S: Spec<Fp, WIDTH, RATE>> {
+    struct HashCircuit<S: Spec<Fp, WIDTH, RATE>, const L: usize> {
        message: ValTensor<Fp>,
        _spec: PhantomData<S>,
    }

-    impl<S: Spec<Fp, WIDTH, RATE>> Circuit<Fp> for HashCircuit<S> {
+    impl<S: Spec<Fp, WIDTH, RATE>, const L: usize> Circuit<Fp> for HashCircuit<S, L> {
        type Config = PoseidonConfig<WIDTH, RATE>;
        type FloorPlanner = ModulePlanner;
        type Params = ();
@@ -442,7 +497,7 @@ mod tests {
        }

        fn configure(meta: &mut ConstraintSystem<Fp>) -> PoseidonConfig<WIDTH, RATE> {
-            PoseidonChip::<PoseidonSpec, WIDTH, RATE>::configure(meta, ())
+            PoseidonChip::<PoseidonSpec, WIDTH, RATE, L>::configure(meta, ())
        }

        fn synthesize(
@@ -450,7 +505,7 @@ mod tests {
            config: PoseidonConfig<WIDTH, RATE>,
            mut layouter: impl Layouter<Fp>,
        ) -> Result<(), Error> {
-            let chip: PoseidonChip<PoseidonSpec, WIDTH, RATE> = PoseidonChip::new(config);
+            let chip: PoseidonChip<PoseidonSpec, WIDTH, RATE, L> = PoseidonChip::new(config);
            chip.layout(
                &mut layouter,
                &[self.message.clone()],
@@ -462,33 +517,18 @@ 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>::run(message.to_vec()).unwrap();
+        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE, 2>::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> {
+        let circuit = HashCircuit::<PoseidonSpec, 2> {
            message: message.into(),
            _spec: PhantomData,
        };
@@ -501,13 +541,13 @@ mod tests {
        let rng = rand::rngs::OsRng;

        let message = [Fp::random(rng), Fp::random(rng), Fp::random(rng)];
-        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.to_vec()).unwrap();
+        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE, 3>::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> {
+        let circuit = HashCircuit::<PoseidonSpec, 3> {
            message: message.into(),
            _spec: PhantomData,
        };
@@ -523,21 +563,23 @@ mod tests {
        #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
        env_logger::init();

-        for i in (32..128).step_by(32) {
+        {
+            let i = 32;
            // print a bunch of new lines
-            log::info!(
+            println!(
                "i is {} -------------------------------------------------",
                i
            );

            let message: Vec<Fp> = (0..i).map(|_| Fp::random(rng)).collect::<Vec<_>>();
-            let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.clone()).unwrap();
+            let output =
+                PoseidonChip::<PoseidonSpec, WIDTH, RATE, 32>::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> {
+            let circuit = HashCircuit::<PoseidonSpec, 32> {
                message: message.into(),
                _spec: PhantomData,
            };
@@ -554,13 +596,13 @@ mod tests {

        let mut message: Vec<Fp> = (0..2048).map(|_| Fp::random(rng)).collect::<Vec<_>>();

-        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.clone()).unwrap();
+        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE, 25>::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> {
+        let circuit = HashCircuit::<PoseidonSpec, 25> {
            message: message.into(),
            _spec: PhantomData,
        };
--- a/src/circuit/ops/base.rs
+++ b/src/circuit/ops/base.rs
@@ -17,14 +17,12 @@ pub enum BaseOp {
    Sub,
    SumInit,
    Sum,
+    IsBoolean,
 }

 /// Matches a [BaseOp] to an operation over inputs
 impl BaseOp {
-    /// forward func for non-accumulating operations
-    /// # Panics
-    /// Panics if called on an accumulating operation
-    /// # Examples
+    /// forward func
    pub fn nonaccum_f<
        T: TensorType + Add<Output = T> + Sub<Output = T> + Mul<Output = T> + Neg<Output = T>,
    >(
@@ -36,13 +34,12 @@ 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 for accumulating operations
-    /// # Panics
-    /// Panics if called on a non-accumulating operation
+    /// forward func
    pub fn accum_f<
        T: TensorType + Add<Output = T> + Sub<Output = T> + Mul<Output = T> + Neg<Output = T>,
    >(
@@ -77,6 +74,7 @@ impl BaseOp {
            BaseOp::Mult => "MULT",
            BaseOp::Sum => "SUM",
            BaseOp::SumInit => "SUMINIT",
+            BaseOp::IsBoolean => "ISBOOLEAN",
        }
    }

@@ -92,6 +90,7 @@ impl BaseOp {
            BaseOp::Mult => (0, 1),
            BaseOp::Sum => (-1, 2),
            BaseOp::SumInit => (0, 1),
+            BaseOp::IsBoolean => (0, 1),
        }
    }

@@ -107,6 +106,7 @@ impl BaseOp {
            BaseOp::Mult => 2,
            BaseOp::Sum => 1,
            BaseOp::SumInit => 1,
+            BaseOp::IsBoolean => 0,
        }
    }

@@ -122,6 +122,7 @@ impl BaseOp {
            BaseOp::SumInit => 0,
            BaseOp::CumProd => 1,
            BaseOp::CumProdInit => 0,
+            BaseOp::IsBoolean => 0,
        }
    }
 }
--- a/src/circuit/ops/chip.rs
+++ b/src/circuit/ops/chip.rs
@@ -2,7 +2,7 @@ use std::str::FromStr;

 use halo2_proofs::{
    circuit::Layouter,
-    plonk::{ConstraintSystem, Constraints, Expression, Selector, TableColumn},
+    plonk::{ConstraintSystem, Constraints, Expression, Selector},
    poly::Rotation,
 };
 use log::debug;
@@ -20,6 +20,7 @@ use crate::{
    circuit::{
        ops::base::BaseOp,
        table::{Range, RangeCheck, Table},
+        utils,
    },
    tensor::{Tensor, TensorType, ValTensor, VarTensor},
 };
@@ -74,12 +75,51 @@ impl FromStr for CheckMode {
    }
 }

-impl CheckMode {
-    /// Returns the value of the check mode
-    pub fn is_safe(&self) -> bool {
-        match self {
-            CheckMode::SAFE => true,
-            CheckMode::UNSAFE => false,
+#[allow(missing_docs)]
+/// An enum representing the tolerance we can accept for the accumulated arguments, either absolute or percentage
+#[derive(Clone, Default, Debug, PartialEq, PartialOrd, Serialize, Deserialize, Copy)]
+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),
        }
    }
 }
@@ -108,6 +148,29 @@ 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_bound(ob: &pyo3::Bound<'source, pyo3::PyAny>) -> PyResult<Self> {
+        if let Ok((val, scale)) = <(f32, f32)>::extract_bound(ob) {
+            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 {
@@ -142,16 +205,15 @@ 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 output_selectors: Vec<Selector>,
+    pub reference_selectors: Vec<Selector>,
    /// Inputs:
    pub inputs: Vec<VarTensor>,
    /// tables
-    pub outputs: Vec<VarTensor>,
+    pub references: Vec<VarTensor>,
 }

 impl Shuffles {
@@ -162,13 +224,9 @@ impl Shuffles {

        Self {
            input_selectors: BTreeMap::new(),
-            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(),
-            ],
+            reference_selectors: vec![],
+            inputs: vec![dummy_var.clone(), dummy_var.clone()],
+            references: vec![single_col_dummy_var.clone(), single_col_dummy_var.clone()],
        }
    }
 }
@@ -268,8 +326,6 @@ 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> {
@@ -282,7 +338,6 @@ 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,
        }
    }
@@ -309,18 +364,13 @@ 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());
            }
        }

@@ -354,13 +404,24 @@ 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 = {
-                    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 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 res = base_op.nonaccum_f((qis[0].clone(), qis[1].clone()));
-                    vec![expected_output[base_op.constraint_idx()].clone() - res]
+                        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]
+                    }
                };

                Constraints::with_selector(selector, constraints)
@@ -415,7 +476,6 @@ 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,
        }
@@ -446,9 +506,21 @@ 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) {
-            let table =
-                Table::<F>::configure(cs, lookup_range, logrows, nl, &mut self.shared_table_inputs);
+            // 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)
+            };
            self.static_lookups.tables.insert(nl.clone(), table.clone());
            table
        } else {
@@ -499,9 +571,9 @@ 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);
@@ -533,40 +605,6 @@ 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);
@@ -692,8 +730,8 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
    pub fn configure_shuffles(
        &mut self,
        cs: &mut ConstraintSystem<F>,
-        inputs: &[VarTensor; 3],
-        outputs: &[VarTensor; 3],
+        inputs: &[VarTensor; 2],
+        references: &[VarTensor; 2],
    ) -> Result<(), CircuitError>
    where
        F: Field,
@@ -704,14 +742,14 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
            }
        }

-        for t in outputs.iter() {
+        for t in references.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 outputs
+        if references
            .iter()
            .map(|t| t.num_blocks())
            .collect::<Vec<_>>()
@@ -719,23 +757,23 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
            .any(|w| w[0] != w[1])
        {
            return Err(CircuitError::WrongDynamicColumnType(
-                "outputs inner cols".to_string(),
+                "references inner cols".to_string(),
            ));
        }

        let one = Expression::Constant(F::ONE);

-        for q in 0..outputs[0].num_blocks() {
-            let s_output = cs.complex_selector();
+        for q in 0..references[0].num_blocks() {
+            let s_reference = 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("shuffle", |cs| {
+                    cs.lookup_any("lookup", |cs| {
                        let s_inputq = cs.query_selector(s_input);
                        let mut expression = vec![];
-                        let s_outputq = cs.query_selector(s_output);
+                        let s_referenceq = cs.query_selector(s_reference);
                        let mut input_queries = vec![one.clone()];

                        for input in inputs {
@@ -747,9 +785,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
                            });
                        }

-                        let mut output_queries = vec![one.clone()];
-                        for output in outputs {
-                            output_queries.push(match output {
+                        let mut ref_queries = vec![one.clone()];
+                        for reference in references {
+                            ref_queries.push(match reference {
                                VarTensor::Advice { inner: advices, .. } => {
                                    cs.query_advice(advices[q][0], Rotation(0))
                                }
@@ -758,7 +796,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
                        }

                        let lhs = input_queries.into_iter().map(|c| c * s_inputq.clone());
-                        let rhs = output_queries.into_iter().map(|c| c * s_outputq.clone());
+                        let rhs = ref_queries.into_iter().map(|c| c * s_referenceq.clone());
                        expression.extend(lhs.zip(rhs));

                        expression
@@ -769,13 +807,13 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
                        .or_insert(s_input);
                }
            }
-            self.shuffles.output_selectors.push(s_output);
+            self.shuffles.reference_selectors.push(s_reference);
        }

        // if we haven't previously initialized the input/output, do so now
-        if self.shuffles.outputs.is_empty() {
-            debug!("assigning shuffles output");
-            self.shuffles.outputs = outputs.to_vec();
+        if self.shuffles.references.is_empty() {
+            debug!("assigning shuffles reference");
+            self.shuffles.references = references.to_vec();
        }
        if self.shuffles.inputs.is_empty() {
            debug!("assigning shuffles input");
@@ -807,6 +845,7 @@ 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
@@ -844,9 +883,9 @@ 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
@@ -869,40 +908,6 @@ 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);
--- a/src/circuit/ops/errors.rs
+++ b/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
@@ -100,13 +100,4 @@ pub enum CircuitError {
    #[error("invalid input type {0}")]
    /// Invalid input type
    InvalidInputType(String),
-    #[error("an element is missing from the shuffled version of the tensor")]
-    /// An element is missing from the shuffled version of the tensor
-    MissingShuffleElement,
-    /// Visibility has not been set
-    #[error("visibility has not been set")]
-    UnsetVisibility,
-    /// A decomposition base overflowed
-    #[error("decomposition base overflowed")]
-    DecompositionBaseOverflow,
 }
--- a/src/circuit/ops/hybrid.rs
+++ b/src/circuit/ops/hybrid.rs
@@ -1,9 +1,9 @@
 use super::*;
 use crate::{
-    circuit::{layouts, utils},
-    fieldutils::{integer_rep_to_felt, IntegerRep},
+    circuit::{layouts, utils, Tolerance},
+    fieldutils::integer_rep_to_felt,
    graph::multiplier_to_scale,
-    tensor::{self, DataFormat, Tensor, TensorType, ValTensor},
+    tensor::{self, Tensor, TensorType, ValTensor},
 };
 use halo2curves::ff::PrimeField;
 use serde::{Deserialize, Serialize};
@@ -57,13 +57,11 @@ 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>,
@@ -78,9 +76,7 @@ pub enum HybridOp {
        output_scale: utils::F32,
        axes: Vec<usize>,
    },
-    Output {
-        decomp: bool,
-    },
+    RangeCheck(Tolerance),
    Greater,
    GreaterEqual,
    Less,
@@ -155,10 +151,10 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                padding,
                stride,
                kernel_shape,
-                normalized, data_format
+                normalized,
            } => format!(
-                "SUMPOOL (padding={:?}, stride={:?}, kernel_shape={:?}, normalized={}, data_format={:?})",
-                padding, stride, kernel_shape, normalized, data_format
+                "SUMPOOL (padding={:?}, stride={:?}, kernel_shape={:?}, normalized={})",
+                padding, stride, kernel_shape, normalized
            ),
            HybridOp::ReduceMax { axes } => format!("REDUCEMAX (axes={:?})", axes),
            HybridOp::ReduceArgMax { dim } => format!("REDUCEARGMAX (dim={})", dim),
@@ -166,10 +162,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                padding,
                stride,
                pool_dims,
-                data_format,
            } => format!(
-                "MaxPool (padding={:?}, stride={:?}, pool_dims={:?}, data_format={:?})",
-                padding, stride, pool_dims, data_format
+                "MaxPool (padding={:?}, stride={:?}, pool_dims={:?})",
+                padding, stride, pool_dims
            ),
            HybridOp::ReduceMin { axes } => format!("REDUCEMIN (axes={:?})", axes),
            HybridOp::ReduceArgMin { dim } => format!("REDUCEARGMIN (dim={})", dim),
@@ -183,9 +178,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                    input_scale, output_scale, axes
                )
            }
-            HybridOp::Output { decomp } => {
-                format!("OUTPUT (decomp={})", decomp)
-            }
+            HybridOp::RangeCheck(p) => format!("RANGECHECK (tol={:?})", p),
            HybridOp::Greater => "GREATER".to_string(),
            HybridOp::GreaterEqual => "GREATEREQUAL".to_string(),
            HybridOp::Less => "LESS".to_string(),
@@ -241,7 +234,6 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                stride,
                kernel_shape,
                normalized,
-                data_format,
            } => layouts::sumpool(
                config,
                region,
@@ -250,7 +242,6 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                stride,
                kernel_shape,
                *normalized,
-                *data_format,
            )?,
            HybridOp::Recip {
                input_scale,
@@ -259,8 +250,8 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                config,
                region,
                values[..].try_into()?,
-                integer_rep_to_felt(input_scale.0 as IntegerRep),
-                integer_rep_to_felt(output_scale.0 as IntegerRep),
+                integer_rep_to_felt(input_scale.0 as i128),
+                integer_rep_to_felt(output_scale.0 as i128),
            )?,
            HybridOp::Div { denom, .. } => {
                if denom.0.fract() == 0.0 {
@@ -268,7 +259,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                        config,
                        region,
                        values[..].try_into()?,
-                        integer_rep_to_felt(denom.0 as IntegerRep),
+                        integer_rep_to_felt(denom.0 as i128),
                    )?
                } else {
                    layouts::nonlinearity(
@@ -291,7 +282,6 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                padding,
                stride,
                pool_dims,
-                data_format,
            } => layouts::max_pool(
                config,
                region,
@@ -299,7 +289,6 @@ 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)?
@@ -325,9 +314,13 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                *output_scale,
                axes,
            )?,
-            HybridOp::Output { decomp } => {
-                layouts::output(config, region, values[..].try_into()?, *decomp)?
-            }
+            HybridOp::RangeCheck(tol) => layouts::range_check_percent(
+                config,
+                region,
+                values[..].try_into()?,
+                tol.scale,
+                tol.val,
+            )?,
            HybridOp::Greater => layouts::greater(config, region, values[..].try_into()?)?,
            HybridOp::GreaterEqual => {
                layouts::greater_equal(config, region, values[..].try_into()?)?
--- a/src/circuit/ops/layouts.rs
+++ b/src/circuit/ops/layouts.rs
--- a/src/circuit/ops/mod.rs
+++ b/src/circuit/ops/mod.rs
@@ -1,8 +1,6 @@
 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,
@@ -98,8 +96,6 @@ pub enum InputType {
    Int,
    ///
    TDim,
-    ///
-    Unknown,
 }

 impl InputType {
@@ -136,7 +132,6 @@ impl InputType {
                let int_input = input.clone().to_i64().unwrap();
                *input = T::from_i64(int_input).unwrap();
            }
-            InputType::Unknown => {}
        }
    }
 }
@@ -157,30 +152,6 @@ impl std::str::FromStr for InputType {
    }
 }

-#[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!(),
-        }
-    }
-}
-
 ///
 #[derive(Clone, Debug, PartialEq, Eq, Hash, PartialOrd, Ord, Serialize, Deserialize)]
 pub struct Input {
@@ -188,8 +159,6 @@ 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 {
@@ -227,7 +196,6 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Input
                    config,
                    region,
                    values[..].try_into()?,
-                    self.decomp,
                )?)),
            }
        } else {
@@ -283,26 +251,20 @@ 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>, decomp: bool) -> Self {
+    pub fn new(quantized_values: Tensor<F>, raw_values: Tensor<f32>) -> 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 = match self.quantized_values.visibility() {
-            Some(v) => v,
-            None => return Err(CircuitError::UnsetVisibility),
-        };
+        let visibility = self.quantized_values.visibility().unwrap();
        self.quantized_values = quantize_tensor(self.raw_values.clone(), new_scale, &visibility)?;
        Ok(())
    }
@@ -319,8 +281,13 @@ 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
@@ -341,12 +308,7 @@ impl<
            self.quantized_values.clone().try_into()?
        };
        // we gotta constrain it once if its used multiple times
-        Ok(Some(layouts::identity(
-            config,
-            region,
-            &[value],
-            self.decomp,
-        )?))
+        Ok(Some(layouts::identity(config, region, &[value])?))
    }

    fn clone_dyn(&self) -> Box<dyn Op<F>> {
--- a/src/circuit/ops/poly.rs
+++ b/src/circuit/ops/poly.rs
@@ -4,7 +4,6 @@ use crate::{
        utils::{self, F32},
    },
    tensor::{self, Tensor, TensorError},
-    tensor::{DataFormat, KernelFormat},
 };

 use super::{base::BaseOp, *};
@@ -44,12 +43,10 @@ pub enum PolyOp {
        padding: Vec<(usize, usize)>,
        stride: Vec<usize>,
        group: usize,
-        data_format: DataFormat,
-        kernel_format: KernelFormat,
    },
    Downsample {
        axis: usize,
-        stride: isize,
+        stride: usize,
        modulo: usize,
    },
    DeConv {
@@ -57,8 +54,6 @@ pub enum PolyOp {
        output_padding: Vec<usize>,
        stride: Vec<usize>,
        group: usize,
-        data_format: DataFormat,
-        kernel_format: KernelFormat,
    },
    Add,
    Sub,
@@ -108,8 +103,13 @@ 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 {
@@ -165,12 +165,10 @@ impl<
                stride,
                padding,
                group,
-                data_format,
-                kernel_format,
            } => {
                format!(
-                    "CONV (stride={:?}, padding={:?}, group={}, data_format={:?}, kernel_format={:?})",
-                    stride, padding, group, data_format, kernel_format
+                    "CONV (stride={:?}, padding={:?}, group={})",
+                    stride, padding, group
                )
            }
            PolyOp::DeConv {
@@ -178,12 +176,10 @@ impl<
                padding,
                output_padding,
                group,
-                data_format,
-                kernel_format,
            } => {
                format!(
-                    "DECONV (stride={:?}, padding={:?}, output_padding={:?}, group={}, data_format={:?}, kernel_format={:?})",
-                    stride, padding, output_padding, group, data_format, kernel_format
+                    "DECONV (stride={:?}, padding={:?}, output_padding={:?}, group={})",
+                    stride, padding, output_padding, group
                )
            }
            PolyOp::Concat { axis } => format!("CONCAT (axis={})", axis),
@@ -246,8 +242,6 @@ impl<
                padding,
                stride,
                group,
-                data_format,
-                kernel_format,
            } => layouts::conv(
                config,
                region,
@@ -255,17 +249,9 @@ 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
@@ -283,12 +269,6 @@ 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,
@@ -317,8 +297,6 @@ impl<
                output_padding,
                stride,
                group,
-                data_format,
-                kernel_format,
            } => layouts::deconv(
                config,
                region,
@@ -327,17 +305,13 @@ 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()?, false)?
-            }
+            PolyOp::Identity { .. } => layouts::identity(config, region, values[..].try_into()?)?,
            PolyOp::Reshape(d) | PolyOp::Flatten(d) => layouts::reshape(values[..].try_into()?, d)?,
            PolyOp::Pad(p) => {
                if values.len() != 1 {
--- a/src/circuit/table.rs
+++ b/src/circuit/table.rs
@@ -132,16 +132,21 @@ 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> {
@@ -163,7 +168,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
        range: Range,
        logrows: usize,
        nonlinearity: &LookupOp,
-        preexisting_inputs: &mut Vec<TableColumn>,
+        preexisting_inputs: Option<Vec<TableColumn>>,
    ) -> Table<F> {
        let factors = cs.blinding_factors() + RESERVED_BLINDING_ROWS_PAD;
        let col_size = Self::cal_col_size(logrows, factors);
@@ -172,28 +177,28 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {

        debug!("table range: {:?}", range);

-        // 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());
+        let table_inputs = preexisting_inputs.unwrap_or_else(|| {
+            let mut cols = vec![];
+            for _ in 0..num_cols {
+                cols.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 = preexisting_inputs
+        let table_outputs = table_inputs
            .iter()
            .map(|_| cs.lookup_table_column())
            .collect::<Vec<_>>();

        Table {
            nonlinearity: nonlinearity.clone(),
-            table_inputs: preexisting_inputs.clone(),
+            table_inputs,
            table_outputs,
            is_assigned: false,
            selector_constructor: SelectorConstructor::new(num_cols),
@@ -350,11 +355,16 @@ 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
--- a/src/circuit/tests.rs
+++ b/src/circuit/tests.rs
@@ -1,6 +1,5 @@
 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},
@@ -1041,10 +1040,6 @@ 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)
        }

@@ -1066,8 +1061,6 @@ 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)
@@ -1178,7 +1171,7 @@ mod conv_col_ultra_overflow {

    use super::*;

-    const K: usize = 6;
+    const K: usize = 4;
    const LEN: usize = 10;

    #[derive(Clone)]
@@ -1198,10 +1191,9 @@ mod conv_col_ultra_overflow {
        }

        fn configure(cs: &mut ConstraintSystem<F>) -> Self::Config {
-            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);
+            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);
            Self::Config::configure(cs, &[a, b], &output, CheckMode::SAFE)
        }

@@ -1223,8 +1215,6 @@ 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)
@@ -1382,8 +1372,6 @@ 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);
@@ -1788,18 +1776,13 @@ 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(), c.clone()],
-                    &[d.clone(), e.clone(), f.clone()],
-                )
+                .configure_shuffles(cs, &[a.clone(), b.clone()], &[d.clone(), e.clone()])
                .unwrap();
            config
        }
@@ -1820,7 +1803,6 @@ mod shuffle {
                                &mut region,
                                &self.inputs[i],
                                &self.references[i],
-                                layouts::SortCollisionMode::Unsorted,
                            )
                            .map_err(|_| Error::Synthesis)?;
                        }
@@ -2006,7 +1988,7 @@ 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>::configure(cs, ());
+            let poseidon = PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::configure(cs, ());

            MyCircuitConfig { base, poseidon }
        }
@@ -2016,7 +1998,7 @@ mod add_with_overflow_and_poseidon {
            mut config: Self::Config,
            mut layouter: impl Layouter<Fr>,
        ) -> Result<(), Error> {
-            let poseidon_chip: PoseidonChip<PoseidonSpec, WIDTH, RATE> =
+            let poseidon_chip: PoseidonChip<PoseidonSpec, WIDTH, RATE, WIDTH> =
                PoseidonChip::new(config.poseidon.clone());

            let assigned_inputs_a =
@@ -2051,9 +2033,11 @@ 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>::run(a.clone()).unwrap()[0][0];
+        let commitment_a =
+            PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::run(a.clone()).unwrap()[0][0];

-        let commitment_b = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(b.clone()).unwrap()[0][0];
+        let commitment_b =
+            PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::run(b.clone()).unwrap()[0][0];

        // parameters
        let a = Tensor::from(a.into_iter().map(Value::known));
@@ -2075,11 +2059,13 @@ 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>::run(a.clone()).unwrap()[0][0] + Fr::one();
+        let commitment_a = PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::run(a.clone())
+            .unwrap()[0][0]
+            + Fr::one();

-        let commitment_b =
-            PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(b.clone()).unwrap()[0][0] + Fr::one();
+        let commitment_b = PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::run(b.clone())
+            .unwrap()[0][0]
+            + Fr::one();

        // parameters
        let a = Tensor::from(a.into_iter().map(Value::known));
--- a/src/commands.rs
+++ b/src/commands.rs
@@ -1,6 +1,6 @@
 use alloy::primitives::Address as H160;
 use clap::{Command, Parser, Subcommand};
-use clap_complete::{Generator, Shell, generate};
+use clap_complete::{generate, Generator, Shell};
 #[cfg(feature = "python-bindings")]
 use pyo3::{conversion::FromPyObject, exceptions::PyValueError, prelude::*};
 use serde::{Deserialize, Serialize};
@@ -8,7 +8,7 @@ use std::path::PathBuf;
 use std::str::FromStr;
 use tosubcommand::{ToFlags, ToSubcommand};

-use crate::{Commitments, RunArgs, pfsys::ProofType};
+use crate::{pfsys::ProofType, Commitments, RunArgs};

 use crate::circuit::CheckMode;
 use crate::graph::TestDataSource;
@@ -83,15 +83,13 @@ pub const DEFAULT_VK_SOL: &str = "vk.sol";
 /// Default VK abi path
 pub const DEFAULT_VK_ABI: &str = "vk.abi";
 /// Default scale rebase multipliers for calibration
-pub const DEFAULT_SCALE_REBASE_MULTIPLIERS: &str = "1,10";
+pub const DEFAULT_SCALE_REBASE_MULTIPLIERS: &str = "1,2,10";
 /// Default use reduced srs for verification
 pub const DEFAULT_USE_REDUCED_SRS_FOR_VERIFICATION: &str = "false";
 /// Default only check for range check rebase
 pub const DEFAULT_ONLY_RANGE_CHECK_REBASE: &str = "false";
 /// Default commitment
 pub const DEFAULT_COMMITMENT: &str = "kzg";
-/// Default seed used to generate random data
-pub const DEFAULT_SEED: &str = "21242";

 #[cfg(feature = "python-bindings")]
 /// Converts TranscriptType into a PyObject (Required for TranscriptType to be compatible with Python)
@@ -360,13 +358,8 @@ pub fn get_styles() -> clap::builder::Styles {
 }

 /// Print completions for the given generator
-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(),
-    );
+pub fn print_completions<G: Generator>(gen: G, cmd: &mut Command) {
+    generate(gen, cmd, cmd.get_name().to_string(), &mut std::io::stdout());
 }

 #[allow(missing_docs)]
@@ -401,9 +394,8 @@ pub enum Commands {
    /// Generates the witness from an input file.
    GenWitness {
        /// The path to the .json data file
-        /// You can also pass the input data as a string, eg. --data '{"input_data": [1.0,2.0,3.0]}' directly and skip the file
        #[arg(short = 'D', long, default_value = DEFAULT_DATA, value_hint = clap::ValueHint::FilePath)]
-        data: Option<String>,
+        data: Option<PathBuf>,
        /// 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>,
@@ -430,27 +422,12 @@ 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 to output
-        #[arg(short = 'D', long, default_value = DEFAULT_DATA, value_hint = clap::ValueHint::FilePath)]
-        data: Option<PathBuf>,
-        /// Hand-written parser for graph variables, eg. batch_size=1
-        #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(short = 'V', long, value_parser = crate::parse_key_val::<String, usize>, default_value = "batch_size->1", value_delimiter = ',', value_hint = clap::ValueHint::Other))]
-        variables: Vec<(String, usize)>,
-        /// random seed for reproducibility (optional)
-        #[arg(long, value_hint = clap::ValueHint::Other, default_value = DEFAULT_SEED)]
-        seed: u64,
-    },
+
    /// Calibrates the proving scale, lookup bits and logrows from a circuit settings file.
    CalibrateSettings {
        /// The path to the .json calibration data file.
-        /// You can also pass the input data as a string, eg. --data '{"input_data": [1.0,2.0,3.0]}' directly and skip the file
        #[arg(short = 'D', long, default_value = DEFAULT_CALIBRATION_FILE, value_hint = clap::ValueHint::FilePath)]
-        data: Option<String>,
+        data: Option<PathBuf>,
        /// The path to the .onnx model file
        #[arg(short = 'M', long, default_value = DEFAULT_MODEL, value_hint = clap::ValueHint::FilePath)]
        model: Option<PathBuf>,
@@ -633,9 +610,8 @@ pub enum Commands {
    #[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)
-        /// You can also pass the input data as a string, eg. --data '{"input_data": [1.0,2.0,3.0]}' directly and skip the file
        #[arg(short = 'D', long, value_hint = clap::ValueHint::FilePath)]
-        data: Option<String>,
+        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>,
@@ -661,9 +637,8 @@ pub enum Commands {
        #[arg(long, value_hint = clap::ValueHint::Other)]
        addr: H160Flag,
        /// The path to the .json data file.
-        /// You can also pass the input data as a string, eg. --data '{"input_data": [1.0,2.0,3.0]}' directly and skip the file
        #[arg(short = 'D', long, value_hint = clap::ValueHint::FilePath)]
-        data: Option<String>,
+        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>,
@@ -780,7 +755,7 @@ pub enum Commands {
        /// 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<String>,
+        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>,
@@ -875,9 +850,8 @@ pub enum Commands {
    #[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)
-        /// You can also pass the input data as a string, eg. --data '{"input_data": [1.0,2.0,3.0]}' directly and skip the file
        #[arg(short = 'D', long, default_value = DEFAULT_DATA, value_hint = clap::ValueHint::FilePath)]
-        data: Option<String>,
+        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>,
--- a/src/eth.rs
+++ b/src/eth.rs
@@ -383,7 +383,7 @@ pub async fn deploy_contract_via_solidity(
 ///
 pub async fn deploy_da_verifier_via_solidity(
    settings_path: PathBuf,
-    input: String,
+    input: PathBuf,
    sol_code_path: PathBuf,
    rpc_url: Option<&str>,
    runs: usize,
@@ -391,7 +391,7 @@ pub async fn deploy_da_verifier_via_solidity(
 ) -> Result<H160, EthError> {
    let (client, client_address) = setup_eth_backend(rpc_url, private_key).await?;

-    let input = GraphData::from_str(&input).map_err(|_| EthError::GraphData)?;
+    let input = GraphData::from_path(input).map_err(|_| EthError::GraphData)?;

    let settings = GraphSettings::load(&settings_path).map_err(|_| EthError::GraphSettings)?;

@@ -517,7 +517,6 @@ pub async fn deploy_da_verifier_via_solidity(
    }
 }

-#[allow(clippy::too_many_arguments)]
 async fn deploy_multi_da_contract(
    client: EthersClient,
    contract_instance_offset: usize,
@@ -688,10 +687,10 @@ fn parse_call_to_account(call_to_account: CallToAccount) -> Result<ParsedCallToA

 pub async fn update_account_calls(
    addr: H160,
-    input: String,
+    input: PathBuf,
    rpc_url: Option<&str>,
 ) -> Result<(), EthError> {
-    let input = GraphData::from_str(&input).map_err(|_| EthError::GraphData)?;
+    let input = GraphData::from_path(input).map_err(|_| EthError::GraphData)?;

    // The data that will be stored in the test contracts that will eventually be read from.
    let mut calls_to_accounts = vec![];
--- a/src/execute.rs
+++ b/src/execute.rs
@@ -1,6 +1,5 @@
-use crate::EZKL_BUF_CAPACITY;
-use crate::circuit::CheckMode;
 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, fix_da_multi_sol,
@@ -13,21 +12,21 @@ use crate::graph::{GraphCircuit, GraphSettings, GraphWitness, Model};
 use crate::graph::{TestDataSource, TestSources};
 use crate::pfsys::evm::aggregation_kzg::{AggregationCircuit, PoseidonTranscript};
 use crate::pfsys::{
-    ProofSplitCommit, create_proof_circuit, swap_proof_commitments_polycommit, verify_proof_circuit,
+    create_keys, load_pk, load_vk, save_params, save_pk, Snark, StrategyType, TranscriptType,
 };
 use crate::pfsys::{
-    Snark, StrategyType, TranscriptType, create_keys, load_pk, load_vk, save_params, save_pk,
+    create_proof_circuit, swap_proof_commitments_polycommit, verify_proof_circuit, ProofSplitCommit,
 };
 use crate::pfsys::{save_vk, srs::*};
 use crate::tensor::TensorError;
+use crate::EZKL_BUF_CAPACITY;
+use crate::{commands::*, EZKLError};
 use crate::{Commitments, RunArgs};
-use crate::{EZKLError, commands::*};
 use colored::Colorize;
 #[cfg(unix)]
 use gag::Gag;
 use halo2_proofs::dev::VerifyFailure;
 use halo2_proofs::plonk::{self, Circuit};
-use halo2_proofs::poly::VerificationStrategy;
 use halo2_proofs::poly::commitment::{CommitmentScheme, Params};
 use halo2_proofs::poly::commitment::{ParamsProver, Verifier};
 use halo2_proofs::poly::ipa::commitment::{IPACommitmentScheme, ParamsIPA};
@@ -40,6 +39,7 @@ use halo2_proofs::poly::kzg::strategy::AccumulatorStrategy as KZGAccumulatorStra
 use halo2_proofs::poly::kzg::{
    commitment::ParamsKZG, strategy::SingleStrategy as KZGSingleStrategy,
 };
+use halo2_proofs::poly::VerificationStrategy;
 use halo2_proofs::transcript::{EncodedChallenge, TranscriptReadBuffer};
 use halo2_solidity_verifier;
 use halo2curves::bn256::{Bn256, Fr, G1Affine};
@@ -50,12 +50,12 @@ use instant::Instant;
 use itertools::Itertools;
 use log::debug;
 use log::{info, trace, warn};
-use serde::Serialize;
 use serde::de::DeserializeOwned;
+use serde::Serialize;
 use snark_verifier::loader::native::NativeLoader;
-use snark_verifier::system::halo2::Config;
 use snark_verifier::system::halo2::compile;
 use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
+use snark_verifier::system::halo2::Config;
 use std::fs::File;
 use std::io::BufWriter;
 use std::io::{Cursor, Write};
@@ -65,8 +65,6 @@ use std::str::FromStr;
 use std::time::Duration;
 use tabled::Tabled;
 use thiserror::Error;
-use tract_onnx::prelude::IntoTensor;
-use tract_onnx::prelude::Tensor as TractTensor;

 use lazy_static::lazy_static;

@@ -118,7 +116,7 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
        } => gen_srs_cmd(
            srs_path,
            logrows as u32,
-            commitment.unwrap_or_else(|| Commitments::from_str(DEFAULT_COMMITMENT).unwrap()),
+            commitment.unwrap_or(Commitments::from_str(DEFAULT_COMMITMENT).unwrap()),
        ),
        Commands::GetSrs {
            srs_path,
@@ -136,17 +134,6 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
            settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
            args,
        ),
-        Commands::GenRandomData {
-            model,
-            data,
-            variables,
-            seed,
-        } => gen_random_data(
-            model.unwrap_or(DEFAULT_MODEL.into()),
-            data.unwrap_or(DEFAULT_DATA.into()),
-            variables,
-            seed,
-        ),
        Commands::CalibrateSettings {
            model,
            settings_path,
@@ -516,9 +503,7 @@ 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"
@@ -727,7 +712,7 @@ pub(crate) fn table(model: PathBuf, run_args: RunArgs) -> Result<String, EZKLErr

 pub(crate) async fn gen_witness(
    compiled_circuit_path: PathBuf,
-    data: String,
+    data: PathBuf,
    output: Option<PathBuf>,
    vk_path: Option<PathBuf>,
    srs_path: Option<PathBuf>,
@@ -735,7 +720,7 @@ 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::from_str(&data)?;
+    let data: GraphData = GraphData::from_path(data)?;
    let settings = circuit.settings().clone();

    let vk = if let Some(vk) = vk_path {
@@ -843,71 +828,6 @@ pub(crate) fn gen_circuit_settings(
    Ok(String::new())
 }

-/// Generate a circuit settings file
-pub(crate) fn gen_random_data(
-    model_path: PathBuf,
-    data_path: PathBuf,
-    variables: Vec<(String, usize)>,
-    seed: u64,
-) -> Result<String, EZKLError> {
-    let mut file = std::fs::File::open(&model_path).map_err(|e| {
-        crate::graph::errors::GraphError::ReadWriteFileError(
-            model_path.display().to_string(),
-            e.to_string(),
-        )
-    })?;
-
-    let (tract_model, _symbol_values) = Model::load_onnx_using_tract(&mut file, &variables)?;
-
-    let input_facts = tract_model
-        .input_outlets()
-        .map_err(|e| EZKLError::from(e.to_string()))?
-        .iter()
-        .map(|&i| tract_model.outlet_fact(i))
-        .collect::<tract_onnx::prelude::TractResult<Vec<_>>>()
-        .map_err(|e| EZKLError::from(e.to_string()))?;
-
-    /// Generates a random tensor of a given size and type.
-    fn random(
-        sizes: &[usize],
-        datum_type: tract_onnx::prelude::DatumType,
-        seed: u64,
-    ) -> TractTensor {
-        use rand::{Rng, SeedableRng};
-        let mut rng = rand::rngs::StdRng::seed_from_u64(seed);
-
-        let mut tensor = TractTensor::zero::<f32>(sizes).unwrap();
-        let slice = tensor.as_slice_mut::<f32>().unwrap();
-        slice.iter_mut().for_each(|x| *x = rng.r#gen());
-        tensor.cast_to_dt(datum_type).unwrap().into_owned()
-    }
-
-    fn tensor_for_fact(fact: &tract_onnx::prelude::TypedFact, seed: u64) -> TractTensor {
-        if let Some(value) = &fact.konst {
-            return value.clone().into_tensor();
-        }
-
-        random(
-            fact.shape
-                .as_concrete()
-                .expect("Expected concrete shape, found: {fact:?}"),
-            fact.datum_type,
-            seed,
-        )
-    }
-
-    let generated = input_facts
-        .iter()
-        .map(|v| tensor_for_fact(v, seed))
-        .collect_vec();
-
-    let data = GraphData::from_tract_data(&generated)?;
-
-    data.save(data_path)?;
-
-    Ok(String::new())
-}
-
 // not for wasm targets
 pub(crate) fn init_spinner() -> ProgressBar {
    let pb = indicatif::ProgressBar::new_spinner();
@@ -1046,7 +966,7 @@ impl AccuracyResults {
 #[allow(clippy::too_many_arguments)]
 pub(crate) async fn calibrate(
    model_path: PathBuf,
-    data: String,
+    data: PathBuf,
    settings_path: PathBuf,
    target: CalibrationTarget,
    lookup_safety_margin: f64,
@@ -1060,7 +980,7 @@ pub(crate) async fn calibrate(

    use crate::fieldutils::IntegerRep;

-    let data = GraphData::from_str(&data)?;
+    let data = GraphData::from_path(data)?;
    // load the pre-generated settings
    let settings = GraphSettings::load(&settings_path)?;
    // now retrieve the run args
@@ -1524,7 +1444,7 @@ pub(crate) async fn create_evm_data_attestation(
    settings_path: PathBuf,
    sol_code_path: PathBuf,
    abi_path: PathBuf,
-    input: String,
+    input: PathBuf,
    witness: Option<PathBuf>,
 ) -> Result<String, EZKLError> {
    #[allow(unused_imports)]
@@ -1537,8 +1457,7 @@ pub(crate) async fn create_evm_data_attestation(
    trace!("params computed");

    // if input is not provided, we just instantiate dummy input data
-    let data =
-        GraphData::from_str(&input).unwrap_or_else(|_| GraphData::new(DataSource::File(vec![])));
+    let data = GraphData::from_path(input).unwrap_or(GraphData::new(DataSource::File(vec![])));

    // The number of input and output instances we attest to for the single call data attestation
    let mut input_len = None;
@@ -1627,7 +1546,7 @@ pub(crate) async fn create_evm_data_attestation(
 }

 pub(crate) async fn deploy_da_evm(
-    data: String,
+    data: PathBuf,
    settings_path: PathBuf,
    sol_code_path: PathBuf,
    rpc_url: Option<String>,
@@ -1870,7 +1789,7 @@ pub(crate) fn setup(
 }

 pub(crate) async fn setup_test_evm_witness(
-    data_path: String,
+    data_path: PathBuf,
    compiled_circuit_path: PathBuf,
    test_data: PathBuf,
    rpc_url: Option<String>,
@@ -1879,7 +1798,7 @@ pub(crate) async fn setup_test_evm_witness(
 ) -> Result<String, EZKLError> {
    use crate::graph::TestOnChainData;

-    let mut data = GraphData::from_str(&data_path)?;
+    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
@@ -1907,7 +1826,7 @@ pub(crate) async fn setup_test_evm_witness(
 use crate::pfsys::ProofType;
 pub(crate) async fn test_update_account_calls(
    addr: H160Flag,
-    data: String,
+    data: PathBuf,
    rpc_url: Option<String>,
 ) -> Result<String, EZKLError> {
    use crate::eth::update_account_calls;
@@ -2129,7 +2048,6 @@ pub(crate) fn mock_aggregate(
    Ok(String::new())
 }

-#[allow(clippy::too_many_arguments)]
 pub(crate) fn setup_aggregate(
    sample_snarks: Vec<PathBuf>,
    vk_path: PathBuf,
--- a/src/fieldutils.rs
+++ b/src/fieldutils.rs
@@ -5,12 +5,10 @@ use halo2curves::ff::PrimeField;
 /// Integer representation of a PrimeField element.
 pub type IntegerRep = i128;

-/// Converts an integer rep to a PrimeField element.
+/// Converts an i64 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)
    }
@@ -34,9 +32,6 @@ 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());
@@ -56,7 +51,7 @@ mod test {
    use halo2curves::pasta::Fp as F;

    #[test]
-    fn integerreptofelt() {
+    fn test_conv() {
        let res: F = integer_rep_to_felt(-15);
        assert_eq!(res, -F::from(15));

@@ -74,24 +69,8 @@ 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: IntegerRep = felt_to_integer_rep::<F>(fieldx);
+            let xf: i128 = 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);
-    }
 }
--- a/src/graph/errors.rs
+++ b/src/graph/errors.rs
@@ -11,12 +11,6 @@ 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),
@@ -33,7 +27,7 @@ pub enum GraphError {
    #[error("a node is missing required params: {0}")]
    MissingParams(String),
    /// A node has missing parameters
-    #[error("a node has misformed params: {0}")]
+    #[error("a node is 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")]
@@ -119,13 +113,13 @@ 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
@@ -149,13 +143,4 @@ 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),
 }
--- a/src/graph/input.rs
+++ b/src/graph/input.rs
@@ -14,6 +14,7 @@ use pyo3::prelude::*;
 use pyo3::types::PyDict;
 #[cfg(feature = "python-bindings")]
 use pyo3::ToPyObject;
+use serde::ser::SerializeStruct;
 use serde::{Deserialize, Deserializer, Serialize, Serializer};
 use std::io::BufReader;
 use std::io::BufWriter;
@@ -24,7 +25,6 @@ use tract_onnx::tract_core::{
    tract_data::{prelude::Tensor as TractTensor, TVec},
    value::TValue,
 };
-
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use tract_onnx::tract_hir::tract_num_traits::ToPrimitive;

@@ -32,95 +32,30 @@ type Decimals = u8;
 type Call = String;
 type RPCUrl = String;

-/// Represents different types of values that can be stored in a file source
-/// Used for handling various input types in zero-knowledge proofs
+///
 #[derive(Clone, Debug, PartialOrd, PartialEq)]
 pub enum FileSourceInner {
-    /// Floating point value (64-bit)
+    /// Inner elements of float inputs coming from a file
    Float(f64),
-    /// Boolean value
+    /// Inner elements of bool inputs coming from a file
    Bool(bool),
-    /// Field element value for direct use in circuits
+    /// Inner elements of inputs coming from a witness
    Field(Fp),
 }

 impl FileSourceInner {
-    /// Returns true if the value is a floating point number
+    ///
    pub fn is_float(&self) -> bool {
        matches!(self, FileSourceInner::Float(_))
    }
-
-    /// Returns true if the value is a boolean
+    ///
    pub fn is_bool(&self) -> bool {
        matches!(self, FileSourceInner::Bool(_))
    }
-
-    /// Returns true if the value is a field element
+    ///
    pub fn is_field(&self) -> bool {
        matches!(self, FileSourceInner::Field(_))
    }
-
-    /// Creates a new floating point value
-    pub fn new_float(f: f64) -> Self {
-        FileSourceInner::Float(f)
-    }
-
-    /// Creates a new field element value
-    pub fn new_field(f: Fp) -> Self {
-        FileSourceInner::Field(f)
-    }
-
-    /// Creates a new boolean value
-    pub fn new_bool(f: bool) -> Self {
-        FileSourceInner::Bool(f)
-    }
-
-    /// Adjusts the value according to the specified input type
-    ///
-    /// # Arguments
-    /// * `input_type` - Type specification to convert the value to
-    pub fn as_type(&mut self, input_type: &InputType) {
-        match self {
-            FileSourceInner::Float(f) => input_type.roundtrip(f),
-            FileSourceInner::Bool(_) => assert!(matches!(input_type, InputType::Bool)),
-            FileSourceInner::Field(_) => {}
-        }
-    }
-
-    /// Converts the value to a field element using appropriate scaling
-    ///
-    /// # Arguments
-    /// * `scale` - Scaling factor for floating point conversion
-    pub fn to_field(&self, scale: crate::Scale) -> Fp {
-        match self {
-            FileSourceInner::Float(f) => {
-                integer_rep_to_felt(quantize_float(f, 0.0, scale).unwrap())
-            }
-            FileSourceInner::Bool(f) => {
-                if *f {
-                    Fp::one()
-                } else {
-                    Fp::zero()
-                }
-            }
-            FileSourceInner::Field(f) => *f,
-        }
-    }
-
-    /// Converts the value to a floating point number
-    pub fn to_float(&self) -> f64 {
-        match self {
-            FileSourceInner::Float(f) => *f,
-            FileSourceInner::Bool(f) => {
-                if *f {
-                    1.0
-                } else {
-                    0.0
-                }
-            }
-            FileSourceInner::Field(f) => crate::fieldutils::felt_to_integer_rep(*f) as f64,
-        }
-    }
 }

 impl Serialize for FileSourceInner {
@@ -136,8 +71,8 @@ impl Serialize for FileSourceInner {
    }
 }

-// Deserialization implementation for FileSourceInner
-// Uses JSON deserialization to handle the different variants
+// !!! ALWAYS USE JSON SERIALIZATION FOR GRAPH INPUT
+// UNTAGGED ENUMS WONT WORK :( as highlighted here:
 impl<'de> Deserialize<'de> for FileSourceInner {
    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
    where
@@ -164,16 +99,70 @@ impl<'de> Deserialize<'de> for FileSourceInner {
    }
 }

-/// A collection of input values from a file source
-/// Organized as a vector of vectors where each inner vector represents a row/entry
+/// Elements of inputs coming from a file
 pub type FileSource = Vec<Vec<FileSourceInner>>;

-/// Represents different types of calls for fetching on-chain data
+impl FileSourceInner {
+    /// Create a new FileSourceInner
+    pub fn new_float(f: f64) -> Self {
+        FileSourceInner::Float(f)
+    }
+    /// Create a new FileSourceInner
+    pub fn new_field(f: Fp) -> Self {
+        FileSourceInner::Field(f)
+    }
+    /// Create a new FileSourceInner
+    pub fn new_bool(f: bool) -> Self {
+        FileSourceInner::Bool(f)
+    }
+
+    ///
+    pub fn as_type(&mut self, input_type: &InputType) {
+        match self {
+            FileSourceInner::Float(f) => input_type.roundtrip(f),
+            FileSourceInner::Bool(_) => assert!(matches!(input_type, InputType::Bool)),
+            FileSourceInner::Field(_) => {}
+        }
+    }
+
+    /// Convert to a field element
+    pub fn to_field(&self, scale: crate::Scale) -> Fp {
+        match self {
+            FileSourceInner::Float(f) => {
+                integer_rep_to_felt(quantize_float(f, 0.0, scale).unwrap())
+            }
+            FileSourceInner::Bool(f) => {
+                if *f {
+                    Fp::one()
+                } else {
+                    Fp::zero()
+                }
+            }
+            FileSourceInner::Field(f) => *f,
+        }
+    }
+    /// Convert to a float
+    pub fn to_float(&self) -> f64 {
+        match self {
+            FileSourceInner::Float(f) => *f,
+            FileSourceInner::Bool(f) => {
+                if *f {
+                    1.0
+                } else {
+                    0.0
+                }
+            }
+            FileSourceInner::Field(f) => crate::fieldutils::felt_to_integer_rep(*f) as f64,
+        }
+    }
+}
+
+/// Call type for attested inputs on-chain
 #[derive(Clone, Debug, PartialOrd, PartialEq)]
 pub enum Calls {
-    /// Multiple calls to different accounts, each returning individual values
+    /// Vector of calls to accounts, each returning an attested data point
    Multiple(Vec<CallsToAccount>),
-    /// Single call returning an array of values
+    /// Single call to account, returning an array of attested data points
    Single(CallToAccount),
 }

@@ -182,6 +171,32 @@ impl Default for Calls {
        Calls::Multiple(Vec::new())
    }
 }
+/// Inner elements of inputs/outputs coming from on-chain
+#[derive(Clone, Debug, Deserialize, Serialize, Default, PartialOrd, PartialEq)]
+pub struct OnChainSource {
+    /// Calls to accounts
+    pub calls: Calls,
+    /// RPC url
+    pub rpc: RPCUrl,
+}
+
+impl OnChainSource {
+    /// Create a new OnChainSource with multiple calls
+    pub fn new_multiple(calls: Vec<CallsToAccount>, rpc: RPCUrl) -> Self {
+        OnChainSource {
+            calls: Calls::Multiple(calls),
+            rpc,
+        }
+    }
+
+    /// Create a new OnChainSource with a single call
+    pub fn new_single(call: CallToAccount, rpc: RPCUrl) -> Self {
+        OnChainSource {
+            calls: Calls::Single(call),
+            rpc,
+        }
+    }
+}

 impl Serialize for Calls {
    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
@@ -203,6 +218,7 @@ impl<'de> Deserialize<'de> for Calls {
        D: Deserializer<'de>,
    {
        let this_json: Box<serde_json::value::RawValue> = Deserialize::deserialize(deserializer)?;
+
        let multiple_try: Result<Vec<CallsToAccount>, _> = serde_json::from_str(this_json.get());
        if let Ok(t) = multiple_try {
            return Ok(Calls::Multiple(t));
@@ -212,52 +228,111 @@ impl<'de> Deserialize<'de> for Calls {
            return Ok(Calls::Single(t));
        }

-        Err(serde::de::Error::custom("failed to deserialize Calls"))
+        Err(serde::de::Error::custom(
+            "failed to deserialize FileSourceInner",
+        ))
    }
 }
-/// Configuration for accessing on-chain data sources
+
+#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
+/// Inner elements of inputs/outputs coming from postgres DB
 #[derive(Clone, Debug, Deserialize, Serialize, Default, PartialOrd, PartialEq)]
-pub struct OnChainSource {
-    /// Call specifications for fetching data
-    pub calls: Calls,
-    /// RPC endpoint URL for accessing the chain
-    pub rpc: RPCUrl,
+pub struct PostgresSource {
+    /// postgres host
+    pub host: RPCUrl,
+    /// user to connect to postgres
+    pub user: String,
+    /// password to connect to postgres
+    pub password: String,
+    /// query to execute
+    pub query: String,
+    /// dbname
+    pub dbname: String,
+    /// port
+    pub port: String,
+}
+
+#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
+impl PostgresSource {
+    /// Create a new PostgresSource
+    pub fn new(
+        host: RPCUrl,
+        port: String,
+        user: String,
+        query: String,
+        dbname: String,
+        password: String,
+    ) -> Self {
+        PostgresSource {
+            host,
+            user,
+            password,
+            query,
+            dbname,
+            port,
+        }
+    }
+
+    /// Fetch data from postgres
+    pub async fn fetch(&self) -> Result<Vec<Vec<pg_bigdecimal::PgNumeric>>, GraphError> {
+        // clone to move into thread
+        let user = self.user.clone();
+        let host = self.host.clone();
+        let query = self.query.clone();
+        let dbname = self.dbname.clone();
+        let port = self.port.clone();
+        let password = self.password.clone();
+
+        let config = if password.is_empty() {
+            format!(
+                "host={} user={} dbname={} port={}",
+                host, user, dbname, port
+            )
+        } else {
+            format!(
+                "host={} user={} dbname={} port={} password={}",
+                host, user, dbname, port, password
+            )
+        };
+
+        let mut client = Client::connect(&config).await?;
+        let mut res: Vec<pg_bigdecimal::PgNumeric> = Vec::new();
+        // extract rows from query
+        for row in client.query(&query, &[]).await? {
+            // extract features from row
+            for i in 0..row.len() {
+                res.push(row.get(i));
+            }
+        }
+        Ok(vec![res])
+    }
+
+    /// Fetch data from postgres and format it as a FileSource
+    pub async fn fetch_and_format_as_file(&self) -> Result<Vec<Vec<FileSourceInner>>, GraphError> {
+        Ok(self
+            .fetch()
+            .await?
+            .iter()
+            .map(|d| {
+                d.iter()
+                    .map(|d| {
+                        FileSourceInner::Float(
+                            d.n.as_ref()
+                                .unwrap()
+                                .to_f64()
+                                .ok_or("could not convert decimal to f64")
+                                .unwrap(),
+                        )
+                    })
+                    .collect()
+            })
+            .collect())
+    }
 }

 impl OnChainSource {
-    /// Creates a new OnChainSource with multiple calls
-    ///
-    /// # Arguments
-    /// * `calls` - Vector of call specifications
-    /// * `rpc` - RPC endpoint URL
-    pub fn new_multiple(calls: Vec<CallsToAccount>, rpc: RPCUrl) -> Self {
-        OnChainSource {
-            calls: Calls::Multiple(calls),
-            rpc,
-        }
-    }
-
-    /// Creates a new OnChainSource with a single call
-    ///
-    /// # Arguments
-    /// * `call` - Call specification
-    /// * `rpc` - RPC endpoint URL
-    pub fn new_single(call: CallToAccount, rpc: RPCUrl) -> Self {
-        OnChainSource {
-            calls: Calls::Single(call),
-            rpc,
-        }
-    }
-
    #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-    /// Creates test data for the OnChain data source
-    /// Used for testing and development purposes
-    ///
-    /// # Arguments
-    /// * `data` - Sample file data to use
-    /// * `scales` - Scaling factors for each input
-    /// * `shapes` - Shapes of the input tensors
-    /// * `rpc` - Optional RPC endpoint override
+    /// Create dummy local on-chain data to test the OnChain data source
    pub async fn test_from_file_data(
        data: &FileSource,
        scales: Vec<crate::Scale>,
@@ -324,40 +399,48 @@ impl OnChainSource {
    }
 }

-/// Specification for view-only calls to fetch on-chain data
-/// Used for data attestation in smart contract verification
+/// Defines the view only calls to accounts to fetch the on-chain input data.
+/// This data will be included as part of the first elements in the publicInputs
+/// for the sol evm verifier and will be  verifyWithDataAttestation.sol
 #[derive(Clone, Debug, Deserialize, Serialize, Default, PartialOrd, PartialEq)]
 pub struct CallsToAccount {
-    /// Vector of (call data, decimals) pairs
-    /// call_data: ABI-encoded function call
-    /// decimals: Number of decimal places for float conversion
+    /// A vector of tuples, where index 0 of tuples
+    /// are the byte strings representing the ABI encoded function calls to
+    /// read the data from the address. This call must return a single
+    /// elementary type (<https://docs.soliditylang.org/en/v0.8.20/abi-spec.html#types>).
+    /// The second index of the tuple is the number of decimals for f32 conversion.
+    /// We don't support dynamic types currently.
    pub call_data: Vec<(Call, Decimals)>,
-    /// Contract address to call
+    /// Address of the contract to read the data from.
    pub address: String,
 }

-/// Specification for a single view-only call returning an array
+/// Defines a view only call to accounts to fetch the on-chain input data.
+/// This data will be included as part of the first elements in the publicInputs
+/// for the sol evm verifier and will be  verifyWithDataAttestation.sol
 #[derive(Clone, Debug, Deserialize, Serialize, Default, PartialOrd, PartialEq)]
 pub struct CallToAccount {
-    /// ABI-encoded function call data
+    /// The call_data is a byte strings representing the ABI encoded function call to
+    /// read the data from the address. This call must return a single array of integers that can be
+    /// be safely cast to the int128 type in solidity.
    pub call_data: Call,
-    /// Number of decimal places for float conversion
+    /// The number of decimals for f32 conversion of all of the elements returned from the
+    /// call.
    pub decimals: Decimals,
-    /// Contract address to call
+    /// Address of the contract to read the data from.
    pub address: String,
-    /// Expected length of returned array
+    /// The number of elements returned from the call.
    pub len: usize,
 }
-
-/// Represents different sources of input/output data for the EZKL model
+/// Enum that defines source of the inputs/outputs to the EZKL model
 #[derive(Clone, Debug, Serialize, PartialOrd, PartialEq)]
 #[serde(untagged)]
 pub enum DataSource {
-    /// Data from a JSON file containing arrays of values
+    /// .json File data source.
    File(FileSource),
-    /// Data fetched from blockchain contracts
+    /// On-chain data source. The first element is the calls to the account, and the second is the RPC url.
    OnChain(OnChainSource),
-    /// Data from a PostgreSQL database
+    /// Postgres DB
    #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
    DB(PostgresSource),
 }
@@ -400,7 +483,8 @@ impl From<OnChainSource> for DataSource {
    }
 }

-// Note: Always use JSON serialization for untagged enums
+// !!! ALWAYS USE JSON SERIALIZATION FOR GRAPH INPUT
+// UNTAGGED ENUMS WONT WORK :( as highlighted here:
 impl<'de> Deserialize<'de> for DataSource {
    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
    where
@@ -408,19 +492,15 @@ impl<'de> Deserialize<'de> for DataSource {
    {
        let this_json: Box<serde_json::value::RawValue> = Deserialize::deserialize(deserializer)?;

-        // Try deserializing as FileSource first
        let first_try: Result<FileSource, _> = serde_json::from_str(this_json.get());
+
        if let Ok(t) = first_try {
            return Ok(DataSource::File(t));
        }
-
-        // Try deserializing as OnChainSource
        let second_try: Result<OnChainSource, _> = serde_json::from_str(this_json.get());
        if let Ok(t) = second_try {
            return Ok(DataSource::OnChain(t));
        }
-
-        // Try deserializing as PostgresSource if feature enabled
        #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
        {
            let third_try: Result<PostgresSource, _> = serde_json::from_str(this_json.get());
@@ -433,29 +513,22 @@ impl<'de> Deserialize<'de> for DataSource {
    }
 }

-/// Container for input and output data for graph computations
-///
-/// Important: Always use JSON serialization for GraphData to handle enum variants correctly
-#[derive(Clone, Debug, Deserialize, Default, PartialEq, Serialize)]
+/// Input to graph as a datasource
+/// Always use JSON serialization for GraphData. Seriously.
+#[derive(Clone, Debug, Deserialize, Default, PartialEq)]
 pub struct GraphData {
-    /// Input data for the model/graph
-    /// Can be empty if inputs come from on-chain sources
+    /// Inputs to the model / computational graph (can be empty vectors if inputs are coming from on-chain).
    pub input_data: DataSource,
-
-    /// Optional output data for the model/graph
-    /// Can be empty if outputs come from on-chain sources
+    /// Outputs of the model / computational graph (can be empty vectors if outputs are coming from on-chain).
    pub output_data: Option<DataSource>,
 }

 impl UnwindSafe for GraphData {}

 impl GraphData {
+    // not wasm
    #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-    /// Converts the input data to tract's tensor format
-    ///
-    /// # Arguments
-    /// * `shapes` - Expected shapes for each input tensor
-    /// * `datum_types` - Expected data types for each input
+    /// Convert the input data to tract data
    pub fn to_tract_data(
        &self,
        shapes: &[Vec<usize>],
@@ -484,43 +557,7 @@ impl GraphData {
        Ok(inputs)
    }

-    #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-    /// Converts tract tensor data into GraphData format
    ///
-    /// # Arguments
-    /// * `tensors` - Array of tract tensors to convert
-    ///
-    /// # Returns
-    /// A new GraphData instance containing the converted tensor data
-    pub fn from_tract_data(tensors: &[TractTensor]) -> Result<Self, GraphError> {
-        use tract_onnx::prelude::DatumType;
-
-        let mut input_data = vec![];
-        for tensor in tensors {
-            match tensor.datum_type() {
-                tract_onnx::prelude::DatumType::Bool => {
-                    let tensor = tensor.to_array_view::<bool>()?;
-                    let tensor = tensor.iter().map(|e| FileSourceInner::Bool(*e)).collect();
-                    input_data.push(tensor);
-                }
-                _ => {
-                    let cast_tensor = tensor.cast_to_dt(DatumType::F64)?;
-                    let tensor = cast_tensor.to_array_view::<f64>()?;
-                    let tensor = tensor.iter().map(|e| FileSourceInner::Float(*e)).collect();
-                    input_data.push(tensor);
-                }
-            }
-        }
-        Ok(GraphData {
-            input_data: DataSource::File(input_data),
-            output_data: None,
-        })
-    }
-
-    /// Creates a new GraphData instance with given input data
-    ///
-    /// # Arguments
-    /// * `input_data` - The input data source
    pub fn new(input_data: DataSource) -> Self {
        GraphData {
            input_data,
@@ -528,34 +565,7 @@ impl GraphData {
        }
    }

-    /// Loads graph input data from a string, first seeing if it is a file path or JSON data
-    /// If it is a file path, it will load the data from the file
-    /// Otherwise, it will attempt to parse the string as JSON data
-    /// 
-    /// # Arguments
-    /// * `data` - String containing the input data
-    /// # Returns
-    /// A new GraphData instance containing the loaded data
-    pub fn from_str(data: &str) -> Result<Self, GraphError> {
-        let graph_input = serde_json::from_str(data); 
-        match graph_input {
-            Ok(graph_input) => {
-                return Ok(graph_input);
-            }
-            Err(_) => {
-                let path = std::path::PathBuf::from(data);
-                GraphData::from_path(path)
-            }
-        }
-    }
-
-    /// Loads graph input data from a file
-    ///
-    /// # Arguments
-    /// * `path` - Path to the input file
-    ///
-    /// # Returns
-    /// A new GraphData instance containing the loaded data
+    /// Load the model input from a file
    pub fn from_path(path: std::path::PathBuf) -> Result<Self, GraphError> {
        let reader = std::fs::File::open(&path).map_err(|e| {
            GraphError::ReadWriteFileError(path.display().to_string(), e.to_string())
@@ -569,35 +579,23 @@ impl GraphData {
        Ok(graph_input)
    }

-    /// Saves the graph data to a file
-    ///
-    /// # Arguments
-    /// * `path` - Path where to save the data
+    /// Save the model input to a file
    pub fn save(&self, path: std::path::PathBuf) -> Result<(), GraphError> {
        let file = std::fs::File::create(path.clone()).map_err(|e| {
            GraphError::ReadWriteFileError(path.display().to_string(), e.to_string())
        })?;
+        // buf writer
        let writer = BufWriter::with_capacity(*EZKL_BUF_CAPACITY, file);
        serde_json::to_writer(writer, self)?;
        Ok(())
    }

-    /// Splits the input data into multiple batches based on input shapes
    ///
-    /// # Arguments
-    /// * `input_shapes` - Vector of shapes for each input tensor
-    ///
-    /// # Returns
-    /// Vector of GraphData instances, one for each batch
-    ///
-    /// # Errors
-    /// Returns error if:
-    /// - Data is from on-chain source
-    /// - Input size is not evenly divisible by batch size
    pub async fn split_into_batches(
        &self,
        input_shapes: Vec<Vec<usize>>,
    ) -> Result<Vec<Self>, GraphError> {
+        // split input data into batches
        let mut batched_inputs = vec![];

        let iterable = match self {
@@ -621,25 +619,17 @@ impl GraphData {
            } => data.fetch_and_format_as_file().await?,
        };

-        // Process each input tensor according to its shape
        for (i, shape) in input_shapes.iter().enumerate() {
+            // ensure the input is evenly divisible by batch_size
            let input_size = shape.clone().iter().product::<usize>();
            let input = &iterable[i];
-
-            // Validate input size is divisible by batch size
            if input.len() % input_size != 0 {
                return Err(GraphError::InvalidDims(
                    0,
-                    format!(
-                        "calibration data length (={}) must be evenly divisible by the original input_size(={})",
-                        input.len(),
-                        input_size
-                    ),
-                    
+                    "calibration data length must be evenly divisible by the original input_size"
+                        .to_string(),
                ));
            }
-
-            // Split input into batches
            let mut batches = vec![];
            for batch in input.chunks(input_size) {
                batches.push(batch.to_vec());
@@ -647,18 +637,18 @@ impl GraphData {
            batched_inputs.push(batches);
        }

-        // Merge batches across inputs
+        // now merge all the batches for each input into a vector of batches
+        // first assert each input has the same number of batches
        let num_batches = if batched_inputs.is_empty() {
            0
        } else {
            let num_batches = batched_inputs[0].len();
-            // Verify all inputs have same number of batches
            for input in batched_inputs.iter() {
                assert_eq!(input.len(), num_batches);
            }
            num_batches
        };
-
+        // now merge the batches
        let mut input_batches = vec![];
        for i in 0..num_batches {
            let mut batch = vec![];
@@ -668,12 +658,11 @@ impl GraphData {
            input_batches.push(DataSource::File(batch));
        }

-        // Ensure at least one batch exists
        if input_batches.is_empty() {
            input_batches.push(DataSource::File(vec![vec![]]));
        }

-        // Create GraphData instance for each batch
+        // create a new GraphWitness for each batch
        let batches = input_batches
            .into_iter()
            .map(GraphData::new)
@@ -685,7 +674,6 @@ impl GraphData {

 #[cfg(feature = "python-bindings")]
 impl ToPyObject for CallsToAccount {
-    /// Converts CallsToAccount to Python object
    fn to_object(&self, py: Python) -> PyObject {
        let dict = PyDict::new(py);
        dict.set_item("account", &self.address).unwrap();
@@ -694,165 +682,6 @@ impl ToPyObject for CallsToAccount {
    }
 }

-// Additional Python bindings for various types...
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    #[test]
-    fn test_postgres_source_new() {
-        #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-        {
-            let source = PostgresSource::new(
-                "localhost".to_string(),
-                "5432".to_string(),
-                "user".to_string(),
-                "SELECT * FROM table".to_string(),
-                "database".to_string(),
-                "password".to_string(),
-            );
-
-            assert_eq!(source.host, "localhost");
-            assert_eq!(source.port, "5432");
-            assert_eq!(source.user, "user");
-            assert_eq!(source.query, "SELECT * FROM table");
-            assert_eq!(source.dbname, "database");
-            assert_eq!(source.password, "password");
-        }
-    }
-
-    #[test]
-    fn test_data_source_serialization_round_trip() {
-        // Test backwards compatibility with old format
-        let source = DataSource::from(vec![vec![0.053_262_424, 0.074_970_566, 0.052_355_476]]);
-        let serialized = serde_json::to_string(&source).unwrap();
-        const JSON: &str = r#"[[0.053262424,0.074970566,0.052355476]]"#;
-        assert_eq!(serialized, JSON);
-
-        let expect = serde_json::from_str::<DataSource>(JSON)
-            .map_err(|e| e.to_string())
-            .unwrap();
-        assert_eq!(expect, source);
-    }
-
-    #[test]
-    fn test_graph_input_serialization_round_trip() {
-        // Test serialization/deserialization of graph input
-        let file = GraphData::new(DataSource::from(vec![vec![
-            0.05326242372393608,
-            0.07497056573629379,
-            0.05235547572374344,
-        ]]));
-
-        let serialized = serde_json::to_string(&file).unwrap();
-        const JSON: &str = r#"{"input_data":[[0.05326242372393608,0.07497056573629379,0.05235547572374344]],"output_data":null}"#;
-        assert_eq!(serialized, JSON);
-
-        let graph_input3 = serde_json::from_str::<GraphData>(JSON)
-            .map_err(|e| e.to_string())
-            .unwrap();
-        assert_eq!(graph_input3, file);
-    }
-
-    #[test]
-    fn test_python_compat() {
-        // Test compatibility with mclbn256 library serialization
-        let source = Fp::from_raw([18445520602771460712, 838677322461845011, 3079992810, 0]);
-        let original_addr = "0x000000000000000000000000b794f5ea0ba39494ce839613fffba74279579268";
-        assert_eq!(format!("{:?}", source), original_addr);
-    }
-}
-
-/// Source data from a PostgreSQL database
-#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-#[derive(Clone, Debug, Deserialize, Serialize, Default, PartialOrd, PartialEq)]
-pub struct PostgresSource {
-    /// Database host address
-    pub host: RPCUrl,
-    /// Database user name
-    pub user: String,
-    /// Database password
-    pub password: String,
-    /// SQL query to execute
-    pub query: String,
-    /// Database name
-    pub dbname: String,
-    /// Database port
-    pub port: String,
-}
-
-#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-impl PostgresSource {
-    /// Creates a new PostgreSQL data source
-    pub fn new(
-        host: RPCUrl,
-        port: String,
-        user: String,
-        query: String,
-        dbname: String,
-        password: String,
-    ) -> Self {
-        PostgresSource {
-            host,
-            user,
-            password,
-            query,
-            dbname,
-            port,
-        }
-    }
-
-    /// Fetches data from the PostgreSQL database
-    pub async fn fetch(&self) -> Result<Vec<Vec<pg_bigdecimal::PgNumeric>>, GraphError> {
-        // Configuration string
-        let config = if self.password.is_empty() {
-            format!(
-                "host={} user={} dbname={} port={}",
-                self.host, self.user, self.dbname, self.port
-            )
-        } else {
-            format!(
-                "host={} user={} dbname={} port={} password={}",
-                self.host, self.user, self.dbname, self.port, self.password
-            )
-        };
-
-        let mut client = Client::connect(&config).await?;
-        let mut res: Vec<pg_bigdecimal::PgNumeric> = Vec::new();
-
-        // Extract rows from query
-        for row in client.query(&self.query, &[]).await? {
-            for i in 0..row.len() {
-                res.push(row.get(i));
-            }
-        }
-        Ok(vec![res])
-    }
-
-    /// Fetches and formats data as FileSource
-    pub async fn fetch_and_format_as_file(&self) -> Result<Vec<Vec<FileSourceInner>>, GraphError> {
-        Ok(self
-            .fetch()
-            .await?
-            .iter()
-            .map(|d| {
-                d.iter()
-                    .map(|d| {
-                        FileSourceInner::Float(
-                            d.n.as_ref()
-                                .unwrap()
-                                .to_f64()
-                                .ok_or("could not convert decimal to f64")
-                                .unwrap(),
-                        )
-                    })
-                    .collect()
-            })
-            .collect())
-    }
-}
-
 #[cfg(feature = "python-bindings")]
 impl ToPyObject for CallToAccount {
    fn to_object(&self, py: Python) -> PyObject {
@@ -887,7 +716,6 @@ impl ToPyObject for DataSource {
                    .unwrap();
                dict.to_object(py)
            }
-            #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
            DataSource::DB(source) => {
                let dict = PyDict::new(py);
                dict.set_item("host", &source.host).unwrap();
@@ -912,3 +740,69 @@ impl ToPyObject for FileSourceInner {
        }
    }
 }
+
+impl Serialize for GraphData {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        let mut state = serializer.serialize_struct("GraphData", 4)?;
+        state.serialize_field("input_data", &self.input_data)?;
+        state.serialize_field("output_data", &self.output_data)?;
+        state.end()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    // this is for backwards compatibility with the old format
+    fn test_data_source_serialization_round_trip() {
+        let source = DataSource::from(vec![vec![0.053_262_424, 0.074_970_566, 0.052_355_476]]);
+
+        let serialized = serde_json::to_string(&source).unwrap();
+
+        const JSON: &str = r#"[[0.053262424,0.074970566,0.052355476]]"#;
+
+        assert_eq!(serialized, JSON);
+
+        let expect = serde_json::from_str::<DataSource>(JSON)
+            .map_err(|e| e.to_string())
+            .unwrap();
+
+        assert_eq!(expect, source);
+    }
+
+    #[test]
+    // this is for backwards compatibility with the old format
+    fn test_graph_input_serialization_round_trip() {
+        let file = GraphData::new(DataSource::from(vec![vec![
+            0.05326242372393608,
+            0.07497056573629379,
+            0.05235547572374344,
+        ]]));
+
+        let serialized = serde_json::to_string(&file).unwrap();
+
+        const JSON: &str = r#"{"input_data":[[0.05326242372393608,0.07497056573629379,0.05235547572374344]],"output_data":null}"#;
+
+        assert_eq!(serialized, JSON);
+
+        let graph_input3 = serde_json::from_str::<GraphData>(JSON)
+            .map_err(|e| e.to_string())
+            .unwrap();
+        assert_eq!(graph_input3, file);
+    }
+
+    //  test for the compatibility with the serialized elements from the mclbn256 library
+    #[test]
+    fn test_python_compat() {
+        let source = Fp::from_raw([18445520602771460712, 838677322461845011, 3079992810, 0]);
+
+        let original_addr = "0x000000000000000000000000b794f5ea0ba39494ce839613fffba74279579268";
+
+        assert_eq!(format!("{:?}", source), original_addr);
+    }
+}
--- a/src/graph/mod.rs
+++ b/src/graph/mod.rs
@@ -281,7 +281,7 @@ impl GraphWitness {

        let reader = std::io::BufReader::with_capacity(*EZKL_BUF_CAPACITY, file);
        let witness: GraphWitness =
-            serde_json::from_reader(reader).map_err(Into::<GraphError>::into)?;
+            serde_json::from_reader(reader).map_err(|e| Into::<GraphError>::into(e))?;

        // check versions match
        crate::check_version_string_matches(witness.version.as_deref().unwrap_or(""));
@@ -455,10 +455,6 @@ pub struct GraphSettings {
    pub num_blinding_factors: Option<usize>,
    /// unix time timestamp
    pub timestamp: Option<u128>,
-    /// Model inputs types (if any)
-    pub input_types: Option<Vec<InputType>>,
-    /// Model outputs types (if any)
-    pub output_types: Option<Vec<InputType>>,
 }

 impl GraphSettings {
@@ -623,6 +619,11 @@ impl GraphSettings {
        }
    }

+    ///
+    pub fn uses_modules(&self) -> bool {
+        !self.module_sizes.max_constraints() > 0
+    }
+
    /// if any visibility is encrypted or hashed
    pub fn module_requires_fixed(&self) -> bool {
        self.run_args.input_visibility.is_hashed()
@@ -765,7 +766,7 @@ pub struct TestOnChainData {
    pub data: std::path::PathBuf,
    /// rpc endpoint
    pub rpc: Option<String>,
-    /// data sources for the on chain data
+    ///
    pub data_sources: TestSources,
 }

@@ -953,7 +954,7 @@ impl GraphCircuit {
            DataSource::File(file_data) => {
                self.load_file_data(file_data, &shapes, scales, input_types)
            }
-            _ => Err(GraphError::OnChainDataSource),
+            _ => unreachable!("cannot load from on-chain data"),
        }
    }

--- a/src/graph/model.rs
+++ b/src/graph/model.rs
@@ -1,6 +1,7 @@
 use super::errors::GraphError;
 use super::extract_const_quantized_values;
 use super::node::*;
+use super::scale_to_multiplier;
 use super::vars::*;
 use super::GraphSettings;
 use crate::circuit::hybrid::HybridOp;
@@ -378,18 +379,13 @@ pub struct ParsedNodes {
    pub nodes: BTreeMap<usize, NodeType>,
    inputs: Vec<usize>,
    outputs: Vec<Outlet>,
-    output_types: Vec<InputType>,
 }

 impl ParsedNodes {
-    /// Returns the output types of the computational graph.
-    pub fn get_output_types(&self) -> Vec<InputType> {
-        self.output_types.clone()
-    }
-
    /// Returns the number of the computational graph's inputs
    pub fn num_inputs(&self) -> usize {
-        self.inputs.len()
+        let input_nodes = self.inputs.iter();
+        input_nodes.len()
    }

    /// Input types
@@ -429,7 +425,8 @@ impl ParsedNodes {

    /// Returns the number of the computational graph's outputs
    pub fn num_outputs(&self) -> usize {
-        self.outputs.len()
+        let output_nodes = self.outputs.iter();
+        output_nodes.len()
    }

    /// Returns shapes of the computational graph's outputs
@@ -496,16 +493,6 @@ impl Model {
        Ok(om)
    }

-    /// Gets the input types from the parsed nodes
-    pub fn get_input_types(&self) -> Result<Vec<InputType>, GraphError> {
-        self.graph.get_input_types()
-    }
-
-    /// Gets the output types from the parsed nodes
-    pub fn get_output_types(&self) -> Vec<InputType> {
-        self.graph.get_output_types()
-    }
-
    ///
    pub fn save(&self, path: PathBuf) -> Result<(), GraphError> {
        let f = std::fs::File::create(&path).map_err(|e| {
@@ -589,11 +576,6 @@ impl Model {
            required_range_checks: res.range_checks.into_iter().collect(),
            model_output_scales: self.graph.get_output_scales()?,
            model_input_scales: self.graph.get_input_scales(),
-            input_types: match self.get_input_types() {
-                Ok(x) => Some(x),
-                Err(_) => None,
-            },
-            output_types: Some(self.get_output_types()),
            num_dynamic_lookups: res.num_dynamic_lookups,
            total_dynamic_col_size: res.dynamic_lookup_col_coord,
            num_shuffles: res.num_shuffles,
@@ -639,23 +621,19 @@ impl Model {
    /// * `scale` - The scale to use for quantization.
    /// * `public_params` - Whether to make the params public.
    #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-    pub(crate) fn load_onnx_using_tract(
+    fn load_onnx_using_tract(
        reader: &mut dyn std::io::Read,
-        variables: &[(String, usize)],
+        run_args: &RunArgs,
    ) -> Result<TractResult, GraphError> {
        use tract_onnx::tract_hir::internal::GenericFactoid;

        let mut model = tract_onnx::onnx().model_for_read(reader)?;

        let variables: std::collections::HashMap<String, usize> =
-            std::collections::HashMap::from_iter(variables.iter().map(|(k, v)| (k.clone(), *v)));
+            std::collections::HashMap::from_iter(run_args.variables.clone());

        for (i, id) in model.clone().inputs.iter().enumerate() {
            let input = model.node_mut(id.node);
-
-            if input.outputs.len() == 0 {
-                return Err(GraphError::MissingOutput(id.node));
-            }
            let mut fact: InferenceFact = input.outputs[0].fact.clone();

            for (i, x) in fact.clone().shape.dims().enumerate() {
@@ -677,7 +655,7 @@ impl Model {
        }

        let mut symbol_values = SymbolValues::default();
-        for (symbol, value) in variables.iter() {
+        for (symbol, value) in run_args.variables.iter() {
            let symbol = model.symbols.sym(symbol);
            symbol_values = symbol_values.with(&symbol, *value as i64);
            debug!("set {} to {}", symbol, value);
@@ -705,7 +683,7 @@ impl Model {
    ) -> Result<ParsedNodes, GraphError> {
        let start_time = instant::Instant::now();

-        let (model, symbol_values) = Self::load_onnx_using_tract(reader, &run_args.variables)?;
+        let (model, symbol_values) = Self::load_onnx_using_tract(reader, run_args)?;

        let scales = VarScales::from_args(run_args);
        let nodes = Self::nodes_from_graph(
@@ -724,11 +702,6 @@ impl Model {
            nodes,
            inputs: model.inputs.iter().map(|o| o.node).collect(),
            outputs: model.outputs.iter().map(|o| (o.node, o.slot)).collect(),
-            output_types: model
-                .outputs
-                .iter()
-                .map(|o| Ok::<InputType, GraphError>(model.outlet_fact(*o)?.datum_type.into()))
-                .collect::<Result<Vec<_>, GraphError>>()?,
        };

        let duration = start_time.elapsed();
@@ -887,15 +860,6 @@ impl Model {
                        nodes: subgraph_nodes,
                        inputs: model.inputs.iter().map(|o| o.node).collect(),
                        outputs: model.outputs.iter().map(|o| (o.node, o.slot)).collect(),
-                        output_types: model
-                            .outputs
-                            .iter()
-                            .map(|o| {
-                                Ok::<InputType, GraphError>(
-                                    model.outlet_fact(*o)?.datum_type.into(),
-                                )
-                            })
-                            .collect::<Result<Vec<_>, GraphError>>()?,
                    };

                    let om = Model {
@@ -942,7 +906,6 @@ impl Model {
                            n.opkind = SupportedOp::Input(Input {
                                scale,
                                datum_type: inp.datum_type,
-                                decomp: !run_args.ignore_range_check_inputs_outputs,
                            });
                            input_idx += 1;
                            n.out_scale = scale;
@@ -1001,7 +964,7 @@ impl Model {
            GraphError::ReadWriteFileError(model_path.display().to_string(), e.to_string())
        })?;

-        let (model, _) = Model::load_onnx_using_tract(&mut file, &run_args.variables)?;
+        let (model, _) = Model::load_onnx_using_tract(&mut file, run_args)?;

        let datum_types: Vec<DatumType> = model
            .input_outlets()?
@@ -1053,10 +1016,6 @@ impl Model {
        let required_lookups = settings.required_lookups.clone();
        let required_range_checks = settings.required_range_checks.clone();

-        if vars.advices.len() < 3 {
-            return Err(GraphError::InsufficientAdviceColumns(3));
-        }
-
        let mut base_gate = PolyConfig::configure(
            meta,
            vars.advices[0..2].try_into()?,
@@ -1076,10 +1035,6 @@ impl Model {
        }

        if settings.requires_dynamic_lookup() {
-            if vars.advices.len() < 6 {
-                return Err(GraphError::InsufficientAdviceColumns(6));
-            }
-
            base_gate.configure_dynamic_lookup(
                meta,
                vars.advices[0..3].try_into()?,
@@ -1088,13 +1043,10 @@ impl Model {
        }

        if settings.requires_shuffle() {
-            if vars.advices.len() < 6 {
-                return Err(GraphError::InsufficientAdviceColumns(6));
-            }
            base_gate.configure_shuffles(
                meta,
-                vars.advices[0..3].try_into()?,
-                vars.advices[3..6].try_into()?,
+                vars.advices[0..2].try_into()?,
+                vars.advices[3..5].try_into()?,
            )?;
        }

@@ -1109,7 +1061,6 @@ impl Model {
    /// * `vars` - The variables for the circuit.
    /// * `witnessed_outputs` - The values to compare against.
    /// * `constants` - The constants for the circuit.
-    #[allow(clippy::too_many_arguments)]
    pub fn layout(
        &self,
        mut config: ModelConfig,
@@ -1172,10 +1123,17 @@ impl Model {
                    })?;

                if run_args.output_visibility.is_public() || run_args.output_visibility.is_fixed() {
+                    let output_scales = self.graph.get_output_scales().map_err(|e| {
+                        error!("{}", e);
+                        halo2_proofs::plonk::Error::Synthesis
+                    })?;
                    let res = outputs
                        .iter()
                        .enumerate()
                        .map(|(i, output)| {
+                            let mut tolerance = run_args.tolerance;
+                            tolerance.scale = scale_to_multiplier(output_scales[i]).into();
+
                            let comparators = if run_args.output_visibility == Visibility::Public {
                                let res = vars
                                    .instance
@@ -1197,9 +1155,7 @@ impl Model {
                                .layout(
                                    &mut thread_safe_region,
                                    &[output.clone(), comparators],
-                                    Box::new(HybridOp::Output {
-                                        decomp: !run_args.ignore_range_check_inputs_outputs,
-                                    }),
+                                    Box::new(HybridOp::RangeCheck(tolerance)),
                                )
                                .map_err(|e| e.into())
                        })
@@ -1459,9 +1415,11 @@ impl Model {
        let outputs = self.layout_nodes(&mut model_config, &mut region, &mut results)?;

        if self.visibility.output.is_public() || self.visibility.output.is_fixed() {
+            let output_scales = self.graph.get_output_scales()?;
            let res = outputs
                .iter()
-                .map(|output| {
+                .enumerate()
+                .map(|(i, output)| {
                    let mut comparator: ValTensor<Fp> = (0..output.len())
                        .map(|_| {
                            if !self.visibility.output.is_fixed() {
@@ -1474,12 +1432,13 @@ impl Model {
                        .into();
                    comparator.reshape(output.dims())?;

+                    let mut tolerance = run_args.tolerance;
+                    tolerance.scale = scale_to_multiplier(output_scales[i]).into();
+
                    dummy_config.layout(
                        &mut region,
                        &[output.clone(), comparator],
-                        Box::new(HybridOp::Output {
-                            decomp: !run_args.ignore_range_check_inputs_outputs,
-                        }),
+                        Box::new(HybridOp::RangeCheck(tolerance)),
                    )
                })
                .collect::<Result<Vec<_>, _>>();
@@ -1501,7 +1460,7 @@ impl Model {
            .iter()
            .map(|x| {
                x.get_felt_evals()
-                    .unwrap_or_else(|_| Tensor::new(Some(&[Fp::ZERO]), &[1]).unwrap())
+                    .unwrap_or(Tensor::new(Some(&[Fp::ZERO]), &[1]).unwrap())
            })
            .collect();

@@ -1571,7 +1530,6 @@ impl Model {
                        let mut op = crate::circuit::Constant::new(
                            c.quantized_values.clone(),
                            c.raw_values.clone(),
-                            c.decomp,
                        );
                        op.pre_assign(consts[const_idx].clone());
                        n.opkind = SupportedOp::Constant(op);
@@ -1599,16 +1557,4 @@ impl Model {
        }
        Ok(instance_shapes)
    }
-
-    /// Input types of the computational graph's public inputs (if any)
-    pub fn instance_types(&self) -> Result<Vec<InputType>, GraphError> {
-        let mut instance_types = vec![];
-        if self.visibility.input.is_public() {
-            instance_types.extend(self.graph.get_input_types()?);
-        }
-        if self.visibility.output.is_public() {
-            instance_types.extend(self.graph.get_output_types());
-        }
-        Ok(instance_types)
-    }
 }
--- a/src/graph/modules.rs
+++ b/src/graph/modules.rs
@@ -14,11 +14,14 @@ use serde::{Deserialize, Serialize};
 use super::errors::GraphError;
 use super::{VarVisibility, Visibility};

+/// poseidon len to hash in tree
+pub const POSEIDON_LEN_GRAPH: usize = 32;
 /// Poseidon number of instances
 pub const POSEIDON_INSTANCES: usize = 1;

 /// Poseidon module type
-pub type ModulePoseidon = PoseidonChip<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>;
+pub type ModulePoseidon =
+    PoseidonChip<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, POSEIDON_LEN_GRAPH>;
 /// Poseidon module config
 pub type ModulePoseidonConfig = PoseidonConfig<POSEIDON_WIDTH, POSEIDON_RATE>;

@@ -281,6 +284,7 @@ impl GraphModules {
                log::error!("Poseidon config not initialized");
                return Err(Error::Synthesis);
            }
+            // If the module is encrypted, then we need to encrypt the inputs
        }

        Ok(())
--- a/src/graph/node.rs
+++ b/src/graph/node.rs
@@ -1,19 +1,10 @@
-// Import dependencies for scaling operations
 use super::scale_to_multiplier;
-
-// Import ONNX-specific utilities when EZKL feature is enabled
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use super::utilities::node_output_shapes;
-
-// Import scale management types for EZKL
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use super::VarScales;
-
-// Import visibility settings for EZKL
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use super::Visibility;
-
-// Import operation types for different circuit components
 use crate::circuit::hybrid::HybridOp;
 use crate::circuit::lookup::LookupOp;
 use crate::circuit::poly::PolyOp;
@@ -22,49 +13,28 @@ use crate::circuit::Constant;
 use crate::circuit::Input;
 use crate::circuit::Op;
 use crate::circuit::Unknown;
-
-// Import graph error types for EZKL
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use crate::graph::errors::GraphError;
-
-// Import ONNX operation conversion utilities
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use crate::graph::new_op_from_onnx;
-
-// Import tensor error handling
 use crate::tensor::TensorError;
-
-// Import curve-specific field type
 use halo2curves::bn256::Fr as Fp;
-
-// Import logging for EZKL
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use log::trace;
-
-// Import serialization traits
 use serde::Deserialize;
 use serde::Serialize;
-
-// Import data structures for EZKL
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use std::collections::BTreeMap;
-
-// Import formatting traits for EZKL
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use std::fmt;
-
-// Import table display formatting for EZKL
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use tabled::Tabled;
-
-// Import ONNX-specific types and traits for EZKL
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use tract_onnx::{
    self,
    prelude::{Node as OnnxNode, SymbolValues, TypedFact, TypedOp},
 };

-/// Helper function to format vectors for display
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 fn display_vector<T: fmt::Debug>(v: &Vec<T>) -> String {
    if !v.is_empty() {
@@ -74,35 +44,29 @@ fn display_vector<T: fmt::Debug>(v: &Vec<T>) -> String {
    }
 }

-/// Helper function to format operation kinds for display
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 fn display_opkind(v: &SupportedOp) -> String {
    v.as_string()
 }

-/// A wrapper for an operation that has been rescaled to handle different precision requirements.
-/// This enables operations to work with inputs that have been scaled to different fixed-point representations.
+/// A wrapper for an operation that has been rescaled.
 #[derive(Clone, Debug, Serialize, Deserialize)]
 pub struct Rescaled {
-    /// The underlying operation that needs to be rescaled
+    /// The operation that has to be rescaled.
    pub inner: Box<SupportedOp>,
-    /// Vector of (index, scale) pairs defining how each input should be scaled
+    /// The scale of the operation's inputs.
    pub scale: Vec<(usize, u128)>,
 }

-/// Implementation of the Op trait for Rescaled operations
 impl Op<Fp> for Rescaled {
-    /// Convert to Any type for runtime type checking
    fn as_any(&self) -> &dyn std::any::Any {
        self
    }

-    /// Get string representation of the operation
    fn as_string(&self) -> String {
        format!("RESCALED INPUT ({})", self.inner.as_string())
    }

-    /// Calculate output scale based on input scales
    fn out_scale(&self, in_scales: Vec<crate::Scale>) -> Result<crate::Scale, CircuitError> {
        let in_scales = in_scales
            .into_iter()
@@ -113,7 +77,6 @@ impl Op<Fp> for Rescaled {
        Op::<Fp>::out_scale(&*self.inner, in_scales)
    }

-    /// Layout the operation in the circuit
    fn layout(
        &self,
        config: &mut crate::circuit::BaseConfig<Fp>,
@@ -130,40 +93,28 @@ impl Op<Fp> for Rescaled {
        self.inner.layout(config, region, res)
    }

-    /// Create a cloned boxed copy of this operation
    fn clone_dyn(&self) -> Box<dyn Op<Fp>> {
-        Box::new(self.clone())
+        Box::new(self.clone()) // Forward to the derive(Clone) impl
    }
 }

-/// A wrapper for operations that require scale rebasing
-/// This handles cases where operation scales need to be adjusted to a target scale
-/// while preserving the numerical relationships
+/// A wrapper for an operation that has been rescaled.
 #[derive(Clone, Debug, Serialize, Deserialize)]
 pub struct RebaseScale {
-    /// The operation that needs to be rescaled
+    /// The operation that has to be rescaled.
    pub inner: Box<SupportedOp>,
-    /// Operation used for rebasing, typically division
+    /// rebase op
    pub rebase_op: HybridOp,
-    /// Scale that we're rebasing to
+    /// scale being rebased to
    pub target_scale: i32,
-    /// Original scale of operation's inputs before rebasing
+    /// The original scale of the operation's inputs.
    pub original_scale: i32,
-    /// Scaling multiplier used in rebasing
+    /// multiplier
    pub multiplier: f64,
 }

 impl RebaseScale {
-    /// Creates a rebased version of an operation if needed
    ///
-    /// # Arguments
-    /// * `inner` - Operation to potentially rebase
-    /// * `global_scale` - Base scale for the system
-    /// * `op_out_scale` - Current output scale of the operation
-    /// * `scale_rebase_multiplier` - Factor determining when rebasing should occur
-    ///
-    /// # Returns
-    /// Original or rebased operation depending on scale relationships
    pub fn rebase(
        inner: SupportedOp,
        global_scale: crate::Scale,
@@ -204,15 +155,7 @@ impl RebaseScale {
        }
    }

-    /// Creates a rebased operation with increased scale
    ///
-    /// # Arguments
-    /// * `inner` - Operation to potentially rebase
-    /// * `target_scale` - Scale to rebase to
-    /// * `op_out_scale` - Current output scale of the operation
-    ///
-    /// # Returns
-    /// Original or rebased operation with increased scale
    pub fn rebase_up(
        inner: SupportedOp,
        target_scale: crate::Scale,
@@ -249,12 +192,10 @@ impl RebaseScale {
 }

 impl Op<Fp> for RebaseScale {
-    /// Convert to Any type for runtime type checking
    fn as_any(&self) -> &dyn std::any::Any {
        self
    }

-    /// Get string representation of the operation
    fn as_string(&self) -> String {
        format!(
            "REBASED (div={:?}, rebasing_op={}) ({})",
@@ -264,12 +205,10 @@ impl Op<Fp> for RebaseScale {
        )
    }

-    /// Calculate output scale based on input scales
    fn out_scale(&self, _: Vec<crate::Scale>) -> Result<crate::Scale, CircuitError> {
        Ok(self.target_scale)
    }

-    /// Layout the operation in the circuit
    fn layout(
        &self,
        config: &mut crate::circuit::BaseConfig<Fp>,
@@ -283,40 +222,34 @@ impl Op<Fp> for RebaseScale {
        self.rebase_op.layout(config, region, &[original_res])
    }

-    /// Create a cloned boxed copy of this operation
    fn clone_dyn(&self) -> Box<dyn Op<Fp>> {
-        Box::new(self.clone())
+        Box::new(self.clone()) // Forward to the derive(Clone) impl
    }
 }

-/// Represents all supported operation types in the circuit
-/// Each variant encapsulates a different type of operation with specific behavior
+/// A single operation in a [crate::graph::Model].
 #[derive(Clone, Debug, Serialize, Deserialize)]
 pub enum SupportedOp {
-    /// Linear operations (polynomial-based)
+    /// A linear operation.
    Linear(PolyOp),
-    /// Nonlinear operations requiring lookup tables
+    /// A nonlinear operation.
    Nonlinear(LookupOp),
-    /// Mixed operations combining different approaches
+    /// A hybrid operation.
    Hybrid(HybridOp),
-    /// Input values to the circuit
+    ///
    Input(Input),
-    /// Constant values in the circuit
+    ///
    Constant(Constant<Fp>),
-    /// Placeholder for unsupported operations
+    ///
    Unknown(Unknown),
-    /// Operations requiring rescaling of inputs
+    ///
    Rescaled(Rescaled),
-    /// Operations requiring scale rebasing
+    ///
    RebaseScale(RebaseScale),
 }

 impl SupportedOp {
-    /// Checks if the operation is a lookup operation
    ///
-    /// # Returns
-    /// * `true` if operation requires lookup table
-    /// * `false` otherwise
    pub fn is_lookup(&self) -> bool {
        match self {
            SupportedOp::Nonlinear(_) => true,
@@ -324,12 +257,7 @@ impl SupportedOp {
            _ => false,
        }
    }
-
-    /// Returns input operation if this is an input
    ///
-    /// # Returns
-    /// * `Some(Input)` if this is an input operation
-    /// * `None` otherwise
    pub fn get_input(&self) -> Option<Input> {
        match self {
            SupportedOp::Input(op) => Some(op.clone()),
@@ -337,11 +265,7 @@ impl SupportedOp {
        }
    }

-    /// Returns reference to rebased operation if this is a rebased operation
    ///
-    /// # Returns
-    /// * `Some(&RebaseScale)` if this is a rebased operation
-    /// * `None` otherwise
    pub fn get_rebased(&self) -> Option<&RebaseScale> {
        match self {
            SupportedOp::RebaseScale(op) => Some(op),
@@ -349,11 +273,7 @@ impl SupportedOp {
        }
    }

-    /// Returns reference to lookup operation if this is a lookup operation
    ///
-    /// # Returns
-    /// * `Some(&LookupOp)` if this is a lookup operation
-    /// * `None` otherwise
    pub fn get_lookup(&self) -> Option<&LookupOp> {
        match self {
            SupportedOp::Nonlinear(op) => Some(op),
@@ -361,11 +281,7 @@ impl SupportedOp {
        }
    }

-    /// Returns reference to constant if this is a constant
    ///
-    /// # Returns
-    /// * `Some(&Constant)` if this is a constant
-    /// * `None` otherwise
    pub fn get_constant(&self) -> Option<&Constant<Fp>> {
        match self {
            SupportedOp::Constant(op) => Some(op),
@@ -373,11 +289,7 @@ impl SupportedOp {
        }
    }

-    /// Returns mutable reference to constant if this is a constant
    ///
-    /// # Returns
-    /// * `Some(&mut Constant)` if this is a constant
-    /// * `None` otherwise
    pub fn get_mutable_constant(&mut self) -> Option<&mut Constant<Fp>> {
        match self {
            SupportedOp::Constant(op) => Some(op),
@@ -385,19 +297,18 @@ impl SupportedOp {
        }
    }

-    /// Creates a homogeneously rescaled version of this operation if needed
-    /// Only available with EZKL feature enabled
    #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
    fn homogenous_rescale(
        &self,
        in_scales: Vec<crate::Scale>,
    ) -> Result<Box<dyn Op<Fp>>, GraphError> {
        let inputs_to_scale = self.requires_homogenous_input_scales();
+        // creates a rescaled op if the inputs are not homogenous
        let op = self.clone_dyn();
        super::homogenize_input_scales(op, in_scales, inputs_to_scale)
    }

-    /// Returns reference to underlying Op implementation
+    /// Since each associated value of `SupportedOp` implements `Op`, let's define a helper method to retrieve it.
    fn as_op(&self) -> &dyn Op<Fp> {
        match self {
            SupportedOp::Linear(op) => op,
@@ -411,10 +322,9 @@ impl SupportedOp {
        }
    }

-    /// Checks if this is an identity operation
-    ///
+    /// check if is the identity operation
    /// # Returns
-    /// * `true` if this operation passes input through unchanged
+    /// * `true` if the operation is the identity operation
    /// * `false` otherwise
    pub fn is_identity(&self) -> bool {
        match self {
@@ -451,11 +361,9 @@ impl From<Box<dyn Op<Fp>>> for SupportedOp {
        if let Some(op) = value.as_any().downcast_ref::<Unknown>() {
            return SupportedOp::Unknown(op.clone());
        };
-
        if let Some(op) = value.as_any().downcast_ref::<Rescaled>() {
            return SupportedOp::Rescaled(op.clone());
        };
-
        if let Some(op) = value.as_any().downcast_ref::<RebaseScale>() {
            return SupportedOp::RebaseScale(op.clone());
        };
@@ -467,7 +375,6 @@ impl From<Box<dyn Op<Fp>>> for SupportedOp {
 }

 impl Op<Fp> for SupportedOp {
-    /// Layout this operation in the circuit
    fn layout(
        &self,
        config: &mut crate::circuit::BaseConfig<Fp>,
@@ -477,61 +384,54 @@ impl Op<Fp> for SupportedOp {
        self.as_op().layout(config, region, values)
    }

-    /// Check if this is an input operation
    fn is_input(&self) -> bool {
        self.as_op().is_input()
    }

-    /// Check if this is a constant operation
    fn is_constant(&self) -> bool {
        self.as_op().is_constant()
    }

-    /// Get which inputs require homogeneous scales
    fn requires_homogenous_input_scales(&self) -> Vec<usize> {
        self.as_op().requires_homogenous_input_scales()
    }

-    /// Create a clone of this operation
    fn clone_dyn(&self) -> Box<dyn Op<Fp>> {
        self.as_op().clone_dyn()
    }

-    /// Get string representation
    fn as_string(&self) -> String {
        self.as_op().as_string()
    }

-    /// Convert to Any type
    fn as_any(&self) -> &dyn std::any::Any {
        self
    }

-    /// Calculate output scale from input scales
    fn out_scale(&self, in_scales: Vec<crate::Scale>) -> Result<crate::Scale, CircuitError> {
        self.as_op().out_scale(in_scales)
    }
 }

-/// Represents a connection to another node's output
-/// First element is node index, second is output slot index
+/// A node's input is a tensor from another node's output.
 pub type Outlet = (usize, usize);

-/// Represents a single computational node in the circuit graph
-/// Contains all information needed to execute and connect operations
+/// A single operation in a [crate::graph::Model].
 #[derive(Clone, Debug, Serialize, Deserialize)]
 pub struct Node {
-    /// The operation this node performs
+    /// [Op] i.e what operation this node represents.
    pub opkind: SupportedOp,
-    /// Fixed point scale factor for this node's output
+    /// The denominator in the fixed point representation for the node's output. Tensors of differing scales should not be combined.
    pub out_scale: i32,
-    /// Connections to other nodes' outputs that serve as inputs
+    // Usually there is a simple in and out shape of the node as an operator.  For example, an Affine node has three input_shapes (one for the input, weight, and bias),
+    // but in_dim is [in], out_dim is [out]
+    /// The indices of the node's inputs.
    pub inputs: Vec<Outlet>,
-    /// Shape of this node's output tensor
+    /// Dimensions of output.
    pub out_dims: Vec<usize>,
-    /// Unique identifier for this node
+    /// The node's unique identifier.
    pub idx: usize,
-    /// Number of times this node's output is used
+    /// The node's num of uses
    pub num_uses: usize,
 }

@@ -569,19 +469,12 @@ impl PartialEq for Node {
 }

 impl Node {
-    /// Creates a new Node from an ONNX node
-    /// Only available when EZKL feature is enabled
-    ///
-    /// # Arguments
-    /// * `node` - Source ONNX node
-    /// * `other_nodes` - Map of existing nodes in the graph
-    /// * `scales` - Scale factors for variables
-    /// * `idx` - Unique identifier for this node
-    /// * `symbol_values` - ONNX symbol values
-    /// * `run_args` - Runtime configuration arguments
-    ///
-    /// # Returns
-    /// New Node instance or error if creation fails
+    /// Converts a tract [OnnxNode] into an ezkl [Node].
+    /// # Arguments:
+    /// * `node` - [OnnxNode]
+    /// * `other_nodes` - [BTreeMap] of other previously initialized [Node]s in the computational graph.
+    /// * `public_params` - flag if parameters of model are public
+    /// * `idx` - The node's unique identifier.
    #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
    #[allow(clippy::too_many_arguments)]
    pub fn new(
@@ -719,14 +612,16 @@ impl Node {
        })
    }

-    /// Check if this node performs softmax operation
+    /// check if it is a softmax node
    pub fn is_softmax(&self) -> bool {
-        matches!(self.opkind, SupportedOp::Hybrid(HybridOp::Softmax { .. }))
+        if let SupportedOp::Hybrid(HybridOp::Softmax { .. }) = self.opkind {
+            true
+        } else {
+            false
+        }
    }
 }

-/// Helper function to rescale constants that are only used once
-/// Only available when EZKL feature is enabled
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 fn rescale_const_with_single_use(
    constant: &mut Constant<Fp>,
--- a/src/graph/utilities.rs
+++ b/src/graph/utilities.rs
@@ -1,14 +1,14 @@
+use super::errors::GraphError;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use super::VarScales;
-use super::errors::GraphError;
 use super::{Rescaled, SupportedOp, Visibility};
-use crate::circuit::Op;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use crate::circuit::hybrid::HybridOp;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use crate::circuit::lookup::LookupOp;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use crate::circuit::poly::PolyOp;
+use crate::circuit::Op;
 use crate::fieldutils::IntegerRep;
 use crate::tensor::{Tensor, TensorError, TensorType};
 use halo2curves::bn256::Fr as Fp;
@@ -22,7 +22,6 @@ use std::sync::Arc;
 use tract_onnx::prelude::{DatumType, Node as OnnxNode, TypedFact, TypedOp};
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use tract_onnx::tract_core::ops::{
-    Downsample,
    array::{
        Gather, GatherElements, GatherNd, MultiBroadcastTo, OneHot, ScatterElements, ScatterNd,
        Slice, Topk,
@@ -32,6 +31,7 @@ use tract_onnx::tract_core::ops::{
    einsum::EinSum,
    element_wise::ElementWiseOp,
    nn::{LeakyRelu, Reduce, Softmax},
+    Downsample,
 };
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use tract_onnx::tract_hir::{
@@ -39,15 +39,16 @@ use tract_onnx::tract_hir::{
    ops::array::{Pad, PadMode, TypedConcat},
    ops::cnn::PoolSpec,
    ops::konst::Const,
+    ops::nn::DataFormat,
    tract_core::ops::cast::Cast,
-    tract_core::ops::cnn::{MaxPool, SumPool},
+    tract_core::ops::cnn::{conv::KernelFormat, MaxPool, SumPool},
 };

-/// Quantizes an iterable of f64 to a [Tensor] of IntegerRep using a fixed point representation.
-/// NAN gets mapped to 0. INFINITY and NEG_INFINITY error out.
+/// Quantizes an iterable of f32s to a [Tensor] of i32s using a fixed point representation.
 /// Arguments
 ///
-/// * `elem` - the element to quantize.
+/// * `vec` - the vector to quantize.
+/// * `dims` - the dimensionality of the resulting [Tensor].
 /// * `shift` - offset used in the fixed point representation.
 /// * `scale` - `2^scale` used in the fixed point representation.
 pub fn quantize_float(
@@ -58,7 +59,7 @@ pub fn quantize_float(
    let mult = scale_to_multiplier(scale);
    let max_value = ((IntegerRep::MAX as f64 - shift) / mult).round(); // the maximum value that can be represented w/o sig bit truncation

-    if *elem > max_value || *elem < -max_value {
+    if *elem > max_value {
        return Err(TensorError::SigBitTruncationError);
    }

@@ -84,7 +85,7 @@ pub fn scale_to_multiplier(scale: crate::Scale) -> f64 {
    f64::powf(2., scale as f64)
 }

-/// Converts a fixed point multiplier to a scale (log base 2).
+/// Converts a scale (log base 2) to a fixed point multiplier.
 pub fn multiplier_to_scale(mult: f64) -> crate::Scale {
    mult.log2().round() as crate::Scale
 }
@@ -227,7 +228,10 @@ pub fn extract_tensor_value(
                .iter()
                .map(|x| match x.to_i64() {
                    Ok(v) => Ok(v as f32),
-                    Err(_) => Err(GraphError::UnsupportedDataType(0, "TDim".to_string())),
+                    Err(_) => match x.to_i64() {
+                        Ok(v) => Ok(v as f32),
+                        Err(_) => Err(GraphError::UnsupportedDataType(0, "TDim".to_string())),
+                    },
                })
                .collect();

@@ -273,10 +277,12 @@ pub fn new_op_from_onnx(
    symbol_values: &SymbolValues,
    run_args: &crate::RunArgs,
 ) -> Result<(SupportedOp, Vec<usize>), GraphError> {
-    use crate::circuit::InputType;
    use std::f64::consts::E;
+
    use tract_onnx::tract_core::ops::array::Trilu;

+    use crate::circuit::InputType;
+
    let input_scales = inputs
        .iter()
        .flat_map(|x| x.out_scales())
@@ -306,9 +312,6 @@ pub fn new_op_from_onnx(
    let mut deleted_indices = vec![];
    let node = match node.op().name().as_ref() {
        "ShiftLeft" => {
-            if inputs.len() != 2 {
-                return Err(GraphError::InvalidDims(idx, "shift left".to_string()));
-            };
            // load shift amount
            if let Some(c) = inputs[1].opkind().get_mutable_constant() {
                inputs[1].decrement_use();
@@ -321,13 +324,10 @@ pub fn new_op_from_onnx(
                    out_scale: Some(input_scales[0] - raw_values[0] as i32),
                })
            } else {
-                return Err(GraphError::OpMismatch(idx, "shift left".to_string()));
+                return Err(GraphError::OpMismatch(idx, "ShiftLeft".to_string()));
            }
        }
        "ShiftRight" => {
-            if inputs.len() != 2 {
-                return Err(GraphError::InvalidDims(idx, "shift right".to_string()));
-            };
            // load shift amount
            if let Some(c) = inputs[1].opkind().get_mutable_constant() {
                inputs[1].decrement_use();
@@ -340,7 +340,7 @@ pub fn new_op_from_onnx(
                    out_scale: Some(input_scales[0] + raw_values[0] as i32),
                })
            } else {
-                return Err(GraphError::OpMismatch(idx, "shift right".to_string()));
+                return Err(GraphError::OpMismatch(idx, "ShiftRight".to_string()));
            }
        }
        "MultiBroadcastTo" => {
@@ -363,10 +363,7 @@ pub fn new_op_from_onnx(
                }
            }

-            if input_ops.len() != 3 {
-                return Err(GraphError::InvalidDims(idx, "range".to_string()));
-            }
-
+            assert_eq!(input_ops.len(), 3, "Range requires 3 inputs");
            let input_ops = input_ops
                .iter()
                .map(|x| x.get_constant().ok_or(GraphError::NonConstantRange))
@@ -381,11 +378,7 @@ pub fn new_op_from_onnx(
            // Quantize the raw value (integers)
            let quantized_value = quantize_tensor(raw_value.clone(), 0, &Visibility::Fixed)?;

-            let c = crate::circuit::ops::Constant::new(
-                quantized_value,
-                raw_value,
-                !run_args.ignore_range_check_inputs_outputs,
-            );
+            let c = crate::circuit::ops::Constant::new(quantized_value, raw_value);
            // Create a constant op
            SupportedOp::Constant(c)
        }
@@ -426,10 +419,6 @@ pub fn new_op_from_onnx(
            if let Some(c) = inputs[1].opkind().get_mutable_constant() {
                inputs[1].decrement_use();
                deleted_indices.push(inputs.len() - 1);
-                if inputs[0].out_dims().is_empty() || inputs[0].out_dims()[0].len() <= axis {
-                    return Err(GraphError::InvalidDims(idx, "gather".to_string()));
-                }
-
                op = SupportedOp::Hybrid(crate::circuit::ops::hybrid::HybridOp::Gather {
                    dim: axis,
                    constant_idx: Some(c.raw_values.map(|x| {
@@ -447,7 +436,6 @@ pub fn new_op_from_onnx(
                inputs[1].replace_opkind(SupportedOp::Input(crate::circuit::ops::Input {
                    scale: 0,
                    datum_type: InputType::TDim,
-                    decomp: false,
                }));
                inputs[1].bump_scale(0);
            }
@@ -459,17 +447,8 @@ pub fn new_op_from_onnx(
        "Topk" => {
            let op = load_op::<Topk>(node.op(), idx, node.op().name().to_string())?;
            let axis = op.axis;
-
-            if inputs.len() != 2 {
-                return Err(GraphError::InvalidDims(idx, "topk".to_string()));
-            };
-
            // if param_visibility.is_public() {
            let k = if let Some(c) = inputs[1].opkind().get_mutable_constant() {
-                if c.raw_values.len() != 1 {
-                    return Err(GraphError::InvalidDims(idx, "topk".to_string()));
-                }
-
                inputs[1].decrement_use();
                deleted_indices.push(inputs.len() - 1);
                c.raw_values.map(|x| x as usize)[0]
@@ -509,10 +488,6 @@ pub fn new_op_from_onnx(
            if let Some(c) = inputs[1].opkind().get_mutable_constant() {
                inputs[1].decrement_use();
                deleted_indices.push(1);
-                if c.raw_values.is_empty() {
-                    return Err(GraphError::InvalidDims(idx, "scatter elements".to_string()));
-                }
-
                op = SupportedOp::Linear(crate::circuit::ops::poly::PolyOp::ScatterElements {
                    dim: axis,
                    constant_idx: Some(c.raw_values.map(|x| x as usize)),
@@ -524,7 +499,6 @@ pub fn new_op_from_onnx(
                inputs[1].replace_opkind(SupportedOp::Input(crate::circuit::ops::Input {
                    scale: 0,
                    datum_type: InputType::TDim,
-                    decomp: !run_args.ignore_range_check_inputs_outputs,
                }));
                inputs[1].bump_scale(0);
            }
@@ -548,9 +522,6 @@ pub fn new_op_from_onnx(
            if let Some(c) = inputs[1].opkind().get_mutable_constant() {
                inputs[1].decrement_use();
                deleted_indices.push(1);
-                if c.raw_values.is_empty() {
-                    return Err(GraphError::InvalidDims(idx, "scatter nd".to_string()));
-                }
                op = SupportedOp::Linear(crate::circuit::ops::poly::PolyOp::ScatterND {
                    constant_idx: Some(c.raw_values.map(|x| x as usize)),
                })
@@ -561,7 +532,6 @@ pub fn new_op_from_onnx(
                inputs[1].replace_opkind(SupportedOp::Input(crate::circuit::ops::Input {
                    scale: 0,
                    datum_type: InputType::TDim,
-                    decomp: !run_args.ignore_range_check_inputs_outputs,
                }));
                inputs[1].bump_scale(0);
            }
@@ -585,9 +555,6 @@ pub fn new_op_from_onnx(
            if let Some(c) = inputs[1].opkind().get_mutable_constant() {
                inputs[1].decrement_use();
                deleted_indices.push(1);
-                if c.raw_values.is_empty() {
-                    return Err(GraphError::InvalidDims(idx, "gather nd".to_string()));
-                }
                op = SupportedOp::Linear(crate::circuit::ops::poly::PolyOp::GatherND {
                    batch_dims,
                    indices: Some(c.raw_values.map(|x| x as usize)),
@@ -599,7 +566,6 @@ pub fn new_op_from_onnx(
                inputs[1].replace_opkind(SupportedOp::Input(crate::circuit::ops::Input {
                    scale: 0,
                    datum_type: InputType::TDim,
-                    decomp: !run_args.ignore_range_check_inputs_outputs,
                }));
                inputs[1].bump_scale(0);
            }
@@ -623,9 +589,6 @@ pub fn new_op_from_onnx(
            if let Some(c) = inputs[1].opkind().get_mutable_constant() {
                inputs[1].decrement_use();
                deleted_indices.push(1);
-                if c.raw_values.is_empty() {
-                    return Err(GraphError::InvalidDims(idx, "gather elements".to_string()));
-                }
                op = SupportedOp::Linear(crate::circuit::ops::poly::PolyOp::GatherElements {
                    dim: axis,
                    constant_idx: Some(c.raw_values.map(|x| x as usize)),
@@ -637,7 +600,6 @@ pub fn new_op_from_onnx(
                inputs[1].replace_opkind(SupportedOp::Input(crate::circuit::ops::Input {
                    scale: 0,
                    datum_type: InputType::TDim,
-                    decomp: !run_args.ignore_range_check_inputs_outputs,
                }));
                inputs[1].bump_scale(0);
            }
@@ -712,11 +674,7 @@ pub fn new_op_from_onnx(
                constant_scale,
                &run_args.param_visibility,
            )?;
-            let c = crate::circuit::ops::Constant::new(
-                quantized_value,
-                raw_value,
-                run_args.ignore_range_check_inputs_outputs,
-            );
+            let c = crate::circuit::ops::Constant::new(quantized_value, raw_value);
            // Create a constant op
            SupportedOp::Constant(c)
        }
@@ -726,9 +684,7 @@ pub fn new_op_from_onnx(
            };
            let op = load_op::<Reduce>(node.op(), idx, node.op().name().to_string())?;
            let axes: Vec<usize> = op.axes.into_iter().collect();
-            if axes.len() != 1 {
-                return Err(GraphError::InvalidDims(idx, "argmax".to_string()));
-            }
+            assert_eq!(axes.len(), 1, "only support argmax over one axis");

            SupportedOp::Hybrid(HybridOp::ReduceArgMax { dim: axes[0] })
        }
@@ -738,9 +694,7 @@ pub fn new_op_from_onnx(
            };
            let op = load_op::<Reduce>(node.op(), idx, node.op().name().to_string())?;
            let axes: Vec<usize> = op.axes.into_iter().collect();
-            if axes.len() != 1 {
-                return Err(GraphError::InvalidDims(idx, "argmin".to_string()));
-            }
+            assert_eq!(axes.len(), 1, "only support argmin over one axis");

            SupportedOp::Hybrid(HybridOp::ReduceArgMin { dim: axes[0] })
        }
@@ -849,9 +803,6 @@ pub fn new_op_from_onnx(
            }
        }
        "Recip" => {
-            if inputs.len() != 1 {
-                return Err(GraphError::InvalidDims(idx, "recip".to_string()));
-            };
            let in_scale = input_scales[0];
            let max_scale = std::cmp::max(scales.get_max(), in_scale);
            // If the input scale is larger than the params scale
@@ -895,9 +846,6 @@ pub fn new_op_from_onnx(
            scale: scale_to_multiplier(input_scales[0]).into(),
        }),
        "Rsqrt" => {
-            if input_scales.len() != 1 {
-                return Err(GraphError::InvalidDims(idx, "rsqrt".to_string()));
-            };
            let in_scale = input_scales[0];
            let max_scale = std::cmp::max(scales.get_max(), in_scale);
            SupportedOp::Hybrid(HybridOp::Rsqrt {
@@ -979,19 +927,13 @@ pub fn new_op_from_onnx(
                DatumType::F64 => (scales.input, InputType::F64),
                _ => return Err(GraphError::UnsupportedDataType(idx, format!("{:?}", dt))),
            };
-            SupportedOp::Input(crate::circuit::ops::Input {
-                scale,
-                datum_type,
-                decomp: !run_args.ignore_range_check_inputs_outputs,
-            })
+            SupportedOp::Input(crate::circuit::ops::Input { scale, datum_type })
        }
        "Cast" => {
            let op = load_op::<Cast>(node.op(), idx, node.op().name().to_string())?;
            let dt = op.to;

-            if input_scales.len() != 1 {
-                return Err(GraphError::InvalidDims(idx, "cast".to_string()));
-            };
+            assert_eq!(input_scales.len(), 1);

            match dt {
                DatumType::Bool
@@ -1041,11 +983,6 @@ pub fn new_op_from_onnx(

            if const_idx.len() == 1 {
                let const_idx = const_idx[0];
-
-                if inputs.len() <= const_idx {
-                    return Err(GraphError::InvalidDims(idx, "mul".to_string()));
-                }
-
                if let Some(c) = inputs[const_idx].opkind().get_mutable_constant() {
                    if c.raw_values.len() == 1 && c.raw_values[0] < 1. {
                        // if not divisible by 2 then we need to add a range check
@@ -1120,9 +1057,6 @@ pub fn new_op_from_onnx(
                    return Err(GraphError::OpMismatch(idx, "softmax".to_string()));
                }
            };
-            if input_scales.len() != 1 {
-                return Err(GraphError::InvalidDims(idx, "softmax".to_string()));
-            }

            let in_scale = input_scales[0];
            let max_scale = std::cmp::max(scales.get_max(), in_scale);
@@ -1145,6 +1079,13 @@ pub fn new_op_from_onnx(

            let pool_spec: &PoolSpec = &sumpool_node.pool_spec;

+            // only support pytorch type formatting for now
+            if pool_spec.data_format != DataFormat::NCHW {
+                return Err(GraphError::MissingParams(
+                    "data in wrong format".to_string(),
+                ));
+            }
+
            let stride = extract_strides(pool_spec)?;
            let padding = extract_padding(pool_spec, &input_dims[0])?;
            let kernel_shape = &pool_spec.kernel_shape;
@@ -1153,45 +1094,24 @@ pub fn new_op_from_onnx(
                padding,
                stride: stride.to_vec(),
                pool_dims: kernel_shape.to_vec(),
-                data_format: pool_spec.data_format.into(),
-            })
-        }
-        "Ceil" => {
-            if input_scales.len() != 1 {
-                return Err(GraphError::InvalidDims(idx, "ceil".to_string()));
-            }
-            SupportedOp::Hybrid(HybridOp::Ceil {
-                scale: scale_to_multiplier(input_scales[0]).into(),
-                legs: run_args.decomp_legs,
-            })
-        }
-        "Floor" => {
-            if input_scales.len() != 1 {
-                return Err(GraphError::InvalidDims(idx, "floor".to_string()));
-            }
-            SupportedOp::Hybrid(HybridOp::Floor {
-                scale: scale_to_multiplier(input_scales[0]).into(),
-                legs: run_args.decomp_legs,
-            })
-        }
-        "Round" => {
-            if input_scales.len() != 1 {
-                return Err(GraphError::InvalidDims(idx, "round".to_string()));
-            }
-            SupportedOp::Hybrid(HybridOp::Round {
-                scale: scale_to_multiplier(input_scales[0]).into(),
-                legs: run_args.decomp_legs,
-            })
-        }
-        "RoundHalfToEven" => {
-            if input_scales.len() != 1 {
-                return Err(GraphError::InvalidDims(idx, "roundhalftoeven".to_string()));
-            }
-            SupportedOp::Hybrid(HybridOp::RoundHalfToEven {
-                scale: scale_to_multiplier(input_scales[0]).into(),
-                legs: run_args.decomp_legs,
            })
        }
+        "Ceil" => SupportedOp::Hybrid(HybridOp::Ceil {
+            scale: scale_to_multiplier(input_scales[0]).into(),
+            legs: run_args.decomp_legs,
+        }),
+        "Floor" => SupportedOp::Hybrid(HybridOp::Floor {
+            scale: scale_to_multiplier(input_scales[0]).into(),
+            legs: run_args.decomp_legs,
+        }),
+        "Round" => SupportedOp::Hybrid(HybridOp::Round {
+            scale: scale_to_multiplier(input_scales[0]).into(),
+            legs: run_args.decomp_legs,
+        }),
+        "RoundHalfToEven" => SupportedOp::Hybrid(HybridOp::RoundHalfToEven {
+            scale: scale_to_multiplier(input_scales[0]).into(),
+            legs: run_args.decomp_legs,
+        }),
        "Sign" => SupportedOp::Linear(PolyOp::Sign),
        "Pow" => {
            // Extract the slope layer hyperparams from a const
@@ -1201,9 +1121,7 @@ pub fn new_op_from_onnx(
                inputs[1].decrement_use();
                deleted_indices.push(1);
                if c.raw_values.len() > 1 {
-                    return Err(GraphError::NonScalarPower);
-                } else if c.raw_values.is_empty() {
-                    return Err(GraphError::InvalidDims(idx, "pow".to_string()));
+                    unimplemented!("only support scalar pow")
                }

                let exponent = c.raw_values[0];
@@ -1220,9 +1138,7 @@ pub fn new_op_from_onnx(
                inputs[0].decrement_use();
                deleted_indices.push(0);
                if c.raw_values.len() > 1 {
-                    return Err(GraphError::NonScalarBase);
-                } else if c.raw_values.is_empty() {
-                    return Err(GraphError::InvalidDims(idx, "pow".to_string()));
+                    unimplemented!("only support scalar base")
                }

                let base = c.raw_values[0];
@@ -1232,14 +1148,10 @@ pub fn new_op_from_onnx(
                    base: base.into(),
                })
            } else {
-                return Err(GraphError::InvalidDims(idx, "pow".to_string()));
+                unimplemented!("only support constant base or pow for now")
            }
        }
        "Div" => {
-            if inputs.len() != 2 {
-                return Err(GraphError::InvalidDims(idx, "div".to_string()));
-            }
-
            let const_idx = inputs
                .iter()
                .enumerate()
@@ -1247,15 +1159,14 @@ pub fn new_op_from_onnx(
                .map(|(i, _)| i)
                .collect::<Vec<_>>();

-            if const_idx.len() > 1 || const_idx.is_empty() {
+            if const_idx.len() > 1 {
                return Err(GraphError::InvalidDims(idx, "div".to_string()));
            }

            let const_idx = const_idx[0];
+
            if const_idx != 1 {
-                return Err(GraphError::MisformedParams(
-                    "only support div with constant as second input".to_string(),
-                ));
+                unimplemented!("only support div with constant as second input")
            }

            if let Some(c) = inputs[const_idx].opkind().get_mutable_constant() {
@@ -1265,28 +1176,14 @@ pub fn new_op_from_onnx(
                    // get the non constant index
                    let denom = c.raw_values[0];

-                    let op = SupportedOp::Hybrid(HybridOp::Div {
+                    SupportedOp::Hybrid(HybridOp::Div {
                        denom: denom.into(),
-                    });
-
-                    // if the input is scale 0 we re up to the max scale
-                    if input_scales[0] == 0 {
-                        SupportedOp::Rescaled(Rescaled {
-                            inner: Box::new(op),
-                            scale: vec![(0, scale_to_multiplier(scales.get_max()) as u128)],
-                        })
-                    } else {
-                        op
-                    }
+                    })
                } else {
-                    return Err(GraphError::MisformedParams(
-                        "only support non zero divisors of size 1".to_string(),
-                    ));
+                    unimplemented!("only support non zero divisors of size 1")
                }
            } else {
-                return Err(GraphError::MisformedParams(
-                    "only support div with constant as second input".to_string(),
-                ));
+                unimplemented!("only support div with constant as second input")
            }
        }
        "Cube" => SupportedOp::Linear(PolyOp::Pow(3)),
@@ -1307,6 +1204,15 @@ pub fn new_op_from_onnx(
                }
            }

+            if ((conv_node.pool_spec.data_format != DataFormat::NCHW)
+                && (conv_node.pool_spec.data_format != DataFormat::CHW))
+                || (conv_node.kernel_fmt != KernelFormat::OIHW)
+            {
+                return Err(GraphError::MisformedParams(
+                    "data or kernel in wrong format".to_string(),
+                ));
+            }
+
            let pool_spec = &conv_node.pool_spec;

            let stride = extract_strides(pool_spec)?;
@@ -1334,8 +1240,6 @@ pub fn new_op_from_onnx(
                padding,
                stride,
                group,
-                data_format: conv_node.pool_spec.data_format.into(),
-                kernel_format: conv_node.kernel_fmt.into(),
            })
        }
        "Not" => SupportedOp::Linear(PolyOp::Not),
@@ -1359,6 +1263,14 @@ pub fn new_op_from_onnx(
                }
            }

+            if (deconv_node.pool_spec.data_format != DataFormat::NCHW)
+                || (deconv_node.kernel_format != KernelFormat::OIHW)
+            {
+                return Err(GraphError::MisformedParams(
+                    "data or kernel in wrong format".to_string(),
+                ));
+            }
+
            let pool_spec = &deconv_node.pool_spec;

            let stride = extract_strides(pool_spec)?;
@@ -1384,8 +1296,6 @@ pub fn new_op_from_onnx(
                output_padding: deconv_node.adjustments.to_vec(),
                stride,
                group: deconv_node.group,
-                data_format: deconv_node.pool_spec.data_format.into(),
-                kernel_format: deconv_node.kernel_format.into(),
            })
        }
        "Downsample" => {
@@ -1398,7 +1308,7 @@ pub fn new_op_from_onnx(

            SupportedOp::Linear(PolyOp::Downsample {
                axis: downsample_node.axis,
-                stride: downsample_node.stride,
+                stride: downsample_node.stride as usize,
                modulo: downsample_node.modulo,
            })
        }
@@ -1413,7 +1323,7 @@ pub fn new_op_from_onnx(
            if !resize_node.contains("interpolator: Nearest")
                && !resize_node.contains("nearest: Floor")
            {
-                return Err(GraphError::InvalidInterpolation);
+                unimplemented!("Only nearest neighbor interpolation is supported")
            }
            // check if optional scale factor is present
            if inputs.len() != 2 && inputs.len() != 3 {
@@ -1469,6 +1379,13 @@ pub fn new_op_from_onnx(

            let pool_spec: &PoolSpec = &sumpool_node.pool_spec;

+            // only support pytorch type formatting for now
+            if pool_spec.data_format != DataFormat::NCHW {
+                return Err(GraphError::MissingParams(
+                    "data in wrong format".to_string(),
+                ));
+            }
+
            let stride = extract_strides(pool_spec)?;
            let padding = extract_padding(pool_spec, &input_dims[0])?;

@@ -1477,7 +1394,6 @@ pub fn new_op_from_onnx(
                stride: stride.to_vec(),
                kernel_shape: pool_spec.kernel_shape.to_vec(),
                normalized: sumpool_node.normalize,
-                data_format: pool_spec.data_format.into(),
            })
        }
        "Pad" => {
@@ -1511,10 +1427,6 @@ pub fn new_op_from_onnx(
            SupportedOp::Linear(PolyOp::Reshape(output_shape))
        }
        "Flatten" => {
-            if inputs.len() != 1 || inputs[0].out_dims().is_empty() {
-                return Err(GraphError::InvalidDims(idx, "flatten".to_string()));
-            };
-
            let new_dims: Vec<usize> = vec![inputs[0].out_dims()[0].iter().product::<usize>()];
            SupportedOp::Linear(PolyOp::Flatten(new_dims))
        }
@@ -1588,10 +1500,12 @@ pub fn homogenize_input_scales(
    input_scales: Vec<crate::Scale>,
    inputs_to_scale: Vec<usize>,
 ) -> Result<Box<dyn Op<Fp>>, GraphError> {
-    let relevant_input_scales = inputs_to_scale
-        .iter()
-        .filter(|idx| input_scales.len() > **idx)
-        .map(|&idx| input_scales[idx])
+    let relevant_input_scales = input_scales
+        .clone()
+        .into_iter()
+        .enumerate()
+        .filter(|(idx, _)| inputs_to_scale.contains(idx))
+        .map(|(_, scale)| scale)
        .collect_vec();

    if inputs_to_scale.is_empty() {
@@ -1632,30 +1546,10 @@ pub fn homogenize_input_scales(
 }

 #[cfg(test)]
-/// tests for the utility module
 pub mod tests {

    use super::*;

-    // quantization tests
-    #[test]
-    fn test_quantize_tensor() {
-        let tensor: Tensor<f32> = (0..10).map(|x| x as f32).into();
-        let reference: Tensor<Fp> = (0..10).map(|x| x.into()).into();
-        let scale = 0;
-        let visibility = &Visibility::Public;
-        let quantized: Tensor<Fp> = quantize_tensor(tensor, scale, visibility).unwrap();
-        assert_eq!(quantized.len(), 10);
-        assert_eq!(quantized, reference);
-    }
-
-    #[test]
-    fn test_quantize_edge_cases() {
-        assert_eq!(quantize_float(&f64::NAN, 0.0, 0).unwrap(), 0);
-        assert!(quantize_float(&f64::INFINITY, 0.0, 0).is_err());
-        assert!(quantize_float(&f64::NEG_INFINITY, 0.0, 0).is_err());
-    }
-
    #[test]
    fn test_flatten_valtensors() {
        let tensor1: Tensor<Fp> = (0..10).map(|x| x.into()).into();
--- a/src/graph/vars.rs
+++ b/src/graph/vars.rs
@@ -11,34 +11,35 @@ use log::debug;
 use pyo3::{
    exceptions::PyValueError, FromPyObject, IntoPy, PyObject, PyResult, Python, ToPyObject,
 };
+
 use serde::{Deserialize, Serialize};
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use tosubcommand::ToFlags;

 use self::errors::GraphError;
+
 use super::*;

-/// Defines the visibility level of values within the zero-knowledge circuit
-/// Controls how values are handled during proof generation and verification
+/// Label enum to track whether model input, model parameters, and model output are public, private, or hashed
 #[derive(Clone, Debug, Serialize, Deserialize, PartialEq, Eq, PartialOrd, Ord, Default)]
 pub enum Visibility {
-    /// Value is private to the prover and not included in proof
+    /// Mark an item as private to the prover (not in the proof submitted for verification)
    #[default]
    Private,
-    /// Value is public and included in proof for verification
+    /// Mark an item as public (sent in the proof submitted for verification)
    Public,
-    /// Value is hashed and the hash is included in proof
+    /// Mark an item as publicly committed to (hash sent in the proof submitted for verification)
    Hashed {
-        /// Controls how the hash is handled in proof
-        /// true - hash is included directly in proof (public)
-        /// false - hash is used as advice and passed to computational graph
+        /// Whether the hash is used as an instance (sent in the proof submitted for verification)
+        /// if false the hash is used as an advice (not in the proof submitted for verification) and is then sent to the computational graph
+        /// if true the hash is used as an instance (sent in the proof submitted for verification) the *inputs* to the hashing function are then sent to the computational graph
        hash_is_public: bool,
-        /// Specifies which outputs this hash affects
+        ///
        outlets: Vec<usize>,
    },
-    /// Value is committed using KZG commitment scheme
+    /// Mark an item as publicly committed to (KZG commitment sent in the proof submitted for verification)
    KZGCommit,
-    /// Value is assigned as a constant in the circuit
+    /// assigned as a constant in the circuit
    Fixed,
 }

@@ -65,17 +66,15 @@ impl Display for Visibility {

 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 impl ToFlags for Visibility {
-    /// Converts visibility to command line flags
    fn to_flags(&self) -> Vec<String> {
        vec![format!("{}", self)]
    }
 }

 impl<'a> From<&'a str> for Visibility {
-    /// Converts string representation to Visibility
    fn from(s: &'a str) -> Self {
        if s.contains("hashed/private") {
-            // Split on last occurrence of '/'
+            // split on last occurrence of '/'
            let (_, outlets) = s.split_at(s.rfind('/').unwrap());
            let outlets = outlets
                .trim_start_matches('/')
@@ -107,8 +106,8 @@ impl<'a> From<&'a str> for Visibility {
 }

 #[cfg(feature = "python-bindings")]
+/// Converts Visibility into a PyObject (Required for Visibility to be compatible with Python)
 impl IntoPy<PyObject> for Visibility {
-    /// Converts Visibility to Python object
    fn into_py(self, py: Python) -> PyObject {
        match self {
            Visibility::Private => "private".to_object(py),
@@ -135,13 +134,14 @@ impl IntoPy<PyObject> for Visibility {
 }

 #[cfg(feature = "python-bindings")]
+/// Obtains Visibility from PyObject (Required for Visibility to be compatible with Python)
 impl<'source> FromPyObject<'source> for Visibility {
-    /// Extracts Visibility from Python object
    fn extract_bound(ob: &pyo3::Bound<'source, pyo3::PyAny>) -> PyResult<Self> {
        let strval = String::extract_bound(ob)?;
        let strval = strval.as_str();

        if strval.contains("hashed/private") {
+            // split on last occurence of '/'
            let (_, outlets) = strval.split_at(strval.rfind('/').unwrap());
            let outlets = outlets
                .trim_start_matches('/')
@@ -174,32 +174,29 @@ impl<'source> FromPyObject<'source> for Visibility {
 }

 impl Visibility {
-    /// Returns true if visibility is Fixed
+    #[allow(missing_docs)]
    pub fn is_fixed(&self) -> bool {
        matches!(&self, Visibility::Fixed)
    }
-
-    /// Returns true if visibility is Private or hashed private
+    #[allow(missing_docs)]
    pub fn is_private(&self) -> bool {
        matches!(&self, Visibility::Private) || self.is_hashed_private()
    }

-    /// Returns true if visibility is Public
+    #[allow(missing_docs)]
    pub fn is_public(&self) -> bool {
        matches!(&self, Visibility::Public)
    }
-
-    /// Returns true if visibility involves hashing
+    #[allow(missing_docs)]
    pub fn is_hashed(&self) -> bool {
        matches!(&self, Visibility::Hashed { .. })
    }
-
-    /// Returns true if visibility uses KZG commitment
+    #[allow(missing_docs)]
    pub fn is_polycommit(&self) -> bool {
        matches!(&self, Visibility::KZGCommit)
    }

-    /// Returns true if visibility is hashed with public hash
+    #[allow(missing_docs)]
    pub fn is_hashed_public(&self) -> bool {
        if let Visibility::Hashed {
            hash_is_public: true,
@@ -210,8 +207,7 @@ impl Visibility {
        }
        false
    }
-
-    /// Returns true if visibility is hashed with private hash
+    #[allow(missing_docs)]
    pub fn is_hashed_private(&self) -> bool {
        if let Visibility::Hashed {
            hash_is_public: false,
@@ -223,12 +219,11 @@ impl Visibility {
        false
    }

-    /// Returns true if visibility requires additional processing
+    #[allow(missing_docs)]
    pub fn requires_processing(&self) -> bool {
        matches!(&self, Visibility::Hashed { .. }) | matches!(&self, Visibility::KZGCommit)
    }
-
-    /// Returns vector of output indices that this visibility setting affects
+    #[allow(missing_docs)]
    pub fn overwrites_inputs(&self) -> Vec<usize> {
        if let Visibility::Hashed { outlets, .. } = self {
            return outlets.clone();
@@ -237,14 +232,14 @@ impl Visibility {
    }
 }

-/// Manages scaling factors for different parts of the model
+/// Represents the scale of the model input, model parameters.
 #[derive(Clone, Debug, Default, Deserialize, Serialize, PartialEq, PartialOrd)]
 pub struct VarScales {
-    /// Scale factor for input values
+    ///
    pub input: crate::Scale,
-    /// Scale factor for parameter values
+    ///
    pub params: crate::Scale,
-    /// Multiplier for scale rebasing
+    ///
    pub rebase_multiplier: u32,
 }

@@ -255,17 +250,17 @@ impl std::fmt::Display for VarScales {
 }

 impl VarScales {
-    /// Returns maximum scale value
+    ///
    pub fn get_max(&self) -> crate::Scale {
        std::cmp::max(self.input, self.params)
    }

-    /// Returns minimum scale value
+    ///
    pub fn get_min(&self) -> crate::Scale {
        std::cmp::min(self.input, self.params)
    }

-    /// Creates VarScales from runtime arguments
+    /// Place in [VarScales] struct.
    pub fn from_args(args: &RunArgs) -> Self {
        Self {
            input: args.input_scale,
@@ -275,17 +270,16 @@ impl VarScales {
    }
 }

-/// Controls visibility settings for different parts of the model
+/// Represents whether the model input, model parameters, and model output are Public or Private to the prover.
 #[derive(Clone, Debug, Deserialize, Serialize, PartialEq, PartialOrd)]
 pub struct VarVisibility {
-    /// Visibility of model inputs
+    /// Input to the model or computational graph
    pub input: Visibility,
-    /// Visibility of model parameters (weights, biases)
+    /// Parameters, such as weights and biases, in the model
    pub params: Visibility,
-    /// Visibility of model outputs
+    /// Output of the model or computational graph
    pub output: Visibility,
 }
-
 impl std::fmt::Display for VarVisibility {
    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
        write!(
@@ -307,7 +301,8 @@ impl Default for VarVisibility {
 }

 impl VarVisibility {
-    /// Creates visibility settings from runtime arguments
+    /// Read from cli args whether the model input, model parameters, and model output are Public or Private to the prover.
+    /// Place in [VarVisibility] struct.
    pub fn from_args(args: &RunArgs) -> Result<Self, GraphError> {
        let input_vis = &args.input_visibility;
        let params_vis = &args.param_visibility;
@@ -318,17 +313,17 @@ impl VarVisibility {
        }

        if !output_vis.is_public()
-            && !params_vis.is_public()
-            && !input_vis.is_public()
-            && !output_vis.is_fixed()
-            && !params_vis.is_fixed()
-            && !input_vis.is_fixed()
-            && !output_vis.is_hashed()
-            && !params_vis.is_hashed()
-            && !input_vis.is_hashed()
-            && !output_vis.is_polycommit()
-            && !params_vis.is_polycommit()
-            && !input_vis.is_polycommit()
+            & !params_vis.is_public()
+            & !input_vis.is_public()
+            & !output_vis.is_fixed()
+            & !params_vis.is_fixed()
+            & !input_vis.is_fixed()
+            & !output_vis.is_hashed()
+            & !params_vis.is_hashed()
+            & !input_vis.is_hashed()
+            & !output_vis.is_polycommit()
+            & !params_vis.is_polycommit()
+            & !input_vis.is_polycommit()
        {
            return Err(GraphError::Visibility);
        }
@@ -340,17 +335,17 @@ impl VarVisibility {
    }
 }

-/// Container for circuit columns used by a model
+/// A wrapper for holding all columns that will be assigned to by a model.
 #[derive(Clone, Debug)]
 pub struct ModelVars<F: PrimeField + TensorType + PartialOrd> {
-    /// Advice columns for circuit assignments
+    #[allow(missing_docs)]
    pub advices: Vec<VarTensor>,
-    /// Optional instance column for public inputs
+    #[allow(missing_docs)]
    pub instance: Option<ValTensor<F>>,
 }

 impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ModelVars<F> {
-    /// Gets reference to instance column if it exists
+    /// Get instance col
    pub fn get_instance_col(&self) -> Option<&Column<Instance>> {
        if let Some(instance) = &self.instance {
            match instance {
@@ -362,14 +357,14 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ModelVars<F> {
        }
    }

-    /// Sets initial offset for instance values
+    /// Set the initial instance offset
    pub fn set_initial_instance_offset(&mut self, offset: usize) {
        if let Some(instance) = &mut self.instance {
            instance.set_initial_instance_offset(offset);
        }
    }

-    /// Gets total length of instance data
+    /// Get the total instance len
    pub fn get_instance_len(&self) -> usize {
        if let Some(instance) = &self.instance {
            instance.get_total_instance_len()
@@ -378,21 +373,21 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ModelVars<F> {
        }
    }

-    /// Increments instance index
+    /// Increment the instance offset
    pub fn increment_instance_idx(&mut self) {
        if let Some(instance) = &mut self.instance {
            instance.increment_idx();
        }
    }

-    /// Sets instance index to specific value
+    /// Reset the instance offset
    pub fn set_instance_idx(&mut self, val: usize) {
        if let Some(instance) = &mut self.instance {
            instance.set_idx(val);
        }
    }

-    /// Gets current instance index
+    /// Get the instance offset
    pub fn get_instance_idx(&self) -> usize {
        if let Some(instance) = &self.instance {
            instance.get_idx()
@@ -401,7 +396,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ModelVars<F> {
        }
    }

-    /// Initializes instance column with specified dimensions and scale
+    ///
    pub fn instantiate_instance(
        &mut self,
        cs: &mut ConstraintSystem<F>,
@@ -422,7 +417,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ModelVars<F> {
        };
    }

-    /// Creates new ModelVars with allocated columns based on settings
+    /// Allocate all columns that will be assigned to by a model.
    pub fn new(cs: &mut ConstraintSystem<F>, params: &GraphSettings) -> Self {
        debug!("number of blinding factors: {}", cs.blinding_factors());

@@ -440,7 +435,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ModelVars<F> {
            .collect_vec();

        if requires_dynamic_lookup || requires_shuffle {
-            let num_cols = 3;
+            let num_cols = if requires_dynamic_lookup { 3 } else { 2 };
            for _ in 0..num_cols {
                let dynamic_lookup =
                    VarTensor::new_advice(cs, logrows, 1, dynamic_lookup_and_shuffle_size);
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -28,9 +28,6 @@

 //! A library for turning computational graphs, such as neural networks, into ZK-circuits.
 //!
-use log::warn;
-#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-use mimalloc as _;

 /// Error type
 // #[cfg_attr(not(feature = "ezkl"), derive(uniffi::Error))]
@@ -97,11 +94,12 @@ impl From<String> for EZKLError {

 use std::str::FromStr;

-use circuit::{table::Range, CheckMode};
+use circuit::{table::Range, CheckMode, Tolerance};
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use clap::Args;
+#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use fieldutils::IntegerRep;
-use graph::{Visibility, MAX_PUBLIC_SRS};
+use graph::Visibility;
 use halo2_proofs::poly::{
    ipa::commitment::IPACommitmentScheme, kzg::commitment::KZGCommitmentScheme,
 };
@@ -167,6 +165,7 @@ pub mod srs_sha;
 pub mod tensor;
 #[cfg(feature = "ios-bindings")]
 uniffi::setup_scaffolding!();
+
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use lazy_static::lazy_static;

@@ -181,9 +180,11 @@ lazy_static! {
        .unwrap_or("8000".to_string())
        .parse()
        .unwrap();
+
    /// The serialization format for the keys
    pub static ref EZKL_KEY_FORMAT: String = std::env::var("EZKL_KEY_FORMAT")
        .unwrap_or("raw-bytes".to_string());
+
 }

 #[cfg(any(not(feature = "ezkl"), target_arch = "wasm32"))]
@@ -265,101 +266,80 @@ impl From<String> for Commitments {
 }

 /// Parameters specific to a proving run
-///
-/// RunArgs contains all configuration parameters needed to control the proving process,
-/// including scaling factors, visibility settings, and circuit parameters.
 #[derive(Debug, Deserialize, Serialize, Clone, PartialEq, PartialOrd)]
 #[cfg_attr(
    all(feature = "ezkl", not(target_arch = "wasm32")),
    derive(Args, ToFlags)
 )]
 pub struct RunArgs {
-    /// Fixed point scaling factor for quantizing inputs
-    /// Higher values provide more precision but increase circuit complexity
+    /// The tolerance for error on model outputs
+    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(short = 'T', long, default_value = "0", value_hint = clap::ValueHint::Other))]
+    pub tolerance: Tolerance,
+    /// The denominator in the fixed point representation used when quantizing inputs
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(short = 'S', long, default_value = "7", value_hint = clap::ValueHint::Other))]
    pub input_scale: Scale,
-    /// Fixed point scaling factor for quantizing parameters
-    /// Higher values provide more precision but increase circuit complexity
+    /// The denominator in the fixed point representation used when quantizing parameters
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "7", value_hint = clap::ValueHint::Other))]
    pub param_scale: Scale,
-    /// Scale rebase threshold multiplier
-    /// When scale exceeds input_scale * multiplier, it is rebased to input_scale
-    /// Advanced parameter that should be used with caution
-    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "1", value_hint = clap::ValueHint::Other))]
+    /// if the scale is ever > scale_rebase_multiplier * input_scale then the scale is rebased to input_scale (this a more advanced parameter, use with caution)
+    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "1",  value_hint = clap::ValueHint::Other))]
    pub scale_rebase_multiplier: u32,
-    /// Range for lookup table input column values
-    /// Specified as (min, max) pair
+    /// The min and max elements in the lookup table input column
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(short = 'B', long, value_parser = parse_key_val::<IntegerRep, IntegerRep>, default_value = "-32768->32768"))]
    pub lookup_range: Range,
-    /// Log2 of the number of rows in the circuit
-    /// Controls circuit size and proving time
+    /// The log_2 number of rows
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(short = 'K', long, default_value = "17", value_hint = clap::ValueHint::Other))]
    pub logrows: u32,
-    /// Number of inner columns per block
-    /// Affects circuit layout and efficiency
+    /// The log_2 number of rows
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(short = 'N', long, default_value = "2", value_hint = clap::ValueHint::Other))]
    pub num_inner_cols: usize,
-    /// Graph variables for parameterizing the computation
-    /// Format: "name->value", e.g. "batch_size->1"
+    /// 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 = parse_key_val::<String, usize>, default_value = "batch_size->1", value_delimiter = ',', value_hint = clap::ValueHint::Other))]
    pub variables: Vec<(String, usize)>,
-    /// Visibility setting for input values
-    /// Controls whether inputs are public or private in the circuit
+    /// Flags whether inputs are public, private, fixed, hashed, polycommit
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "private", value_hint = clap::ValueHint::Other))]
    pub input_visibility: Visibility,
-    /// Visibility setting for output values
-    /// Controls whether outputs are public or private in the circuit
+    /// Flags whether outputs are public, private, fixed, hashed, polycommit
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "public", value_hint = clap::ValueHint::Other))]
    pub output_visibility: Visibility,
-    /// Visibility setting for parameters
-    /// Controls how parameters are handled in the circuit
+    /// Flags whether params are fixed, private, hashed, polycommit
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "private", value_hint = clap::ValueHint::Other))]
    pub param_visibility: Visibility,
-    /// Whether to rebase constants with zero fractional part to scale 0
-    /// Can improve efficiency for integer constants
+    #[cfg_attr(
+        all(feature = "ezkl", not(target_arch = "wasm32")),
+        arg(long, default_value = "false")
+    )]
+    /// Should constants with 0.0 fraction be rebased to scale 0
    #[cfg_attr(
        all(feature = "ezkl", not(target_arch = "wasm32")),
        arg(long, default_value = "false")
    )]
    pub rebase_frac_zero_constants: bool,
-    /// Circuit checking mode
-    /// Controls level of constraint verification
+    /// check mode (safe, unsafe, etc)
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "unsafe", value_hint = clap::ValueHint::Other))]
    pub check_mode: CheckMode,
-    /// Commitment scheme for circuit proving
-    /// Affects proof size and verification time
+    /// commitment scheme
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "kzg", value_hint = clap::ValueHint::Other))]
    pub commitment: Option<Commitments>,
-    /// Base for number decomposition
-    /// Must be a power of 2
+    /// the base used for decompositions
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "16384", value_hint = clap::ValueHint::Other))]
    pub decomp_base: usize,
-    /// Number of decomposition legs
-    /// Controls decomposition granularity
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "2", value_hint = clap::ValueHint::Other))]
+    /// the number of legs used for decompositions
    pub decomp_legs: usize,
-    /// Whether to use bounded lookup for logarithm computation
    #[cfg_attr(
        all(feature = "ezkl", not(target_arch = "wasm32")),
        arg(long, default_value = "false")
    )]
+    /// use unbounded lookup for the log
    pub bounded_log_lookup: bool,
-    /// Range check inputs and outputs (turn off if the inputs are felts)
-    #[cfg_attr(
-        all(feature = "ezkl", not(target_arch = "wasm32")),
-        arg(long, default_value = "false")
-    )]
-    pub ignore_range_check_inputs_outputs: bool,
 }

 impl Default for RunArgs {
-    /// Creates a new RunArgs instance with default values
-    ///
-    /// Default configuration is optimized for common use cases
-    /// while maintaining reasonable proving time and circuit size
    fn default() -> Self {
        Self {
            bounded_log_lookup: false,
+            tolerance: Tolerance::default(),
            input_scale: 7,
            param_scale: 7,
            scale_rebase_multiplier: 1,
@@ -375,139 +355,54 @@ impl Default for RunArgs {
            commitment: None,
            decomp_base: 16384,
            decomp_legs: 2,
-            ignore_range_check_inputs_outputs: false,
        }
    }
 }

 impl RunArgs {
-    /// Validates the RunArgs configuration
    ///
-    /// Performs comprehensive validation of all parameters to ensure they are within
-    /// acceptable ranges and follow required constraints. Returns accumulated errors
-    /// if any validations fail.
-    ///
-    /// # Returns
-    /// - Ok(()) if all validations pass
-    /// - Err(String) with detailed error message if any validation fails
    pub fn validate(&self) -> Result<(), String> {
-        let mut errors = Vec::new();
-
-        // check if the largest represented integer in the decomposed form overflows IntegerRep
-        //  try it with the largest possible value
-        let max_decomp = (self.decomp_base as IntegerRep).checked_pow(self.decomp_legs as u32);
-        if max_decomp.is_none() {
-            errors.push(format!(
-                "decomp_base^decomp_legs overflows IntegerRep: {}^{}",
-                self.decomp_base, self.decomp_legs
-            ));
-        }
-
-        // Visibility validations
        if self.param_visibility == Visibility::Public {
-            errors.push(
-                "Parameters cannot be public instances. Use 'fixed' or 'kzgcommit' instead"
-                    .to_string(),
+            return Err(
+                "params cannot be public instances, you are probably trying to use `fixed` or `kzgcommit`"
+                    .into(),
            );
        }
-
-        // Scale validations
        if self.scale_rebase_multiplier < 1 {
-            errors.push("scale_rebase_multiplier must be >= 1".to_string());
+            return Err("scale_rebase_multiplier must be >= 1".into());
        }
-
-        // if any of the scales are too small
-        if self.input_scale < 8 || self.param_scale < 8 {
-            warn!("low scale values (<8) may impact precision");
-        }
-
-        // Lookup range validations
        if self.lookup_range.0 > self.lookup_range.1 {
-            errors.push(format!(
-                "Invalid lookup range: min ({}) is greater than max ({})",
-                self.lookup_range.0, self.lookup_range.1
-            ));
+            return Err("lookup_range min is greater than max".into());
        }
-
-        // Size validations
        if self.logrows < 1 {
-            errors.push("logrows must be >= 1".to_string());
+            return Err("logrows must be >= 1".into());
        }
-
        if self.num_inner_cols < 1 {
-            errors.push("num_inner_cols must be >= 1".to_string());
+            return Err("num_inner_cols must be >= 1".into());
        }
-
-        let batch_size = self.variables.iter().find(|(name, _)| name == "batch_size");
-        if let Some(batch_size) = batch_size {
-            if batch_size.1 == 0 {
-                errors.push("'batch_size' cannot be 0".to_string());
-            }
-        }
-
-        // Decomposition validations
-        if self.decomp_base == 0 {
-            errors.push("decomp_base cannot be 0".to_string());
-        }
-
-        if self.decomp_legs == 0 {
-            errors.push("decomp_legs cannot be 0".to_string());
-        }
-
-        // Performance validations
-        if self.logrows > MAX_PUBLIC_SRS {
-            warn!("logrows exceeds maximum public SRS size");
-        }
-
-        // Performance warnings
-        if self.input_scale > 20 || self.param_scale > 20 {
-            warn!("High scale values (>20) may impact performance");
-        }
-
-        if errors.is_empty() {
-            Ok(())
-        } else {
-            Err(errors.join("\n"))
+        if self.tolerance.val > 0.0 && self.output_visibility != Visibility::Public {
+            return Err("tolerance > 0.0 requires output_visibility to be public".into());
        }
+        Ok(())
    }

-    /// Exports the configuration as JSON
-    ///
-    /// Serializes the RunArgs instance to a JSON string
-    ///
-    /// # Returns
-    /// * `Ok(String)` containing JSON representation
-    /// * `Err` if serialization fails
+    /// Export the ezkl configuration as json
    pub fn as_json(&self) -> Result<String, Box<dyn std::error::Error>> {
-        let res = serde_json::to_string(&self)?;
-        Ok(res)
+        let serialized = match serde_json::to_string(&self) {
+            Ok(s) => s,
+            Err(e) => {
+                return Err(Box::new(e));
+            }
+        };
+        Ok(serialized)
    }
-
-    /// Parses configuration from JSON
-    ///
-    /// Deserializes a RunArgs instance from a JSON string
-    ///
-    /// # Arguments
-    /// * `arg_json` - JSON string containing configuration
-    ///
-    /// # Returns
-    /// * `Ok(RunArgs)` if parsing succeeds
-    /// * `Err` if parsing fails
+    /// Parse an ezkl configuration from a json
    pub fn from_json(arg_json: &str) -> Result<Self, serde_json::Error> {
        serde_json::from_str(arg_json)
    }
 }

-// Additional helper functions for the module
-
-/// Parses a key-value pair from a string in the format "key->value"
-///
-/// # Arguments
-/// * `s` - Input string in the format "key->value"
-///
-/// # Returns
-/// * `Ok((T, U))` - Parsed key and value
-/// * `Err` - If parsing fails
+/// Parse a single key-value pair
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 fn parse_key_val<T, U>(
    s: &str,
@@ -520,15 +415,14 @@ where
 {
    let pos = s
        .find("->")
-        .ok_or_else(|| format!("invalid KEY->VALUE: no `->` found in `{s}`"))?;
-    Ok((s[..pos].parse()?, s[pos + 2..].parse()?))
+        .ok_or_else(|| format!("invalid x->y: no `->` found in `{s}`"))?;
+    let a = s[..pos].parse()?;
+    let b = s[pos + 2..].parse()?;
+    Ok((a, b))
 }

-/// Verifies that a version string matches the expected artifact version
-/// Logs warnings for version mismatches or unversioned artifacts
-///
-/// # Arguments
-/// * `artifact_version` - Version string from the artifact
+/// Check if the version string matches the artifact version
+/// If the version string does not match the artifact version, log a warning
 pub fn check_version_string_matches(artifact_version: &str) {
    if artifact_version == "0.0.0"
        || artifact_version == "source - no compatibility guaranteed"
@@ -553,81 +447,3 @@ pub fn check_version_string_matches(artifact_version: &str) {
        );
    }
 }
-
-#[cfg(test)]
-#[allow(clippy::field_reassign_with_default)]
-mod tests {
-    use super::*;
-
-    #[test]
-    fn test_valid_default_args() {
-        let args = RunArgs::default();
-        assert!(args.validate().is_ok());
-    }
-
-    #[test]
-    fn test_invalid_param_visibility() {
-        let mut args = RunArgs::default();
-        args.param_visibility = Visibility::Public;
-        let err = args.validate().unwrap_err();
-        assert!(err.contains("Parameters cannot be public instances"));
-    }
-
-    #[test]
-    fn test_invalid_scale_rebase() {
-        let mut args = RunArgs::default();
-        args.scale_rebase_multiplier = 0;
-        let err = args.validate().unwrap_err();
-        assert!(err.contains("scale_rebase_multiplier must be >= 1"));
-    }
-
-    #[test]
-    fn test_invalid_lookup_range() {
-        let mut args = RunArgs::default();
-        args.lookup_range = (100, -100);
-        let err = args.validate().unwrap_err();
-        assert!(err.contains("Invalid lookup range"));
-    }
-
-    #[test]
-    fn test_invalid_logrows() {
-        let mut args = RunArgs::default();
-        args.logrows = 0;
-        let err = args.validate().unwrap_err();
-        assert!(err.contains("logrows must be >= 1"));
-    }
-
-    #[test]
-    fn test_invalid_inner_cols() {
-        let mut args = RunArgs::default();
-        args.num_inner_cols = 0;
-        let err = args.validate().unwrap_err();
-        assert!(err.contains("num_inner_cols must be >= 1"));
-    }
-
-    #[test]
-    fn test_zero_batch_size() {
-        let mut args = RunArgs::default();
-        args.variables = vec![("batch_size".to_string(), 0)];
-        let err = args.validate().unwrap_err();
-        assert!(err.contains("'batch_size' cannot be 0"));
-    }
-
-    #[test]
-    fn test_json_serialization() {
-        let args = RunArgs::default();
-        let json = args.as_json().unwrap();
-        let deserialized = RunArgs::from_json(&json).unwrap();
-        assert_eq!(args, deserialized);
-    }
-
-    #[test]
-    fn test_multiple_validation_errors() {
-        let mut args = RunArgs::default();
-        args.logrows = 0;
-        args.lookup_range = (100, -100);
-        let err = args.validate().unwrap_err();
-        // Should contain multiple error messages
-        assert!(err.matches("\n").count() >= 1);
-    }
-}
--- a/src/pfsys/evm/aggregation_kzg.rs
+++ b/src/pfsys/evm/aggregation_kzg.rs
@@ -133,6 +133,7 @@ pub fn aggregate<'a>(
                .collect_vec()
        }));

+        // loader.ctx().constrain_equal(cell_0, cell_1)
        let mut transcript = PoseidonTranscript::<Rc<Halo2Loader>, _>::new(loader, snark.proof());
        let proof = PlonkSuccinctVerifier::read_proof(svk, &protocol, &instances, &mut transcript)
            .map_err(|_| plonk::Error::Synthesis)?;
@@ -308,11 +309,11 @@ impl AggregationCircuit {
        })
    }

-    /// Number of limbs used for decomposition
+    ///
    pub fn num_limbs() -> usize {
        LIMBS
    }
-    /// Number of bits used for decomposition
+    ///
    pub fn num_bits() -> usize {
        BITS
    }
--- a/src/pfsys/mod.rs
+++ b/src/pfsys/mod.rs
@@ -17,16 +17,16 @@ use crate::{Commitments, EZKL_BUF_CAPACITY, EZKL_KEY_FORMAT};
 use clap::ValueEnum;
 use halo2_proofs::circuit::Value;
 use halo2_proofs::plonk::{
-    Circuit, ProvingKey, VerifyingKey, create_proof, keygen_pk, keygen_vk_custom, verify_proof,
+    create_proof, keygen_pk, keygen_vk_custom, verify_proof, Circuit, ProvingKey, VerifyingKey,
 };
-use halo2_proofs::poly::VerificationStrategy;
 use halo2_proofs::poly::commitment::{CommitmentScheme, Params, ParamsProver, Prover, Verifier};
 use halo2_proofs::poly::ipa::commitment::IPACommitmentScheme;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::VerificationStrategy;
 use halo2_proofs::transcript::{EncodedChallenge, TranscriptReadBuffer, TranscriptWriterBuffer};
-use halo2curves::CurveAffine;
 use halo2curves::ff::{FromUniformBytes, PrimeField, WithSmallOrderMulGroup};
 use halo2curves::serde::SerdeObject;
+use halo2curves::CurveAffine;
 use instant::Instant;
 use log::{debug, info, trace};
 #[cfg(not(feature = "det-prove"))]
@@ -51,9 +51,6 @@ use pyo3::types::PyDictMethods;

 use halo2curves::bn256::{Bn256, Fr, G1Affine};

-/// Converts a string to a `SerdeFormat`.
-/// # Panics
-/// Panics if the provided `s` is not a valid `SerdeFormat` (i.e. not one of "processed", "raw-bytes-unchecked", or "raw-bytes").
 fn serde_format_from_str(s: &str) -> halo2_proofs::SerdeFormat {
    match s {
        "processed" => halo2_proofs::SerdeFormat::Processed,
@@ -324,7 +321,7 @@ where
 }

 #[cfg(feature = "python-bindings")]
-use pyo3::{PyObject, Python, ToPyObject, types::PyDict};
+use pyo3::{types::PyDict, PyObject, Python, ToPyObject};
 #[cfg(feature = "python-bindings")]
 impl<F: PrimeField + SerdeObject + Serialize, C: CurveAffine + Serialize> ToPyObject for Snark<F, C>
 where
@@ -348,15 +345,14 @@ where
 }

 impl<
-    F: PrimeField + SerdeObject + Serialize + FromUniformBytes<64> + DeserializeOwned,
-    C: CurveAffine + Serialize + DeserializeOwned,
-> Snark<F, C>
+        F: PrimeField + SerdeObject + Serialize + FromUniformBytes<64> + DeserializeOwned,
+        C: CurveAffine + Serialize + DeserializeOwned,
+    > Snark<F, C>
 where
    C::Scalar: Serialize + DeserializeOwned,
    C::ScalarExt: Serialize + DeserializeOwned,
 {
    /// Create a new application snark from proof and instance variables ready for aggregation
-    #[allow(clippy::too_many_arguments)]
    pub fn new(
        protocol: Option<PlonkProtocol<C>>,
        instances: Vec<Vec<F>>,
@@ -532,6 +528,7 @@ pub fn create_keys<Scheme: CommitmentScheme, C: Circuit<Scheme::Scalar>>(
    disable_selector_compression: bool,
 ) -> Result<ProvingKey<Scheme::Curve>, halo2_proofs::plonk::Error>
 where
+    C: Circuit<Scheme::Scalar>,
    <Scheme as CommitmentScheme>::Scalar: FromUniformBytes<64>,
 {
    //	Real proof
@@ -797,6 +794,7 @@ pub fn load_vk<Scheme: CommitmentScheme, C: Circuit<Scheme::Scalar>>(
    params: <C as Circuit<Scheme::Scalar>>::Params,
 ) -> Result<VerifyingKey<Scheme::Curve>, PfsysError>
 where
+    C: Circuit<Scheme::Scalar>,
    Scheme::Curve: SerdeObject + CurveAffine,
    Scheme::Scalar: PrimeField + SerdeObject + FromUniformBytes<64>,
 {
@@ -819,6 +817,7 @@ pub fn load_pk<Scheme: CommitmentScheme, C: Circuit<Scheme::Scalar>>(
    params: <C as Circuit<Scheme::Scalar>>::Params,
 ) -> Result<ProvingKey<Scheme::Curve>, PfsysError>
 where
+    C: Circuit<Scheme::Scalar>,
    Scheme::Curve: SerdeObject + CurveAffine,
    Scheme::Scalar: PrimeField + SerdeObject + FromUniformBytes<64>,
 {
--- a/src/tensor/errors.rs
+++ b/src/tensor/errors.rs
@@ -1,6 +1,6 @@
 use thiserror::Error;

-use super::{ops::DecompositionError, DataFormat};
+use super::ops::DecompositionError;

 /// A wrapper for tensor related errors.
 #[derive(Debug, Error)]
@@ -38,13 +38,4 @@ pub enum TensorError {
    /// Decomposition error
    #[error("decomposition error: {0}")]
    DecompositionError(#[from] DecompositionError),
-    /// Invalid argument
-    #[error("invalid argument: {0}")]
-    InvalidArgument(String),
-    /// Index out of bounds
-    #[error("index {0} out of bounds for dimension {1}")]
-    IndexOutOfBounds(usize, usize),
-    /// Invalid data conversion
-    #[error("invalid data conversion from format {0} to {1}")]
-    InvalidDataConversion(DataFormat, DataFormat),
 }
--- a/src/tensor/mod.rs
+++ b/src/tensor/mod.rs
@@ -9,7 +9,6 @@ pub mod var;

 pub use errors::TensorError;

-use core::hash::Hash;
 use halo2curves::ff::PrimeField;
 use maybe_rayon::{
    prelude::{
@@ -25,9 +24,12 @@ use std::path::PathBuf;
 pub use val::*;
 pub use var::*;

+#[cfg(feature = "metal")]
+use instant::Instant;
+
 use crate::{
    circuit::utils,
-    fieldutils::{IntegerRep, integer_rep_to_felt},
+    fieldutils::{integer_rep_to_felt, IntegerRep},
    graph::Visibility,
 };

@@ -38,6 +40,8 @@ use halo2_proofs::{
    poly::Rotation,
 };
 use itertools::Itertools;
+#[cfg(feature = "metal")]
+use metal::{Device, MTLResourceOptions, MTLSize};
 use std::error::Error;
 use std::fmt::Debug;
 use std::io::Read;
@@ -45,6 +49,31 @@ use std::iter::Iterator;
 use std::ops::{Add, Deref, DerefMut, Div, Mul, Neg, Range, Sub};
 use std::{cmp::max, ops::Rem};

+#[cfg(feature = "metal")]
+use std::collections::HashMap;
+
+#[cfg(feature = "metal")]
+const LIB_DATA: &[u8] = include_bytes!("metal/tensor_ops.metallib");
+
+#[cfg(feature = "metal")]
+lazy_static::lazy_static! {
+    static ref DEVICE: Device = Device::system_default().expect("no device found");
+
+    static ref LIB: metal::Library = DEVICE.new_library_with_data(LIB_DATA).unwrap();
+
+    static ref QUEUE: metal::CommandQueue = DEVICE.new_command_queue();
+
+    static ref PIPELINES: HashMap<String, metal::ComputePipelineState> = {
+        let mut map = HashMap::new();
+        for name in ["add", "sub", "mul"] {
+            let function = LIB.get_function(name, None).unwrap();
+            let pipeline = DEVICE.new_compute_pipeline_state_with_function(&function).unwrap();
+            map.insert(name.to_string(), pipeline);
+        }
+        map
+    };
+}
+
 /// The (inner) type of tensor elements.
 pub trait TensorType: Clone + Debug + 'static {
    /// Returns the zero value.
@@ -62,7 +91,7 @@ pub trait TensorType: Clone + Debug + 'static {
 }

 macro_rules! tensor_type {
-    ($rust_type:ty, $tensor_type:ident, $zero:expr_2021, $one:expr_2021) => {
+    ($rust_type:ty, $tensor_type:ident, $zero:expr, $one:expr) => {
        impl TensorType for $rust_type {
            fn zero() -> Option<Self> {
                Some($zero)
@@ -415,7 +444,7 @@ impl<T: Clone + TensorType + PrimeField> Tensor<T> {
                Err(_) => {
                    return Err(TensorError::FileLoadError(
                        "Failed to read tensor".to_string(),
-                    ));
+                    ))
                }
            }
        }
@@ -804,12 +833,6 @@ impl<T: Clone + TensorType> Tensor<T> {
        num_repeats: usize,
        initial_offset: usize,
    ) -> Result<Tensor<T>, TensorError> {
-        if n == 0 {
-            return Err(TensorError::InvalidArgument(
-                "Cannot duplicate every 0th element".to_string(),
-            ));
-        }
-
        let mut inner: Vec<T> = Vec::with_capacity(self.inner.len());
        let mut offset = initial_offset;
        for (i, elem) in self.inner.clone().into_iter().enumerate() {
@@ -839,17 +862,11 @@ impl<T: Clone + TensorType> Tensor<T> {
        num_repeats: usize,
        initial_offset: usize,
    ) -> Result<Tensor<T>, TensorError> {
-        if n == 0 {
-            return Err(TensorError::InvalidArgument(
-                "Cannot remove every 0th element".to_string(),
-            ));
-        }
-
        // Pre-calculate capacity to avoid reallocations
        let estimated_size = self.inner.len() - (self.inner.len() / n) * num_repeats;
        let mut inner = Vec::with_capacity(estimated_size);

-        // Use iterator directly instead of creating intermediate collectionsif
+        // Use iterator directly instead of creating intermediate collections
        let mut i = 0;
        while i < self.inner.len() {
            // Add the current element
@@ -868,6 +885,7 @@ impl<T: Clone + TensorType> Tensor<T> {
    }

    /// Remove indices
+    /// WARN: assumes indices are in ascending order for speed
    /// ```
    /// use ezkl::tensor::Tensor;
    /// use ezkl::fieldutils::IntegerRep;
@@ -894,11 +912,7 @@ impl<T: Clone + TensorType> Tensor<T> {
        }
        // remove indices
        for elem in indices.iter().rev() {
-            if *elem < self.len() {
-                inner.remove(*elem);
-            } else {
-                return Err(TensorError::IndexOutOfBounds(*elem, self.len()));
-            }
+            inner.remove(*elem);
        }

        Tensor::new(Some(&inner), &[inner.len()])
@@ -926,9 +940,6 @@ impl<T: Clone + TensorType> Tensor<T> {
                ));
            }
            self.dims = vec![];
-        }
-        if self.dims() == &[0] && new_dims.iter().product::<usize>() == 1 {
-            self.dims = Vec::from(new_dims);
        } else {
            let product = if new_dims != [0] {
                new_dims.iter().product::<usize>()
@@ -1107,10 +1118,6 @@ impl<T: Clone + TensorType> Tensor<T> {
            let mut output = self.clone();
            output.reshape(shape)?;
            return Ok(output);
-        } else if self.dims() == &[0] && shape.iter().product::<usize>() == 1 {
-            let mut output = self.clone();
-            output.reshape(shape)?;
-            return Ok(output);
        }

        if self.dims().len() > shape.len() {
@@ -1261,7 +1268,7 @@ impl<T: Clone + TensorType> Tensor<T> {
            None => {
                return Err(TensorError::DimError(
                    "Cannot get last element of empty tensor".to_string(),
-                ));
+                ))
            }
        };

@@ -1286,7 +1293,7 @@ impl<T: Clone + TensorType> Tensor<T> {
            None => {
                return Err(TensorError::DimError(
                    "Cannot get first element of empty tensor".to_string(),
-                ));
+                ))
            }
        };

@@ -1397,6 +1404,10 @@ impl<T: TensorType + Add<Output = T> + std::marker::Send + std::marker::Sync> Ad
        let lhs = self.expand(&broadcasted_shape).unwrap();
        let rhs = rhs.expand(&broadcasted_shape).unwrap();

+        #[cfg(feature = "metal")]
+        let res = metal_tensor_op(&lhs, &rhs, "add");
+
+        #[cfg(not(feature = "metal"))]
        let res = {
            let mut res: Tensor<T> = lhs
                .par_iter()
@@ -1494,6 +1505,10 @@ impl<T: TensorType + Sub<Output = T> + std::marker::Send + std::marker::Sync> Su
        let lhs = self.expand(&broadcasted_shape).unwrap();
        let rhs = rhs.expand(&broadcasted_shape).unwrap();

+        #[cfg(feature = "metal")]
+        let res = metal_tensor_op(&lhs, &rhs, "sub");
+
+        #[cfg(not(feature = "metal"))]
        let res = {
            let mut res: Tensor<T> = lhs
                .par_iter()
@@ -1561,6 +1576,10 @@ impl<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::Sync> Mu
        let lhs = self.expand(&broadcasted_shape).unwrap();
        let rhs = rhs.expand(&broadcasted_shape).unwrap();

+        #[cfg(feature = "metal")]
+        let res = metal_tensor_op(&lhs, &rhs, "mul");
+
+        #[cfg(not(feature = "metal"))]
        let res = {
            let mut res: Tensor<T> = lhs
                .par_iter()
@@ -1666,9 +1685,7 @@ impl<T: TensorType + Div<Output = T> + std::marker::Send + std::marker::Sync> Di
 }

 // implement remainder
-impl<T: TensorType + Rem<Output = T> + std::marker::Send + std::marker::Sync + PartialEq> Rem
-    for Tensor<T>
-{
+impl<T: TensorType + Rem<Output = T> + std::marker::Send + std::marker::Sync> Rem for Tensor<T> {
    type Output = Result<Tensor<T>, TensorError>;

    /// Elementwise remainder of a tensor with another tensor.
@@ -1697,24 +1714,9 @@ impl<T: TensorType + Rem<Output = T> + std::marker::Send + std::marker::Sync + P
        let mut lhs = self.expand(&broadcasted_shape).unwrap();
        let rhs = rhs.expand(&broadcasted_shape).unwrap();

-        lhs.par_iter_mut()
-            .zip(rhs)
-            .map(|(o, r)| match T::zero() {
-                Some(zero) => {
-                    if r != zero {
-                        *o = o.clone() % r;
-                        Ok(())
-                    } else {
-                        Err(TensorError::InvalidArgument(
-                            "Cannot divide by zero in remainder".to_string(),
-                        ))
-                    }
-                }
-                _ => Err(TensorError::InvalidArgument(
-                    "Undefined zero value".to_string(),
-                )),
-            })
-            .collect::<Result<Vec<_>, _>>()?;
+        lhs.par_iter_mut().zip(rhs).for_each(|(o, r)| {
+            *o = o.clone() % r;
+        });

        Ok(lhs)
    }
@@ -1749,6 +1751,7 @@ impl<T: TensorType + Rem<Output = T> + std::marker::Send + std::marker::Sync + P
 /// assert_eq!(c, vec![2, 3]);
 ///
 /// ```
+
 pub fn get_broadcasted_shape(
    shape_a: &[usize],
    shape_b: &[usize],
@@ -1756,247 +1759,23 @@ pub fn get_broadcasted_shape(
    let num_dims_a = shape_a.len();
    let num_dims_b = shape_b.len();

-    if num_dims_a == num_dims_b {
-        let mut broadcasted_shape = Vec::with_capacity(num_dims_a);
-        for (dim_a, dim_b) in shape_a.iter().zip(shape_b.iter()) {
-            let max_dim = dim_a.max(dim_b);
-            broadcasted_shape.push(*max_dim);
+    match (num_dims_a, num_dims_b) {
+        (a, b) if a == b => {
+            let mut broadcasted_shape = Vec::with_capacity(num_dims_a);
+            for (dim_a, dim_b) in shape_a.iter().zip(shape_b.iter()) {
+                let max_dim = dim_a.max(dim_b);
+                broadcasted_shape.push(*max_dim);
+            }
+            Ok(broadcasted_shape)
        }
-        Ok(broadcasted_shape)
-    } else if num_dims_a < num_dims_b {
-        Ok(shape_b.to_vec())
-    } else if num_dims_a > num_dims_b {
-        Ok(shape_a.to_vec())
-    } else {
-        Err(TensorError::DimError(
+        (a, b) if a < b => Ok(shape_b.to_vec()),
+        (a, b) if a > b => Ok(shape_a.to_vec()),
+        _ => Err(TensorError::DimError(
            "Unknown condition for broadcasting".to_string(),
-        ))
+        )),
    }
 }
 ////////////////////////
-///
-
-/// The shape of data for some operations
-#[derive(Clone, Debug, Serialize, Deserialize, PartialEq, Default, Copy)]
-pub enum DataFormat {
-    /// NCHW
-    #[default]
-    NCHW,
-    /// NHWC
-    NHWC,
-    /// CHW
-    CHW,
-    /// HWC
-    HWC,
-}
-
-// as str
-impl core::fmt::Display for DataFormat {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        match self {
-            DataFormat::NCHW => write!(f, "NCHW"),
-            DataFormat::NHWC => write!(f, "NHWC"),
-            DataFormat::CHW => write!(f, "CHW"),
-            DataFormat::HWC => write!(f, "HWC"),
-        }
-    }
-}
-
-impl DataFormat {
-    /// Get the format's canonical form
-    pub fn canonical(&self) -> DataFormat {
-        match self {
-            DataFormat::NHWC => DataFormat::NCHW,
-            DataFormat::HWC => DataFormat::CHW,
-            _ => self.clone(),
-        }
-    }
-
-    /// no batch dim
-    pub fn has_no_batch(&self) -> bool {
-        match self {
-            DataFormat::CHW | DataFormat::HWC => true,
-            _ => false,
-        }
-    }
-
-    /// Convert tensor to canonical format (NCHW or CHW)
-    pub fn to_canonical<F: PrimeField + TensorType + PartialOrd + Hash>(
-        &self,
-        tensor: &mut ValTensor<F>,
-    ) -> Result<(), TensorError> {
-        match self {
-            DataFormat::NHWC => {
-                // For ND: Move channels from last axis to position after batch
-                let ndims = tensor.dims().len();
-                if ndims > 2 {
-                    tensor.move_axis(ndims - 1, 1)?;
-                }
-            }
-            DataFormat::HWC => {
-                // For ND: Move channels from last axis to first position
-                let ndims = tensor.dims().len();
-                if ndims > 1 {
-                    tensor.move_axis(ndims - 1, 0)?;
-                }
-            }
-            _ => {} // NCHW/CHW are already in canonical format
-        }
-        Ok(())
-    }
-
-    /// Convert tensor from canonical format to target format
-    pub fn from_canonical<F: PrimeField + TensorType + PartialOrd + Hash>(
-        &self,
-        tensor: &mut ValTensor<F>,
-    ) -> Result<(), TensorError> {
-        match self {
-            DataFormat::NHWC => {
-                // Move channels from position 1 to end
-                let ndims = tensor.dims().len();
-                if ndims > 2 {
-                    tensor.move_axis(1, ndims - 1)?;
-                }
-            }
-            DataFormat::HWC => {
-                // Move channels from position 0 to end
-                let ndims = tensor.dims().len();
-                if ndims > 1 {
-                    tensor.move_axis(0, ndims - 1)?;
-                }
-            }
-            _ => {} // NCHW/CHW don't need conversion
-        }
-        Ok(())
-    }
-
-    /// Get the position of the channel dimension
-    pub fn get_channel_dim(&self, ndims: usize) -> usize {
-        match self {
-            DataFormat::NCHW => 1,
-            DataFormat::NHWC => ndims - 1,
-            DataFormat::CHW => 0,
-            DataFormat::HWC => ndims - 1,
-        }
-    }
-}
-/// The shape of the kernel for some operations
-#[derive(Clone, Debug, Serialize, Deserialize, PartialEq, Default, Copy)]
-pub enum KernelFormat {
-    /// HWIO
-    HWIO,
-    /// OIHW
-    #[default]
-    OIHW,
-    /// OHWI
-    OHWI,
-}
-
-impl core::fmt::Display for KernelFormat {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        match self {
-            KernelFormat::HWIO => write!(f, "HWIO"),
-            KernelFormat::OIHW => write!(f, "OIHW"),
-            KernelFormat::OHWI => write!(f, "OHWI"),
-        }
-    }
-}
-
-impl KernelFormat {
-    /// Get the format's canonical form
-    pub fn canonical(&self) -> KernelFormat {
-        match self {
-            KernelFormat::HWIO => KernelFormat::OIHW,
-            KernelFormat::OHWI => KernelFormat::OIHW,
-            _ => self.clone(),
-        }
-    }
-
-    /// Convert kernel to canonical format (OIHW)
-    pub fn to_canonical<F: PrimeField + TensorType + PartialOrd + Hash>(
-        &self,
-        kernel: &mut ValTensor<F>,
-    ) -> Result<(), TensorError> {
-        match self {
-            KernelFormat::HWIO => {
-                let kdims = kernel.dims().len();
-                // Move output channels from last to first
-                kernel.move_axis(kdims - 1, 0)?;
-                // Move input channels from new last to second position
-                kernel.move_axis(kdims - 1, 1)?;
-            }
-            KernelFormat::OHWI => {
-                let kdims = kernel.dims().len();
-                // Move input channels from last to second position
-                kernel.move_axis(kdims - 1, 1)?;
-            }
-            _ => {} // OIHW is already canonical
-        }
-        Ok(())
-    }
-
-    /// Convert kernel from canonical format to target format
-    pub fn from_canonical<F: PrimeField + TensorType + PartialOrd + Hash>(
-        &self,
-        kernel: &mut ValTensor<F>,
-    ) -> Result<(), TensorError> {
-        match self {
-            KernelFormat::HWIO => {
-                let kdims = kernel.dims().len();
-                // Move input channels from second position to last
-                kernel.move_axis(1, kdims - 1)?;
-                // Move output channels from first to last
-                kernel.move_axis(0, kdims - 1)?;
-            }
-            KernelFormat::OHWI => {
-                let kdims = kernel.dims().len();
-                // Move input channels from second position to last
-                kernel.move_axis(1, kdims - 1)?;
-            }
-            _ => {} // OIHW doesn't need conversion
-        }
-        Ok(())
-    }
-
-    /// Get the position of input and output channel dimensions
-    pub fn get_channel_dims(&self, ndims: usize) -> (usize, usize) {
-        // (input_ch, output_ch)
-        match self {
-            KernelFormat::OIHW => (1, 0),
-            KernelFormat::HWIO => (ndims - 2, ndims - 1),
-            KernelFormat::OHWI => (ndims - 1, 0),
-        }
-    }
-}
-
-#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-impl From<tract_onnx::tract_hir::ops::nn::DataFormat> for DataFormat {
-    fn from(fmt: tract_onnx::tract_hir::ops::nn::DataFormat) -> Self {
-        match fmt {
-            tract_onnx::tract_hir::ops::nn::DataFormat::NCHW => DataFormat::NCHW,
-            tract_onnx::tract_hir::ops::nn::DataFormat::NHWC => DataFormat::NHWC,
-            tract_onnx::tract_hir::ops::nn::DataFormat::CHW => DataFormat::CHW,
-            tract_onnx::tract_hir::ops::nn::DataFormat::HWC => DataFormat::HWC,
-        }
-    }
-}
-
-#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-impl From<tract_onnx::tract_hir::tract_core::ops::cnn::conv::KernelFormat> for KernelFormat {
-    fn from(fmt: tract_onnx::tract_hir::tract_core::ops::cnn::conv::KernelFormat) -> Self {
-        match fmt {
-            tract_onnx::tract_hir::tract_core::ops::cnn::conv::KernelFormat::HWIO => {
-                KernelFormat::HWIO
-            }
-            tract_onnx::tract_hir::tract_core::ops::cnn::conv::KernelFormat::OIHW => {
-                KernelFormat::OIHW
-            }
-            tract_onnx::tract_hir::tract_core::ops::cnn::conv::KernelFormat::OHWI => {
-                KernelFormat::OHWI
-            }
-        }
-    }
-}

 #[cfg(test)]
 mod tests {
@@ -2032,4 +1811,66 @@ mod tests {
        let b = Tensor::<IntegerRep>::new(Some(&[1, 4]), &[2, 1]).unwrap();
        assert_eq!(a.get_slice(&[0..2, 0..1]).unwrap(), b);
    }
+
+    #[test]
+    #[cfg(feature = "metal")]
+    fn tensor_metal_int() {
+        let a = Tensor::<i64>::new(Some(&[1, 2, 3, 4]), &[2, 2]).unwrap();
+        let b = Tensor::<i64>::new(Some(&[1, 2, 3, 4]), &[2, 2]).unwrap();
+        let c = metal_tensor_op(&a, &b, "add");
+        assert_eq!(c, Tensor::new(Some(&[2, 4, 6, 8]), &[2, 2]).unwrap());
+
+        let c = metal_tensor_op(&a, &b, "sub");
+        assert_eq!(c, Tensor::new(Some(&[0, 0, 0, 0]), &[2, 2]).unwrap());
+
+        let c = metal_tensor_op(&a, &b, "mul");
+        assert_eq!(c, Tensor::new(Some(&[1, 4, 9, 16]), &[2, 2]).unwrap());
+    }
+
+    #[test]
+    #[cfg(feature = "metal")]
+    fn tensor_metal_felt() {
+        use halo2curves::bn256::Fr;
+
+        let a = Tensor::<Fr>::new(
+            Some(&[Fr::from(1), Fr::from(2), Fr::from(3), Fr::from(4)]),
+            &[2, 2],
+        )
+        .unwrap();
+        let b = Tensor::<Fr>::new(
+            Some(&[Fr::from(1), Fr::from(2), Fr::from(3), Fr::from(4)]),
+            &[2, 2],
+        )
+        .unwrap();
+
+        let c = metal_tensor_op(&a, &b, "add");
+        assert_eq!(
+            c,
+            Tensor::<Fr>::new(
+                Some(&[Fr::from(2), Fr::from(4), Fr::from(6), Fr::from(8)]),
+                &[2, 2],
+            )
+            .unwrap()
+        );
+
+        let c = metal_tensor_op(&a, &b, "sub");
+        assert_eq!(
+            c,
+            Tensor::<Fr>::new(
+                Some(&[Fr::from(0), Fr::from(0), Fr::from(0), Fr::from(0)]),
+                &[2, 2],
+            )
+            .unwrap()
+        );
+
+        let c = metal_tensor_op(&a, &b, "mul");
+        assert_eq!(
+            c,
+            Tensor::<Fr>::new(
+                Some(&[Fr::from(1), Fr::from(4), Fr::from(9), Fr::from(16)]),
+                &[2, 2],
+            )
+            .unwrap()
+        );
+    }
 }
--- a/src/tensor/ops.rs
+++ b/src/tensor/ops.rs
@@ -27,7 +27,7 @@ pub fn get_rep(
    n: usize,
 ) -> Result<Vec<IntegerRep>, DecompositionError> {
    // check if x is too large
-    if (*x).abs() > ((base as i128).pow(n as u32)) - 1 {
+    if x.abs() > (base.pow(n as u32) as IntegerRep) - 1 {
        return Err(DecompositionError::TooLarge(*x, base, n));
    }
    let mut rep = vec![0; n + 1];
@@ -43,8 +43,8 @@ pub fn get_rep(
    let mut x = x.abs();
    //
    for i in (1..rep.len()).rev() {
-        rep[i] = x % base as IntegerRep;
-        x /= base as IntegerRep;
+        rep[i] = x % base as i128;
+        x /= base as i128;
    }

    Ok(rep)
@@ -127,7 +127,7 @@ pub fn decompose(
        .flatten()
        .collect::<Vec<IntegerRep>>();

-    let output = Tensor::<IntegerRep>::new(Some(&resp), &dims)?;
+    let output = Tensor::<i128>::new(Some(&resp), &dims)?;

    Ok(output)
 }
@@ -385,12 +385,6 @@ pub fn resize<T: TensorType + Send + Sync>(
 pub fn add<T: TensorType + Add<Output = T> + std::marker::Send + std::marker::Sync>(
    t: &[Tensor<T>],
 ) -> Result<Tensor<T>, TensorError> {
-    if t.len() == 1 {
-        return Ok(t[0].clone());
-    } else if t.is_empty() {
-        return Err(TensorError::DimMismatch("add".to_string()));
-    }
-
    // calculate value of output
    let mut output: Tensor<T> = t[0].clone();

@@ -439,11 +433,6 @@ pub fn add<T: TensorType + Add<Output = T> + std::marker::Send + std::marker::Sy
 pub fn sub<T: TensorType + Sub<Output = T> + std::marker::Send + std::marker::Sync>(
    t: &[Tensor<T>],
 ) -> Result<Tensor<T>, TensorError> {
-    if t.len() == 1 {
-        return Ok(t[0].clone());
-    } else if t.is_empty() {
-        return Err(TensorError::DimMismatch("sub".to_string()));
-    }
    // calculate value of output
    let mut output: Tensor<T> = t[0].clone();

@@ -490,11 +479,6 @@ pub fn sub<T: TensorType + Sub<Output = T> + std::marker::Send + std::marker::Sy
 pub fn mult<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::Sync>(
    t: &[Tensor<T>],
 ) -> Result<Tensor<T>, TensorError> {
-    if t.len() == 1 {
-        return Ok(t[0].clone());
-    } else if t.is_empty() {
-        return Err(TensorError::DimMismatch("mult".to_string()));
-    }
    // calculate value of output
    let mut output: Tensor<T> = t[0].clone();

@@ -535,101 +519,30 @@ pub fn mult<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::S
 /// let result = downsample(&x, 1, 2, 2).unwrap();
 /// let expected = Tensor::<IntegerRep>::new(Some(&[3, 6]), &[2, 1]).unwrap();
 /// assert_eq!(result, expected);
-/// let x = Tensor::<IntegerRep>::new(
-///     Some(&[1, 2, 3, 4, 5, 6]),
-///     &[2, 3],
-/// ).unwrap();
-///
-/// // Test case 1: Negative stride along dimension 0
-/// // This should flip the order along dimension 0
-/// let result = downsample(&x, 0, -1, 0).unwrap();
-/// let expected = Tensor::<IntegerRep>::new(
-///     Some(&[4, 5, 6, 1, 2, 3]), // Flipped order of rows
-///     &[2, 3]
-/// ).unwrap();
-/// assert_eq!(result, expected);
-///
-/// // Test case 2: Negative stride along dimension 1
-/// // This should flip the order along dimension 1
-/// let result = downsample(&x, 1, -1, 0).unwrap();
-/// let expected = Tensor::<IntegerRep>::new(
-///     Some(&[3, 2, 1, 6, 5, 4]), // Flipped order of columns
-///     &[2, 3]
-/// ).unwrap();
-/// assert_eq!(result, expected);
-///
-/// // Test case 3: Negative stride with stride magnitude > 1
-/// // This should both skip and flip
-/// let result = downsample(&x, 1, -2, 0).unwrap();
-/// let expected = Tensor::<IntegerRep>::new(
-///     Some(&[3, 1, 6, 4]), // Take every 2nd element in reverse
-///     &[2, 2]
-/// ).unwrap();
-/// assert_eq!(result, expected);
-///
-/// // Test case 4: Negative stride with non-zero modulo
-/// // This should start at (size - 1 - modulo) and reverse
-/// let result = downsample(&x, 1, -2, 1).unwrap();
-/// let expected = Tensor::<IntegerRep>::new(
-///     Some(&[2, 5]), // Start at second element from end, take every 2nd in reverse
-///     &[2, 1]
-/// ).unwrap();
-/// assert_eq!(result, expected);
-///
-/// // Create a larger test case for more complex downsampling
-/// let y = Tensor::<IntegerRep>::new(
-///     Some(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]),
-///     &[3, 4],
-/// ).unwrap();
-///
-/// // Test case 5: Negative stride with modulo on larger tensor
-/// let result = downsample(&y, 1, -2, 1).unwrap();
-/// let expected = Tensor::<IntegerRep>::new(
-///     Some(&[3, 1, 7, 5, 11, 9]), // Start at one after reverse, take every 2nd
-///     &[3, 2]
-/// ).unwrap();
-/// assert_eq!(result, expected);
-/// ```
 pub fn downsample<T: TensorType + Send + Sync>(
    input: &Tensor<T>,
    dim: usize,
-    stride: isize, // Changed from usize to isize to support negative strides
+    stride: usize,
    modulo: usize,
 ) -> Result<Tensor<T>, TensorError> {
-    // Handle negative stride case
-    if stride == 0 {
-        return Err(TensorError::DimMismatch(
-            "downsample stride cannot be zero".to_string(),
-        ));
-    }
-
-    let stride_abs = stride.unsigned_abs();
    let mut output_shape = input.dims().to_vec();
+    // now downsample along axis dim offset by modulo, rounding up (+1 if remaidner is non-zero)
+    let remainder = (input.dims()[dim] - modulo) % stride;
+    let div = (input.dims()[dim] - modulo) / stride;
+    output_shape[dim] = div + (remainder > 0) as usize;
+    let mut output = Tensor::<T>::new(None, &output_shape)?;

-    if modulo >= input.dims()[dim] {
+    if modulo > input.dims()[dim] {
        return Err(TensorError::DimMismatch("downsample".to_string()));
    }

-    // Calculate output shape based on the absolute value of stride
-    let remainder = (input.dims()[dim] - modulo) % stride_abs;
-    let div = (input.dims()[dim] - modulo) / stride_abs;
-    output_shape[dim] = div + (remainder > 0) as usize;
-
-    let mut output = Tensor::<T>::new(None, &output_shape)?;
-
-    // Calculate indices based on stride direction
+    // now downsample along axis dim offset by modulo
    let indices = (0..output_shape.len())
        .map(|i| {
            if i == dim {
                let mut index = vec![0; output_shape[i]];
-                for (j, idx) in index.iter_mut().enumerate() {
-                    if stride > 0 {
-                        // Positive stride: move forward from modulo
-                        *idx = j * stride_abs + modulo;
-                    } else {
-                        // Negative stride: move backward from (size - 1 - modulo)
-                        *idx = (input.dims()[dim] - 1 - modulo) - j * stride_abs;
-                    }
+                for (i, idx) in index.iter_mut().enumerate() {
+                    *idx = i * stride + modulo;
                }
                index
            } else {
@@ -1397,6 +1310,7 @@ pub fn pad<T: TensorType>(
 ///
 /// # Errors
 /// Returns a TensorError if the tensors in `inputs` have incompatible dimensions for concatenation along the specified `axis`.
+
 pub fn concat<T: TensorType + Send + Sync>(
    inputs: &[&Tensor<T>],
    axis: usize,
@@ -2172,6 +2086,7 @@ pub mod nonlinearities {
    /// let expected = Tensor::<IntegerRep>::new(Some(&[4, 25, 8, 1, 1, 0]), &[2, 3]).unwrap();
    /// assert_eq!(result, expected);
    /// ```
+
    pub fn tanh(a: &Tensor<IntegerRep>, scale_input: f64) -> Tensor<IntegerRep> {
        a.par_enum_map(|_, a_i| {
            let kix = (a_i as f64) / scale_input;
--- a/src/tensor/val.rs
+++ b/src/tensor/val.rs
--- a/src/tensor/var.rs
+++ b/src/tensor/var.rs
@@ -2,38 +2,36 @@ use std::collections::HashSet;

 use log::{debug, error, warn};

-use crate::circuit::{CheckMode, region::ConstantsMap};
+use crate::circuit::{region::ConstantsMap, CheckMode};

 use super::*;
-/// A wrapper around Halo2's Column types that represents a tensor of variables in the circuit.
-/// VarTensors are used to store and manage circuit columns, typically for assigning ValTensor
-/// values during circuit layout. The tensor organizes storage into blocks of columns, where each
-/// block contains multiple columns and each column contains multiple rows.
+/// A wrapper around Halo2's `Column<Fixed>` or `Column<Advice>`.
+/// Typically assign [ValTensor]s to [VarTensor]s when laying out a circuit.
 #[derive(Clone, Default, Debug, PartialEq, Eq)]
 pub enum VarTensor {
    /// A VarTensor for holding Advice values, which are assigned at proving time.
    Advice {
        /// Vec of Advice columns, we have [[xx][xx][xx]...] where each inner vec is xx columns
        inner: Vec<Vec<Column<Advice>>>,
-        /// The number of columns in each inner block
+        ///
        num_inner_cols: usize,
        /// Number of rows available to be used in each column of the storage
        col_size: usize,
    },
-    /// A placeholder tensor used for testing or temporary storage
+    /// Dummy var
    Dummy {
-        /// The number of columns in each inner block
+        ///
        num_inner_cols: usize,
        /// Number of rows available to be used in each column of the storage
        col_size: usize,
    },
-    /// An empty tensor with no storage
+    /// Empty var
    #[default]
    Empty,
 }

 impl VarTensor {
-    /// Returns the name of the tensor variant as a static string
+    /// name of the tensor
    pub fn name(&self) -> &'static str {
        match self {
            VarTensor::Advice { .. } => "Advice",
@@ -42,35 +40,22 @@ impl VarTensor {
        }
    }

-    /// Returns true if the tensor is an Advice variant
+    ///
    pub fn is_advice(&self) -> bool {
        matches!(self, VarTensor::Advice { .. })
    }

-    /// Calculates the maximum number of usable rows in the constraint system
    ///
-    /// # Arguments
-    /// * `cs` - The constraint system
-    /// * `logrows` - Log base 2 of the total number of rows (including system and blinding rows)
-    ///
-    /// # Returns
-    /// The maximum number of usable rows after accounting for blinding factors
    pub fn max_rows<F: PrimeField>(cs: &ConstraintSystem<F>, logrows: usize) -> usize {
        let base = 2u32;
        base.pow(logrows as u32) as usize - cs.blinding_factors() - 1
    }

-    /// Creates a new VarTensor::Advice with unblinded columns. Unblinded columns are used when
-    /// the values do not need to be hidden in the proof.
-    ///
-    /// # Arguments
-    /// * `cs` - The constraint system to create columns in
-    /// * `logrows` - Log base 2 of the total number of rows
-    /// * `num_inner_cols` - Number of columns in each inner block
-    /// * `capacity` - Total number of advice cells to allocate
-    ///
-    /// # Returns
-    /// A new VarTensor::Advice with unblinded columns enabled for equality constraints
+    /// Create a new VarTensor::Advice that is unblinded
+    /// Arguments
+    /// * `cs` - The constraint system
+    /// * `logrows` - log2 number of rows in the matrix, including any system and blinding rows.
+    /// * `capacity` - The number of advice cells to allocate
    pub fn new_unblinded_advice<F: PrimeField>(
        cs: &mut ConstraintSystem<F>,
        logrows: usize,
@@ -108,17 +93,11 @@ impl VarTensor {
        }
    }

-    /// Creates a new VarTensor::Advice with standard (blinded) columns, used when
-    /// the values need to be hidden in the proof.
-    ///
-    /// # Arguments
-    /// * `cs` - The constraint system to create columns in
-    /// * `logrows` - Log base 2 of the total number of rows
-    /// * `num_inner_cols` - Number of columns in each inner block
-    /// * `capacity` - Total number of advice cells to allocate
-    ///
-    /// # Returns
-    /// A new VarTensor::Advice with blinded columns enabled for equality constraints
+    /// Create a new VarTensor::Advice
+    /// Arguments
+    /// * `cs` - The constraint system
+    /// * `logrows` - log2 number of rows in the matrix, including any system and blinding rows.
+    /// * `capacity` - The number of advice cells to allocate
    pub fn new_advice<F: PrimeField>(
        cs: &mut ConstraintSystem<F>,
        logrows: usize,
@@ -154,17 +133,11 @@ impl VarTensor {
        }
    }

-    /// Initializes fixed columns in the constraint system to support the VarTensor::Advice
-    /// Fixed columns are used for constant values that are known at circuit creation time.
-    ///
-    /// # Arguments
-    /// * `cs` - The constraint system to create columns in
-    /// * `logrows` - Log base 2 of the total number of rows
-    /// * `num_constants` - Number of constant values needed
-    /// * `module_requires_fixed` - Whether the module requires at least one fixed column
-    ///
-    /// # Returns
-    /// The number of fixed columns created
+    /// Initializes fixed columns to support the VarTensor::Advice
+    /// Arguments
+    /// * `cs` - The constraint system
+    /// * `logrows` - log2 number of rows in the matrix, including any system and blinding rows.
+    /// * `capacity` - The number of advice cells to allocate
    pub fn constant_cols<F: PrimeField>(
        cs: &mut ConstraintSystem<F>,
        logrows: usize,
@@ -196,14 +169,7 @@ impl VarTensor {
        modulo
    }

-    /// Creates a new dummy VarTensor for testing or temporary storage
-    ///
-    /// # Arguments
-    /// * `logrows` - Log base 2 of the total number of rows
-    /// * `num_inner_cols` - Number of columns in each inner block
-    ///
-    /// # Returns
-    /// A new VarTensor::Dummy with the specified dimensions
+    /// Create a new VarTensor::Dummy
    pub fn dummy(logrows: usize, num_inner_cols: usize) -> Self {
        let base = 2u32;
        let max_rows = base.pow(logrows as u32) as usize - 6;
@@ -213,7 +179,7 @@ impl VarTensor {
        }
    }

-    /// Returns the number of blocks in the tensor
+    /// Gets the dims of the object the VarTensor represents
    pub fn num_blocks(&self) -> usize {
        match self {
            VarTensor::Advice { inner, .. } => inner.len(),
@@ -221,7 +187,7 @@ impl VarTensor {
        }
    }

-    /// Returns the number of columns in each inner block
+    /// Num inner cols
    pub fn num_inner_cols(&self) -> usize {
        match self {
            VarTensor::Advice { num_inner_cols, .. } | VarTensor::Dummy { num_inner_cols, .. } => {
@@ -231,7 +197,7 @@ impl VarTensor {
        }
    }

-    /// Returns the total number of columns across all blocks
+    /// Total number of columns
    pub fn num_cols(&self) -> usize {
        match self {
            VarTensor::Advice { inner, .. } => inner[0].len() * inner.len(),
@@ -239,7 +205,7 @@ impl VarTensor {
        }
    }

-    /// Returns the maximum number of rows in each column
+    /// Gets the size of each column
    pub fn col_size(&self) -> usize {
        match self {
            VarTensor::Advice { col_size, .. } | VarTensor::Dummy { col_size, .. } => *col_size,
@@ -247,7 +213,7 @@ impl VarTensor {
        }
    }

-    /// Returns the total size of each block (num_inner_cols * col_size)
+    /// Gets the size of each column
    pub fn block_size(&self) -> usize {
        match self {
            VarTensor::Advice {
@@ -264,13 +230,7 @@ impl VarTensor {
        }
    }

-    /// Converts a linear coordinate to (block, column, row) coordinates in the storage
-    ///
-    /// # Arguments
-    /// * `linear_coord` - The linear index to convert
-    ///
-    /// # Returns
-    /// A tuple of (block_index, column_index, row_index)
+    /// Take a linear coordinate and output the (column, row) position in the storage block.
    pub fn cartesian_coord(&self, linear_coord: usize) -> (usize, usize, usize) {
        // x indexes over blocks of size num_inner_cols
        let x = linear_coord / self.block_size();
@@ -283,17 +243,7 @@ impl VarTensor {
 }

 impl VarTensor {
-    /// Queries a range of cells in the tensor during circuit synthesis
-    ///
-    /// # Arguments
-    /// * `meta` - Virtual cells accessor
-    /// * `x` - Block index
-    /// * `y` - Column index within block
-    /// * `z` - Starting row offset
-    /// * `rng` - Number of consecutive rows to query
-    ///
-    /// # Returns
-    /// A tensor of expressions representing the queried cells
+    /// Retrieve the value of a specific cell in the tensor.
    pub fn query_rng<F: PrimeField>(
        &self,
        meta: &mut VirtualCells<'_, F>,
@@ -318,16 +268,7 @@ impl VarTensor {
        }
    }

-    /// Queries an entire block of cells at a given offset
-    ///
-    /// # Arguments
-    /// * `meta` - Virtual cells accessor
-    /// * `x` - Block index
-    /// * `z` - Row offset
-    /// * `rng` - Number of consecutive rows to query
-    ///
-    /// # Returns
-    /// A tensor of expressions representing the queried block
+    /// Retrieve the value of a specific block at an offset in the tensor.
    pub fn query_whole_block<F: PrimeField>(
        &self,
        meta: &mut VirtualCells<'_, F>,
@@ -352,16 +293,7 @@ impl VarTensor {
        }
    }

-    /// Assigns a constant value to a specific cell in the tensor
-    ///
-    /// # Arguments
-    /// * `region` - The region to assign values in
-    /// * `offset` - Base offset for the assignment
-    /// * `coord` - Coordinate within the tensor
-    /// * `constant` - The constant value to assign
-    ///
-    /// # Returns
-    /// The assigned cell or an error if assignment fails
+    /// Assigns a constant value to a specific cell in the tensor.
    pub fn assign_constant<F: PrimeField + TensorType + PartialOrd>(
        &self,
        region: &mut Region<F>,
@@ -381,17 +313,7 @@ impl VarTensor {
        }
    }

-    /// Assigns values from a ValTensor to this tensor, excluding specified positions
-    ///
-    /// # Arguments
-    /// * `region` - The region to assign values in
-    /// * `offset` - Base offset for assignments
-    /// * `values` - The ValTensor containing values to assign
-    /// * `omissions` - Set of positions to skip during assignment
-    /// * `constants` - Map for tracking constant assignments
-    ///
-    /// # Returns
-    /// The assigned ValTensor or an error if assignment fails
+    /// Assigns [ValTensor] to the columns of the inner tensor.
    pub fn assign_with_omissions<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
        &self,
        region: &mut Region<F>,
@@ -403,10 +325,7 @@ impl VarTensor {
        let mut assigned_coord = 0;
        let mut res: ValTensor<F> = match values {
            ValTensor::Instance { .. } => {
-                error!(
-                    "assignment with omissions is not supported on instance columns. increase K if you require more rows."
-                );
-                Err(halo2_proofs::plonk::Error::Synthesis)
+                unimplemented!("cannot assign instance to advice columns with omissions")
            }
            ValTensor::Value { inner: v, .. } => Ok::<ValTensor<F>, halo2_proofs::plonk::Error>(
                v.enum_map(|coord, k| {
@@ -425,16 +344,7 @@ impl VarTensor {
        Ok(res)
    }

-    /// Assigns values from a ValTensor to this tensor
-    ///
-    /// # Arguments
-    /// * `region` - The region to assign values in
-    /// * `offset` - Base offset for assignments
-    /// * `values` - The ValTensor containing values to assign
-    /// * `constants` - Map for tracking constant assignments
-    ///
-    /// # Returns
-    /// The assigned ValTensor or an error if assignment fails
+    /// Assigns [ValTensor] to the columns of the inner tensor.
    pub fn assign<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
        &self,
        region: &mut Region<F>,
@@ -486,23 +396,14 @@ impl VarTensor {
        Ok(res)
    }

-    /// Returns the remaining available space in a column for assignments
-    ///
-    /// # Arguments
-    /// * `offset` - Current offset in the column
-    /// * `values` - The ValTensor to check space for
-    ///
-    /// # Returns
-    /// The number of rows that need to be flushed or an error if space is insufficient
+    /// Helper function to get the remaining size of the column
    pub fn get_column_flush<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
        &self,
        offset: usize,
        values: &ValTensor<F>,
    ) -> Result<usize, halo2_proofs::plonk::Error> {
        if values.len() > self.col_size() {
-            error!(
-                "There are too many values to flush for this column size, try setting the logrows to a higher value (eg. --logrows 22 on the cli)"
-            );
+            error!("Values are too large for the column");
            return Err(halo2_proofs::plonk::Error::Synthesis);
        }

@@ -526,16 +427,8 @@ impl VarTensor {
        Ok(flush_len)
    }

-    /// Assigns values to a single column, avoiding column overflow by flushing to the next column if needed
-    ///
-    /// # Arguments
-    /// * `region` - The region to assign values in
-    /// * `offset` - Base offset for assignments
-    /// * `values` - The ValTensor containing values to assign
-    /// * `constants` - Map for tracking constant assignments
-    ///
-    /// # Returns
-    /// A tuple of (assigned ValTensor, number of rows flushed) or an error if assignment fails
+    /// Assigns [ValTensor] to the columns of the inner tensor. Whereby the values are assigned to a single column, without overflowing.
+    /// So for instance if we are assigning 10 values and we are at index 18 of the column, and the columns are of length 20, we skip the last 2 values of current column and start from the beginning of the next column.
    pub fn assign_exact_column<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
        &self,
        region: &mut Region<F>,
@@ -550,17 +443,8 @@ impl VarTensor {
        Ok((assigned_vals, flush_len))
    }

-    /// Assigns values with duplication in dummy mode, used for testing and simulation
-    ///
-    /// # Arguments
-    /// * `row` - Starting row for assignment
-    /// * `offset` - Base offset for assignments
-    /// * `values` - The ValTensor containing values to assign
-    /// * `single_inner_col` - Whether to treat as a single column
-    /// * `constants` - Map for tracking constant assignments
-    ///
-    /// # Returns
-    /// A tuple of (assigned ValTensor, total length used) or an error if assignment fails
+    /// Assigns specific values (`ValTensor`) to the columns of the inner tensor but allows for column wrapping for accumulated operations.
+    /// Duplication occurs by copying the last cell of the column to the first cell next column and creating a copy constraint between the two.
    pub fn dummy_assign_with_duplication<
        F: PrimeField + TensorType + PartialOrd + std::hash::Hash,
    >(
@@ -572,13 +456,8 @@ impl VarTensor {
        constants: &mut ConstantsMap<F>,
    ) -> Result<(ValTensor<F>, usize), halo2_proofs::plonk::Error> {
        match values {
-            ValTensor::Instance { .. } => {
-                error!(
-                    "duplication is not supported on instance columns. increase K if you require more rows."
-                );
-                Err(halo2_proofs::plonk::Error::Synthesis)
-            }
-            ValTensor::Value { inner: v, dims, .. } => {
+            ValTensor::Instance { .. } => unimplemented!("duplication is not supported on instance columns. increase K if you require more rows."),
+            ValTensor::Value { inner: v, dims , ..} => {
                let duplication_freq = if single_inner_col {
                    self.col_size()
                } else {
@@ -591,20 +470,21 @@ impl VarTensor {
                    self.num_inner_cols()
                };

-                let duplication_offset = if single_inner_col { row } else { offset };
+                let duplication_offset = if single_inner_col {
+                    row
+                } else {
+                    offset
+                };
+

                // duplicates every nth element to adjust for column overflow
-                let mut res: ValTensor<F> = v
-                    .duplicate_every_n(duplication_freq, num_repeats, duplication_offset)
-                    .unwrap()
-                    .into();
+                let mut res: ValTensor<F> = v.duplicate_every_n(duplication_freq, num_repeats, duplication_offset).unwrap().into();

                let constants_map = res.create_constants_map();
                constants.extend(constants_map);

                let total_used_len = res.len();
-                res.remove_every_n(duplication_freq, num_repeats, duplication_offset)
-                    .unwrap();
+                res.remove_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();

                res.reshape(dims).unwrap();
                res.set_scale(values.scale());
@@ -614,16 +494,7 @@ impl VarTensor {
        }
    }

-    /// Assigns values with duplication but without enforcing constraints between duplicated values
-    ///
-    /// # Arguments
-    /// * `region` - The region to assign values in
-    /// * `offset` - Base offset for assignments
-    /// * `values` - The ValTensor containing values to assign
-    /// * `constants` - Map for tracking constant assignments
-    ///
-    /// # Returns
-    /// A tuple of (assigned ValTensor, total length used) or an error if assignment fails
+    /// Assigns specific values (`ValTensor`) to the columns of the inner tensor but allows for column wrapping for accumulated operations.
    pub fn assign_with_duplication_unconstrained<
        F: PrimeField + TensorType + PartialOrd + std::hash::Hash,
    >(
@@ -634,13 +505,9 @@ impl VarTensor {
        constants: &mut ConstantsMap<F>,
    ) -> Result<(ValTensor<F>, usize), halo2_proofs::plonk::Error> {
        match values {
-            ValTensor::Instance { .. } => {
-                error!(
-                    "duplication is not supported on instance columns. increase K if you require more rows."
-                );
-                Err(halo2_proofs::plonk::Error::Synthesis)
-            }
-            ValTensor::Value { inner: v, dims, .. } => {
+            ValTensor::Instance { .. } => unimplemented!("duplication is not supported on instance columns. increase K if you require more rows."),
+            ValTensor::Value { inner: v, dims , ..} => {
+
                let duplication_freq = self.block_size();

                let num_repeats = self.num_inner_cols();
@@ -648,31 +515,17 @@ impl VarTensor {
                let duplication_offset = offset;

                // duplicates every nth element to adjust for column overflow
-                let v = v
-                    .duplicate_every_n(duplication_freq, num_repeats, duplication_offset)
-                    .map_err(|e| {
-                        error!("Error duplicating values: {:?}", e);
-                        halo2_proofs::plonk::Error::Synthesis
-                    })?;
+                let v = v.duplicate_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();
                let mut res: ValTensor<F> = {
                    v.enum_map(|coord, k| {
-                        let cell =
-                            self.assign_value(region, offset, k.clone(), coord, constants)?;
-                        Ok::<_, halo2_proofs::plonk::Error>(cell)
-                    })?
-                    .into()
-                };
-                let total_used_len = res.len();
-                res.remove_every_n(duplication_freq, num_repeats, duplication_offset)
-                    .map_err(|e| {
-                        error!("Error duplicating values: {:?}", e);
-                        halo2_proofs::plonk::Error::Synthesis
-                    })?;
+                    let cell = self.assign_value(region, offset, k.clone(), coord, constants)?;
+                    Ok::<_, halo2_proofs::plonk::Error>(cell)

-                res.reshape(dims).map_err(|e| {
-                    error!("Error duplicating values: {:?}", e);
-                    halo2_proofs::plonk::Error::Synthesis
-                })?;
+                })?.into()};
+                let total_used_len = res.len();
+                res.remove_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();
+
+                res.reshape(dims).unwrap();
                res.set_scale(values.scale());

                Ok((res, total_used_len))
@@ -680,18 +533,8 @@ impl VarTensor {
        }
    }

-    /// Assigns values with duplication and enforces equality constraints between duplicated values
-    ///
-    /// # Arguments
-    /// * `region` - The region to assign values in
-    /// * `row` - Starting row for assignment
-    /// * `offset` - Base offset for assignments
-    /// * `values` - The ValTensor containing values to assign
-    /// * `check_mode` - Mode for checking equality constraints
-    /// * `constants` - Map for tracking constant assignments
-    ///
-    /// # Returns
-    /// A tuple of (assigned ValTensor, total length used) or an error if assignment fails
+    /// Assigns specific values (`ValTensor`) to the columns of the inner tensor but allows for column wrapping for accumulated operations.
+    /// Duplication occurs by copying the last cell of the column to the first cell next column and creating a copy constraint between the two.
    pub fn assign_with_duplication_constrained<
        F: PrimeField + TensorType + PartialOrd + std::hash::Hash,
    >(
@@ -706,71 +549,54 @@ impl VarTensor {
        let mut prev_cell = None;

        match values {
-            ValTensor::Instance { .. } => {
-                error!(
-                    "duplication is not supported on instance columns. increase K if you require more rows."
-                );
-                Err(halo2_proofs::plonk::Error::Synthesis)
-            }
-            ValTensor::Value { inner: v, dims, .. } => {
+            ValTensor::Instance { .. } => unimplemented!("duplication is not supported on instance columns. increase K if you require more rows."),
+            ValTensor::Value { inner: v, dims , ..} => {
+
                let duplication_freq = self.col_size();
                let num_repeats = 1;
                let duplication_offset = row;

                // duplicates every nth element to adjust for column overflow
-                let v = v
-                    .duplicate_every_n(duplication_freq, num_repeats, duplication_offset)
-                    .unwrap();
-                let mut res: ValTensor<F> = v
-                    .enum_map(|coord, k| {
-                        let step = self.num_inner_cols();
+                let v = v.duplicate_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();
+                let mut res: ValTensor<F> = {
+                    v.enum_map(|coord, k| {

-                        let (x, y, z) = self.cartesian_coord(offset + coord * step);
-                        if matches!(check_mode, CheckMode::SAFE) && coord > 0 && z == 0 && y == 0 {
-                            // assert that duplication occurred correctly
-                            assert_eq!(
-                                Into::<IntegerRep>::into(k.clone()),
-                                Into::<IntegerRep>::into(v[coord - 1].clone())
-                            );
-                        };
+                    let step = self.num_inner_cols();

-                        let cell =
-                            self.assign_value(region, offset, k.clone(), coord * step, constants)?;
+                    let (x, y, z) = self.cartesian_coord(offset + coord * step);
+                    if matches!(check_mode, CheckMode::SAFE) && coord > 0 && z == 0 && y == 0 {
+                        // assert that duplication occurred correctly
+                        assert_eq!(Into::<IntegerRep>::into(k.clone()), Into::<IntegerRep>::into(v[coord - 1].clone()));
+                    };

-                        let at_end_of_column = z == duplication_freq - 1;
-                        let at_beginning_of_column = z == 0;
+                    let cell = self.assign_value(region, offset, k.clone(), coord * step, constants)?;

-                        if at_end_of_column {
-                            // if we are at the end of the column, we need to copy the cell to the next column
-                            prev_cell = Some(cell.clone());
-                        } else if coord > 0 && at_beginning_of_column {
-                            if let Some(prev_cell) = prev_cell.as_ref() {
-                                let cell = if let Some(cell) = cell.cell() {
-                                    cell
-                                } else {
-                                    error!("Error getting cell: {:?}", (x, y));
-                                    return Err(halo2_proofs::plonk::Error::Synthesis);
-                                };
-                                let prev_cell = if let Some(prev_cell) = prev_cell.cell() {
-                                    prev_cell
-                                } else {
-                                    error!("Error getting prev cell: {:?}", (x, y));
-                                    return Err(halo2_proofs::plonk::Error::Synthesis);
-                                };
-                                region.constrain_equal(prev_cell, cell)?;
-                            } else {
-                                error!("Previous cell was not set");
-                                return Err(halo2_proofs::plonk::Error::Synthesis);
-                            }
+                    let at_end_of_column = z == duplication_freq - 1;
+                    let at_beginning_of_column = z == 0;
+
+                    if at_end_of_column {
+                        // if we are at the end of the column, we need to copy the cell to the next column
+                        prev_cell = Some(cell.clone());
+                    } else if coord > 0 && at_beginning_of_column  {
+                        if let Some(prev_cell) = prev_cell.as_ref() {
+                            let cell = cell.cell().ok_or({
+                                error!("Error getting cell: {:?}", (x,y));
+                                halo2_proofs::plonk::Error::Synthesis})?;
+                            let prev_cell = prev_cell.cell().ok_or({
+                                error!("Error getting cell: {:?}", (x,y));
+                                halo2_proofs::plonk::Error::Synthesis})?;
+                            region.constrain_equal(prev_cell,cell)?;
+                        } else {
+                            error!("Previous cell was not set");
+                            return Err(halo2_proofs::plonk::Error::Synthesis);
                        }
+                    }

-                        Ok(cell)
-                    })?
-                    .into();
+                    Ok(cell)

+                })?.into()};
                let total_used_len = res.len();
-                res.remove_every_n(duplication_freq, num_repeats, duplication_offset)
-                    .unwrap();
+                res.remove_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();

                res.reshape(dims).unwrap();
                res.set_scale(values.scale());
@@ -780,17 +606,6 @@ impl VarTensor {
        }
    }

-    /// Assigns a single value to the tensor. This is a helper function used by other assignment methods.
-    ///
-    /// # Arguments
-    /// * `region` - The region to assign values in
-    /// * `offset` - Base offset for the assignment
-    /// * `k` - The value to assign
-    /// * `coord` - The coordinate where to assign the value
-    /// * `constants` - Map for tracking constant assignments
-    ///
-    /// # Returns
-    /// The assigned value or an error if assignment fails
    fn assign_value<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
        &self,
        region: &mut Region<F>,
@@ -801,49 +616,32 @@ impl VarTensor {
    ) -> Result<ValType<F>, halo2_proofs::plonk::Error> {
        let (x, y, z) = self.cartesian_coord(offset + coord);
        let res = match k {
-            // Handle direct value assignment
            ValType::Value(v) => match &self {
                VarTensor::Advice { inner: advices, .. } => {
                    ValType::PrevAssigned(region.assign_advice(|| "k", advices[x][y], z, || v)?)
                }
-                _ => {
-                    error!("VarTensor was not initialized");
-                    return Err(halo2_proofs::plonk::Error::Synthesis);
-                }
+                _ => unimplemented!(),
            },
-            // Handle copying previously assigned value
            ValType::PrevAssigned(v) => match &self {
                VarTensor::Advice { inner: advices, .. } => {
                    ValType::PrevAssigned(v.copy_advice(|| "k", region, advices[x][y], z)?)
                }
-                _ => {
-                    error!("VarTensor was not initialized");
-                    return Err(halo2_proofs::plonk::Error::Synthesis);
-                }
+                _ => unimplemented!(),
            },
-            // Handle copying previously assigned constant
            ValType::AssignedConstant(v, val) => match &self {
                VarTensor::Advice { inner: advices, .. } => {
                    ValType::AssignedConstant(v.copy_advice(|| "k", region, advices[x][y], z)?, val)
                }
-                _ => {
-                    error!("VarTensor was not initialized");
-                    return Err(halo2_proofs::plonk::Error::Synthesis);
-                }
+                _ => unimplemented!(),
            },
-            // Handle assigning evaluated value
            ValType::AssignedValue(v) => match &self {
                VarTensor::Advice { inner: advices, .. } => ValType::PrevAssigned(
                    region
                        .assign_advice(|| "k", advices[x][y], z, || v)?
                        .evaluate(),
                ),
-                _ => {
-                    error!("VarTensor was not initialized");
-                    return Err(halo2_proofs::plonk::Error::Synthesis);
-                }
+                _ => unimplemented!(),
            },
-            // Handle constant value assignment with caching
            ValType::Constant(v) => {
                if let std::collections::hash_map::Entry::Vacant(e) = constants.entry(v) {
                    let value = ValType::AssignedConstant(
--- a/tests/assets/model.compiled
+++ b/tests/assets/model.compiled
--- a/tests/assets/pk.key
+++ b/tests/assets/pk.key
--- a/tests/assets/proof.json
+++ b/tests/assets/proof.json
--- a/tests/assets/settings.json
+++ b/tests/assets/settings.json
@@ -28,12 +28,11 @@
        "commitment": "KZG",
        "decomp_base": 128,
        "decomp_legs": 2,
-        "bounded_log_lookup": false,
-        "ignore_range_check_inputs_outputs": false
+        "bounded_log_lookup": false
    },
-    "num_rows": 236,
-    "total_assignments": 472,
-    "total_const_size": 4,
+    "num_rows": 46,
+    "total_assignments": 92,
+    "total_const_size": 3,
    "total_dynamic_col_size": 0,
    "max_dynamic_input_len": 0,
    "num_dynamic_lookups": 0,
--- a/tests/assets/vk.key
+++ b/tests/assets/vk.key
--- a/tests/integration_tests.rs
+++ b/tests/integration_tests.rs
--- a/tests/py_integration_tests.rs
+++ b/tests/py_integration_tests.rs
@@ -46,28 +46,7 @@ mod py_tests {
            assert!(status.success());
        });
        // set VOICE_DATA_DIR environment variable
-        unsafe {
-            std::env::set_var("VOICE_DATA_DIR", format!("{}", voice_data_dir));
-        }
-    }
-
-    fn download_catdog_data() {
-        let cat_and_dog_data_dir = shellexpand::tilde("~/data/catdog_data");
-
-        DOWNLOAD_VOICE_DATA.call_once(|| {
-            let status = Command::new("bash")
-                .args([
-                    "examples/notebooks/cat_and_dog_data.sh",
-                    &cat_and_dog_data_dir,
-                ])
-                .status()
-                .expect("failed to execute process");
-            assert!(status.success());
-        });
-        // set VOICE_DATA_DIR environment variable
-        unsafe {
-            std::env::set_var("CATDOG_DATA_DIR", format!("{}", cat_and_dog_data_dir));
-        }
+        std::env::set_var("VOICE_DATA_DIR", format!("{}", voice_data_dir));
    }

    fn setup_py_env() {
@@ -93,10 +72,11 @@ mod py_tests {
                    "torchtext==0.17.2",
                    "torchvision==0.17.2",
                    "pandas==2.2.1",
+                    "numpy==1.26.4",
                    "seaborn==0.13.2",
                    "notebook==7.1.2",
                    "nbconvert==7.16.3",
-                    "onnx==1.17.0",
+                    "onnx==1.16.0",
                    "kaggle==1.6.8",
                    "py-solc-x==2.0.3",
                    "web3==7.5.0",
@@ -110,13 +90,12 @@ mod py_tests {
                    "xgboost==2.0.3",
                    "hummingbird-ml==0.4.11",
                    "lightgbm==4.3.0",
-                    "numpy==1.26.4",
                ])
                .status()
                .expect("failed to execute process");
            assert!(status.success());
            let status = Command::new("pip")
-                .args(["install", "numpy==1.26.4"])
+                .args(["install", "numpy==1.23"])
                .status()
                .expect("failed to execute process");

@@ -147,10 +126,10 @@ mod py_tests {
    }

    const TESTS: [&str; 35] = [
-        "mnist_gan.ipynb",                         // 0
-        "ezkl_demo_batch.ipynb",                   // 1
-        "proof_splitting.ipynb",                   // 2
-        "variance.ipynb",                          // 3
+        "ezkl_demo_batch.ipynb",                   // 0
+        "proof_splitting.ipynb",                   // 1
+        "variance.ipynb",                          // 2
+        "mnist_gan.ipynb",                         // 3
        "keras_simple_demo.ipynb",                 // 4
        "mnist_gan_proof_splitting.ipynb",         // 5
        "hashed_vis.ipynb",                        // 6
@@ -246,20 +225,6 @@ mod py_tests {
                anvil_child.kill().unwrap();
            }

-
-            #[test]
-            fn cat_and_dog_notebook_() {
-                crate::py_tests::init_binary();
-                let mut anvil_child = crate::py_tests::start_anvil(false);
-                crate::py_tests::download_catdog_data();
-                let test_dir: TempDir = TempDir::new("cat_and_dog").unwrap();
-                let path = test_dir.path().to_str().unwrap();
-                crate::py_tests::mv_test_(path, "cat_and_dog.ipynb");
-                run_notebook(path, "cat_and_dog.ipynb");
-                test_dir.close().unwrap();
-                anvil_child.kill().unwrap();
-            }
-
            #[test]
            fn reusable_verifier_notebook_() {
                crate::py_tests::init_binary();
--- a/tests/python/binding_tests.py
+++ b/tests/python/binding_tests.py
@@ -48,6 +48,7 @@ def test_py_run_args():
    run_args = ezkl.PyRunArgs()
    run_args.input_visibility = "hashed"
    run_args.output_visibility = "hashed"
+    run_args.tolerance = 1.5


 def test_poseidon_hash():
@@ -58,7 +59,7 @@ def test_poseidon_hash():
    message = [ezkl.float_to_felt(x, 7) for x in message]
    res = ezkl.poseidon_hash(message)
    assert ezkl.felt_to_big_endian(
-        res[0]) == "0x2369898875588bf49b6539376b09705ea69aee318a58e6fcc1e68fc3e7ad81ab"
+        res[0]) == "0x0da7e5e5c8877242fa699f586baf770d731defd54f952d4adeb85047a0e32f45"



@@ -872,8 +873,6 @@ def get_examples():
        'linear_regression',
        "mnist_gan",
        "smallworm",
-        "fr_age",
-        "1d_conv",
    ]
    examples = []
    for subdir, _, _ in os.walk(os.path.join(examples_path, "onnx")):
@@ -900,12 +899,7 @@ async def test_all_examples(model_file, input_file):
    proof_path = os.path.join(folder_path, 'proof.json')

    print("Testing example: ", model_file)
-
-    run_args = ezkl.PyRunArgs()
-    run_args.variables = [("batch_size", 1), ("sequence_length", 100), ("<Sym1>", 1)]
-    run_args.logrows = 22
-
-    res = ezkl.gen_settings(model_file, settings_path, py_run_args=run_args)
+    res = ezkl.gen_settings(model_file, settings_path)
    assert res

    res = await ezkl.calibrate_settings(
--- a/tests/wasm.rs
+++ b/tests/wasm.rs
@@ -11,6 +11,7 @@ mod wasm32 {
    use ezkl::circuit::modules::poseidon::spec::{PoseidonSpec, POSEIDON_RATE, POSEIDON_WIDTH};
    use ezkl::circuit::modules::poseidon::PoseidonChip;
    use ezkl::circuit::modules::Module;
+    use ezkl::graph::modules::POSEIDON_LEN_GRAPH;
    use ezkl::graph::GraphCircuit;
    use ezkl::graph::{GraphSettings, GraphWitness};
    use ezkl::pfsys;
@@ -226,9 +227,11 @@ mod wasm32 {
        let hash: Vec<Vec<Fr>> = serde_json::from_slice(&hash[..]).unwrap();

        let reference_hash =
-            PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::run(message.clone())
-                .map_err(|_| "failed")
-                .unwrap();
+            PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, POSEIDON_LEN_GRAPH>::run(
+                message.clone(),
+            )
+            .map_err(|_| "failed")
+            .unwrap();

        assert_eq!(hash, reference_hash)
    }
@@ -337,7 +340,7 @@ mod wasm32 {
        // Run compiled circuit validation on onnx network (should fail)
        let circuit = compiledCircuitValidation(wasm_bindgen::Clamped(NETWORK.to_vec()));
        assert!(circuit.is_err());
-        // Run compiled circuit validation on compiled network (should pass)
+        // Run compiled circuit validation on comiled network (should pass)
        let circuit = compiledCircuitValidation(wasm_bindgen::Clamped(NETWORK_COMPILED.to_vec()));
        assert!(circuit.is_ok());
        // Run input validation on witness (should fail)
Author	SHA1	Message	Date
dante	9c699e30cb	Merge branch 'main' into ac/artifact-version-warning	2025-01-06 15:49:58 +00:00
dante	3b8e44df9b	chore: version mismatch warnings for artifacts	2025-01-06 15:35:37 +00:00
				`@@ -1 +0,0 @@`
				{"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"]}
				`@@ -1 +0,0 @@`
				`{"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]]}`