chore: bump h2

BREAKING CHANGE: commitment is now an added flag

.github/workflows/large-tests.yml

@@ -11,7 +11,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - name: nanoGPT Mock

.github/workflows/npm.yml

@@ -1,4 +1,4 @@
-name: Build and Publish WASM<>JS Bindings
+name: Build and Publish EZKL npm packages (wasm bindings and in-browser evm verifier)
 
 on:
   workflow_dispatch:
@@ -14,7 +14,7 @@ defaults:
   run:
     working-directory: .
 jobs:
-  wasm-publish:
+  publish-wasm-bindings:
     name: publish-wasm-bindings
     runs-on: ubuntu-latest
     if: startsWith(github.ref, 'refs/tags/')
@@ -22,7 +22,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: jetli/wasm-pack-action@v0.4.0
@@ -30,13 +30,13 @@ jobs:
         run: rustup target add wasm32-unknown-unknown
 
       - name: Add rust-src
-        run: rustup component add rust-src --toolchain nightly-2024-01-04-x86_64-unknown-linux-gnu
+        run: rustup component add rust-src --toolchain nightly-2024-02-06-x86_64-unknown-linux-gnu
       - name: Install binaryen
         run: |
-            set -e
-            curl -L https://github.com/WebAssembly/binaryen/releases/download/version_116/binaryen-version_116-x86_64-linux.tar.gz | tar xzf -
-            export PATH=$PATH:$PWD/binaryen-version_116/bin
-            wasm-opt --version
+          set -e
+          curl -L https://github.com/WebAssembly/binaryen/releases/download/version_116/binaryen-version_116-x86_64-linux.tar.gz | tar xzf -
+          export PATH=$PATH:$PWD/binaryen-version_116/bin
+          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"
@@ -92,7 +92,7 @@ jobs:
             const jsonObject = JSONBig.parse(string);
             return jsonObject;
           }
-          
+
           function serialize(data) { // data is an object // return a Uint8ClampedArray
             // Step 1: Stringify the Object with BigInt support
             if (typeof data === "object") {
@@ -100,11 +100,11 @@ jobs:
             }
             // Step 2: Encode the JSON String
             const uint8Array = new TextEncoder().encode(data);
-          
+
             // Step 3: Convert to Uint8ClampedArray
             return new Uint8ClampedArray(uint8Array.buffer);
           }
-          
+
           module.exports = {
             deserialize,
             serialize
@@ -123,7 +123,7 @@ jobs:
             const jsonObject = parse(string);
             return jsonObject;
           }
-          
+
           export function serialize(data) { // data is an object // return a Uint8ClampedArray
             // Step 1: Stringify the Object with BigInt support
             if (typeof data === "object") {
@@ -131,7 +131,7 @@ jobs:
             }
             // Step 2: Encode the JSON String
             const uint8Array = new TextEncoder().encode(data);
-          
+
             // Step 3: Convert to Uint8ClampedArray
             return new Uint8ClampedArray(uint8Array.buffer);
           }
@@ -174,3 +174,40 @@ jobs:
           npm publish
         env:
           NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
+
+  in-browser-evm-ver-publish:
+    name: publish-in-browser-evm-verifier-package
+    needs: ["publish-wasm-bindings"]
+    runs-on: ubuntu-latest
+    if: startsWith(github.ref, 'refs/tags/')
+    steps:
+      - uses: actions/checkout@v4
+      - name: Update version in package.json
+        shell: bash
+        env:
+          RELEASE_TAG: ${{ github.ref_name }}
+        run: |
+          sed -i "s|\"version\": \".*\"|\"version\": \"${{ github.ref_name }}\"|" in-browser-evm-verifier/package.json
+      - name: Update @ezkljs/engine version in package.json
+        shell: bash
+        env:
+          RELEASE_TAG: ${{ github.ref_name }}
+        run: |
+          sed -i "s|\"@ezkljs/engine\": \".*\"|\"@ezkljs/engine\": \"${{ github.ref_name }}\"|" in-browser-evm-verifier/package.json
+      - name: Update the engine import in in-browser-evm-verifier to use @ezkljs/engine package instead of the local one;
+        run: |
+          sed -i "s|import { encodeVerifierCalldata } from '../nodejs/ezkl';|import { encodeVerifierCalldata } from '@ezkljs/engine';|" in-browser-evm-verifier/src/index.ts
+      - name: Set up Node.js
+        uses: actions/setup-node@v3
+        with:
+          node-version: "18.12.1"
+          registry-url: "https://registry.npmjs.org"
+      - name: Publish to npm
+        run: |
+          cd in-browser-evm-verifier
+          npm install
+          npm run build
+          npm ci
+          npm publish
+        env:
+          NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}

.github/workflows/pypi-gpu.yml

@@ -26,7 +26,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: x64
 
       - name: Set pyproject.toml version to match github tag

.github/workflows/pypi.yml

@@ -25,7 +25,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: x64
 
       - name: Set Cargo.toml version to match github tag
@@ -70,7 +70,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: ${{ matrix.target }}
 
       - name: Set Cargo.toml version to match github tag
@@ -115,7 +115,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: x64
 
       - name: Set Cargo.toml version to match github tag
@@ -139,6 +139,20 @@ jobs:
           target: ${{ matrix.target }}
           manylinux: auto
           args: --release --out dist --features python-bindings
+          before-script-linux: |
+            # If we're running on rhel centos, install needed packages.
+            if command -v yum &> /dev/null; then
+                yum update -y && yum install -y perl-core openssl openssl-devel pkgconfig libatomic
+
+                # If we're running on i686 we need to symlink libatomic
+                # in order to build openssl with -latomic flag.
+                if [[ ! -d "/usr/lib64" ]]; then
+                    ln -s /usr/lib/libatomic.so.1 /usr/lib/libatomic.so
+                fi
+            else
+                # If we're running on debian-based system.
+                apt update -y && apt-get install -y libssl-dev openssl pkg-config
+            fi
 
       - name: Install built wheel
         if: matrix.target == 'x86_64'
@@ -162,7 +176,7 @@ jobs:
   #   - uses: actions/checkout@v4
   #   - uses: actions/setup-python@v4
   #     with:
-  #       python-version: 3.7
+  #       python-version: 3.12
 
   #   - name: Install cross-compilation tools for aarch64
   #     if: matrix.target == 'aarch64'
@@ -214,7 +228,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
           architecture: x64
 
       - name: Set Cargo.toml version to match github tag
@@ -249,7 +263,7 @@ jobs:
             apk add py3-pip
             pip3 install -U pip
             python3 -m venv .venv
-            source .venv/bin/activate            
+            source .venv/bin/activate
             pip3 install ezkl --no-index --find-links /io/dist/ --force-reinstall
             python3 -c "import ezkl"
 
@@ -273,7 +287,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: 3.12
 
       - name: Set Cargo.toml version to match github tag
         shell: bash

.github/workflows/release.yml

@@ -32,7 +32,7 @@ jobs:
           token: ${{ secrets.RELEASE_TOKEN }}
           tag_name: ${{ env.EZKL_VERSION }}
 
-  build-release-gpu: 
+  build-release-gpu:
     name: build-release-gpu
     needs: ["create-release"]
     runs-on: GPU
@@ -45,7 +45,7 @@ jobs:
     steps:
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - name: Checkout repo
@@ -60,16 +60,15 @@ jobs:
       - name: Set Cargo.toml version to match github tag
         shell: bash
         run: |
-            mv Cargo.toml Cargo.toml.orig
-            sed "s/0\\.0\\.0/${EZKL_VERSION//v}/" Cargo.toml.orig >Cargo.toml
-            mv Cargo.lock Cargo.lock.orig
-            sed "s/0\\.0\\.0/${EZKL_VERSION//v}/" Cargo.lock.orig >Cargo.lock
-
+          mv Cargo.toml Cargo.toml.orig
+          sed "s/0\\.0\\.0/${EZKL_VERSION//v}/" Cargo.toml.orig >Cargo.toml
+          mv Cargo.lock Cargo.lock.orig
+          sed "s/0\\.0\\.0/${EZKL_VERSION//v}/" Cargo.lock.orig >Cargo.lock
 
       - name: Install dependencies
         shell: bash
         run: |
-            sudo apt-get update
+          sudo apt-get update
 
       - name: Build release binary
         run: cargo build --release -Z sparse-registry --features icicle
@@ -91,7 +90,6 @@ jobs:
           asset_name: ${{ env.ASSET }}
           asset_content_type: application/octet-stream
 
-
   build-release:
     name: build-release
     needs: ["create-release"]

.github/workflows/rust.yml

@@ -26,7 +26,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - name: Build
@@ -38,7 +38,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - name: Docs
@@ -50,7 +50,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -73,7 +73,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -106,7 +106,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -139,7 +139,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -172,7 +172,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -184,12 +184,12 @@ jobs:
 
   wasm32-tests:
     runs-on: ubuntu-latest
-    # needs: [build,  library-tests, docs]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: jetli/wasm-pack-action@v0.4.0
@@ -199,7 +199,7 @@ jobs:
       - name: Install wasm32-unknown-unknown
         run: rustup target add wasm32-unknown-unknown
       - name: Add rust-src
-        run: rustup component add rust-src --toolchain nightly-2024-01-04-x86_64-unknown-linux-gnu
+        run: rustup component add rust-src --toolchain nightly-2024-02-06-x86_64-unknown-linux-gnu
       - name: Run wasm verifier tests
         # on mac:
         #   AR=/opt/homebrew/opt/llvm/bin/llvm-ar CC=/opt/homebrew/opt/llvm/bin/clang wasm-pack test --firefox --headless -- -Z build-std="panic_abort,std" --features web
@@ -207,12 +207,12 @@ jobs:
 
   tutorial:
     runs-on: ubuntu-latest
-    needs: [build, library-tests, docs]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -224,12 +224,12 @@ jobs:
 
   mock-proving-tests:
     runs-on: non-gpu
-    # needs: [build,  library-tests, docs]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -281,12 +281,12 @@ jobs:
 
   prove-and-verify-evm-tests:
     runs-on: non-gpu
-    needs: [build, library-tests]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -303,16 +303,30 @@ jobs:
         with:
           node-version: "18.12.1"
           cache: "pnpm"
-      - name: Install dependencies
+      - name: "Add rust-src"
+        run: rustup component add rust-src --toolchain nightly-2024-02-06-x86_64-unknown-linux-gnu
+      - name: Install dependencies for js tests and in-browser-evm-verifier package
         run: |
           pnpm install --no-frozen-lockfile
+          pnpm install --dir ./in-browser-evm-verifier --no-frozen-lockfile
         env:
           CI: false
           NODE_ENV: development
+      - name: Build wasm package for nodejs target.
+        run: |
+          wasm-pack build --release --target nodejs --out-dir ./in-browser-evm-verifier/nodejs . -- -Z build-std="panic_abort,std"
+      - name: Replace memory definition in nodejs
+        run: |
+          sed -i "3s|.*|imports['env'] = {memory: new WebAssembly.Memory({initial:20,maximum:65536,shared:true})}|" in-browser-evm-verifier/nodejs/ezkl.js
+      - name: Build @ezkljs/verify package
+        run: |
+          cd in-browser-evm-verifier
+          pnpm build:commonjs
+          cd ..
       - name: Install solc
         run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
       - name: Install Anvil
-        run: cargo install --git https://github.com/foundry-rs/foundry --rev b320f350156a0fb15c2eb13dc380deb2367c4474 --profile local --locked anvil --force
+        run: cargo install --git https://github.com/foundry-rs/foundry --rev c2233ec9fe61e0920c61c6d779bc707252852037 --profile local --locked anvil --force
       - name: KZG prove and verify tests (EVM + VK rendered seperately)
         run: cargo nextest run --release --verbose tests_evm::kzg_evm_prove_and_verify_render_seperately_ --test-threads 1
       - name: KZG prove and verify tests (EVM + kzg all)
@@ -340,12 +354,12 @@ jobs:
 
   prove-and-verify-tests:
     runs-on: non-gpu
-    needs: [build, library-tests]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: jetli/wasm-pack-action@v0.4.0
@@ -353,7 +367,7 @@ jobs:
         run: rustup target add wasm32-unknown-unknown
 
       - name: Add rust-src
-        run: rustup component add rust-src --toolchain nightly-2024-01-04-x86_64-unknown-linux-gnu
+        run: rustup component add rust-src --toolchain nightly-2024-02-06-x86_64-unknown-linux-gnu
       - uses: actions/checkout@v3
       - name: Use pnpm 8
         uses: pnpm/action-setup@v2
@@ -364,7 +378,7 @@ jobs:
         with:
           node-version: "18.12.1"
           cache: "pnpm"
-      - name: Install dependencies
+      - name: Install dependencies for js tests
         run: |
           pnpm install --no-frozen-lockfile
         env:
@@ -380,6 +394,12 @@ jobs:
       - name: Replace memory definition in nodejs
         run: |
           sed -i "3s|.*|imports['env'] = {memory: new WebAssembly.Memory({initial:20,maximum:65536,shared:true})}|" tests/wasm/nodejs/ezkl.js
+      - name: KZG prove and verify tests (public outputs)
+        run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_tight_lookup_::t
+      - name: IPA prove and verify tests
+        run: cargo nextest run --release --verbose tests::ipa_prove_and_verify_::t --test-threads 1
+      - name: IPA prove and verify tests (ipa outputs)
+        run: cargo nextest run --release --verbose tests::ipa_prove_and_verify_ipa_output
       - name: KZG prove and verify tests (public outputs + column overflow)
         run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_with_overflow_::w
       - name: KZG prove and verify tests single inner col
@@ -396,8 +416,6 @@ jobs:
         run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_with_overflow_fixed_params_
       - name: KZG prove and verify tests (public outputs)
         run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_::t
-      - name: KZG prove and verify tests (public outputs + column overflow)
-        run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_::t
       - name: KZG prove and verify tests (public inputs)
         run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_public_input
       - name: KZG prove and verify tests (fixed params)
@@ -413,11 +431,11 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - name: Add rust-src
-        run: rustup component add rust-src --toolchain nightly-2024-01-04-x86_64-unknown-linux-gnu
+        run: rustup component add rust-src --toolchain nightly-2024-02-06-x86_64-unknown-linux-gnu
       - uses: actions/checkout@v3
       - uses: baptiste0928/cargo-install@v1
         with:
@@ -440,44 +458,21 @@ jobs:
       - name: KZG prove and verify tests (hashed outputs)
         run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_hashed --features icicle  --test-threads 1
 
-  fuzz-tests:
-    runs-on: ubuntu-latest-32-cores
-    needs: [build, library-tests, python-tests]
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
-        with:
-          toolchain: nightly-2024-01-04
-          override: true
-          components: rustfmt, clippy
-      - uses: baptiste0928/cargo-install@v1
-        with:
-          crate: cargo-nextest
-          locked: true
-      - name: Install solc
-        run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
-      - name: Install Anvil
-        run: cargo install --git https://github.com/foundry-rs/foundry --rev b320f350156a0fb15c2eb13dc380deb2367c4474 --profile local --locked anvil --force
-      - name: fuzz tests (EVM)
-        run: cargo nextest run --release --verbose tests_evm::kzg_evm_fuzz_ --test-threads 2
-      # - name: fuzz tests
-      #   run: cargo nextest run --release --verbose tests::kzg_fuzz_ --test-threads 6
-
   prove-and-verify-mock-aggr-tests:
     runs-on: self-hosted
-    needs: [build, library-tests]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
         with:
           crate: cargo-nextest
           locked: true
-      - name: Mock aggr tests
+      - name: Mock aggr tests (KZG)
         run: cargo nextest run --release --verbose tests_aggr::kzg_aggr_mock_prove_and_verify_  --test-threads 8
 
   prove-and-verify-aggr-tests-gpu:
@@ -488,7 +483,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -500,29 +495,29 @@ jobs:
 
   prove-and-verify-aggr-tests:
     runs-on: large-self-hosted
-    needs: [build, library-tests]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
         with:
           crate: cargo-nextest
           locked: true
-      - name: KZG )tests
+      - name: KZG tests
         run: cargo nextest run --release --verbose tests_aggr::kzg_aggr_prove_and_verify_  --test-threads 4 -- --include-ignored
 
   prove-and-verify-aggr-evm-tests:
     runs-on: large-self-hosted
-    needs: [build, library-tests]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -532,7 +527,7 @@ jobs:
       - name: Install solc
         run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
       - name: Install Anvil
-        run: cargo install --git https://github.com/foundry-rs/foundry --rev b320f350156a0fb15c2eb13dc380deb2367c4474 --profile local --locked anvil --force
+        run: cargo install --git https://github.com/foundry-rs/foundry --rev c2233ec9fe61e0920c61c6d779bc707252852037 --profile local --locked anvil --force
       - name: KZG prove and verify aggr tests
         run: cargo nextest run  --release --verbose tests_evm::kzg_evm_aggr_prove_and_verify_::t --test-threads 4 -- --include-ignored
 
@@ -543,7 +538,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -562,18 +557,20 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.7"
+          python-version: "3.12"
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
+      - name: Install cmake
+        run: sudo apt-get install -y cmake
       - name: Install solc
         run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
       - name: Setup Virtual Env and Install python dependencies
-        run: python -m venv .env; source .env/bin/activate; pip install -r requirements.txt;
+        run: python -m venv .env --clear; source .env/bin/activate; pip install -r requirements.txt;
       - name: Install Anvil
-        run: cargo install --git https://github.com/foundry-rs/foundry --rev b320f350156a0fb15c2eb13dc380deb2367c4474 --profile local --locked anvil --force
+        run: cargo install --git https://github.com/foundry-rs/foundry --rev c2233ec9fe61e0920c61c6d779bc707252852037 --profile local --locked anvil --force
       - name: Build python ezkl
         run: source .env/bin/activate; unset CONDA_PREFIX; maturin develop --features python-bindings --release
       - name: Run pytest
@@ -581,15 +578,15 @@ jobs:
 
   accuracy-measurement-tests:
     runs-on: ubuntu-latest-32-cores
-    # needs: [build, library-tests, docs]
+    needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.7"
+          python-version: "3.12"
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -597,7 +594,7 @@ jobs:
           crate: cargo-nextest
           locked: true
       - name: Setup Virtual Env and Install python dependencies
-        run: python -m venv .env; source .env/bin/activate; pip install -r requirements.txt;
+        run: python -m venv .env --clear; source .env/bin/activate; pip install -r requirements.txt;
       - name: Build python ezkl
         run: source .env/bin/activate; unset CONDA_PREFIX; maturin develop --features python-bindings --release
       - name: Div rebase
@@ -617,10 +614,10 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.9"
+          python-version: "3.11"
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-01-04
+          toolchain: nightly-2024-02-06
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -630,11 +627,15 @@ jobs:
       - name: Install solc
         run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
       - name: Install Anvil
-        run: cargo install --git https://github.com/foundry-rs/foundry --rev b320f350156a0fb15c2eb13dc380deb2367c4474 --profile local --locked anvil --force
+        run: cargo install --git https://github.com/foundry-rs/foundry --rev c2233ec9fe61e0920c61c6d779bc707252852037 --profile local --locked anvil --force
+      - name: Install pip
+        run: python -m ensurepip --upgrade
       - name: Setup Virtual Env and Install python dependencies
-        run: python -m venv .env; source .env/bin/activate; pip install -r requirements.txt;
+        run: python -m venv .env --clear; source .env/bin/activate; pip install -r requirements.txt; python -m ensurepip --upgrade
       - name: Build python ezkl
         run: source .env/bin/activate; unset CONDA_PREFIX; maturin develop --features python-bindings --release
+      - name: Tictactoe tutorials
+        run: source .env/bin/activate; cargo nextest run py_tests::tests::tictactoe_ --test-threads 1
       # - name: authenticate-kaggle-cli
       #   shell: bash
       #   env:
@@ -645,12 +646,10 @@ jobs:
       #     echo $KAGGLE_API_KEY > /home/ubuntu/.kaggle/kaggle.json
       #     chmod 600 /home/ubuntu/.kaggle/kaggle.json
       - name: All notebooks
-        run: source .env/bin/activate; cargo nextest run py_tests::tests::run_notebook_ --no-capture
+        run: source .env/bin/activate; cargo nextest run py_tests::tests::run_notebook_ --test-threads 1
       - name: Voice tutorial
         run: source .env/bin/activate; cargo nextest run py_tests::tests::voice_
       - name: NBEATS tutorial
         run: source .env/bin/activate; cargo nextest run py_tests::tests::nbeats_
-      - name: Tictactoe tutorials
-        run: source .env/bin/activate; cargo nextest run py_tests::tests::tictactoe_ --no-capture
       # - name: Postgres tutorials
       #   run: source .env/bin/activate; cargo nextest run py_tests::tests::postgres_ --test-threads 1

.gitignore

@@ -45,6 +45,8 @@ var/
 *.whl
 *.bak
 node_modules
+/dist
 timingData.json
 !tests/wasm/pk.key
-!tests/wasm/vk.key
+!tests/wasm/vk.key
+!tests/wasm/vk_aggr.key

Cargo.toml

@@ -15,73 +15,97 @@ crate-type = ["cdylib", "rlib"]
 
 
 [dependencies]
-halo2_gadgets = { git = "https://github.com/zkonduit/halo2", branch= "main" }
-halo2_proofs = { git = "https://github.com/zkonduit/halo2", branch= "main"  }
-halo2curves = { git = "https://github.com/privacy-scaling-explorations/halo2curves", rev="9fff22c", features=["derive_serde"] }
+mimalloc = "0.1"
+halo2_gadgets = { git = "https://github.com/zkonduit/halo2", branch = "ac/optional-selector-poly" }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2?branch=ac/cache-lookup-commitments#127938f23e7aece10b0f32d2ffc07a6c9f244d03", package = "halo2_proofs", branch = "ac/cache-lookup-commitments" }
+halo2curves = { git = "https://github.com/privacy-scaling-explorations/halo2curves", rev = "b753a832e92d5c86c5c997327a9cf9de86a18851", features = [
+    "derive_serde",
+] }
 rand = { version = "0.8", default_features = false }
 itertools = { version = "0.10.3", default_features = false }
-clap = { version = "4.3.3", features = ["derive"]}
+clap = { version = "4.5.3", features = ["derive"] }
 serde = { version = "1.0.126", features = ["derive"], optional = true }
-serde_json = { version = "1.0.97", default_features = false, features = ["float_roundtrip", "raw_value"], optional = true }
+serde_json = { version = "1.0.97", default_features = false, features = [
+    "float_roundtrip",
+    "raw_value",
+], optional = true }
 log = { version = "0.4.17", default_features = false, optional = true }
 thiserror = { version = "1.0.38", default_features = false }
 hex = { version = "0.4.3", default_features = false }
 halo2_wrong_ecc = { git = "https://github.com/zkonduit/halo2wrong", branch = "ac/chunked-mv-lookup", package = "ecc" }
-snark-verifier = { git = "https://github.com/zkonduit/snark-verifier", branch = "ac/chunked-mv-lookup", features=["derive_serde"]}
-halo2_solidity_verifier = { git = "https://github.com/alexander-camuto/halo2-solidity-verifier",  branch= "main" }
-maybe-rayon = { version = "0.1.1",  default_features = false }
+snark-verifier = { git = "https://github.com/zkonduit/snark-verifier", branch = "ac/chunked-mv-lookup",  features = [
+    "derive_serde",
+] }
+halo2_solidity_verifier = { git = "https://github.com/alexander-camuto/halo2-solidity-verifier", branch = "ac/update-h2-curves" }
+maybe-rayon = { version = "0.1.1", default_features = false }
 bincode = { version = "1.3.3", default_features = false }
 ark-std = { version = "^0.3.0", default-features = false }
 unzip-n = "0.1.2"
 num = "0.4.1"
 portable-atomic = "1.6.0"
-tosubcommand = { git =  "https://github.com/zkonduit/enum_to_subcommand", package = "tosubcommand" }
+tosubcommand = { git = "https://github.com/zkonduit/enum_to_subcommand", package = "tosubcommand" }
 
 
 # evm related deps
 [target.'cfg(not(target_arch = "wasm32"))'.dependencies]
-ethers = { version = "2.0.11", default_features = false, features = ["ethers-solc"] }
-indicatif = {version = "0.17.5", features = ["rayon"]}
-gag =  { version = "1.0.0", default_features = false}
+ethers = { version = "2.0.11", default_features = false, features = [
+    "ethers-solc",
+] }
+indicatif = { version = "0.17.5", features = ["rayon"] }
+gag = { version = "1.0.0", default_features = false }
 instant = { version = "0.1" }
-reqwest = { version = "0.11.14", default-features = false, features = ["default-tls", "multipart", "stream"] }
+reqwest = { version = "0.11.14", default-features = false, features = [
+    "default-tls",
+    "multipart",
+    "stream",
+] }
 openssl = { version = "0.10.55", features = ["vendored"] }
 postgres = "0.19.5"
 pg_bigdecimal = "0.1.5"
 lazy_static = "1.4.0"
-colored_json =  { version = "3.0.1",  default_features = false, optional = true}
+colored_json = { version = "3.0.1", default_features = false, optional = true }
 plotters = { version = "0.3.0", default_features = false, optional = true }
 regex = { version = "1", default_features = false }
-tokio = { version = "1.26.0",  default_features = false, features = ["macros", "rt"] }
+tokio = { version = "1.26.0", default_features = false, features = [
+    "macros",
+    "rt",
+] }
 tokio-util = { version = "0.7.9", features = ["codec"] }
-pyo3 = { version = "0.20.2", features = ["extension-module", "abi3-py37", "macros"],  default_features = false, optional = true }
-pyo3-asyncio = { version = "0.20.0",  features = ["attributes", "tokio-runtime"],  default_features = false, optional = true }
+pyo3 = { version = "0.20.2", features = [
+    "extension-module",
+    "abi3-py37",
+    "macros",
+], default_features = false, optional = true }
+pyo3-asyncio = { version = "0.20.0", features = [
+    "attributes",
+    "tokio-runtime",
+], default_features = false, optional = true }
 pyo3-log = { version = "0.9.0", default_features = false, optional = true }
-tract-onnx = { git = "https://github.com/sonos/tract/", rev= "7b1aa33b2f7d1f19b80e270c83320f0f94daff69", default_features = false, optional = true }
+tract-onnx = { git = "https://github.com/sonos/tract/", rev = "40c64319291184814d9fea5fdf4fa16f5a4f7116", default_features = false, optional = true }
 tabled = { version = "0.12.0", optional = true }
 
 
 [target.'cfg(not(all(target_arch = "wasm32", target_os = "unknown")))'.dependencies]
-colored = { version = "2.0.0",  default_features = false, optional = true}
-env_logger = { version = "0.10.0",  default_features = false, optional = true}
+colored = { version = "2.0.0", default_features = false, optional = true }
+env_logger = { version = "0.10.0", default_features = false, optional = true }
 chrono = "0.4.31"
 sha256 = "1.4.0"
 
 [target.'cfg(target_arch = "wasm32")'.dependencies]
 getrandom = { version = "0.2.8", features = ["js"] }
-instant = { version = "0.1", features = [ "wasm-bindgen", "inaccurate" ] }
+instant = { version = "0.1", features = ["wasm-bindgen", "inaccurate"] }
 
 [target.'cfg(all(target_arch = "wasm32", target_os = "unknown"))'.dependencies]
-wasm-bindgen-rayon = { version = "1.0", optional=true }
-wasm-bindgen-test = "0.3.34"
-serde-wasm-bindgen = "0.4"
-wasm-bindgen = { version = "0.2.81", features = ["serde-serialize"]}
+wasm-bindgen-rayon = { version = "1.2.1", optional = true }
+wasm-bindgen-test = "0.3.42"
+serde-wasm-bindgen = "0.6.5"
+wasm-bindgen = { version = "0.2.92", features = ["serde-serialize"] }
 console_error_panic_hook = "0.1.7"
 wasm-bindgen-console-logger = "0.1.1"
 
 
 [dev-dependencies]
-criterion = {version = "0.3",  features = ["html_reports"]}
+criterion = { version = "0.3", features = ["html_reports"] }
 tempfile = "3.3.0"
 lazy_static = "1.4.0"
 mnist = "0.5"
@@ -152,12 +176,27 @@ required-features = ["ezkl"]
 
 [features]
 web = ["wasm-bindgen-rayon"]
-default = ["ezkl", "mv-lookup"]
-render = ["halo2_proofs/dev-graph", "plotters"]
+default = ["ezkl", "mv-lookup", "precompute-coset"]
 onnx = ["dep:tract-onnx"]
 python-bindings = ["pyo3", "pyo3-log", "pyo3-asyncio"]
-ezkl = ["onnx", "serde", "serde_json", "log", "colored", "env_logger", "tabled/color", "colored_json", "halo2_proofs/circuit-params"]
-mv-lookup = ["halo2_proofs/mv-lookup", "snark-verifier/mv-lookup", "halo2_solidity_verifier/mv-lookup"]
+ezkl = [
+    "onnx",
+    "serde",
+    "serde_json",
+    "log",
+    "colored",
+    "env_logger",
+    "tabled/color",
+    "colored_json",
+    "halo2_proofs/circuit-params",
+]
+mv-lookup = [
+    "halo2_proofs/mv-lookup",
+    "snark-verifier/mv-lookup",
+    "halo2_solidity_verifier/mv-lookup",
+]
+asm = ["halo2curves/asm", "halo2_proofs/asm"]
+precompute-coset = ["halo2_proofs/precompute-coset"]
 det-prove = []
 icicle = ["halo2_proofs/icicle_gpu"]
 empty-cmd = []
@@ -165,7 +204,11 @@ no-banner = []
 
 # icicle patch to 0.1.0 if feature icicle is enabled
 [patch.'https://github.com/ingonyama-zk/icicle']
-icicle = { git = "https://github.com/ingonyama-zk/icicle?rev=45b00fb", package = "icicle", branch = "fix/vhnat/ezkl-build-fix"}
+icicle = { git = "https://github.com/ingonyama-zk/icicle?rev=45b00fb", package = "icicle", branch = "fix/vhnat/ezkl-build-fix" }
+
+[patch.'https://github.com/zkonduit/halo2']
+halo2_proofs = { git = "https://github.com/zkonduit/halo2?branch=ac/cache-lookup-commitments#127938f23e7aece10b0f32d2ffc07a6c9f244d03", package = "halo2_proofs", branch = "ac/cache-lookup-commitments" }
 
 [profile.release]
-rustflags = [ "-C", "relocation-model=pic" ]
+rustflags = ["-C", "relocation-model=pic"]
+

README.md

@@ -31,9 +31,9 @@ EZKL
 
 [![Notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/zkonduit/ezkl/blob/main/examples/notebooks/simple_demo_all_public.ipynb) 
 
-In the backend we use [Halo2](https://github.com/privacy-scaling-explorations/halo2) as a proof system.
+In the backend we use the collaboratively-developed [Halo2](https://github.com/privacy-scaling-explorations/halo2) as a proof system.
 
-The generated proofs can then be used on-chain to verify computation, only the Ethereum Virtual Machine (EVM) is supported at the moment.
+The generated proofs can then be verified with much less computational resources, including on-chain (with the Ethereum Virtual Machine), in a browser, or on a device. 
 
 - If you have any questions, we'd love for you to open up a discussion topic in [Discussions](https://github.com/zkonduit/ezkl/discussions). Alternatively, you can join the ✨[EZKL Community Telegram Group](https://t.me/+QRzaRvTPIthlYWMx)💫.
 
@@ -45,6 +45,8 @@ The generated proofs can then be used on-chain to verify computation, only the E
 
 ### getting started ⚙️
 
+The easiest way to get started is to try out a notebook. 
+
 #### Python
 Install the python bindings by calling.
 
@@ -70,10 +72,14 @@ curl https://raw.githubusercontent.com/zkonduit/ezkl/main/install_ezkl_cli.sh |
 
 https://user-images.githubusercontent.com/45801863/236771676-5bbbbfd1-ba6f-418a-902e-20738ce0e9f0.mp4
 
-For more details visit the [docs](https://docs.ezkl.xyz).
+For more details visit the [docs](https://docs.ezkl.xyz). The CLI is faster than Python, as it has less overhead. For even more speed and convenience, check out the [remote proving service](https://ei40vx5x6j0.typeform.com/to/sFv1oxvb), which feels like the CLI but is backed by a tuned cluster.
 
 Build the auto-generated rust documentation and open the docs in your browser locally. `cargo doc --open`
 
+#### In-browser EVM verifier
+
+As an alternative to running the native Halo2 verifier as a WASM binding in the browser, you can use the in-browser EVM verifier. The source code of which you can find in the `in-browser-evm-verifier` directory and a README with instructions on how to use it.
+
 
 ### building the project 🔨
 
@@ -120,17 +126,6 @@ unset ENABLE_ICICLE_GPU
 
 **NOTE:** Even with the above environment variable set, icicle is disabled for circuits where k <= 8. To change the value of `k` where icicle is enabled, you can set the environment variable `ICICLE_SMALL_K`.
 
-### repos
-
-The EZKL project has several libraries and repos. 
-
-| Repo | Description |
-| --- | --- |
-| [@zkonduit/ezkl](https://github.com/zkonduit/ezkl) | the main ezkl repo in rust with wasm and python bindings |
-| [@zkonduit/ezkljs](https://github.com/zkonduit/ezkljs) | typescript and javascript tooling to help integrate ezkl into web apps |
-
-----------------------
-
 ### contributing 🌎
 
 If you're interested in contributing and are unsure where to start, reach out to one of the maintainers:
@@ -147,7 +142,7 @@ More broadly:
 - To report bugs or request new features [create a new issue within Issues](https://github.com/zkonduit/ezkl/issues) to inform the greater community.
 
 
-Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you shall be licensed to Zkonduit Inc. under the terms and conditions specified in the [CLA](https://github.com/zkonduit/ezkl/blob/main/cla.md), which you agree to by intentionally submitting a contribution. In particular, you have the right to submit the contribution and we can distribute it under the Apache 2.0 license, among other terms and conditions. 
+Any contribution intentionally submitted for inclusion in the work by you shall be licensed to Zkonduit Inc. under the terms and conditions specified in the [CLA](https://github.com/zkonduit/ezkl/blob/main/cla.md), which you agree to by intentionally submitting a contribution. In particular, you have the right to submit the contribution and we can distribute it, among other terms and conditions. 
 
 ### no security guarantees
 
@@ -155,4 +150,7 @@ 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. 
 
+### 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,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

benches/accum_conv.rs

@@ -2,11 +2,13 @@ use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Through
 use ezkl::circuit::poly::PolyOp;
 use ezkl::circuit::*;
 use ezkl::pfsys::create_keys;
-use ezkl::pfsys::create_proof_circuit_kzg;
+use ezkl::pfsys::create_proof_circuit;
 use ezkl::pfsys::srs::gen_srs;
 use ezkl::pfsys::TranscriptType;
 use ezkl::tensor::*;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::kzg::multiopen::ProverSHPLONK;
+use halo2_proofs::poly::kzg::multiopen::VerifierSHPLONK;
 use halo2_proofs::poly::kzg::strategy::SingleStrategy;
 use halo2_proofs::{
     arithmetic::Field,
@@ -15,6 +17,7 @@ use halo2_proofs::{
 };
 use halo2curves::bn256::{Bn256, Fr};
 use rand::rngs::OsRng;
+use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 
 static mut KERNEL_HEIGHT: usize = 2;
 static mut KERNEL_WIDTH: usize = 2;
@@ -121,28 +124,35 @@ fn runcnvrl(c: &mut Criterion) {
             group.throughput(Throughput::Elements(*size as u64));
             group.bench_with_input(BenchmarkId::new("pk", size), &size, |b, &_| {
                 b.iter(|| {
-                    create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(
-                        &circuit, &params, true,
-                    )
-                    .unwrap();
+                    create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
+                        .unwrap();
                 });
             });
 
-            let pk =
-                create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
-                    .unwrap();
+            let pk = create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
+                .unwrap();
 
             group.throughput(Throughput::Elements(*size as u64));
             group.bench_with_input(BenchmarkId::new("prove", size), &size, |b, &_| {
                 b.iter(|| {
-                    let prover = create_proof_circuit_kzg(
+                    let prover = create_proof_circuit::<
+                        KZGCommitmentScheme<_>,
+                        MyCircuit,
+                        ProverSHPLONK<_>,
+                        VerifierSHPLONK<_>,
+                        SingleStrategy<_>,
+                        _,
+                        EvmTranscript<_, _, _, _>,
+                        EvmTranscript<_, _, _, _>,
+                    >(
                         circuit.clone(),
+                        vec![],
                         &params,
-                        None,
                         &pk,
-                        TranscriptType::EVM,
-                        SingleStrategy::new(&params),
                         CheckMode::UNSAFE,
+                        ezkl::Commitments::KZG,
+                        TranscriptType::EVM,
+                        None,
                         None,
                     );
                     prover.unwrap();

benches/accum_dot.rs

@@ -1,11 +1,13 @@
 use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
 use ezkl::circuit::poly::PolyOp;
 use ezkl::circuit::*;
-use ezkl::pfsys::create_proof_circuit_kzg;
+use ezkl::pfsys::create_proof_circuit;
 use ezkl::pfsys::TranscriptType;
 use ezkl::pfsys::{create_keys, srs::gen_srs};
 use ezkl::tensor::*;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::kzg::multiopen::ProverSHPLONK;
+use halo2_proofs::poly::kzg::multiopen::VerifierSHPLONK;
 use halo2_proofs::poly::kzg::strategy::SingleStrategy;
 use halo2_proofs::{
     arithmetic::Field,
@@ -14,6 +16,7 @@ use halo2_proofs::{
 };
 use halo2curves::bn256::{Bn256, Fr};
 use rand::rngs::OsRng;
+use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;
 
 static mut LEN: usize = 4;
@@ -90,25 +93,35 @@ fn rundot(c: &mut Criterion) {
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("pk", len), &len, |b, &_| {
             b.iter(|| {
-                create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
+                create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
                     .unwrap();
             });
         });
 
-        let pk = create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
-            .unwrap();
+        let pk =
+            create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true).unwrap();
 
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("prove", len), &len, |b, &_| {
             b.iter(|| {
-                let prover = create_proof_circuit_kzg(
+                let prover = create_proof_circuit::<
+                    KZGCommitmentScheme<_>,
+                    MyCircuit,
+                    ProverSHPLONK<_>,
+                    VerifierSHPLONK<_>,
+                    SingleStrategy<_>,
+                    _,
+                    EvmTranscript<_, _, _, _>,
+                    EvmTranscript<_, _, _, _>,
+                >(
                     circuit.clone(),
+                    vec![],
                     &params,
-                    None,
                     &pk,
-                    TranscriptType::EVM,
-                    SingleStrategy::new(&params),
                     CheckMode::UNSAFE,
+                    ezkl::Commitments::KZG,
+                    TranscriptType::EVM,
+                    None,
                     None,
                 );
                 prover.unwrap();

benches/accum_einsum_matmul.rs

@@ -1,11 +1,13 @@
 use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
 use ezkl::circuit::poly::PolyOp;
 use ezkl::circuit::*;
-use ezkl::pfsys::create_proof_circuit_kzg;
+use ezkl::pfsys::create_proof_circuit;
 use ezkl::pfsys::TranscriptType;
 use ezkl::pfsys::{create_keys, srs::gen_srs};
 use ezkl::tensor::*;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::kzg::multiopen::ProverSHPLONK;
+use halo2_proofs::poly::kzg::multiopen::VerifierSHPLONK;
 use halo2_proofs::poly::kzg::strategy::SingleStrategy;
 use halo2_proofs::{
     arithmetic::Field,
@@ -14,6 +16,7 @@ use halo2_proofs::{
 };
 use halo2curves::bn256::{Bn256, Fr};
 use rand::rngs::OsRng;
+use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;
 
 static mut LEN: usize = 4;
@@ -94,25 +97,35 @@ fn runmatmul(c: &mut Criterion) {
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("pk", len), &len, |b, &_| {
             b.iter(|| {
-                create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
+                create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
                     .unwrap();
             });
         });
 
-        let pk = create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
-            .unwrap();
+        let pk =
+            create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true).unwrap();
 
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("prove", len), &len, |b, &_| {
             b.iter(|| {
-                let prover = create_proof_circuit_kzg(
+                let prover = create_proof_circuit::<
+                    KZGCommitmentScheme<_>,
+                    MyCircuit,
+                    ProverSHPLONK<_>,
+                    VerifierSHPLONK<_>,
+                    SingleStrategy<_>,
+                    _,
+                    EvmTranscript<_, _, _, _>,
+                    EvmTranscript<_, _, _, _>,
+                >(
                     circuit.clone(),
+                    vec![],
                     &params,
-                    None,
                     &pk,
-                    TranscriptType::EVM,
-                    SingleStrategy::new(&params),
                     CheckMode::UNSAFE,
+                    ezkl::Commitments::KZG,
+                    TranscriptType::EVM,
+                    None,
                     None,
                 );
                 prover.unwrap();

benches/accum_matmul_relu.rs

@@ -4,17 +4,20 @@ use ezkl::circuit::*;
 
 use ezkl::circuit::lookup::LookupOp;
 use ezkl::circuit::poly::PolyOp;
-use ezkl::pfsys::create_proof_circuit_kzg;
+use ezkl::pfsys::create_proof_circuit;
 use ezkl::pfsys::TranscriptType;
 use ezkl::pfsys::{create_keys, srs::gen_srs};
 use ezkl::tensor::*;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::kzg::multiopen::ProverSHPLONK;
+use halo2_proofs::poly::kzg::multiopen::VerifierSHPLONK;
 use halo2_proofs::poly::kzg::strategy::SingleStrategy;
 use halo2_proofs::{
     circuit::{Layouter, SimpleFloorPlanner, Value},
     plonk::{Circuit, ConstraintSystem, Error},
 };
 use halo2curves::bn256::{Bn256, Fr};
+use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;
 
 const BITS: Range = (-32768, 32768);
@@ -112,25 +115,35 @@ fn runmatmul(c: &mut Criterion) {
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("pk", len), &len, |b, &_| {
             b.iter(|| {
-                create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
+                create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
                     .unwrap();
             });
         });
 
-        let pk = create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
-            .unwrap();
+        let pk =
+            create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true).unwrap();
 
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("prove", len), &len, |b, &_| {
             b.iter(|| {
-                let prover = create_proof_circuit_kzg(
+                let prover = create_proof_circuit::<
+                    KZGCommitmentScheme<_>,
+                    MyCircuit,
+                    ProverSHPLONK<_>,
+                    VerifierSHPLONK<_>,
+                    SingleStrategy<_>,
+                    _,
+                    EvmTranscript<_, _, _, _>,
+                    EvmTranscript<_, _, _, _>,
+                >(
                     circuit.clone(),
+                    vec![],
                     &params,
-                    None,
                     &pk,
+                    CheckMode::UNSAFE,
+                    ezkl::Commitments::KZG,
                     TranscriptType::EVM,
-                    SingleStrategy::new(&params),
-                    CheckMode::SAFE,
+                    None,
                     None,
                 );
                 prover.unwrap();

benches/accum_matmul_relu_overflow.rs

@@ -4,17 +4,20 @@ use ezkl::circuit::*;
 use ezkl::circuit::lookup::LookupOp;
 use ezkl::circuit::poly::PolyOp;
 use ezkl::circuit::table::Range;
-use ezkl::pfsys::create_proof_circuit_kzg;
+use ezkl::pfsys::create_proof_circuit;
 use ezkl::pfsys::TranscriptType;
 use ezkl::pfsys::{create_keys, srs::gen_srs};
 use ezkl::tensor::*;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::kzg::multiopen::ProverSHPLONK;
+use halo2_proofs::poly::kzg::multiopen::VerifierSHPLONK;
 use halo2_proofs::poly::kzg::strategy::SingleStrategy;
 use halo2_proofs::{
     circuit::{Layouter, SimpleFloorPlanner, Value},
     plonk::{Circuit, ConstraintSystem, Error},
 };
 use halo2curves::bn256::{Bn256, Fr};
+use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;
 
 const BITS: Range = (-8180, 8180);
@@ -115,25 +118,35 @@ fn runmatmul(c: &mut Criterion) {
         group.throughput(Throughput::Elements(k as u64));
         group.bench_with_input(BenchmarkId::new("pk", k), &k, |b, &_| {
             b.iter(|| {
-                create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
+                create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
                     .unwrap();
             });
         });
 
-        let pk = create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
-            .unwrap();
+        let pk =
+            create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true).unwrap();
 
         group.throughput(Throughput::Elements(k as u64));
         group.bench_with_input(BenchmarkId::new("prove", k), &k, |b, &_| {
             b.iter(|| {
-                let prover = create_proof_circuit_kzg(
+                let prover = create_proof_circuit::<
+                    KZGCommitmentScheme<_>,
+                    MyCircuit,
+                    ProverSHPLONK<_>,
+                    VerifierSHPLONK<_>,
+                    SingleStrategy<_>,
+                    _,
+                    EvmTranscript<_, _, _, _>,
+                    EvmTranscript<_, _, _, _>,
+                >(
                     circuit.clone(),
+                    vec![],
                     &params,
-                    None,
                     &pk,
+                    CheckMode::UNSAFE,
+                    ezkl::Commitments::KZG,
                     TranscriptType::EVM,
-                    SingleStrategy::new(&params),
-                    CheckMode::SAFE,
+                    None,
                     None,
                 );
                 prover.unwrap();

benches/accum_sum.rs

@@ -1,11 +1,13 @@
 use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
 use ezkl::circuit::poly::PolyOp;
 use ezkl::circuit::*;
-use ezkl::pfsys::create_proof_circuit_kzg;
+use ezkl::pfsys::create_proof_circuit;
 use ezkl::pfsys::TranscriptType;
 use ezkl::pfsys::{create_keys, srs::gen_srs};
 use ezkl::tensor::*;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::kzg::multiopen::ProverSHPLONK;
+use halo2_proofs::poly::kzg::multiopen::VerifierSHPLONK;
 use halo2_proofs::poly::kzg::strategy::SingleStrategy;
 use halo2_proofs::{
     arithmetic::Field,
@@ -14,6 +16,7 @@ use halo2_proofs::{
 };
 use halo2curves::bn256::{Bn256, Fr};
 use rand::rngs::OsRng;
+use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;
 
 static mut LEN: usize = 4;
@@ -86,25 +89,35 @@ fn runsum(c: &mut Criterion) {
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("pk", len), &len, |b, &_| {
             b.iter(|| {
-                create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
+                create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
                     .unwrap();
             });
         });
 
-        let pk = create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
-            .unwrap();
+        let pk =
+            create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true).unwrap();
 
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("prove", len), &len, |b, &_| {
             b.iter(|| {
-                let prover = create_proof_circuit_kzg(
+                let prover = create_proof_circuit::<
+                    KZGCommitmentScheme<_>,
+                    MyCircuit,
+                    ProverSHPLONK<_>,
+                    VerifierSHPLONK<_>,
+                    SingleStrategy<_>,
+                    _,
+                    EvmTranscript<_, _, _, _>,
+                    EvmTranscript<_, _, _, _>,
+                >(
                     circuit.clone(),
+                    vec![],
                     &params,
-                    None,
                     &pk,
-                    TranscriptType::EVM,
-                    SingleStrategy::new(&params),
                     CheckMode::UNSAFE,
+                    ezkl::Commitments::KZG,
+                    TranscriptType::EVM,
+                    None,
                     None,
                 );
                 prover.unwrap();

benches/accum_sumpool.rs

@@ -2,11 +2,13 @@ use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Through
 use ezkl::circuit::hybrid::HybridOp;
 use ezkl::circuit::*;
 use ezkl::pfsys::create_keys;
-use ezkl::pfsys::create_proof_circuit_kzg;
+use ezkl::pfsys::create_proof_circuit;
 use ezkl::pfsys::srs::gen_srs;
 use ezkl::pfsys::TranscriptType;
 use ezkl::tensor::*;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::kzg::multiopen::ProverSHPLONK;
+use halo2_proofs::poly::kzg::multiopen::VerifierSHPLONK;
 use halo2_proofs::poly::kzg::strategy::SingleStrategy;
 use halo2_proofs::{
     arithmetic::Field,
@@ -15,6 +17,7 @@ use halo2_proofs::{
 };
 use halo2curves::bn256::{Bn256, Fr};
 use rand::rngs::OsRng;
+use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 
 static mut IMAGE_HEIGHT: usize = 2;
 static mut IMAGE_WIDTH: usize = 2;
@@ -101,28 +104,35 @@ fn runsumpool(c: &mut Criterion) {
             group.throughput(Throughput::Elements(*size as u64));
             group.bench_with_input(BenchmarkId::new("pk", size), &size, |b, &_| {
                 b.iter(|| {
-                    create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(
-                        &circuit, &params, true,
-                    )
-                    .unwrap();
+                    create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
+                        .unwrap();
                 });
             });
 
-            let pk =
-                create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
-                    .unwrap();
+            let pk = create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
+                .unwrap();
 
             group.throughput(Throughput::Elements(*size as u64));
             group.bench_with_input(BenchmarkId::new("prove", size), &size, |b, &_| {
                 b.iter(|| {
-                    let prover = create_proof_circuit_kzg(
+                    let prover = create_proof_circuit::<
+                        KZGCommitmentScheme<_>,
+                        MyCircuit,
+                        ProverSHPLONK<_>,
+                        VerifierSHPLONK<_>,
+                        SingleStrategy<_>,
+                        _,
+                        EvmTranscript<_, _, _, _>,
+                        EvmTranscript<_, _, _, _>,
+                    >(
                         circuit.clone(),
+                        vec![],
                         &params,
-                        None,
                         &pk,
-                        TranscriptType::EVM,
-                        SingleStrategy::new(&params),
                         CheckMode::UNSAFE,
+                        ezkl::Commitments::KZG,
+                        TranscriptType::EVM,
+                        None,
                         None,
                     );
                     prover.unwrap();

benches/pairwise_add.rs

@@ -1,11 +1,13 @@
 use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
 use ezkl::circuit::poly::PolyOp;
 use ezkl::circuit::*;
-use ezkl::pfsys::create_proof_circuit_kzg;
+use ezkl::pfsys::create_proof_circuit;
 use ezkl::pfsys::TranscriptType;
 use ezkl::pfsys::{create_keys, srs::gen_srs};
 use ezkl::tensor::*;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::kzg::multiopen::ProverSHPLONK;
+use halo2_proofs::poly::kzg::multiopen::VerifierSHPLONK;
 use halo2_proofs::poly::kzg::strategy::SingleStrategy;
 use halo2_proofs::{
     arithmetic::Field,
@@ -14,6 +16,7 @@ use halo2_proofs::{
 };
 use halo2curves::bn256::{Bn256, Fr};
 use rand::rngs::OsRng;
+use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;
 
 static mut LEN: usize = 4;
@@ -84,25 +87,35 @@ fn runadd(c: &mut Criterion) {
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("pk", len), &len, |b, &_| {
             b.iter(|| {
-                create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
+                create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
                     .unwrap();
             });
         });
 
-        let pk = create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
-            .unwrap();
+        let pk =
+            create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true).unwrap();
 
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("prove", len), &len, |b, &_| {
             b.iter(|| {
-                let prover = create_proof_circuit_kzg(
+                let prover = create_proof_circuit::<
+                    KZGCommitmentScheme<_>,
+                    MyCircuit,
+                    ProverSHPLONK<_>,
+                    VerifierSHPLONK<_>,
+                    SingleStrategy<_>,
+                    _,
+                    EvmTranscript<_, _, _, _>,
+                    EvmTranscript<_, _, _, _>,
+                >(
                     circuit.clone(),
+                    vec![],
                     &params,
-                    None,
                     &pk,
+                    CheckMode::UNSAFE,
+                    ezkl::Commitments::KZG,
                     TranscriptType::EVM,
-                    SingleStrategy::new(&params),
-                    CheckMode::SAFE,
+                    None,
                     None,
                 );
                 prover.unwrap();

benches/pairwise_pow.rs

@@ -2,11 +2,13 @@ use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Through
 use ezkl::circuit::poly::PolyOp;
 use ezkl::circuit::region::RegionCtx;
 use ezkl::circuit::*;
-use ezkl::pfsys::create_proof_circuit_kzg;
+use ezkl::pfsys::create_proof_circuit;
 use ezkl::pfsys::TranscriptType;
 use ezkl::pfsys::{create_keys, srs::gen_srs};
 use ezkl::tensor::*;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::kzg::multiopen::ProverSHPLONK;
+use halo2_proofs::poly::kzg::multiopen::VerifierSHPLONK;
 use halo2_proofs::poly::kzg::strategy::SingleStrategy;
 use halo2_proofs::{
     arithmetic::Field,
@@ -15,6 +17,7 @@ use halo2_proofs::{
 };
 use halo2curves::bn256::{Bn256, Fr};
 use rand::rngs::OsRng;
+use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;
 
 static mut LEN: usize = 4;
@@ -83,25 +86,35 @@ fn runpow(c: &mut Criterion) {
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("pk", len), &len, |b, &_| {
             b.iter(|| {
-                create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
+                create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
                     .unwrap();
             });
         });
 
-        let pk = create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
-            .unwrap();
+        let pk =
+            create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true).unwrap();
 
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("prove", len), &len, |b, &_| {
             b.iter(|| {
-                let prover = create_proof_circuit_kzg(
+                let prover = create_proof_circuit::<
+                    KZGCommitmentScheme<_>,
+                    MyCircuit,
+                    ProverSHPLONK<_>,
+                    VerifierSHPLONK<_>,
+                    SingleStrategy<_>,
+                    _,
+                    EvmTranscript<_, _, _, _>,
+                    EvmTranscript<_, _, _, _>,
+                >(
                     circuit.clone(),
+                    vec![],
                     &params,
-                    None,
                     &pk,
+                    CheckMode::UNSAFE,
+                    ezkl::Commitments::KZG,
                     TranscriptType::EVM,
-                    SingleStrategy::new(&params),
-                    CheckMode::SAFE,
+                    None,
                     None,
                 );
                 prover.unwrap();

benches/poseidon.rs

@@ -1,15 +1,18 @@
+use std::collections::HashMap;
+
 use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
 use ezkl::circuit::modules::poseidon::spec::{PoseidonSpec, POSEIDON_RATE, POSEIDON_WIDTH};
 use ezkl::circuit::modules::poseidon::{PoseidonChip, PoseidonConfig};
 use ezkl::circuit::modules::Module;
 use ezkl::circuit::*;
 use ezkl::pfsys::create_keys;
-use ezkl::pfsys::create_proof_circuit_kzg;
+use ezkl::pfsys::create_proof_circuit;
 use ezkl::pfsys::srs::gen_srs;
 use ezkl::pfsys::TranscriptType;
 use ezkl::tensor::*;
 use halo2_proofs::circuit::Value;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::kzg::multiopen::{ProverSHPLONK, VerifierSHPLONK};
 use halo2_proofs::poly::kzg::strategy::SingleStrategy;
 use halo2_proofs::{
     arithmetic::Field,
@@ -18,6 +21,7 @@ use halo2_proofs::{
 };
 use halo2curves::bn256::{Bn256, Fr};
 use rand::rngs::OsRng;
+use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 
 const L: usize = 10;
 
@@ -46,7 +50,7 @@ impl Circuit<Fr> for MyCircuit {
     ) -> Result<(), Error> {
         let chip: PoseidonChip<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, L> =
             PoseidonChip::new(config);
-        chip.layout(&mut layouter, &[self.image.clone()], 0)?;
+        chip.layout(&mut layouter, &[self.image.clone()], 0, &mut HashMap::new())?;
         Ok(())
     }
 }
@@ -62,7 +66,7 @@ fn runposeidon(c: &mut Criterion) {
         let params = gen_srs::<KZGCommitmentScheme<_>>(k);
 
         let message = (0..*size).map(|_| Fr::random(OsRng)).collect::<Vec<_>>();
-        let output =
+        let _output =
             PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, L>::run(message.to_vec())
                 .unwrap();
 
@@ -76,25 +80,35 @@ fn runposeidon(c: &mut Criterion) {
         group.throughput(Throughput::Elements(*size as u64));
         group.bench_with_input(BenchmarkId::new("pk", size), &size, |b, &_| {
             b.iter(|| {
-                create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
+                create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
                     .unwrap();
             });
         });
 
-        let pk = create_keys::<KZGCommitmentScheme<Bn256>, Fr, MyCircuit>(&circuit, &params, true)
-            .unwrap();
+        let pk =
+            create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true).unwrap();
 
         group.throughput(Throughput::Elements(*size as u64));
         group.bench_with_input(BenchmarkId::new("prove", size), &size, |b, &_| {
             b.iter(|| {
-                let prover = create_proof_circuit_kzg(
+                let prover = create_proof_circuit::<
+                    KZGCommitmentScheme<_>,
+                    MyCircuit,
+                    ProverSHPLONK<_>,
+                    VerifierSHPLONK<_>,
+                    SingleStrategy<_>,
+                    _,
+                    EvmTranscript<_, _, _, _>,
+                    EvmTranscript<_, _, _, _>,
+                >(
                     circuit.clone(),
+                    vec![],
                     &params,
-                    Some(output[0].clone()),
                     &pk,
-                    TranscriptType::EVM,
-                    SingleStrategy::new(&params),
                     CheckMode::UNSAFE,
+                    ezkl::Commitments::KZG,
+                    TranscriptType::EVM,
+                    None,
                     None,
                 );
                 prover.unwrap();

benches/relu.rs

@@ -2,11 +2,12 @@ use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Through
 use ezkl::circuit::region::RegionCtx;
 use ezkl::circuit::table::Range;
 use ezkl::circuit::{ops::lookup::LookupOp, BaseConfig as Config, CheckMode};
-use ezkl::pfsys::create_proof_circuit_kzg;
+use ezkl::pfsys::create_proof_circuit;
 use ezkl::pfsys::TranscriptType;
 use ezkl::pfsys::{create_keys, srs::gen_srs};
 use ezkl::tensor::*;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::kzg::multiopen::{ProverSHPLONK, VerifierSHPLONK};
 use halo2_proofs::poly::kzg::strategy::SingleStrategy;
 use halo2_proofs::{
     circuit::{Layouter, SimpleFloorPlanner, Value},
@@ -14,6 +15,7 @@ use halo2_proofs::{
 };
 use halo2curves::bn256::{Bn256, Fr};
 use rand::Rng;
+use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 
 const BITS: Range = (-32768, 32768);
 static mut LEN: usize = 4;
@@ -91,25 +93,35 @@ fn runrelu(c: &mut Criterion) {
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("pk", len), &len, |b, &_| {
             b.iter(|| {
-                create_keys::<KZGCommitmentScheme<Bn256>, Fr, NLCircuit>(&circuit, &params, true)
+                create_keys::<KZGCommitmentScheme<Bn256>, NLCircuit>(&circuit, &params, true)
                     .unwrap();
             });
         });
 
-        let pk = create_keys::<KZGCommitmentScheme<Bn256>, Fr, NLCircuit>(&circuit, &params, true)
-            .unwrap();
+        let pk =
+            create_keys::<KZGCommitmentScheme<Bn256>, NLCircuit>(&circuit, &params, true).unwrap();
 
         group.throughput(Throughput::Elements(len as u64));
         group.bench_with_input(BenchmarkId::new("prove", len), &len, |b, &_| {
             b.iter(|| {
-                let prover = create_proof_circuit_kzg(
+                let prover = create_proof_circuit::<
+                    KZGCommitmentScheme<_>,
+                    NLCircuit,
+                    ProverSHPLONK<_>,
+                    VerifierSHPLONK<_>,
+                    SingleStrategy<_>,
+                    _,
+                    EvmTranscript<_, _, _, _>,
+                    EvmTranscript<_, _, _, _>,
+                >(
                     circuit.clone(),
+                    vec![],
                     &params,
-                    None,
                     &pk,
+                    CheckMode::UNSAFE,
+                    ezkl::Commitments::KZG,
                     TranscriptType::EVM,
-                    SingleStrategy::new(&params),
-                    CheckMode::SAFE,
+                    None,
                     None,
                 );
                 prover.unwrap();

examples/notebooks/ezkl_demo.ipynb

@@ -696,10 +696,12 @@
         "for i, value in enumerate(proof[\"instances\"]):\n",
         "    for j, field_element in enumerate(value):\n",
         "        onchain_input_array.append(ezkl.felt_to_big_endian(field_element))\n",
-        "        formatted_output += str(onchain_input_array[-1])\n",
+        "        formatted_output += '\"' + str(onchain_input_array[-1]) + '\"'\n",
         "        if j != len(value) - 1:\n",
         "            formatted_output += \", \"\n",
-        "    formatted_output += \"]\"\n",
+        "    if i != len(proof[\"instances\"]) - 1:\n",
+        "        formatted_output += \", \"\n",
+        "formatted_output += \"]\"\n",
         "\n",
         "# This will be the values you use onchain\n",
         "# copy them over to remix and see if they verify\n",

examples/notebooks/generalized_inverse.ipynb

@@ -10,7 +10,7 @@
         "\n",
         "## Generalized Inverse\n",
         "\n",
-        "We show how to use EZKL to prove that we know matrices $A$ and its generalized inverse $B$. Since these are large we deal with the KZG commitments, with $a$ the kzgcommit of $A$, $b$ the kzgcommit of $B$, and $ABA = A$.\n"
+        "We show how to use EZKL to prove that we know matrices $A$ and its generalized inverse $B$. Since these are large we deal with the KZG commitments, with $a$ the polycommit of $A$, $b$ the polycommit of $B$, and $ABA = A$.\n"
       ]
     },
     {
@@ -77,7 +77,7 @@
       "outputs": [],
       "source": [
         "gip_run_args = ezkl.PyRunArgs()\n",
-        "gip_run_args.input_visibility = \"kzgcommit\"  # matrix and generalized inverse commitments\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)"
       ]
@@ -340,4 +340,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

examples/notebooks/hashed_vis.ipynb

@@ -161,7 +161,7 @@
                 "- `fixed`: known to the prover and verifier (as a commit), but not modifiable by the prover.\n",
                 "- `hashed`: the hash pre-image is known to the prover, the prover and verifier know the hash. The prover proves that the they know the pre-image to the hash. \n",
                 "- `encrypted`: the non-encrypted element and the secret key used for decryption are known to the prover. The prover and the verifier know the encrypted element, the public key used to encrypt, and the hash of the decryption hey. The prover proves that they know the pre-image of the hashed decryption key and that this key can in fact decrypt the encrypted message.\n",
-                "- `kzgcommit`: unblinded advice column which generates a kzg commitment. This doesn't appear in the instances of the circuit and must instead be inserted directly within the proof bytes.  \n",
+                "- `polycommit`: unblinded advice column which generates a kzg commitment. This doesn't appear in the instances of the circuit and must instead be inserted directly within the proof bytes.  \n",
                 "\n",
                 "\n",
                 "Here we create the following setup:\n",
@@ -510,4 +510,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 2
-}
+}

examples/notebooks/keras_simple_demo.ipynb

@@ -67,6 +67,7 @@
                 "model.add(Dense(128, activation='relu'))\n",
                 "model.add(Dropout(0.5))\n",
                 "model.add(Dense(10, activation='softmax'))\n",
+                "model.output_names=['output']\n",
                 "\n",
                 "\n",
                 "# Train the model as you like here (skipped for brevity)\n",

examples/notebooks/kzg_vis.ipynb

@@ -154,11 +154,11 @@
                 "- `fixed`: known to the prover and verifier (as a commit), but not modifiable by the prover.\n",
                 "- `hashed`: the hash pre-image is known to the prover, the prover and verifier know the hash. The prover proves that the they know the pre-image to the hash. \n",
                 "- `encrypted`: the non-encrypted element and the secret key used for decryption are known to the prover. The prover and the verifier know the encrypted element, the public key used to encrypt, and the hash of the decryption hey. The prover proves that they know the pre-image of the hashed decryption key and that this key can in fact decrypt the encrypted message.\n",
-                "- `kzgcommit`: unblinded advice column which generates a kzg commitment. This doesn't appear in the instances of the circuit and must instead be modified directly within the proof bytes.  \n",
+                "- `polycommit`: unblinded advice column which generates a kzg commitment. This doesn't appear in the instances of the circuit and must instead be modified directly within the proof bytes.  \n",
                 "\n",
                 "Here we create the following setup:\n",
-                "- `input_visibility`: \"kzgcommit\"\n",
-                "- `param_visibility`: \"kzgcommit\"\n",
+                "- `input_visibility`: \"polycommit\"\n",
+                "- `param_visibility`: \"polycommit\"\n",
                 "- `output_visibility`: public\n",
                 "\n",
                 "We encourage you to play around with other setups :) \n",
@@ -186,8 +186,8 @@
                 "data_path = os.path.join('input.json')\n",
                 "\n",
                 "run_args = ezkl.PyRunArgs()\n",
-                "run_args.input_visibility = \"kzgcommit\"\n",
-                "run_args.param_visibility = \"kzgcommit\"\n",
+                "run_args.input_visibility = \"polycommit\"\n",
+                "run_args.param_visibility = \"polycommit\"\n",
                 "run_args.output_visibility = \"public\"\n",
                 "run_args.variables = [(\"batch_size\", 1)]\n",
                 "\n",
@@ -512,4 +512,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 2
-}
+}

examples/notebooks/mnist_gan.ipynb

@@ -38,7 +38,7 @@
                 "import logging\n",
                 "\n",
                 "import tensorflow as tf\n",
-                "from tensorflow.keras.optimizers.legacy import Adam\n",
+                "from tensorflow.keras.optimizers import Adam\n",
                 "from tensorflow.keras.layers import *\n",
                 "from tensorflow.keras.models import Model\n",
                 "from tensorflow.keras.datasets import mnist\n",
@@ -71,9 +71,11 @@
             },
             "outputs": [],
             "source": [
-                "opt = Adam()\n",
                 "ZDIM = 100\n",
                 "\n",
+                "opt = Adam()\n",
+                "\n",
+                "\n",
                 "# discriminator\n",
                 "# 0 if it's fake, 1 if it's real\n",
                 "x = in1 = Input((28,28))\n",
@@ -114,8 +116,11 @@
                 "\n",
                 "gm = Model(in1, x)\n",
                 "gm.compile('adam', 'mse')\n",
+                "gm.output_names=['output']\n",
                 "gm.summary()\n",
                 "\n",
+                "opt = Adam()\n",
+                "\n",
                 "# GAN\n",
                 "dm.trainable = False\n",
                 "x = dm(gm.output)\n",
@@ -415,7 +420,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         }
     },
     "nbformat": 4,

examples/notebooks/mnist_vae.ipynb

@@ -349,6 +349,8 @@
                 "z_log_var = Dense(ZDIM)(x)\n",
                 "z = Lambda(lambda x: x[0] + K.exp(0.5 * x[1]) * K.random_normal(shape=K.shape(x[0])))([z_mu, z_log_var])\n",
                 "dec = get_decoder()\n",
+                "dec.output_names=['output']\n",
+                "\n",
                 "out = dec(z)\n",
                 "\n",
                 "mse_loss = mse(Reshape((28*28,))(in1), Reshape((28*28,))(out)) * 28 * 28\n",

examples/notebooks/proof_splitting.ipynb

@@ -208,7 +208,7 @@
                 "- `private`: known only to the prover\n",
                 "- `hashed`: the hash pre-image is known to the prover, the prover and verifier know the hash. The prover proves that the they know the pre-image to the hash. \n",
                 "- `encrypted`: the non-encrypted element and the secret key used for decryption are known to the prover. The prover and the verifier know the encrypted element, the public key used to encrypt, and the hash of the decryption hey. The prover proves that they know the pre-image of the hashed decryption key and that this key can in fact decrypt the encrypted message.\n",
-                "- `kzgcommit`: unblinded advice column which generates a kzg commitment. This doesn't appear in the instances of the circuit and must instead be modified directly within the proof bytes.  \n",
+                "- `polycommit`: unblinded advice column which generates a kzg commitment. This doesn't appear in the instances of the circuit and must instead be modified directly within the proof bytes.  \n",
                 "\n",
                 "Here we create the following setup:\n",
                 "- `input_visibility`: \"public\"\n",
@@ -234,7 +234,7 @@
                 "run_args.input_scale = 2\n",
                 "run_args.logrows = 8\n",
                 "\n",
-                "ezkl.get_srs(logrows=run_args.logrows)"
+                "ezkl.get_srs(logrows=run_args.logrows, commitment=ezkl.PyCommitments.KZG)"
             ]
         },
         {
@@ -385,9 +385,9 @@
                 "###  KZG commitment intermediate calculations\n",
                 "\n",
                 "This time the visibility parameters are:\n",
-                "- `input_visibility`: \"kzgcommit\"\n",
+                "- `input_visibility`: \"polycommit\"\n",
                 "- `param_visibility`: \"public\"\n",
-                "- `output_visibility`: kzgcommit"
+                "- `output_visibility`: polycommit"
             ]
         },
         {
@@ -399,9 +399,9 @@
                 "import ezkl\n",
                 "\n",
                 "run_args = ezkl.PyRunArgs()\n",
-                "run_args.input_visibility = \"kzgcommit\"\n",
+                "run_args.input_visibility = \"polycommit\"\n",
                 "run_args.param_visibility = \"fixed\"\n",
-                "run_args.output_visibility = \"kzgcommit\"\n",
+                "run_args.output_visibility = \"polycommit\"\n",
                 "run_args.variables = [(\"batch_size\", 1)]\n",
                 "run_args.input_scale = 2\n",
                 "run_args.logrows = 8\n"

examples/notebooks/random_forest.ipynb

@@ -61,11 +61,10 @@
                 "from sklearn.datasets import load_iris\n",
                 "from sklearn.model_selection import train_test_split\n",
                 "from sklearn.ensemble import RandomForestClassifier as Rf\n",
-                "import sk2torch\n",
                 "import torch\n",
                 "import ezkl\n",
                 "import os\n",
-                "from torch import nn\n",
+                "from hummingbird.ml import convert\n",
                 "\n",
                 "\n",
                 "\n",
@@ -77,28 +76,12 @@
                 "clr.fit(X_train, y_train)\n",
                 "\n",
                 "\n",
-                "trees = []\n",
-                "for tree in clr.estimators_:\n",
-                "    trees.append(sk2torch.wrap(tree))\n",
                 "\n",
-                "\n",
-                "class RandomForest(nn.Module):\n",
-                "    def __init__(self, trees):\n",
-                "        super(RandomForest, self).__init__()\n",
-                "        self.trees = nn.ModuleList(trees)\n",
-                "\n",
-                "    def forward(self, x):\n",
-                "        out = self.trees[0](x)\n",
-                "        for tree in self.trees[1:]:\n",
-                "            out += tree(x)\n",
-                "        return out / len(self.trees)\n",
-                "\n",
-                "\n",
-                "torch_rf = RandomForest(trees)\n",
+                "torch_rf = convert(clr, 'torch')\n",
                 "# assert predictions from torch are = to sklearn \n",
                 "diffs = []\n",
                 "for i in range(len(X_test)):\n",
-                "    torch_pred = torch_rf(torch.tensor(X_test[i].reshape(1, -1)))\n",
+                "    torch_pred = torch_rf.predict(torch.tensor(X_test[i].reshape(1, -1)))\n",
                 "    sk_pred = clr.predict(X_test[i].reshape(1, -1))\n",
                 "    diffs.append(torch_pred[0].round() - sk_pred[0])\n",
                 "\n",
@@ -134,14 +117,12 @@
                 "\n",
                 "# export to onnx format\n",
                 "\n",
-                "torch_rf.eval()\n",
-                "\n",
                 "# Input to the model\n",
                 "shape = X_train.shape[1:]\n",
                 "x = torch.rand(1, *shape, requires_grad=False)\n",
-                "torch_out = torch_rf(x)\n",
+                "torch_out = torch_rf.predict(x)\n",
                 "# Export the model\n",
-                "torch.onnx.export(torch_rf,               # model being run\n",
+                "torch.onnx.export(torch_rf.model,               # 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",
@@ -158,7 +139,7 @@
                 "\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",
+                "            output_data=[o.reshape([-1]).tolist() for o in torch_out])\n",
                 "\n",
                 "# Serialize data into file:\n",
                 "json.dump(data, open(\"input.json\", 'w'))\n"
@@ -321,7 +302,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         }
     },
     "nbformat": 4,

examples/notebooks/simple_demo_aggregated_proofs.ipynb

@@ -275,7 +275,6 @@
                 "        proof_path,\n",
                 "        settings_path,\n",
                 "        vk_path,\n",
-                "        \n",
                 "    )\n",
                 "\n",
                 "assert res == True\n",
@@ -291,7 +290,7 @@
             "source": [
                 "# Generate a larger SRS. This is needed for the aggregated proof\n",
                 "\n",
-                "res = ezkl.get_srs(settings_path=None, logrows=21)"
+                "res = ezkl.get_srs(settings_path=None, logrows=21, commitment=ezkl.PyCommitments.KZG)"
             ]
         },
         {

examples/notebooks/solvency.ipynb

@@ -9,7 +9,7 @@
       "source": [
         "## Solvency demo\n",
         "\n",
-        "Here we create a demo of a solvency calculation in the manner of [summa-solvency](https://github.com/summa-dev/summa-solvency). The aim here is to demonstrate the use of the new kzgcommit method detailed [here](https://blog.ezkl.xyz/post/commits/). \n",
+        "Here we create a demo of a solvency calculation in the manner of [summa-solvency](https://github.com/summa-dev/summa-solvency). The aim here is to demonstrate the use of the new polycommit method detailed [here](https://blog.ezkl.xyz/post/commits/). \n",
         "\n",
         "In this setup:\n",
         "- the commitments to users, respective balances, and total balance are known are publicly known to the prover and verifier. \n",
@@ -177,10 +177,10 @@
         "- `private`: known only to the prover\n",
         "- `hashed`: the hash pre-image is known to the prover, the prover and verifier know the hash. The prover proves that the they know the pre-image to the hash. \n",
         "- `encrypted`: the non-encrypted element and the secret key used for decryption are known to the prover. The prover and the verifier know the encrypted element, the public key used to encrypt, and the hash of the decryption hey. The prover proves that they know the pre-image of the hashed decryption key and that this key can in fact decrypt the encrypted message.\n",
-        "- `kzgcommit`: unblinded advice column which generates a kzg commitment. This doesn't appear in the instances of the circuit and must instead be modified directly within the proof bytes.  \n",
+        "- `polycommit`: unblinded advice column which generates a kzg commitment. This doesn't appear in the instances of the circuit and must instead be modified directly within the proof bytes.  \n",
         "\n",
         "Here we create the following setup:\n",
-        "- `input_visibility`: \"kzgcommit\"\n",
+        "- `input_visibility`: \"polycommit\"\n",
         "- `param_visibility`: \"public\"\n",
         "- `output_visibility`: public\n",
         "\n",
@@ -202,8 +202,8 @@
       "outputs": [],
       "source": [
         "run_args = ezkl.PyRunArgs()\n",
-        "# \"kzgcommit\" 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 = \"kzgcommit\"\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",
         "# the parameters are public\n",
         "run_args.param_visibility = \"fixed\"\n",
         "# the output is public (this is the inequality test)\n",
@@ -515,4 +515,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

examples/notebooks/tictactoe_autoencoder.ipynb

@@ -13,7 +13,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 1,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -57,7 +57,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 2,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -119,7 +119,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 3,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -163,7 +163,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 4,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -217,7 +217,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 6,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -227,6 +227,10 @@
                 "        self.length = self.compute_length(self.file_good)\n",
                 "        self.data = self.load_data(self.file_good)\n",
                 "\n",
+                "    def __iter__(self):\n",
+                "        for i in range(len(self.data)):\n",
+                "            yield self.data[i]\n",
+                "\n",
                 "    def parse_json_object(self, line):\n",
                 "        try:\n",
                 "            return json.loads(line)\n",
@@ -749,7 +753,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         }
     },
     "nbformat": 4,

examples/notebooks/tictactoe_binary_classification.ipynb

@@ -209,6 +209,11 @@
                 "        self.length = self.compute_length(self.file_good, self.file_bad)\n",
                 "        self.data = self.load_data(self.file_good, self.file_bad)\n",
                 "\n",
+                "    def __iter__(self):\n",
+                "        for i in range(len(self.data)):\n",
+                "            yield self.data[i]\n",
+                "\n",
+                "\n",
                 "    def parse_json_object(self, line):\n",
                 "        try:\n",
                 "            return json.loads(line)\n",
@@ -637,7 +642,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.2"
         }
     },
     "nbformat": 4,

examples/onnx/1l_lppool/gen.py

@@ -0,0 +1,40 @@
+from torch import nn
+import torch
+import json
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.layer = nn.LPPool2d(2, 1, (1, 1))
+
+    def forward(self, x):
+        return self.layer(x)[0]
+
+
+circuit = Model()
+
+x = torch.empty(1, 3, 2, 2).uniform_(0, 1)
+
+out = circuit(x)
+
+print(out)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))
+

examples/onnx/1l_lppool/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.7549541592597961, 0.990360677242279, 0.9473411440849304, 0.3951031565666199, 0.8500555753707886, 0.9352139830589294, 0.11867779493331909, 0.9493132829666138, 0.6588345766067505, 0.1933223009109497, 0.12139874696731567, 0.8547163605690002]]}

examples/onnx/celu/gen.py

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

examples/onnx/celu/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.35387128591537476, 0.030473172664642334, 0.08707714080810547, 0.2429301142692566, 0.45228832960128784, 0.496021032333374, 0.13245105743408203, 0.8497090339660645]]}

examples/onnx/clip/gen.py

@@ -0,0 +1,41 @@
+from torch import nn
+import torch
+import json
+import numpy as np
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+
+    def forward(self, x):          
+        m = torch.clamp(x, min=0.4, max=0.8)
+        return m
+
+
+circuit = MyModel()
+
+x = torch.empty(1, 2, 2, 8).uniform_(0, 1)
+
+out = circuit(x)
+
+print(out)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))

examples/onnx/clip/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.03297048807144165, 0.46362626552581787, 0.6044231057167053, 0.4949902892112732, 0.48823297023773193, 0.6798646450042725, 0.6824942231178284, 0.03491640090942383, 0.19608813524246216, 0.24129939079284668, 0.9769315123558044, 0.6306831240653992, 0.7690497636795044, 0.252221941947937, 0.9167693853378296, 0.3882681131362915, 0.9307044148445129, 0.33559417724609375, 0.7815426588058472, 0.3435332179069519, 0.7871478796005249, 0.12240773439407349, 0.5295405983924866, 0.4874419569969177, 0.08262640237808228, 0.1124718189239502, 0.5834914445877075, 0.30927878618240356, 0.48899340629577637, 0.9376634955406189, 0.21893149614334106, 0.526070773601532]]}

examples/onnx/clip/network.onnx

@@ -0,0 +1,24 @@
+pytorch2.2.1:±
+?/Constant_output_0	/Constant"Constant*
+value*
JÍÌÌ> 

+C/Constant_1_output_0/Constant_1"Constant*
+value*
JÍÌL? 

+F
+input
+/Constant_output_0
+/Constant_1_output_0output/Clip"Clip
+main_graphZ)
+input 
+

+
+batch_size
+
+
+b*
+output 
+

+
+batch_size
+
+
+B

examples/onnx/gru/gen.py

@@ -0,0 +1,41 @@
+import random
+import math
+import numpy as np
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+import json
+
+
+model = nn.GRU(3, 3)  # Input dim is 3, output dim is 3
+x = torch.randn(1, 3)  # make a sequence of length 5
+
+print(x)
+
+# Flips the neural net into inference mode
+model.eval()
+model.to('cpu')
+
+# Export the model
+torch.onnx.export(model,               # model being run
+                  # model input (or a tuple for multiple inputs)
+                  x,
+                  # where to save the model (can be a file or file-like object)
+                  "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=10,          # 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'}})
+
+data_array = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data_json = dict(input_data=[data_array])
+
+print(data_json)
+
+# Serialize data into file:
+json.dump(data_json, open("input.json", 'w'))

examples/onnx/gru/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.4145222008228302, -0.4043896496295929, 0.7545749545097351]]}

examples/onnx/hard_max/gen.py

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

examples/onnx/hard_max/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.5505883693695068, 0.0766521692276001, 0.12006187438964844, 0.9497959017753601, 0.9100563526153564, 0.968717098236084, 0.5978299379348755, 0.9419963359832764]]}

examples/onnx/hard_sigmoid/gen.py

@@ -9,7 +9,7 @@ class MyModel(nn.Module):
         super(MyModel, self).__init__()
 
     def forward(self, x):          
-        m = nn.Logsoftmax()(x)
+        m = nn.Hardsigmoid()(x)
         
         return m 
 

examples/onnx/hard_sigmoid/input.json

@@ -1 +1 @@
-{"input_data": [[0.2971532940864563, 0.3465197682380676, 0.05381882190704346, 0.058654189109802246, 0.014198064804077148, 0.06088751554489136, 0.1723427176475525, 0.5115123987197876]]}
+{"input_data": [[0.8326942324638367, 0.2796096205711365, 0.600328266620636, 0.3701696991920471, 0.17832040786743164, 0.6247223019599915, 0.501872718334198, 0.6961578726768494]]}

examples/onnx/hard_sigmoid/network.onnx

@@ -1,4 +1,4 @@
-pytorch2.1.0:<3A>

+pytorch2.2.1:<3A>

 ;
 inputoutput/HardSigmoid"HardSigmoid*
 alpha«ª*> 



examples/onnx/hard_swish/gen.py

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

examples/onnx/hard_swish/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.6996762752532959, 0.42992985248565674, 0.5102168321609497, 0.5540387630462646, 0.8489438891410828, 0.8533616065979004, 0.36736780405044556, 0.5859147310256958]]}

examples/onnx/hard_swish/network.onnx

@@ -0,0 +1,15 @@
+pytorch2.2.1:{
+&
+inputoutput
+/HardSwish"	HardSwish
+main_graphZ!
+input
+

+
+batch_size
+b"
+output
+

+
+batch_size
+B

examples/onnx/log_softmax/gen.py

@@ -9,7 +9,7 @@ class MyModel(nn.Module):
         super(MyModel, self).__init__()
 
     def forward(self, x):          
-        m = nn.Hardsigmoid()(x)
+        m = nn.LogSoftmax()(x)
         
         return m 
 

examples/onnx/logsumexp/gen.py

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

examples/onnx/logsumexp/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.7973018884658813, 0.5245689153671265, 0.34149593114852905, 0.1455438733100891, 0.9482707381248474, 0.4221445322036743, 0.001363217830657959, 0.8736765384674072, 0.42954301834106445, 0.7199509739875793, 0.37641745805740356, 0.5920265316963196, 0.42270803451538086, 0.41761744022369385, 0.603948712348938, 0.7250819802284241, 0.047173500061035156, 0.5115441679954529, 0.3743387460708618, 0.16794061660766602, 0.5352339148521423, 0.037976861000061035, 0.65323406457901, 0.5585184097290039, 0.10559147596359253, 0.07827490568161011, 0.6717077493667603, 0.6480781435966492, 0.9780838489532471, 0.8353415131568909, 0.6491701006889343, 0.6573048233985901]]}

examples/onnx/lstm_large/input.json

@@ -0,0 +1,13 @@
+{
+  "input_data": [
+    [
+      0.8894134163856506,
+      0.8894201517105103
+    ]
+  ],
+  "output_data": [
+    [
+      0.8436377
+    ]
+  ]
+}

examples/onnx/mish/gen.py

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

examples/onnx/mish/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.18563222885131836, 0.4843214750289917, 0.9991059899330139, 0.02534431219100952, 0.8105666041374207, 0.9658406376838684, 0.681107759475708, 0.5365872979164124]]}

examples/onnx/mish/network.onnx

@@ -0,0 +1,19 @@
+pytorch2.2.1:ä

+0
+input/Softplus_output_0	/Softplus"Softplus
+1
+/Softplus_output_0/Tanh_output_0/Tanh"Tanh
+*
+input
+/Tanh_output_0output/Mul"Mul
+main_graphZ!
+input
+

+
+batch_size
+b"
+output
+

+
+batch_size
+B

examples/onnx/reducel1/gen.py

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

examples/onnx/reducel1/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.02284395694732666, 0.7941043376922607, 0.07971876859664917, 0.8898420929908752, 0.8233054280281067, 0.11066079139709473, 0.4424799084663391, 0.4355071783065796, 0.6723723411560059, 0.6818525195121765, 0.8726171851158142, 0.17742449045181274, 0.054257750511169434, 0.5775953531265259, 0.7758923172950745, 0.8431423306465149, 0.7602444887161255, 0.29686522483825684, 0.22489851713180542, 0.0675363540649414, 0.981339693069458, 0.15771394968032837, 0.5801441669464111, 0.9044001698493958, 0.49266451597213745, 0.42621421813964844, 0.35345613956451416, 0.042848050594329834, 0.6908614039421082, 0.5422852039337158, 0.01975083351135254, 0.5772860050201416]]}

examples/onnx/reducel2/gen.py

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

examples/onnx/reducel2/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.8709188103675842, 0.11553549766540527, 0.27376580238342285, 0.7518517971038818, 0.7879393100738525, 0.8765475749969482, 0.14315760135650635, 0.8982420563697815, 0.7274006605148315, 0.39007169008255005, 0.729040801525116, 0.11306107044219971, 0.658822774887085, 0.666404664516449, 0.3001367449760437, 0.45343858003616333, 0.7460223436355591, 0.7423691749572754, 0.7544230818748474, 0.5674425959587097, 0.8728761672973633, 0.27062875032424927, 0.1595977544784546, 0.22975260019302368, 0.6711723208427429, 0.8265992403030396, 0.48679041862487793, 0.689740777015686, 0.330846905708313, 0.5630669593811035, 0.8058932423591614, 0.5802426338195801]]}

examples/onnx/tril/gen.py

@@ -0,0 +1,42 @@
+from torch import nn
+import torch
+import json
+import numpy as np
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+
+    def forward(self, x):          
+        m = torch.triu(x)
+        
+        return m 
+
+
+circuit = MyModel()
+
+x = torch.empty(1, 3, 3).uniform_(0, 5)
+
+out = circuit(x)
+
+print(out)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))

examples/onnx/tril/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.4870188236236572, 2.275230646133423, 3.126268148422241, 0.6412187218666077, 0.9967470169067383, 1.9814395904541016, 1.6355383396148682, 0.6397527456283569, 0.7825168967247009]]}

examples/onnx/triu/gen.py

@@ -0,0 +1,42 @@
+from torch import nn
+import torch
+import json
+import numpy as np
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+
+    def forward(self, x):          
+        m = torch.tril(x)
+        
+        return m 
+
+
+circuit = MyModel()
+
+x = torch.empty(1, 3, 3).uniform_(0, 5)
+
+out = circuit(x)
+
+print(out)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))

examples/onnx/triu/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.2898547053337097, 1.8070811033248901, 0.30266255140304565, 3.00581955909729, 0.5379888415336609, 1.7057424783706665, 2.415961265563965, 0.589233934879303, 0.03824889659881592]]}

in-browser-evm-verifier/README.md

@@ -0,0 +1,60 @@
+# inbrowser-evm-verify
+
+We would like the Solidity verifier to be canonical and usually all you ever need. For this, we need to be able to run that verifier in browser.
+
+## How to use (Node js)
+
+```ts 
+import localEVMVerify from '@ezkljs/verify';
+
+// Load in the proof file as a buffer
+const proofFileBuffer = fs.readFileSync(`${path}/${example}/proof.pf`)
+
+// Stringified EZKL evm verifier bytecode (this is just an example don't use in production)
+const bytecode = '0x608060405234801561001057600080fd5b5060d38061001f6000396000f3fe608060405234801561001057600080fd5b50600436106100415760003560e01c8063cfae321714610046575b600080fd5b6100496100f1565b60405161005691906100f1565b60405180910390f35b'
+
+const result = await localEVMVerify(proofFileBuffer, bytecode)
+
+console.log('result', result)
+```
+
+**Note**: Run `ezkl create-evm-verifier` to get the Solidity verifier, with which you can retrieve the bytecode once compiled. We recommend compiling to the Shanghai hardfork target, else you will have to pass an additional parameter specifying the EVM version to the `localEVMVerify` function like so (for Paris hardfork):
+
+```ts
+import localEVMVerify, { hardfork } from '@ezkljs/verify';
+
+const result = await localEVMVerify(proofFileBuffer, bytecode, hardfork['Paris'])
+```
+
+**Note**: You can also verify separated vk verifiers using the `localEVMVerify` function. Just pass the vk verifier bytecode as the third parameter like so:
+```ts
+import localEVMVerify from '@ezkljs/verify';
+
+const result = await localEVMVerify(proofFileBuffer, verifierBytecode, VKBytecode)
+```
+
+
+## How to use (Browser)
+
+```ts
+import localEVMVerify from '@ezkljs/verify';
+
+// Load in the proof file as a buffer using the web apis (fetch, FileReader, etc)
+// We use fetch in this example to load the proof file as a buffer
+const proofFileBuffer = await fetch(`${path}/${example}/proof.pf`).then(res => res.arrayBuffer())
+
+// Stringified EZKL evm verifier bytecode (this is just an example don't use in production)
+const bytecode = '0x608060405234801561001057600080fd5b5060d38061001f6000396000f3fe608060405234801561001057600080fd5b50600436106100415760003560e01c8063cfae321714610046575b600080fd5b6100496100f1565b60405161005691906100f1565b60405180910390f35b'
+
+const result = await browserEVMVerify(proofFileBuffer, bytecode)
+
+console.log('result', result)
+```
+
+Output:
+
+```ts  
+result: true
+```
+
+

in-browser-evm-verifier/package.json

@@ -0,0 +1,42 @@
+{
+  "name": "@ezkljs/verify",
+  "version": "0.0.0",
+  "publishConfig": {
+    "access": "public"
+  },
+  "description": "Evm verify EZKL proofs in the browser.",
+  "main": "dist/commonjs/index.js",
+  "module": "dist/esm/index.js",
+  "types": "dist/commonjs/index.d.ts",
+  "files": [
+    "dist",
+    "LICENSE",
+    "README.md"
+  ],
+  "scripts": {
+    "clean": "rm -r dist || true",
+    "build:commonjs": "tsc --project tsconfig.commonjs.json && resolve-tspaths -p tsconfig.commonjs.json",
+    "build:esm": "tsc --project tsconfig.esm.json && resolve-tspaths -p tsconfig.esm.json",
+    "build": "npm run clean && npm run build:commonjs && npm run build:esm"
+  },
+  "dependencies": {
+    "@ethereumjs/common": "^4.0.0",
+    "@ethereumjs/evm": "^2.0.0",
+    "@ethereumjs/statemanager": "^2.0.0",
+    "@ethereumjs/tx": "^5.0.0",
+    "@ethereumjs/util": "^9.0.0",
+    "@ethereumjs/vm": "^7.0.0",
+    "@ethersproject/abi": "^5.7.0",
+    "@ezkljs/engine": "^9.4.4",
+    "ethers": "^6.7.1",
+    "json-bigint": "^1.0.0"
+  },
+  "devDependencies": {
+    "@types/node": "^20.8.3",
+    "ts-loader": "^9.5.0",
+    "ts-node": "^10.9.1",
+    "resolve-tspaths": "^0.8.16",
+    "tsconfig-paths": "^4.2.0",
+    "typescript": "^5.2.2"
+  }
+}

in-browser-evm-verifier/src/index.ts

@@ -0,0 +1,145 @@
+import { defaultAbiCoder as AbiCoder } from '@ethersproject/abi'
+import { Address, hexToBytes } from '@ethereumjs/util'
+import { Chain, Common, Hardfork } from '@ethereumjs/common'
+import { LegacyTransaction, LegacyTxData } from '@ethereumjs/tx'
+// import { DefaultStateManager } from '@ethereumjs/statemanager'
+// import { Blockchain } from '@ethereumjs/blockchain'
+import { VM } from '@ethereumjs/vm'
+import { EVM } from '@ethereumjs/evm'
+import { buildTransaction, encodeDeployment } from './utils/tx-builder'
+import { getAccountNonce, insertAccount } from './utils/account-utils'
+import { encodeVerifierCalldata } from '../nodejs/ezkl';
+import { error } from 'console'
+
+async function deployContract(
+  vm: VM,
+  common: Common,
+  senderPrivateKey: Uint8Array,
+  deploymentBytecode: string
+): Promise<Address> {
+  // Contracts are deployed by sending their deployment bytecode to the address 0
+  // The contract params should be abi-encoded and appended to the deployment bytecode.
+  // const data =
+  const data = encodeDeployment(deploymentBytecode)
+  const txData = {
+    data,
+    nonce: await getAccountNonce(vm, senderPrivateKey),
+  }
+
+  const tx = LegacyTransaction.fromTxData(
+    buildTransaction(txData) as LegacyTxData,
+    { common, allowUnlimitedInitCodeSize: true },
+  ).sign(senderPrivateKey)
+
+  const deploymentResult = await vm.runTx({
+    tx,
+    skipBlockGasLimitValidation: true,
+    skipNonce: true
+  })
+
+  if (deploymentResult.execResult.exceptionError) {
+    throw deploymentResult.execResult.exceptionError
+  }
+
+  return deploymentResult.createdAddress!
+}
+
+async function verify(
+  vm: VM,
+  contractAddress: Address,
+  caller: Address,
+  proof: Uint8Array | Uint8ClampedArray,
+  vkAddress?: Address | Uint8Array,
+): Promise<boolean> {
+  if (proof instanceof Uint8Array) {
+    proof = new Uint8ClampedArray(proof.buffer)
+  }
+  if (vkAddress) {
+    const vkAddressBytes = hexToBytes(vkAddress.toString())
+    const vkAddressArray = Array.from(vkAddressBytes)
+
+    let string = JSON.stringify(vkAddressArray)
+
+    const uint8Array = new TextEncoder().encode(string);
+
+    // Step 3: Convert to Uint8ClampedArray
+    vkAddress = new Uint8Array(uint8Array.buffer);
+
+    // convert uitn8array of length
+    error('vkAddress', vkAddress)
+  }
+  const data = encodeVerifierCalldata(proof, vkAddress)
+
+  const verifyResult = await vm.evm.runCall({
+    to: contractAddress,
+    caller: caller,
+    origin: caller, // The tx.origin is also the caller here
+    data: data,
+  })
+
+  if (verifyResult.execResult.exceptionError) {
+    throw verifyResult.execResult.exceptionError
+  }
+
+  const results = AbiCoder.decode(['bool'], verifyResult.execResult.returnValue)
+
+  return results[0]
+}
+
+/**
+ * Spins up an ephemeral EVM instance for executing the bytecode of a solidity verifier
+ * @param proof Json serialized proof file
+ * @param bytecode The bytecode of a compiled solidity verifier.
+ * @param bytecode_vk The bytecode of a contract that stores the vk. (Optional, only required if the vk is stored in a separate contract)
+ * @param evmVersion The evm version to use for the verification. (Default: London)
+ * @returns The result of the evm verification.
+ * @throws If the verify transaction reverts
+ */
+export default async function localEVMVerify(
+  proof: Uint8Array | Uint8ClampedArray,
+  bytecode_verifier: string,
+  bytecode_vk?: string,
+  evmVersion?: Hardfork,
+): Promise<boolean> {
+  try {
+    const hardfork = evmVersion ? evmVersion : Hardfork['Shanghai']
+    const common = new Common({ chain: Chain.Mainnet, hardfork })
+    const accountPk = hexToBytes(
+      '0xe331b6d69882b4cb4ea581d88e0b604039a3de5967688d3dcffdd2270c0fd109', // anvil deterministic Pk 
+    )
+
+    const evm = new EVM({
+      allowUnlimitedContractSize: true,
+      allowUnlimitedInitCodeSize: true,
+    })
+
+    const vm = await VM.create({ common, evm })
+    const accountAddress = Address.fromPrivateKey(accountPk)
+
+    await insertAccount(vm, accountAddress)
+
+    const verifierAddress = await deployContract(
+      vm,
+      common,
+      accountPk,
+      bytecode_verifier
+    )
+
+    if (bytecode_vk) {
+      const accountPk = hexToBytes("0xac0974bec39a17e36ba4a6b4d238ff944bacb478cbed5efcae784d7bf4f2ff80"); // anvil deterministic Pk
+      const accountAddress = Address.fromPrivateKey(accountPk)
+      await insertAccount(vm, accountAddress)
+      const output = await deployContract(vm, common, accountPk, bytecode_vk)
+      const result = await verify(vm, verifierAddress, accountAddress, proof, output)
+      return true
+    }
+
+    const result = await verify(vm, verifierAddress, accountAddress, proof)
+
+    return result
+  } catch (error) {
+    // log or re-throw the error, depending on your needs
+    console.error('An error occurred:', error)
+    throw error
+  }
+}

in-browser-evm-verifier/src/utils/account-utils.ts

@@ -0,0 +1,32 @@
+import { VM } from '@ethereumjs/vm'
+import { Account, Address } from '@ethereumjs/util'
+
+export const keyPair = {
+  secretKey:
+    '0x3cd7232cd6f3fc66a57a6bedc1a8ed6c228fff0a327e169c2bcc5e869ed49511',
+  publicKey:
+    '0x0406cc661590d48ee972944b35ad13ff03c7876eae3fd191e8a2f77311b0a3c6613407b5005e63d7d8d76b89d5f900cde691497688bb281e07a5052ff61edebdc0',
+}
+
+export const insertAccount = async (vm: VM, address: Address) => {
+  const acctData = {
+    nonce: 0,
+    balance: BigInt('1000000000000000000'), // 1 eth
+  }
+  const account = Account.fromAccountData(acctData)
+
+  await vm.stateManager.putAccount(address, account)
+}
+
+export const getAccountNonce = async (
+  vm: VM,
+  accountPrivateKey: Uint8Array,
+) => {
+  const address = Address.fromPrivateKey(accountPrivateKey)
+  const account = await vm.stateManager.getAccount(address)
+  if (account) {
+    return account.nonce
+  } else {
+    return BigInt(0)
+  }
+}

in-browser-evm-verifier/src/utils/tx-builder.ts

@@ -0,0 +1,59 @@
+import { Interface, defaultAbiCoder as AbiCoder } from '@ethersproject/abi'
+import {
+  AccessListEIP2930TxData,
+  FeeMarketEIP1559TxData,
+  TxData,
+} from '@ethereumjs/tx'
+
+type TransactionsData =
+  | TxData
+  | AccessListEIP2930TxData
+  | FeeMarketEIP1559TxData
+
+export const encodeFunction = (
+  method: string,
+  params?: {
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    types: any[]
+    values: unknown[]
+  },
+): string => {
+  const parameters = params?.types ?? []
+  const methodWithParameters = `function ${method}(${parameters.join(',')})`
+  const signatureHash = new Interface([methodWithParameters]).getSighash(method)
+  const encodedArgs = AbiCoder.encode(parameters, params?.values ?? [])
+
+  return signatureHash + encodedArgs.slice(2)
+}
+
+export const encodeDeployment = (
+  bytecode: string,
+  params?: {
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    types: any[]
+    values: unknown[]
+  },
+) => {
+  const deploymentData = '0x' + bytecode
+  if (params) {
+    const argumentsEncoded = AbiCoder.encode(params.types, params.values)
+    return deploymentData + argumentsEncoded.slice(2)
+  }
+  return deploymentData
+}
+
+export const buildTransaction = (
+  data: Partial<TransactionsData>,
+): TransactionsData => {
+  const defaultData: Partial<TransactionsData> = {
+    gasLimit: 3_000_000_000_000_000,
+    gasPrice: 7,
+    value: 0,
+    data: '0x',
+  }
+
+  return {
+    ...defaultData,
+    ...data,
+  }
+}

in-browser-evm-verifier/tsconfig.commonjs.json

@@ -0,0 +1,7 @@
+{
+  "extends": "./tsconfig.json",
+  "compilerOptions": {
+    "module": "CommonJS",
+    "outDir": "./dist/commonjs"
+  }
+}

in-browser-evm-verifier/tsconfig.esm.json

@@ -0,0 +1,7 @@
+{
+  "extends": "./tsconfig.json",
+  "compilerOptions": {
+    "module": "ES2020",
+    "outDir": "./dist/esm"
+  }
+}

in-browser-evm-verifier/tsconfig.json

@@ -0,0 +1,62 @@
+{
+  "compilerOptions": {
+    "rootDir": "src",
+    "target": "es2017",
+    "outDir": "dist",
+    "declaration": true,
+    "lib": [
+      "dom",
+      "dom.iterable",
+      "esnext"
+    ],
+    "allowJs": true,
+    "checkJs": true,
+    "skipLibCheck": true,
+    "strict": true,
+    "forceConsistentCasingInFileNames": true,
+    "noEmit": false,
+    "esModuleInterop": true,
+    "module": "CommonJS",
+    "moduleResolution": "node",
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "jsx": "preserve",
+    // "incremental": true,
+    "noUncheckedIndexedAccess": true,
+    "baseUrl": ".",
+    "paths": {
+      "@/*": [
+        "./src/*"
+      ]
+    }
+  },
+  "include": [
+    "src/**/*.ts",
+    "src/**/*.tsx",
+    "src/**/*.cjs",
+    "src/**/*.mjs"
+  ],
+  "exclude": [
+    "node_modules"
+  ],
+  // NEW: Options for file/directory watching
+  "watchOptions": {
+    // Use native file system events for files and directories
+    "watchFile": "useFsEvents",
+    "watchDirectory": "useFsEvents",
+    // Poll files for updates more frequently
+    // when they're updated a lot.
+    "fallbackPolling": "dynamicPriority",
+    // Don't coalesce watch notification
+    "synchronousWatchDirectory": true,
+    // Finally, two additional settings for reducing the amount of possible
+    // files to track  work from these directories
+    "excludeDirectories": [
+      "**/node_modules",
+      "_build"
+    ],
+    "excludeFiles": [
+      "build/fileWhichChangesOften.ts"
+    ]
+  }
+}

package.json

@@ -7,7 +7,7 @@
     "test": "jest"
   },
   "devDependencies": {
-    "@ezkljs/engine": "^2.4.5",
+    "@ezkljs/engine": "^9.4.4",
     "@ezkljs/verify": "^0.0.6",
     "@jest/types": "^29.6.3",
     "@types/file-saver": "^2.0.5",
@@ -27,4 +27,4 @@
     "tsconfig-paths": "^4.2.0",
     "typescript": "5.1.6"
   }
-}
+}

pnpm-lock.yaml

@@ -6,8 +6,8 @@ settings:
 
 devDependencies:
   '@ezkljs/engine':
-    specifier: ^2.4.5
-    version: 2.4.5
+    specifier: ^9.4.4
+    version: 9.4.4
   '@ezkljs/verify':
     specifier: ^0.0.6
     version: 0.0.6(buffer@6.0.3)
@@ -785,6 +785,13 @@ packages:
       json-bigint: 1.0.0
     dev: true
 
+  /@ezkljs/engine@9.4.4:
+    resolution: {integrity: sha512-kNsTmDQa8mIiQ6yjJmBMwVgAAxh4nfs4NCtnewJifonyA8Mfhs+teXwwW8WhERRDoQPUofKO2pT8BPvV/XGIDA==}
+    dependencies:
+      '@types/json-bigint': 1.0.1
+      json-bigint: 1.0.0
+    dev: true
+
   /@ezkljs/verify@0.0.6(buffer@6.0.3):
     resolution: {integrity: sha512-9DHoEhLKl1DBGuUVseXLThuMyYceY08Zymr/OsLH0zbdA9OoISYhb77j4QPm4ANRKEm5dCi8oHDqkwGbFc2xFQ==}
     dependencies:

requirements.txt

@@ -1,14 +1,14 @@
-attrs==22.2.0
-exceptiongroup==1.1.1
-importlib-metadata==6.1.0
+attrs==23.2.0
+exceptiongroup==1.2.0
+importlib-metadata==7.1.0
 iniconfig==2.0.0
-maturin==1.0.1
-packaging==23.0
-pluggy==1.0.0
-pytest==7.2.2
+maturin==1.5.0
+packaging==24.0
+pluggy==1.4.0
+pytest==8.1.1
 tomli==2.0.1
-typing-extensions==4.5.0
-zipp==3.15.0
-onnx==1.14.1
-onnxruntime==1.14.1
-numpy==1.21.6
+typing-extensions==4.10.0
+zipp==3.18.1
+onnx==1.15.0
+onnxruntime==1.17.1
+numpy==1.26.4

rust-toolchain

@@ -1,3 +1,3 @@
 [toolchain]
-channel = "nightly-2023-08-24"
+channel = "nightly-2024-02-06"
 components = ["rustfmt", "clippy"]

src/bin/ezkl.rs

@@ -1,4 +1,6 @@
 // ignore file if compiling for wasm
+#[global_allocator]
+static GLOBAL: mimalloc::MiMalloc = mimalloc::MiMalloc;
 
 #[cfg(not(target_arch = "wasm32"))]
 use clap::Parser;

src/circuit/modules/mod.rs

@@ -2,7 +2,7 @@
 pub mod poseidon;
 
 ///
-pub mod kzg;
+pub mod polycommit;
 
 ///
 pub mod planner;
@@ -15,6 +15,8 @@ pub use planner::*;
 
 use crate::tensor::{TensorType, ValTensor};
 
+use super::region::ConstantsMap;
+
 /// Module trait used to extend ezkl functionality
 pub trait Module<F: PrimeField + TensorType + PartialOrd> {
     /// Config
@@ -39,6 +41,7 @@ pub trait Module<F: PrimeField + TensorType + PartialOrd> {
         &self,
         layouter: &mut impl Layouter<F>,
         input: &[ValTensor<F>],
+        constants: &mut ConstantsMap<F>,
     ) -> Result<Self::InputAssignments, Error>;
     /// Layout
     fn layout(
@@ -46,6 +49,7 @@ pub trait Module<F: PrimeField + TensorType + PartialOrd> {
         layouter: &mut impl Layouter<F>,
         input: &[ValTensor<F>],
         row_offset: usize,
+        constants: &mut ConstantsMap<F>,
     ) -> Result<ValTensor<F>, Error>;
     /// Number of instance values the module uses every time it is applied
     fn instance_increment_input(&self) -> Vec<usize>;

src/circuit/modules/polycommit.rs

@@ -4,49 +4,49 @@ is already implemented in halo2_gadgets, there is no wrapper chip that makes it
 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;
+
 // This chip adds a set of advice columns to the gadget Chip to store the inputs of the hash
 use halo2_proofs::halo2curves::bn256::Fr as Fp;
-use halo2_proofs::poly::commitment::{Blind, Params};
-use halo2_proofs::poly::kzg::commitment::ParamsKZG;
+use halo2_proofs::poly::commitment::{Blind, CommitmentScheme, Params};
 use halo2_proofs::{circuit::*, plonk::*};
-use halo2curves::bn256::{Bn256, G1Affine};
+use halo2curves::bn256::G1Affine;
 use halo2curves::group::prime::PrimeCurveAffine;
 use halo2curves::group::Curve;
 use halo2curves::CurveAffine;
 
+use crate::circuit::region::ConstantsMap;
 use crate::tensor::{Tensor, ValTensor, ValType, VarTensor};
 
 use super::Module;
 
-/// The number of instance columns used by the KZG hash function
+/// The number of instance columns used by the PolyCommit hash function
 pub const NUM_INSTANCE_COLUMNS: usize = 0;
-/// The number of advice columns used by the KZG hash function
+/// The number of advice columns used by the PolyCommit hash function
 pub const NUM_INNER_COLS: usize = 1;
 
 #[derive(Debug, Clone)]
-/// WIDTH, RATE and L are const generics for the struct, which represent the width, rate, and number of inputs for the Poseidon hash function, respectively.
-/// This means they are values that are known at compile time and can be used to specialize the implementation of the struct.
-/// The actual chip provided by halo2_gadgets is added to the parent Chip.
-pub struct KZGConfig {
+/// Configuration for the PolyCommit chip
+pub struct PolyCommitConfig {
     ///
-    pub hash_inputs: VarTensor,
+    pub inputs: VarTensor,
 }
 
 type InputAssignments = ();
 
-/// 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)]
-pub struct KZGChip {
-    config: KZGConfig,
+pub struct PolyCommitChip {
+    config: PolyCommitConfig,
 }
 
-impl KZGChip {
+impl PolyCommitChip {
     /// Commit to the message using the KZG commitment scheme
-    pub fn commit(
-        message: Vec<Fp>,
+    pub fn commit<Scheme: CommitmentScheme<Scalar = Fp, Curve = G1Affine>>(
+        message: Vec<Scheme::Scalar>,
         degree: u32,
         num_unusable_rows: u32,
-        params: &ParamsKZG<Bn256>,
+        params: &Scheme::ParamsProver,
     ) -> Vec<G1Affine> {
         let k = params.k();
         let domain = halo2_proofs::poly::EvaluationDomain::new(degree, k);
@@ -81,14 +81,14 @@ impl KZGChip {
     }
 }
 
-impl Module<Fp> for KZGChip {
-    type Config = KZGConfig;
+impl Module<Fp> for PolyCommitChip {
+    type Config = PolyCommitConfig;
     type InputAssignments = InputAssignments;
     type RunInputs = Vec<Fp>;
     type Params = (usize, usize);
 
     fn name(&self) -> &'static str {
-        "KZG"
+        "PolyCommit"
     }
 
     fn instance_increment_input(&self) -> Vec<usize> {
@@ -102,14 +102,15 @@ impl Module<Fp> for KZGChip {
 
     /// Configuration of the PoseidonChip
     fn configure(meta: &mut ConstraintSystem<Fp>, params: Self::Params) -> Self::Config {
-        let hash_inputs = VarTensor::new_unblinded_advice(meta, params.0, NUM_INNER_COLS, params.1);
-        Self::Config { hash_inputs }
+        let inputs = VarTensor::new_unblinded_advice(meta, params.0, NUM_INNER_COLS, params.1);
+        Self::Config { inputs }
     }
 
     fn layout_inputs(
         &self,
         _: &mut impl Layouter<Fp>,
         _: &[ValTensor<Fp>],
+        _: &mut ConstantsMap<Fp>,
     ) -> Result<Self::InputAssignments, Error> {
         Ok(())
     }
@@ -122,11 +123,24 @@ impl Module<Fp> for KZGChip {
         layouter: &mut impl Layouter<Fp>,
         input: &[ValTensor<Fp>],
         _: usize,
+        constants: &mut ConstantsMap<Fp>,
     ) -> Result<ValTensor<Fp>, Error> {
         assert_eq!(input.len(), 1);
+
+        let local_constants = constants.clone();
         layouter.assign_region(
-            || "kzg commit",
-            |mut region| self.config.hash_inputs.assign(&mut region, 0, &input[0]),
+            || "PolyCommit",
+            |mut region| {
+                let mut local_inner_constants = local_constants.clone();
+                let res = self.config.inputs.assign(
+                    &mut region,
+                    0,
+                    &input[0],
+                    &mut local_inner_constants,
+                )?;
+                *constants = local_inner_constants;
+                Ok(res)
+            },
         )
     }
 
@@ -163,7 +177,7 @@ mod tests {
     }
 
     impl Circuit<Fp> for HashCircuit {
-        type Config = KZGConfig;
+        type Config = PolyCommitConfig;
         type FloorPlanner = ModulePlanner;
         type Params = ();
 
@@ -178,7 +192,7 @@ mod tests {
 
         fn configure(meta: &mut ConstraintSystem<Fp>) -> Self::Config {
             let params = (K, R);
-            KZGChip::configure(meta, params)
+            PolyCommitChip::configure(meta, params)
         }
 
         fn synthesize(
@@ -186,8 +200,13 @@ mod tests {
             config: Self::Config,
             mut layouter: impl Layouter<Fp>,
         ) -> Result<(), Error> {
-            let kzg_chip = KZGChip::new(config);
-            kzg_chip.layout(&mut layouter, &[self.message.clone()], 0);
+            let polycommit_chip = PolyCommitChip::new(config);
+            polycommit_chip.layout(
+                &mut layouter,
+                &[self.message.clone()],
+                0,
+                &mut HashMap::new(),
+            );
 
             Ok(())
         }
@@ -195,7 +214,7 @@ mod tests {
 
     #[test]
     #[ignore]
-    fn kzg_for_a_range_of_input_sizes() {
+    fn polycommit_chip_for_a_range_of_input_sizes() {
         let rng = rand::rngs::OsRng;
 
         #[cfg(not(target_arch = "wasm32"))]
@@ -225,7 +244,7 @@ mod tests {
 
     #[test]
     #[ignore]
-    fn kzg_commit_much_longer_input() {
+    fn polycommit_chip_much_longer_input() {
         #[cfg(not(target_arch = "wasm32"))]
         env_logger::init();
 

src/circuit/modules/poseidon.rs

@@ -18,6 +18,7 @@ use maybe_rayon::slice::ParallelSlice;
 
 use std::marker::PhantomData;
 
+use crate::circuit::region::ConstantsMap;
 use crate::tensor::{Tensor, ValTensor, ValType};
 
 use super::Module;
@@ -172,12 +173,15 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
         &self,
         layouter: &mut impl Layouter<Fp>,
         message: &[ValTensor<Fp>],
+        constants: &mut ConstantsMap<Fp>,
     ) -> Result<Self::InputAssignments, Error> {
         assert_eq!(message.len(), 1);
         let message = message[0].clone();
 
         let start_time = instant::Instant::now();
 
+        let local_constants = constants.clone();
+
         let res = layouter.assign_region(
             || "load message",
             |mut region| {
@@ -199,12 +203,26 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
                                 ValType::PrevAssigned(v) | ValType::AssignedConstant(v, ..) => {
                                     Ok(v.clone())
                                 }
-                                ValType::Constant(f) => region.assign_advice_from_constant(
-                                    || format!("load message_{}", i),
-                                    self.config.hash_inputs[x],
-                                    y,
-                                    *f,
-                                ),
+                                ValType::Constant(f) => {
+                                    if local_constants.contains_key(f) {
+                                        Ok(constants.get(f).unwrap().assigned_cell().ok_or({
+                                            log::error!("constant not previously assigned");
+                                            Error::Synthesis
+                                        })?)
+                                    } 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 => {
                                     log::error!(
                                         "wrong input type {:?}, must be previously assigned",
@@ -270,8 +288,9 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
         layouter: &mut impl Layouter<Fp>,
         input: &[ValTensor<Fp>],
         row_offset: usize,
+        constants: &mut ConstantsMap<Fp>,
     ) -> Result<ValTensor<Fp>, Error> {
-        let (mut input_cells, zero_val) = self.layout_inputs(layouter, input)?;
+        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();
@@ -434,7 +453,7 @@ mod tests {
         *,
     };
 
-    use std::marker::PhantomData;
+    use std::{collections::HashMap, marker::PhantomData};
 
     use halo2_gadgets::poseidon::primitives::Spec;
     use halo2_proofs::{
@@ -477,7 +496,12 @@ mod tests {
             mut layouter: impl Layouter<Fp>,
         ) -> Result<(), Error> {
             let chip: PoseidonChip<PoseidonSpec, WIDTH, RATE, L> = PoseidonChip::new(config);
-            chip.layout(&mut layouter, &[self.message.clone()], 0)?;
+            chip.layout(
+                &mut layouter,
+                &[self.message.clone()],
+                0,
+                &mut HashMap::new(),
+            )?;
 
             Ok(())
         }

src/circuit/ops/chip.rs

@@ -345,7 +345,7 @@ pub struct BaseConfig<F: PrimeField + TensorType + PartialOrd> {
     _marker: PhantomData<F>,
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> BaseConfig<F> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
     /// Returns a new [BaseConfig] with no inputs, no selectors, and no tables.
     pub fn dummy(col_size: usize, num_inner_cols: usize) -> Self {
         Self {

src/circuit/ops/hybrid.rs

@@ -46,7 +46,8 @@ pub enum HybridOp {
         dim: usize,
     },
     Softmax {
-        scale: utils::F32,
+        input_scale: utils::F32,
+        output_scale: utils::F32,
         axes: Vec<usize>,
     },
     RangeCheck(Tolerance),
@@ -70,7 +71,7 @@ pub enum HybridOp {
     },
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> Op<F> for HybridOp {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for HybridOp {
     ///
     fn requires_homogenous_input_scales(&self) -> Vec<usize> {
         match self {
@@ -130,9 +131,16 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for HybridOp {
                 kernel_shape,
                 normalized,
             } => tensor::ops::sumpool(&x, *padding, *stride, *kernel_shape, *normalized)?,
-            HybridOp::Softmax { scale, axes } => {
-                tensor::ops::nonlinearities::softmax_axes(&x, scale.into(), axes)
-            }
+            HybridOp::Softmax {
+                input_scale,
+                output_scale,
+                axes,
+            } => tensor::ops::nonlinearities::softmax_axes(
+                &x,
+                input_scale.into(),
+                output_scale.into(),
+                axes,
+            ),
             HybridOp::RangeCheck(tol) => {
                 let y = inputs[1].clone().map(|x| felt_to_i128(x));
                 tensor::ops::nonlinearities::range_check_percent(&[x, y], 128, 128, tol.val)
@@ -203,8 +211,15 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for HybridOp {
             ),
             HybridOp::ReduceMin { axes } => format!("REDUCEMIN (axes={:?})", axes),
             HybridOp::ReduceArgMin { dim } => format!("REDUCEARGMIN (dim={})", dim),
-            HybridOp::Softmax { scale, axes } => {
-                format!("SOFTMAX (scale={}, axes={:?})", scale, axes)
+            HybridOp::Softmax {
+                input_scale,
+                output_scale,
+                axes,
+            } => {
+                format!(
+                    "SOFTMAX (input_scale={}, output_scale={}, axes={:?})",
+                    input_scale, output_scale, axes
+                )
             }
             HybridOp::RangeCheck(p) => format!("RANGECHECK (tol={:?})", p),
             HybridOp::Greater => "GREATER".into(),
@@ -324,9 +339,18 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for HybridOp {
             HybridOp::ReduceArgMin { dim } => {
                 layouts::argmin_axes(config, region, values[..].try_into()?, *dim)?
             }
-            HybridOp::Softmax { scale, axes } => {
-                layouts::softmax_axes(config, region, values[..].try_into()?, *scale, axes)?
-            }
+            HybridOp::Softmax {
+                input_scale,
+                output_scale,
+                axes,
+            } => layouts::softmax_axes(
+                config,
+                region,
+                values[..].try_into()?,
+                *input_scale,
+                *output_scale,
+                axes,
+            )?,
             HybridOp::RangeCheck(tol) => layouts::range_check_percent(
                 config,
                 region,
@@ -359,8 +383,9 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for HybridOp {
             | HybridOp::ReduceArgMax { .. }
             | HybridOp::OneHot { .. }
             | HybridOp::ReduceArgMin { .. } => 0,
-            HybridOp::Softmax { .. } => 2 * in_scales[0],
-            HybridOp::Recip { output_scale, .. } => multiplier_to_scale(output_scale.0 as f64),
+            HybridOp::Softmax { output_scale, .. } | HybridOp::Recip { output_scale, .. } => {
+                multiplier_to_scale(output_scale.0 as f64)
+            }
             _ => in_scales[0],
         };
         Ok(scale)

src/circuit/ops/layouts.rs

@@ -9,6 +9,7 @@ use halo2curves::ff::PrimeField;
 use itertools::Itertools;
 use log::{error, trace};
 use maybe_rayon::{
+    iter::IntoParallelRefIterator,
     prelude::{IndexedParallelIterator, IntoParallelIterator, ParallelIterator},
     slice::ParallelSliceMut,
 };
@@ -33,7 +34,7 @@ use super::*;
 use crate::circuit::ops::lookup::LookupOp;
 
 /// Same as div but splits the division into N parts
-pub(crate) fn loop_div<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn loop_div<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     value: &[ValTensor<F>; 1],
@@ -68,7 +69,7 @@ pub(crate) fn loop_div<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Div accumulated layout
-pub(crate) fn div<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn div<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     value: &[ValTensor<F>; 1],
@@ -93,9 +94,9 @@ pub(crate) fn div<F: PrimeField + TensorType + PartialOrd>(
     let mut claimed_output: ValTensor<F> = if is_assigned {
         let input_evals = input.get_int_evals()?;
         tensor::ops::nonlinearities::const_div(&input_evals.clone(), felt_to_i128(div) as f64)
-            .iter()
-            .map(|x| Ok(Value::known(i128_to_felt(*x))))
-            .collect::<Result<Tensor<Value<F>>, Box<dyn Error>>>()?
+            .par_iter()
+            .map(|x| Value::known(i128_to_felt(*x)))
+            .collect::<Tensor<Value<F>>>()
             .into()
     } else {
         Tensor::new(
@@ -133,7 +134,7 @@ pub(crate) fn div<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// recip accumulated layout
-pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     value: &[ValTensor<F>; 1],
@@ -166,9 +167,9 @@ pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd>(
             felt_to_i128(input_scale) as f64,
             felt_to_i128(output_scale) as f64,
         )
-        .iter()
-        .map(|x| Ok(Value::known(i128_to_felt(*x))))
-        .collect::<Result<Tensor<Value<F>>, Box<dyn Error>>>()?
+        .par_iter()
+        .map(|x| Value::known(i128_to_felt(*x)))
+        .collect::<Tensor<Value<F>>>()
         .into()
     } else {
         Tensor::new(
@@ -226,7 +227,7 @@ pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Dot product accumulated layout
-pub(crate) fn dot<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn dot<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -337,7 +338,7 @@ pub(crate) fn dot<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Einsum
-pub(crate) fn einsum<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn einsum<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     inputs: &[ValTensor<F>],
@@ -524,14 +525,7 @@ pub(crate) fn einsum<F: PrimeField + TensorType + PartialOrd>(
 
                 // Compute the product of all input tensors
                 for pair in input_pairs {
-                    let product_across_pair = prod(
-                        config,
-                        region,
-                        &[pair.try_into().map_err(|e| {
-                            error!("{}", e);
-                            halo2_proofs::plonk::Error::Synthesis
-                        })?],
-                    )?;
+                    let product_across_pair = prod(config, region, &[pair.into()])?;
 
                     if let Some(product) = prod_res {
                         prod_res = Some(
@@ -563,7 +557,7 @@ pub(crate) fn einsum<F: PrimeField + TensorType + PartialOrd>(
     Ok(output)
 }
 
-fn _sort_ascending<F: PrimeField + TensorType + PartialOrd>(
+fn _sort_ascending<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -574,12 +568,12 @@ fn _sort_ascending<F: PrimeField + TensorType + PartialOrd>(
     let is_assigned = !input.any_unknowns()?;
 
     let sorted = if is_assigned {
-        input
-            .get_int_evals()?
-            .iter()
-            .sorted_by(|a, b| a.cmp(b))
-            .map(|x| Ok(Value::known(i128_to_felt(*x))))
-            .collect::<Result<Tensor<Value<F>>, Box<dyn Error>>>()?
+        let mut int_evals = input.get_int_evals()?;
+        int_evals.par_sort_unstable_by(|a, b| a.cmp(b));
+        int_evals
+            .par_iter()
+            .map(|x| Value::known(i128_to_felt(*x)))
+            .collect::<Tensor<Value<F>>>()
     } else {
         Tensor::new(
             Some(&vec![Value::<F>::unknown(); input.len()]),
@@ -607,7 +601,7 @@ fn _sort_ascending<F: PrimeField + TensorType + PartialOrd>(
 }
 
 ///
-fn _select_topk<F: PrimeField + TensorType + PartialOrd>(
+fn _select_topk<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -622,7 +616,7 @@ fn _select_topk<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Select top k elements
-pub(crate) fn topk_axes<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn topk_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -642,11 +636,12 @@ pub(crate) fn topk_axes<F: PrimeField + TensorType + PartialOrd>(
     Ok(output)
 }
 
-fn select<F: PrimeField + TensorType + PartialOrd>(
+fn select<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
 ) -> Result<ValTensor<F>, Box<dyn Error>> {
+    let start = instant::Instant::now();
     let (mut input, index) = (values[0].clone(), values[1].clone());
     input.flatten();
 
@@ -656,12 +651,13 @@ fn select<F: PrimeField + TensorType + PartialOrd>(
 
     let is_assigned = !input.any_unknowns()? && !index.any_unknowns()?;
 
-    let output: ValTensor<F> = if is_assigned {
+    let output: ValTensor<F> = if is_assigned && region.witness_gen() {
+        let felt_evals = input.get_felt_evals()?;
         index
             .get_int_evals()?
-            .iter()
-            .map(|x| Ok(Value::known(input.get_felt_evals()?.get(&[*x as usize]))))
-            .collect::<Result<Tensor<Value<F>>, Box<dyn Error>>>()?
+            .par_iter()
+            .map(|x| Value::known(felt_evals.get(&[*x as usize])))
+            .collect::<Tensor<Value<F>>>()
     } else {
         Tensor::new(
             Some(&vec![Value::<F>::unknown(); index.len()]),
@@ -673,10 +669,13 @@ fn select<F: PrimeField + TensorType + PartialOrd>(
     let (_, assigned_output) =
         dynamic_lookup(config, region, &[index, output], &[dim_indices, input])?;
 
+    let end = start.elapsed();
+    trace!("select took: {:?}", end);
+
     Ok(assigned_output)
 }
 
-fn one_hot<F: PrimeField + TensorType + PartialOrd>(
+fn one_hot<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -692,7 +691,7 @@ fn one_hot<F: PrimeField + TensorType + PartialOrd>(
     let output: ValTensor<F> = if is_assigned {
         let int_evals = input.get_int_evals()?;
         let res = tensor::ops::one_hot(&int_evals, num_classes, 1)?;
-        res.iter()
+        res.par_iter()
             .map(|x| Value::known(i128_to_felt(*x)))
             .collect::<Tensor<_>>()
     } else {
@@ -728,12 +727,13 @@ fn one_hot<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Dynamic lookup
-pub(crate) fn dynamic_lookup<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn dynamic_lookup<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     lookups: &[ValTensor<F>; 2],
     tables: &[ValTensor<F>; 2],
 ) -> Result<(ValTensor<F>, ValTensor<F>), Box<dyn Error>> {
+    let start = instant::Instant::now();
     // if not all lookups same length err
     if lookups[0].len() != lookups[1].len() {
         return Err("lookups must be same length".into());
@@ -753,20 +753,28 @@ pub(crate) fn dynamic_lookup<F: PrimeField + TensorType + PartialOrd>(
     let _table_1 = region.assign_dynamic_lookup(&config.dynamic_lookups.tables[1], &table_1)?;
     let table_len = table_0.len();
 
+    trace!("assigning tables took: {:?}", start.elapsed());
+
     // now create a vartensor of constants for the dynamic lookup index
     let table_index = create_constant_tensor(F::from(dynamic_lookup_index as u64), table_len);
     let _table_index =
         region.assign_dynamic_lookup(&config.dynamic_lookups.tables[2], &table_index)?;
 
+    trace!("assigning table index took: {:?}", start.elapsed());
+
     let lookup_0 = region.assign(&config.dynamic_lookups.inputs[0], &lookup_0)?;
     let lookup_1 = region.assign(&config.dynamic_lookups.inputs[1], &lookup_1)?;
     let lookup_len = lookup_0.len();
 
+    trace!("assigning lookups took: {:?}", start.elapsed());
+
     // now set the lookup index
     let lookup_index = create_constant_tensor(F::from(dynamic_lookup_index as u64), lookup_len);
 
     let _lookup_index = region.assign(&config.dynamic_lookups.inputs[2], &lookup_index)?;
 
+    trace!("assigning lookup index took: {:?}", start.elapsed());
+
     if !region.is_dummy() {
         (0..table_len)
             .map(|i| {
@@ -802,11 +810,14 @@ pub(crate) fn dynamic_lookup<F: PrimeField + TensorType + PartialOrd>(
     region.increment_dynamic_lookup_index(1);
     region.increment(lookup_len);
 
+    let end = start.elapsed();
+    trace!("dynamic lookup took: {:?}", end);
+
     Ok((lookup_0, lookup_1))
 }
 
 /// Shuffle arg
-pub(crate) fn shuffles<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn shuffles<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     input: &[ValTensor<F>; 1],
@@ -869,7 +880,7 @@ pub(crate) fn shuffles<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// One hot accumulated layout
-pub(crate) fn one_hot_axis<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn one_hot_axis<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -922,7 +933,7 @@ pub(crate) fn one_hot_axis<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Gather accumulated layout
-pub(crate) fn gather<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn gather<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -950,7 +961,7 @@ pub(crate) fn gather<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Gather accumulated layout
-pub(crate) fn gather_elements<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn gather_elements<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -973,7 +984,7 @@ pub(crate) fn gather_elements<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Gather accumulated layout
-pub(crate) fn gather_nd<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn gather_nd<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -1024,7 +1035,7 @@ pub(crate) fn gather_nd<F: PrimeField + TensorType + PartialOrd>(
 /// Takes a tensor representing a multi-dimensional index and returns a tensor representing the linearized index.
 /// The linearized index is the index of the element in the flattened tensor.
 /// FOr instance if the dims is [3,5,2], the linearized index of [2] at dim 1 is 2*5 + 3 = 13
-pub(crate) fn linearize_element_index<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn linearize_element_index<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -1032,6 +1043,7 @@ pub(crate) fn linearize_element_index<F: PrimeField + TensorType + PartialOrd>(
     dim: usize,
     is_flat_index: bool,
 ) -> Result<ValTensor<F>, Box<dyn Error>> {
+    let start_time = instant::Instant::now();
     let index = values[0].clone();
     if !is_flat_index {
         assert_eq!(index.dims().len(), dims.len());
@@ -1105,6 +1117,9 @@ pub(crate) fn linearize_element_index<F: PrimeField + TensorType + PartialOrd>(
 
     region.apply_in_loop(&mut output, inner_loop_function)?;
 
+    let elapsed = start_time.elapsed();
+    trace!("linearize_element_index took: {:?}", elapsed);
+
     Ok(output.into())
 }
 
@@ -1125,7 +1140,7 @@ pub(crate) fn linearize_element_index<F: PrimeField + TensorType + PartialOrd>(
 ///     If indices_shape[-1] == r-b, since the rank of indices is q, indices can be thought of as N (q-b-1)-dimensional tensors containing 1-D tensors of dimension r-b, where N is an integer equals to the product of 1 and all the elements in the batch dimensions of the indices_shape.
 ///     Let us think of each such r-b ranked tensor as indices_slice. Each scalar value corresponding to data[0:b-1,indices_slice] is filled into the corresponding location of the (q-b-1)-dimensional tensor to form the output tensor (Example 1 below)
 ///     If indices_shape[-1] < r-b, since the rank of indices is q, indices can be thought of as N (q-b-1)-dimensional tensor containing 1-D tensors of dimension < r-b. Let us think of each such tensors as indices_slice. Each tensor slice corresponding to data[0:b-1, indices_slice , :] is filled into the corresponding location of the (q-b-1)-dimensional tensor to form the output tensor (Examples 2, 3, 4 and 5 below)
-pub(crate) fn linearize_nd_index<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn linearize_nd_index<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -1222,7 +1237,7 @@ pub(crate) fn linearize_nd_index<F: PrimeField + TensorType + PartialOrd>(
                 .iter()
                 .map(|x| {
                     let slice = x.iter().map(|x| *x..*x + 1).collect::<Vec<_>>();
-                    Ok(index_slice.get_slice(&slice)?)
+                    index_slice.get_slice(&slice)
                 })
                 .collect::<Result<Vec<_>, Box<dyn Error>>>()?
         };
@@ -1232,7 +1247,6 @@ pub(crate) fn linearize_nd_index<F: PrimeField + TensorType + PartialOrd>(
             const_offset += F::from(coord[i] as u64) * dim_multiplier[i];
         }
 
-
         let const_offset = create_constant_tensor(const_offset, 1);
 
         let mut results = vec![];
@@ -1250,16 +1264,18 @@ pub(crate) fn linearize_nd_index<F: PrimeField + TensorType + PartialOrd>(
             let res = sum(config, region, &[res])?;
             results.push(res.get_inner_tensor()?.clone());
             // assert than res is less than the product of the dims
-            assert!(
+            if region.witness_gen() {
+                assert!(
                 res.get_int_evals()?
                     .iter()
                     .all(|x| *x < dims.iter().product::<usize>() as i128),
                 "res is greater than the product of the dims {} (coord={}, index_dim_multiplier={}, res={})",
                 dims.iter().product::<usize>(),
-                index_val.show(), 
+                index_val.show(),
                 index_dim_multiplier.show(),
                 res.show()
             );
+            }
         }
 
         let result_tensor = Tensor::from(results.into_iter());
@@ -1273,7 +1289,9 @@ pub(crate) fn linearize_nd_index<F: PrimeField + TensorType + PartialOrd>(
     Ok(output.into())
 }
 
-pub(crate) fn get_missing_set_elements<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn get_missing_set_elements<
+    F: PrimeField + TensorType + PartialOrd + std::hash::Hash,
+>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -1304,7 +1322,7 @@ pub(crate) fn get_missing_set_elements<F: PrimeField + TensorType + PartialOrd>(
         }
 
         fullset_evals
-            .iter()
+            .par_iter()
             .map(|x| Value::known(i128_to_felt(*x)))
             .collect::<Tensor<Value<F>>>()
             .into()
@@ -1337,7 +1355,7 @@ pub(crate) fn get_missing_set_elements<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Gather accumulated layout
-pub(crate) fn scatter_elements<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn scatter_elements<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 3],
@@ -1354,14 +1372,14 @@ pub(crate) fn scatter_elements<F: PrimeField + TensorType + PartialOrd>(
 
     let is_assigned = !input.any_unknowns()? && !index.any_unknowns()? && !src.any_unknowns()?;
 
-    let claimed_output: ValTensor<F> = if is_assigned {
+    let claimed_output: ValTensor<F> = if is_assigned && region.witness_gen() {
         let input_inner = input.get_int_evals()?;
         let index_inner = index.get_int_evals()?.map(|x| x as usize);
         let src_inner = src.get_int_evals()?;
 
         let res = tensor::ops::scatter(&input_inner, &index_inner, &src_inner, dim)?;
 
-        res.iter()
+        res.par_iter()
             .map(|x| Value::known(i128_to_felt(*x)))
             .collect::<Tensor<Value<F>>>()
             .into()
@@ -1419,7 +1437,7 @@ pub(crate) fn scatter_elements<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Scatter Nd
-pub(crate) fn scatter_nd<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn scatter_nd<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 3],
@@ -1433,14 +1451,14 @@ pub(crate) fn scatter_nd<F: PrimeField + TensorType + PartialOrd>(
 
     let is_assigned = !input.any_unknowns()? && !index.any_unknowns()? && !src.any_unknowns()?;
 
-    let claimed_output: ValTensor<F> = if is_assigned {
+    let claimed_output: ValTensor<F> = if is_assigned && region.witness_gen() {
         let input_inner = input.get_int_evals()?;
         let index_inner = index.get_int_evals()?.map(|x| x as usize);
         let src_inner = src.get_int_evals()?;
 
         let res = tensor::ops::scatter_nd(&input_inner, &index_inner, &src_inner)?;
 
-        res.iter()
+        res.par_iter()
             .map(|x| Value::known(i128_to_felt(*x)))
             .collect::<Tensor<Value<F>>>()
             .into()
@@ -1457,7 +1475,6 @@ pub(crate) fn scatter_nd<F: PrimeField + TensorType + PartialOrd>(
     region.increment(claimed_output.len());
     claimed_output.reshape(input.dims())?;
 
-
     // scatter elements is the inverse of gather elements
     let (gather_src, linear_index) =
         gather_nd(config, region, &[claimed_output.clone(), index.clone()], 0)?;
@@ -1498,7 +1515,7 @@ pub(crate) fn scatter_nd<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// sum accumulated layout
-pub(crate) fn sum<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn sum<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -1581,7 +1598,7 @@ pub(crate) fn sum<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// product accumulated layout
-pub(crate) fn prod<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn prod<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -1661,7 +1678,7 @@ pub(crate) fn prod<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Axes wise op wrapper
-fn axes_wise_op<F: PrimeField + TensorType + PartialOrd>(
+fn axes_wise_op<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -1722,7 +1739,7 @@ fn axes_wise_op<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Sum accumulated layout
-pub(crate) fn prod_axes<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn prod_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -1733,7 +1750,7 @@ pub(crate) fn prod_axes<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Sum accumulated layout
-pub(crate) fn sum_axes<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn sum_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -1744,7 +1761,7 @@ pub(crate) fn sum_axes<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// argmax layout
-pub(crate) fn argmax_axes<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn argmax_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -1762,7 +1779,7 @@ pub(crate) fn argmax_axes<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Max accumulated layout
-pub(crate) fn max_axes<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn max_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -1774,7 +1791,7 @@ pub(crate) fn max_axes<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Argmin layout
-pub(crate) fn argmin_axes<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn argmin_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -1792,7 +1809,7 @@ pub(crate) fn argmin_axes<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Min accumulated layout
-pub(crate) fn min_axes<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn min_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -1804,7 +1821,7 @@ pub(crate) fn min_axes<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Pairwise (elementwise) op layout
-pub(crate) fn pairwise<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn pairwise<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -1959,7 +1976,7 @@ pub(crate) fn pairwise<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// expand the tensor to the given shape
-pub(crate) fn expand<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn expand<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -1972,7 +1989,7 @@ pub(crate) fn expand<F: PrimeField + TensorType + PartialOrd>(
 }
 
 ///
-pub(crate) fn greater<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn greater<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -1995,7 +2012,7 @@ pub(crate) fn greater<F: PrimeField + TensorType + PartialOrd>(
 }
 
 ///
-pub(crate) fn greater_equal<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn greater_equal<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -2018,7 +2035,7 @@ pub(crate) fn greater_equal<F: PrimeField + TensorType + PartialOrd>(
 }
 
 ///
-pub(crate) fn less<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn less<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -2028,7 +2045,7 @@ pub(crate) fn less<F: PrimeField + TensorType + PartialOrd>(
 }
 
 ///
-pub(crate) fn less_equal<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn less_equal<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -2038,7 +2055,7 @@ pub(crate) fn less_equal<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// And boolean operation
-pub(crate) fn and<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn and<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -2049,7 +2066,7 @@ pub(crate) fn and<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Or boolean operation
-pub(crate) fn or<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn or<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -2065,7 +2082,7 @@ pub(crate) fn or<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Equality boolean operation
-pub(crate) fn equals<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn equals<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -2075,7 +2092,7 @@ pub(crate) fn equals<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Equality boolean operation
-pub(crate) fn equals_zero<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn equals_zero<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2109,7 +2126,7 @@ pub(crate) fn equals_zero<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Xor boolean operation
-pub(crate) fn xor<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn xor<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -2135,7 +2152,7 @@ pub(crate) fn xor<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Not boolean operation
-pub(crate) fn not<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn not<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2151,7 +2168,7 @@ pub(crate) fn not<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Iff
-pub(crate) fn iff<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn iff<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 3],
@@ -2175,7 +2192,7 @@ pub(crate) fn iff<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Negation operation accumulated layout
-pub(crate) fn neg<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn neg<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2185,7 +2202,7 @@ pub(crate) fn neg<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Sumpool accumulated layout
-pub(crate) fn sumpool<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn sumpool<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>],
@@ -2239,7 +2256,7 @@ pub(crate) fn sumpool<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Convolution accumulated layout
-pub(crate) fn max_pool2d<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn max_pool2d<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2304,7 +2321,7 @@ pub(crate) fn max_pool2d<F: PrimeField + TensorType + PartialOrd>(
 
 /// DeConvolution accumulated layout
 pub(crate) fn deconv<
-    F: PrimeField + TensorType + PartialOrd + std::marker::Send + std::marker::Sync,
+    F: PrimeField + TensorType + PartialOrd + std::hash::Hash + std::marker::Send + std::marker::Sync,
 >(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
@@ -2397,7 +2414,7 @@ pub(crate) fn deconv<
 
 /// Convolution accumulated layout
 pub(crate) fn conv<
-    F: PrimeField + TensorType + PartialOrd + std::marker::Send + std::marker::Sync,
+    F: PrimeField + TensorType + PartialOrd + std::hash::Hash + std::marker::Send + std::marker::Sync,
 >(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
@@ -2578,7 +2595,7 @@ pub(crate) fn conv<
 }
 
 /// Power accumulated layout
-pub(crate) fn pow<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn pow<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2594,7 +2611,7 @@ pub(crate) fn pow<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Rescaled op accumulated layout
-pub(crate) fn rescale<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn rescale<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>],
@@ -2616,7 +2633,7 @@ pub(crate) fn rescale<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Dummy (no contraints) reshape layout
-pub(crate) fn reshape<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn reshape<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     values: &[ValTensor<F>; 1],
     new_dims: &[usize],
 ) -> Result<ValTensor<F>, Box<dyn Error>> {
@@ -2626,7 +2643,7 @@ pub(crate) fn reshape<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Dummy (no contraints) move_axis layout
-pub(crate) fn move_axis<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn move_axis<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     values: &[ValTensor<F>; 1],
     source: usize,
     destination: usize,
@@ -2637,7 +2654,7 @@ pub(crate) fn move_axis<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// resize layout
-pub(crate) fn resize<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn resize<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2651,7 +2668,7 @@ pub(crate) fn resize<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Slice layout
-pub(crate) fn slice<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn slice<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2660,15 +2677,45 @@ pub(crate) fn slice<F: PrimeField + TensorType + PartialOrd>(
     end: &usize,
 ) -> Result<ValTensor<F>, Box<dyn Error>> {
     // assigns the instance to the advice.
-    let mut output = region.assign(&config.custom_gates.output, &values[0])?;
-    region.increment(output.len());
+    let mut output = values[0].clone();
+
+    let is_assigned = output.all_prev_assigned();
+    if !is_assigned {
+        output = region.assign(&config.custom_gates.output, &values[0])?;
+        region.increment(output.len());
+    }
+
     output.slice(axis, start, end)?;
 
     Ok(output)
 }
 
+/// Trilu layout
+pub(crate) fn trilu<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    values: &[ValTensor<F>; 1],
+    k: &i32,
+    upper: &bool,
+) -> Result<ValTensor<F>, Box<dyn Error>> {
+    // assigns the instance to the advice.
+    let mut output = values[0].clone();
+
+    let is_assigned = output.all_prev_assigned();
+    if !is_assigned {
+        output = region.assign(&config.custom_gates.inputs[0], &values[0])?;
+    }
+
+    let res = tensor::ops::trilu(output.get_inner_tensor()?, *k, *upper)?;
+
+    let output = region.assign(&config.custom_gates.output, &res.into())?;
+    region.increment(output.len());
+
+    Ok(output)
+}
+
 /// Concat layout
-pub(crate) fn concat<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn concat<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     values: &[ValTensor<F>],
     axis: &usize,
 ) -> Result<ValTensor<F>, Box<dyn Error>> {
@@ -2680,7 +2727,7 @@ pub(crate) fn concat<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Identity constraint. Usually used to constrain an instance column to an advice so the returned cells / values can be operated upon.
-pub(crate) fn identity<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn identity<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2695,7 +2742,7 @@ pub(crate) fn identity<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Boolean identity constraint. Usually used to constrain an instance column to an advice so the returned cells / values can be operated upon.
-pub(crate) fn boolean_identity<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn boolean_identity<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2731,7 +2778,7 @@ pub(crate) fn boolean_identity<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Downsample layout
-pub(crate) fn downsample<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn downsample<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2748,7 +2795,7 @@ pub(crate) fn downsample<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// layout for enforcing two sets of cells to be equal
-pub(crate) fn enforce_equality<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn enforce_equality<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],
@@ -2774,7 +2821,7 @@ pub(crate) fn enforce_equality<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// layout for range check.
-pub(crate) fn range_check<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn range_check<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2830,12 +2877,13 @@ pub(crate) fn range_check<F: PrimeField + TensorType + PartialOrd>(
             .collect::<Result<Vec<_>, Box<dyn Error>>>()?;
     }
 
-    if region.throw_range_check_error() {
+    let is_assigned = !w.any_unknowns()?;
+    if is_assigned && region.witness_gen() {
         // assert is within range
         let int_values = w.get_int_evals()?;
-        for v in int_values {
-            if v < range.0 || v > range.1 {
-                log::debug!("Value ({:?}) out of range: {:?}", v, range);
+        for v in int_values.iter() {
+            if v < &range.0 || v > &range.1 {
+                log::error!("Value ({:?}) out of range: {:?}", v, range);
                 return Err(Box::new(TensorError::TableLookupError));
             }
         }
@@ -2855,7 +2903,7 @@ pub(crate) fn range_check<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// layout for nonlinearity check.
-pub(crate) fn nonlinearity<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn nonlinearity<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2957,7 +3005,7 @@ pub(crate) fn nonlinearity<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Argmax
-pub(crate) fn argmax<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn argmax<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -2993,7 +3041,7 @@ pub(crate) fn argmax<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// Argmin
-pub(crate) fn argmin<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn argmin<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -3029,7 +3077,7 @@ pub(crate) fn argmin<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// max layout
-pub(crate) fn max<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn max<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -3039,7 +3087,7 @@ pub(crate) fn max<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// min layout
-pub(crate) fn min<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn min<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -3047,7 +3095,7 @@ pub(crate) fn min<F: PrimeField + TensorType + PartialOrd>(
     _sort_ascending(config, region, values)?.get_slice(&[0..1])
 }
 
-fn multi_dim_axes_op<F: PrimeField + TensorType + PartialOrd>(
+fn multi_dim_axes_op<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
@@ -3150,18 +3198,19 @@ fn multi_dim_axes_op<F: PrimeField + TensorType + PartialOrd>(
 }
 
 /// softmax layout
-pub(crate) fn softmax_axes<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn softmax_axes<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
-    scale: utils::F32,
+    input_scale: utils::F32,
+    output_scale: utils::F32,
     axes: &[usize],
 ) -> Result<ValTensor<F>, Box<dyn Error>> {
     let soft_max_at_scale = move |config: &BaseConfig<F>,
                                   region: &mut RegionCtx<F>,
                                   values: &[ValTensor<F>; 1]|
           -> Result<ValTensor<F>, Box<dyn Error>> {
-        softmax(config, region, values, scale)
+        softmax(config, region, values, input_scale, output_scale)
     };
 
     let output = multi_dim_axes_op(config, region, values, axes, soft_max_at_scale)?;
@@ -3169,33 +3218,66 @@ pub(crate) fn softmax_axes<F: PrimeField + TensorType + PartialOrd>(
     Ok(output)
 }
 
-/// softmax func
-pub(crate) fn softmax<F: PrimeField + TensorType + PartialOrd>(
+/// percent func
+pub(crate) fn percent<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
-    scale: utils::F32,
+    input_scale: utils::F32,
+    output_scale: utils::F32,
 ) -> Result<ValTensor<F>, Box<dyn Error>> {
-    // elementwise exponential
-    let ex = nonlinearity(config, region, values, &LookupOp::Exp { scale })?;
-
+    let is_assigned = values[0].all_prev_assigned();
+    let mut input = values[0].clone();
+    if !is_assigned {
+        input = region.assign(&config.custom_gates.inputs[0], &values[0])?;
+        region.increment(input.len());
+    };
     // sum of exps
-    let denom = sum(config, region, &[ex.clone()])?;
-    // get the inverse
-
-    let felt_scale = F::from(scale.0 as u64);
-    let inv_denom = recip(config, region, &[denom], felt_scale, felt_scale)?;
+    let denom = sum(config, region, &[input.clone()])?;
 
+    let input_felt_scale = F::from(input_scale.0 as u64);
+    let output_felt_scale = F::from(output_scale.0 as u64);
+    let inv_denom = recip(
+        config,
+        region,
+        &[denom],
+        input_felt_scale,
+        output_felt_scale,
+    )?;
     // product of num * (1 / denom) = 2*output_scale
-    let softmax = pairwise(config, region, &[ex, inv_denom], BaseOp::Mult)?;
+    let percent = pairwise(config, region, &[input, inv_denom], BaseOp::Mult)?;
 
-    Ok(softmax)
+    // rebase the percent to 2x the scale
+    loop_div(config, region, &[percent], input_felt_scale)
+}
+
+/// softmax func
+pub(crate) fn softmax<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    values: &[ValTensor<F>; 1],
+    input_scale: utils::F32,
+    output_scale: utils::F32,
+) -> Result<ValTensor<F>, Box<dyn Error>> {
+    // get the max then subtract it
+    let max_val = max(config, region, values)?;
+    // rebase the input to 0
+    let sub = pairwise(config, region, &[values[0].clone(), max_val], BaseOp::Sub)?;
+    // elementwise exponential
+    let ex = nonlinearity(
+        config,
+        region,
+        &[sub],
+        &LookupOp::Exp { scale: input_scale },
+    )?;
+
+    percent(config, region, &[ex.clone()], input_scale, output_scale)
 }
 
 /// Checks that the percent error between the expected public output and the actual output value
 /// is within the percent error expressed by the `tol` input, where `tol == 1.0` means the percent
 /// error tolerance is 1 percent.
-pub(crate) fn range_check_percent<F: PrimeField + TensorType + PartialOrd>(
+pub(crate) fn range_check_percent<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 2],

src/circuit/ops/lookup.rs

@@ -123,6 +123,9 @@ pub enum LookupOp {
         scale: utils::F32,
         a: utils::F32,
     },
+    HardSwish {
+        scale: utils::F32,
+    },
 }
 
 impl LookupOp {
@@ -132,9 +135,56 @@ impl LookupOp {
         let range = range as i128;
         (-range, range)
     }
+
+    /// as path
+    pub fn as_path(&self) -> String {
+        match self {
+            LookupOp::Abs => "abs".into(),
+            LookupOp::Ceil { scale } => format!("ceil_{}", scale),
+            LookupOp::Floor { scale } => format!("floor_{}", scale),
+            LookupOp::Round { scale } => format!("round_{}", scale),
+            LookupOp::RoundHalfToEven { scale } => format!("round_half_to_even_{}", scale),
+            LookupOp::Pow { scale, a } => format!("pow_{}_{}", scale, a),
+            LookupOp::KroneckerDelta => "kronecker_delta".into(),
+            LookupOp::Max { scale, a } => format!("max_{}_{}", scale, a),
+            LookupOp::Min { scale, a } => format!("min_{}_{}", scale, a),
+            LookupOp::Sign => "sign".into(),
+            LookupOp::LessThan { a } => format!("less_than_{}", a),
+            LookupOp::LessThanEqual { a } => format!("less_than_equal_{}", a),
+            LookupOp::GreaterThan { a } => format!("greater_than_{}", a),
+            LookupOp::GreaterThanEqual { a } => format!("greater_than_equal_{}", a),
+            LookupOp::Div { denom } => format!("div_{}", denom),
+            LookupOp::Cast { scale } => format!("cast_{}", scale),
+            LookupOp::Recip {
+                input_scale,
+                output_scale,
+            } => format!("recip_{}_{}", input_scale, output_scale),
+            LookupOp::ReLU => "relu".to_string(),
+            LookupOp::LeakyReLU { slope: a } => format!("leaky_relu_{}", a),
+            LookupOp::Sigmoid { scale } => format!("sigmoid_{}", scale),
+            LookupOp::Sqrt { scale } => format!("sqrt_{}", scale),
+            LookupOp::Rsqrt { scale } => format!("rsqrt_{}", scale),
+            LookupOp::Erf { scale } => format!("erf_{}", scale),
+            LookupOp::Exp { scale } => format!("exp_{}", scale),
+            LookupOp::Ln { scale } => format!("ln_{}", scale),
+            LookupOp::Cos { scale } => format!("cos_{}", scale),
+            LookupOp::ACos { scale } => format!("acos_{}", scale),
+            LookupOp::Cosh { scale } => format!("cosh_{}", scale),
+            LookupOp::ACosh { scale } => format!("acosh_{}", scale),
+            LookupOp::Sin { scale } => format!("sin_{}", scale),
+            LookupOp::ASin { scale } => format!("asin_{}", scale),
+            LookupOp::Sinh { scale } => format!("sinh_{}", scale),
+            LookupOp::ASinh { scale } => format!("asinh_{}", scale),
+            LookupOp::Tan { scale } => format!("tan_{}", scale),
+            LookupOp::ATan { scale } => format!("atan_{}", scale),
+            LookupOp::ATanh { scale } => format!("atanh_{}", scale),
+            LookupOp::Tanh { scale } => format!("tanh_{}", scale),
+            LookupOp::HardSwish { scale } => format!("hardswish_{}", scale),
+        }
+    }
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> Op<F> for LookupOp {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for LookupOp {
     /// Returns a reference to the Any trait.
     fn as_any(&self) -> &dyn Any {
         self
@@ -223,6 +273,9 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for LookupOp {
             LookupOp::ATan { scale } => Ok(tensor::ops::nonlinearities::atan(&x, scale.into())),
             LookupOp::ATanh { scale } => Ok(tensor::ops::nonlinearities::atanh(&x, scale.into())),
             LookupOp::Tanh { scale } => Ok(tensor::ops::nonlinearities::tanh(&x, scale.into())),
+            LookupOp::HardSwish { scale } => {
+                Ok(tensor::ops::nonlinearities::hardswish(&x, scale.into()))
+            }
         }?;
 
         let output = res.map(|x| i128_to_felt(x));
@@ -276,6 +329,7 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for LookupOp {
             LookupOp::ASin { scale } => format!("ASIN(scale={})", scale),
             LookupOp::Sinh { scale } => format!("SINH(scale={})", scale),
             LookupOp::ASinh { scale } => format!("ASINH(scale={})", scale),
+            LookupOp::HardSwish { scale } => format!("HARDSWISH(scale={})", scale),
         }
     }
 

src/circuit/ops/mod.rs

@@ -27,12 +27,14 @@ pub mod region;
 
 /// A struct representing the result of a forward pass.
 #[derive(Clone, Debug, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
-pub struct ForwardResult<F: PrimeField + TensorType + PartialOrd> {
+pub struct ForwardResult<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> {
     pub(crate) output: Tensor<F>,
 }
 
 /// A trait representing operations that can be represented as constraints in a circuit.
-pub trait Op<F: PrimeField + TensorType + PartialOrd>: std::fmt::Debug + Send + Sync + Any {
+pub trait Op<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>:
+    std::fmt::Debug + Send + Sync + Any
+{
     /// Matches a [Op] to an operation in the `tensor::ops` module.
     fn f(&self, x: &[Tensor<F>]) -> Result<ForwardResult<F>, TensorError>;
     /// Returns a string representation of the operation.
@@ -98,7 +100,7 @@ pub trait Op<F: PrimeField + TensorType + PartialOrd>: std::fmt::Debug + Send +
     }
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> Clone for Box<dyn Op<F>> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Clone for Box<dyn Op<F>> {
     fn clone(&self) -> Self {
         self.clone_dyn()
     }
@@ -165,7 +167,7 @@ pub struct Input {
     pub datum_type: InputType,
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> Op<F> for Input {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Input {
     fn out_scale(&self, _: Vec<crate::Scale>) -> Result<crate::Scale, Box<dyn Error>> {
         Ok(self.scale)
     }
@@ -226,7 +228,7 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for Input {
 #[derive(Clone, Debug, PartialEq, Eq, Hash, PartialOrd, Ord, Serialize, Deserialize)]
 pub struct Unknown;
 
-impl<F: PrimeField + TensorType + PartialOrd> Op<F> for Unknown {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Unknown {
     fn out_scale(&self, _: Vec<crate::Scale>) -> Result<crate::Scale, Box<dyn Error>> {
         Ok(0)
     }
@@ -256,7 +258,7 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for Unknown {
 
 ///
 #[derive(Clone, Debug, Serialize, Deserialize)]
-pub struct Constant<F: PrimeField + TensorType + PartialOrd> {
+pub struct Constant<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> {
     ///
     pub quantized_values: Tensor<F>,
     ///
@@ -266,7 +268,7 @@ pub struct Constant<F: PrimeField + TensorType + PartialOrd> {
     pub pre_assigned_val: Option<ValTensor<F>>,
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> Constant<F> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Constant<F> {
     ///
     pub fn new(quantized_values: Tensor<F>, raw_values: Tensor<f32>) -> Self {
         Self {
@@ -293,8 +295,14 @@ impl<F: PrimeField + TensorType + PartialOrd> Constant<F> {
     }
 }
 
-impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<'de>> Op<F>
-    for Constant<F>
+impl<
+        F: PrimeField
+            + TensorType
+            + PartialOrd
+            + std::hash::Hash
+            + Serialize
+            + for<'de> Deserialize<'de>,
+    > Op<F> for Constant<F>
 {
     fn as_any(&self) -> &dyn Any {
         self

src/circuit/ops/poly.rs

@@ -83,10 +83,20 @@ pub enum PolyOp {
     And,
     Or,
     Xor,
+    Trilu {
+        upper: bool,
+        k: i32,
+    },
 }
 
-impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<'de>> Op<F>
-    for PolyOp
+impl<
+        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 {
@@ -114,7 +124,7 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
             PolyOp::Add => "ADD".into(),
             PolyOp::Mult => "MULT".into(),
             PolyOp::Sub => "SUB".into(),
-            PolyOp::Sum { .. } => "SUM".into(),
+            PolyOp::Sum { axes } => format!("SUM (axes={:?})", axes),
             PolyOp::Prod { .. } => "PROD".into(),
             PolyOp::Pow(_) => "POW".into(),
             PolyOp::Conv { .. } => "CONV".into(),
@@ -128,6 +138,7 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
             PolyOp::And => "AND".into(),
             PolyOp::Or => "OR".into(),
             PolyOp::Xor => "XOR".into(),
+            PolyOp::Trilu { upper, k } => format!("TRILU (upper={}, k={})", upper, k),
         }
     }
 
@@ -265,6 +276,7 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
                 };
                 tensor::ops::scatter_nd(&x, &idx, &src)
             }
+            PolyOp::Trilu { upper, k } => tensor::ops::trilu(&inputs[0], *k, *upper),
         }?;
 
         Ok(ForwardResult { output: res })
@@ -384,6 +396,9 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
             PolyOp::Slice { axis, start, end } => {
                 layouts::slice(config, region, values[..].try_into()?, axis, start, end)?
             }
+            PolyOp::Trilu { upper, k } => {
+                layouts::trilu(config, region, values[..].try_into()?, k, upper)?
+            }
         }))
     }