ci: update version string in docs

BREAKING CHANGE: may break w/ old compiled circuits

.github/workflows/engine.yml

@@ -1,4 +1,4 @@
-name: Build and Publish EZKL npm packages (wasm bindings and in-browser evm verifier)
+name: Build and Publish EZKL Engine npm package
 
 on:
   workflow_dispatch:
@@ -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"
@@ -62,7 +62,7 @@ jobs:
               "web/ezkl_bg.wasm",
               "web/ezkl.js",
               "web/ezkl.d.ts",
-              "web/snippets/wasm-bindgen-rayon-7afa899f36665473/src/workerHelpers.js",
+              "web/snippets/**/*",
               "web/package.json",
               "web/utils.js",
               "ezkl.d.ts"
@@ -79,6 +79,10 @@ jobs:
         run: |
           sed -i "3s|.*|imports['env'] = {memory: new WebAssembly.Memory({initial:20,maximum:65536,shared:true})}|" pkg/nodejs/ezkl.js
 
+      - name: Replace `import.meta.url` with `import.meta.resolve` definition in workerHelpers.js
+        run: |
+          find ./pkg/web/snippets -type f -name "*.js" -exec sed -i "s|import.meta.url|import.meta.resolve|" {} +
+
       - name: Add serialize and deserialize methods to nodejs bundle
         run: |
           echo '
@@ -92,7 +96,7 @@ 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 +104,11 @@ 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 +127,7 @@ 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 +135,7 @@ jobs:
             }
             // Step 2: Encode the JSON String
             const uint8Array = new TextEncoder().encode(data);
-          
+
             // Step 3: Convert to Uint8ClampedArray
             return new Uint8ClampedArray(uint8Array.buffer);
           }
@@ -174,40 +178,3 @@ 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/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/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
@@ -128,6 +128,7 @@ jobs:
           mv Cargo.lock Cargo.lock.orig
           sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.lock.orig >Cargo.lock
 
+
       - name: Install required libraries
         shell: bash
         run: |
@@ -139,6 +140,20 @@ 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 +177,7 @@ 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 +229,7 @@ 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 +264,7 @@ 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 +288,7 @@ 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
@@ -345,3 +360,17 @@ jobs:
         with:
           repository-url: https://test.pypi.org/legacy/
           packages-dir: ./
+
+  doc-publish:
+    name: Trigger ReadTheDocs Build
+    runs-on: ubuntu-latest
+    needs: pypi-publish
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Trigger RTDs build
+        uses: dfm/rtds-action@v1
+        with:
+          webhook_url: ${{ secrets.RTDS_WEBHOOK_URL }}
+          webhook_token: ${{ secrets.RTDS_WEBHOOK_TOKEN }}
+          commit_ref: ${{ github.ref_name }}

.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
@@ -303,10 +303,12 @@ jobs:
         with:
           node-version: "18.12.1"
           cache: "pnpm"
+      - 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
+          pnpm install --frozen-lockfile
+          pnpm install --dir ./in-browser-evm-verifier --frozen-lockfile
         env:
           CI: false
           NODE_ENV: development
@@ -324,7 +326,7 @@ 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 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)
@@ -352,12 +354,12 @@ jobs:
 
   prove-and-verify-tests:
     runs-on: non-gpu
-    needs: [build, library-tests]
+    needs: [build, library-tests, docs]
     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
@@ -365,7 +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
@@ -378,7 +380,7 @@ jobs:
           cache: "pnpm"
       - name: Install dependencies for js tests
         run: |
-          pnpm install --no-frozen-lockfile
+          pnpm install --frozen-lockfile
         env:
           CI: false
           NODE_ENV: development
@@ -392,12 +394,18 @@ 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 + column overflow)
+        run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_with_overflow_::w
+      - name: KZG prove and verify tests (public outputs + fixed params + column overflow)
+        run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_with_overflow_fixed_params_
+      - name: KZG prove and verify tests (hashed inputs + column overflow)
+        run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_with_overflow_hashed_inputs_
+      - 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
         run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_single_col
       - name: KZG prove and verify tests triple inner col
@@ -408,12 +416,8 @@ jobs:
         run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_octuple_col --test-threads 8
       - name: KZG prove and verify tests (kzg outputs)
         run: cargo nextest run --release --verbose tests::kzg_prove_and_verify_kzg_output
-      - name: KZG prove and verify tests (public outputs + fixed params + column overflow)
-        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)
@@ -429,11 +433,11 @@ 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:
@@ -456,15 +460,14 @@ 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
 
-
   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
@@ -482,7 +485,7 @@ 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
@@ -494,12 +497,12 @@ 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
@@ -509,16 +512,14 @@ jobs:
       - 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
@@ -528,7 +529,7 @@ 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
 
@@ -539,15 +540,13 @@ 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
         with:
           crate: cargo-nextest
           locked: true
-      - name: Download MNIST
-        run: sh data.sh
       - name: Examples
         run: cargo nextest run --release tests_examples
 
@@ -558,18 +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
@@ -577,15 +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
@@ -593,7 +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
@@ -609,14 +610,32 @@ jobs:
 
   python-integration-tests:
     runs-on: large-self-hosted
+    services:
+      # Label used to access the service container
+      postgres:
+        # Docker Hub image
+        image: postgres
+        env:
+          POSTGRES_USER: ubuntu
+          POSTGRES_HOST_AUTH_METHOD: trust
+        # Set health checks to wait until postgres has started
+        options: >-
+          --health-cmd pg_isready
+          --health-interval 10s
+          --health-timeout 5s
+          --health-retries 5
+          -v /var/run/postgresql:/var/run/postgresql
+        ports:
+          # Maps tcp port 5432 on service container to the host
+          - 5432:5432
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: "3.10"
+          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
@@ -626,11 +645,17 @@ 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: Postgres tutorials
+        run: source .env/bin/activate; cargo nextest run py_tests::tests::postgres_ --no-capture
+      - 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:
@@ -646,7 +671,3 @@ jobs:
         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_
-      # - name: Postgres tutorials
-      #   run: source .env/bin/activate; cargo nextest run py_tests::tests::postgres_ --test-threads 1

.github/workflows/tagging.yml

@@ -14,6 +14,40 @@ jobs:
       - uses: actions/checkout@v4
       - name: Bump version and push tag
         id: tag_version
-        uses: mathieudutour/github-tag-action@v6.1
+        uses: mathieudutour/github-tag-action@v6.2
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Set Cargo.toml version to match github tag for docs
+        shell: bash
+        env:
+          RELEASE_TAG: ${{ steps.tag_version.outputs.new_tag }}
+        run: |
+          mv docs/python/src/conf.py docs/python/src/conf.py.orig
+          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" docs/python/src/conf.py.orig >docs/python/src/conf.py
+          rm docs/python/src/conf.py.orig
+          mv docs/python/requirements-docs.txt docs/python/requirements-docs.txt.orig
+          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" docs/python/requirements-docs.txt.orig >docs/python/requirements-docs.txt
+          rm docs/python/requirements-docs.txt.orig
+
+      - name: Commit files and create tag
+        env:
+          RELEASE_TAG: ${{ steps.tag_version.outputs.new_tag }}
+        run: |
+          git config --local user.email "github-actions[bot]@users.noreply.github.com"
+          git config --local user.name "github-actions[bot]"
+          git fetch --tags
+          git checkout -b release-$RELEASE_TAG
+          git add .
+          git commit -m "ci: update version string in docs"
+          git tag -d $RELEASE_TAG
+          git tag $RELEASE_TAG
+
+      - name: Push changes
+        uses: ad-m/github-push-action@master
+        env:
+          RELEASE_TAG: ${{ steps.tag_version.outputs.new_tag }}
+        with:
+          branch: release-${{ steps.tag_version.outputs.new_tag }}
+          force: true
+          tags: true

.github/workflows/verify.yml

@@ -0,0 +1,65 @@
+name: Build and Publish EZKL npm packages (wasm bindings and in-browser evm verifier)
+
+on:
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: "The tag to release"
+        required: true
+  push:
+    tags:
+      - "*"
+
+defaults:
+  run:
+    working-directory: .
+jobs:
+  in-browser-evm-ver-publish:
+    name: publish-in-browser-evm-verifier-package
+    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#v }}\"|" 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: Fetch integrity
+        run: |
+          ENGINE_INTEGRITY=$(npm view @ezkljs/engine@${{ github.ref_name#v }} dist.integrity)
+          echo "ENGINE_INTEGRITY=$ENGINE_INTEGRITY" >> $GITHUB_ENV
+      - name: Update pnpm-lock.yaml versions and integrity
+        run: |
+          awk -v integrity="$ENGINE_INTEGRITY" -v tag="${{ github.ref_name#v }}" '
+          NR==30{$0="    specifier: \"" tag "\""}
+          NR==31{$0="    version: \"" tag "\""}
+          NR==400{$0="  /@ezkljs/engine@" tag ":"}
+          NR==401{$0="    resolution: {integrity: \"" integrity "\"}"} 1' in-browser-evm-verifier/pnpm-lock.yaml > temp.yaml && mv temp.yaml in-browser-evm-verifier/pnpm-lock.yaml  
+      - name: Use pnpm 8
+        uses: pnpm/action-setup@v2
+        with:
+          version: 8
+      - 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
+          pnpm install --frozen-lockfile
+          pnpm run build
+          pnpm publish
+        env:
+          NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}

.gitignore

@@ -1,6 +1,5 @@
 target
 pkg
-data
 *.csv
 !examples/notebooks/eth_price.csv
 *.ipynb_checkpoints
@@ -48,4 +47,6 @@ node_modules
 /dist
 timingData.json
 !tests/wasm/pk.key
-!tests/wasm/vk.key
+!tests/wasm/vk.key
+docs/python/build
+!tests/wasm/vk_aggr.key

.python-version

@@ -0,0 +1 @@
+3.12.1

.readthedocs.yaml

@@ -0,0 +1,26 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.12"
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+  configuration: ./docs/python/src/conf.py
+
+# Optionally build your docs in additional formats such as PDF and ePub
+# formats:
+#    - pdf
+#    - epub
+
+# Optional but recommended, declare the Python requirements required
+# to build your documentation
+# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
+python:
+  install:
+  - requirements: ./docs/python/requirements-docs.txt

Cargo.toml

@@ -15,14 +15,14 @@ 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" }
+halo2_gadgets = { git = "https://github.com/zkonduit/halo2", branch = "ac/optional-selector-poly" }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2", branch = "ac/optional-selector-poly" }
 halo2curves = { git = "https://github.com/privacy-scaling-explorations/halo2curves", rev = "9fff22c", 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",
@@ -43,6 +43,7 @@ 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" }
+metal = { git = "https://github.com/gfx-rs/metal-rs", optional = true }
 
 
 # evm related deps
@@ -80,9 +81,11 @@ pyo3-asyncio = { version = "0.20.0", features = [
     "tokio-runtime",
 ], default_features = false, optional = true }
 pyo3-log = { version = "0.9.0", default_features = false, optional = true }
-tract-onnx = { git = "https://github.com/sonos/tract/", rev = "7b1aa33b2f7d1f19b80e270c83320f0f94daff69", default_features = false, optional = true }
+tract-onnx = { git = "https://github.com/sonos/tract/", rev = "681a096f02c9d7d363102d9fb0e446d1710ac2c8", default_features = false, optional = true }
 tabled = { version = "0.12.0", optional = true }
 
+objc = { version = "0.2.4", optional = true }
+
 
 [target.'cfg(not(all(target_arch = "wasm32", target_os = "unknown")))'.dependencies]
 colored = { version = "2.0.0", default_features = false, optional = true }
@@ -95,10 +98,10 @@ getrandom = { version = "0.2.8", features = ["js"] }
 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"
 
@@ -198,10 +201,15 @@ det-prove = []
 icicle = ["halo2_proofs/icicle_gpu"]
 empty-cmd = []
 no-banner = []
+metal = ["dep:metal", "dep:objc"]
 
 # icicle patch to 0.1.0 if feature icicle is enabled
 [patch.'https://github.com/ingonyama-zk/icicle']
 icicle = { git = "https://github.com/ingonyama-zk/icicle?rev=45b00fb", package = "icicle", branch = "fix/vhnat/ezkl-build-fix" }
 
+[patch.'https://github.com/zkonduit/halo2']
+halo2_proofs = { git = "https://github.com/zkonduit/halo2?branch=ac/optional-selector-poly#54f54453cf186aa5d89579c4e7663f9a27cfb89a", package = "halo2_proofs", branch = "ac/optional-selector-poly" }
+
+
 [profile.release]
 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.
 
@@ -70,7 +72,7 @@ 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`
 
@@ -124,17 +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:
@@ -151,7 +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
 
@@ -159,4 +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.

abis/QuantizeData.json

@@ -20,9 +20,9 @@
 		"name": "quantize_data",
 		"outputs": [
 			{
-				"internalType": "int128[]",
+				"internalType": "int64[]",
 				"name": "quantized_data",
-				"type": "int128[]"
+				"type": "int64[]"
 			}
 		],
 		"stateMutability": "pure",
@@ -31,9 +31,9 @@
 	{
 		"inputs": [
 			{
-				"internalType": "int128[]",
+				"internalType": "int64[]",
 				"name": "quantized_data",
-				"type": "int128[]"
+				"type": "int64[]"
 			}
 		],
 		"name": "to_field_element",

benches/accum_conv.rs

@@ -70,8 +70,8 @@ impl Circuit<Fr> for MyCircuit {
                         &mut region,
                         &[self.image.clone(), self.kernel.clone(), self.bias.clone()],
                         Box::new(PolyOp::Conv {
-                            padding: [(0, 0); 2],
-                            stride: (1, 1),
+                            padding: vec![(0, 0)],
+                            stride: vec![1; 2],
                         }),
                     )
                     .unwrap();

benches/accum_sumpool.rs

@@ -65,9 +65,9 @@ impl Circuit<Fr> for MyCircuit {
                         &mut region,
                         &[self.image.clone()],
                         Box::new(HybridOp::SumPool {
-                            padding: [(0, 0); 2],
-                            stride: (1, 1),
-                            kernel_shape: (2, 2),
+                            padding: vec![(0, 0); 2],
+                            stride: vec![1, 1],
+                            kernel_shape: vec![2, 2],
                             normalized: false,
                         }),
                     )

benches/poseidon.rs

@@ -1,3 +1,5 @@
+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};
@@ -48,7 +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(())
     }
 }

contracts/QuantizeData.sol

@@ -125,7 +125,7 @@ contract QuantizeData {
     }
 
     function to_field_element(
-        int128[] memory quantized_data
+        int64[] memory quantized_data
     ) public pure returns (uint256[] memory output) {
         output = new uint256[](quantized_data.length);
         for (uint i; i < quantized_data.length; i++) {

data.sh

@@ -1,11 +0,0 @@
-#! /bin/bash
-
-mkdir data
-cd data
-
-wget http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
-wget http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
-wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
-wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
-
-gzip -d *.gz

docs/python/build.sh

@@ -0,0 +1,2 @@
+#!/bin/sh
+sphinx-build ./src build

docs/python/requirements-docs.txt

@@ -0,0 +1,4 @@
+ezkl==11.0.0
+sphinx
+sphinx-rtd-theme
+sphinxcontrib-napoleon

docs/python/src/conf.py

@@ -0,0 +1,29 @@
+import ezkl
+
+project = 'ezkl'
+release = '11.0.0'
+version = release
+
+
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.autosummary',
+    'sphinx.ext.intersphinx',
+    'sphinx.ext.todo',
+    'sphinx.ext.inheritance_diagram',
+    'sphinx.ext.autosectionlabel',
+    'sphinx.ext.napoleon',
+    'sphinx_rtd_theme',
+]
+
+autosummary_generate = True
+autosummary_imported_members = True
+
+templates_path = ['_templates']
+exclude_patterns = []
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = 'sphinx_rtd_theme'
+html_static_path = ['_static']

docs/python/src/index.rst

@@ -0,0 +1,11 @@
+.. extension documentation master file, created by
+   sphinx-quickstart on Mon Jun 19 15:02:05 2023.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+ezkl python bindings
+================================================
+
+.. automodule:: ezkl
+   :members:
+   :undoc-members:

examples/conv2d_mnist/main.rs

@@ -42,8 +42,8 @@ const NUM_INNER_COLS: usize = 1;
 struct Config<
     const LEN: usize, //LEN = CHOUT x OH x OW flattened //not supported yet in rust stable
     const CLASSES: usize,
-    const LOOKUP_MIN: i128,
-    const LOOKUP_MAX: i128,
+    const LOOKUP_MIN: i64,
+    const LOOKUP_MAX: i64,
     // Convolution
     const KERNEL_HEIGHT: usize,
     const KERNEL_WIDTH: usize,
@@ -66,8 +66,8 @@ struct Config<
 struct MyCircuit<
     const LEN: usize, //LEN = CHOUT x OH x OW flattened
     const CLASSES: usize,
-    const LOOKUP_MIN: i128,
-    const LOOKUP_MAX: i128,
+    const LOOKUP_MIN: i64,
+    const LOOKUP_MAX: i64,
     // Convolution
     const KERNEL_HEIGHT: usize,
     const KERNEL_WIDTH: usize,
@@ -90,8 +90,8 @@ struct MyCircuit<
 impl<
         const LEN: usize,
         const CLASSES: usize,
-        const LOOKUP_MIN: i128,
-        const LOOKUP_MAX: i128,
+        const LOOKUP_MIN: i64,
+        const LOOKUP_MAX: i64,
         // Convolution
         const KERNEL_HEIGHT: usize,
         const KERNEL_WIDTH: usize,
@@ -203,8 +203,8 @@ where
                     let mut region = RegionCtx::new(region, 0, NUM_INNER_COLS);
 
                     let op = PolyOp::Conv {
-                        padding: [(PADDING, PADDING); 2],
-                        stride: (STRIDE, STRIDE),
+                        padding: vec![(PADDING, PADDING); 2],
+                        stride: vec![STRIDE; 2],
                     };
                     let x = config
                         .layer_config
@@ -308,6 +308,7 @@ pub fn runconv() {
         tst_lbl: _,
         ..
     } = MnistBuilder::new()
+        .base_path("examples/data")
         .label_format_digit()
         .training_set_length(50_000)
         .validation_set_length(10_000)

examples/mlp_4d_einsum.rs

@@ -23,8 +23,8 @@ struct MyConfig {
 #[derive(Clone)]
 struct MyCircuit<
     const LEN: usize, //LEN = CHOUT x OH x OW flattened
-    const LOOKUP_MIN: i128,
-    const LOOKUP_MAX: i128,
+    const LOOKUP_MIN: i64,
+    const LOOKUP_MAX: i64,
 > {
     // Given the stateless MyConfig type information, a DNN trace is determined by its input and the parameters of its layers.
     // Computing the trace still requires a forward pass. The intermediate activations are stored only by the layouter.
@@ -34,7 +34,7 @@ struct MyCircuit<
     _marker: PhantomData<F>,
 }
 
-impl<const LEN: usize, const LOOKUP_MIN: i128, const LOOKUP_MAX: i128> Circuit<F>
+impl<const LEN: usize, const LOOKUP_MIN: i64, const LOOKUP_MAX: i64> Circuit<F>
     for MyCircuit<LEN, LOOKUP_MIN, LOOKUP_MAX>
 {
     type Config = MyConfig;

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/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/mean_postgres.ipynb

@@ -7,9 +7,9 @@
         "## Mean of ERC20 transfer amounts\n",
         "\n",
         "This notebook shows how to calculate the mean of ERC20 transfer amounts, pulling data in from a Postgres database. First we install and get the necessary libraries running. \n",
-        "The first of which is [e2pg](https://github.com/indexsupply/x/tree/main/docs/e2pg), which is a library that allows us to pull data from the Ethereum blockchain into a Postgres database.\n",
+        "The first of which is [shovel](https://indexsupply.com/shovel/docs/#getting-started), which is a library that allows us to pull data from the Ethereum blockchain into a Postgres database.\n",
         "\n",
-        "Make sure you install postgres if needed https://postgresapp.com/. \n",
+        "Make sure you install postgres if needed https://indexsupply.com/shovel/docs/#getting-started. \n",
         "\n"
       ]
     },
@@ -21,23 +21,81 @@
       "source": [
         "import os\n",
         "import getpass\n",
-        "\n",
+        "import json\n",
+        "import time\n",
+        "import subprocess\n",
         "\n",
         "# swap out for the relevant linux/amd64, darwin/arm64, darwin/amd64, windows/amd64\n",
-        "os.system(\"curl -LO https://indexsupply.net/bin/main/linux/amd64/e2pg\")\n",
-        "os.system(\"chmod +x e2pg\")\n",
+        "os.system(\"curl -LO https://indexsupply.net/bin/1.0/linux/amd64/shovel\")\n",
+        "os.system(\"chmod +x shovel\")\n",
         "\n",
         "\n",
-        "os.environ[\"PG_URL\"] = \"postgresql://\" + getpass.getuser() + \":@localhost:5432/e2pg\"\n",
-        "os.environ[\"RLPS_URL\"] = \"https://1.rlps.indexsupply.net\"\n",
+        "os.environ[\"PG_URL\"] = \"postgres://\" + getpass.getuser() + \":@localhost:5432/shovel\"\n",
+        "\n",
+        "# create a config.json file with the following contents\n",
+        "config = {\n",
+        "  \"pg_url\": \"$PG_URL\",\n",
+        "  \"eth_sources\": [\n",
+        "    {\"name\": \"mainnet\", \"chain_id\": 1, \"url\": \"https://ethereum-rpc.publicnode.com\"},\n",
+        "    {\"name\": \"base\", \"chain_id\": 8453, \"url\": \"https://base-rpc.publicnode.com\"}\n",
+        "  ],\n",
+        "  \"integrations\": [{\n",
+        "    \"name\": \"usdc_transfer\",\n",
+        "    \"enabled\": True,\n",
+        "    \"sources\": [{\"name\": \"mainnet\"}, {\"name\": \"base\"}],\n",
+        "    \"table\": {\n",
+        "      \"name\": \"usdc\",\n",
+        "      \"columns\": [\n",
+        "        {\"name\": \"log_addr\",  \"type\": \"bytea\"},\n",
+        "        {\"name\": \"block_num\", \"type\": \"numeric\"},\n",
+        "        {\"name\": \"f\",         \"type\": \"bytea\"},\n",
+        "        {\"name\": \"t\",         \"type\": \"bytea\"},\n",
+        "        {\"name\": \"v\",         \"type\": \"numeric\"}\n",
+        "      ]\n",
+        "    },\n",
+        "    \"block\": [\n",
+        "      {\"name\": \"block_num\", \"column\": \"block_num\"},\n",
+        "      {\n",
+        "        \"name\": \"log_addr\",\n",
+        "        \"column\": \"log_addr\",\n",
+        "        \"filter_op\": \"contains\",\n",
+        "        \"filter_arg\": [\n",
+        "          \"a0b86991c6218b36c1d19d4a2e9eb0ce3606eb48\",\n",
+        "          \"833589fCD6eDb6E08f4c7C32D4f71b54bdA02913\"\n",
+        "        ]\n",
+        "      }\n",
+        "    ],\n",
+        "    \"event\": {\n",
+        "      \"name\": \"Transfer\",\n",
+        "      \"type\": \"event\",\n",
+        "      \"anonymous\": False,\n",
+        "      \"inputs\": [\n",
+        "        {\"indexed\": True,  \"name\": \"from\",  \"type\": \"address\", \"column\": \"f\"},\n",
+        "        {\"indexed\": True,  \"name\": \"to\",    \"type\": \"address\", \"column\": \"t\"},\n",
+        "        {\"indexed\": False, \"name\": \"value\", \"type\": \"uint256\", \"column\": \"v\"}\n",
+        "      ]\n",
+        "    }\n",
+        "  }]\n",
+        "}\n",
+        "\n",
+        "# write the config to a file\n",
+        "with open(\"config.json\", \"w\") as f:\n",
+        "  f.write(json.dumps(config))\n",
+        "\n",
         "\n",
         "# print the two env variables\n",
         "os.system(\"echo $PG_URL\")\n",
-        "os.system(\"echo $RLPS_URL\")\n",
         "\n",
-        "os.system(\"createdb -h localhost -p 5432 e2pg\")\n",
-        "# equivalent of nohup ./e2pg -reset -e $RLPS_URL -pg $PG_URL &\n",
-        "e2pg_process = os.system(\"nohup ./e2pg -e $RLPS_URL -pg $PG_URL &\")\n",
+        "os.system(\"createdb -h localhost -p 5432 shovel\")\n",
+        "\n",
+        "os.system(\"echo shovel is now installed. starting:\")\n",
+        "\n",
+        "command = [\"./shovel\", \"-config\", \"config.json\"]\n",
+        "subprocess.Popen(command)\n",
+        "\n",
+        "os.system(\"echo shovel started.\")\n",
+        "\n",
+        "time.sleep(5)\n",
         "\n"
       ]
     },
@@ -79,11 +137,13 @@
         "import json\n",
         "import os\n",
         "\n",
-        "# import logging\n",
-        "# # # uncomment for more descriptive logging \n",
-        "# FORMAT = '%(levelname)s %(name)s %(asctime)-15s %(filename)s:%(lineno)d %(message)s'\n",
-        "# logging.basicConfig(format=FORMAT)\n",
-        "# logging.getLogger().setLevel(logging.DEBUG)"
+        "import logging\n",
+        "# # uncomment for more descriptive logging \n",
+        "FORMAT = '%(levelname)s %(name)s %(asctime)-15s %(filename)s:%(lineno)d %(message)s'\n",
+        "logging.basicConfig(format=FORMAT)\n",
+        "logging.getLogger().setLevel(logging.DEBUG)\n",
+        "\n",
+        "print(\"ezkl version: \", ezkl.__version__)"
       ]
     },
     {
@@ -176,6 +236,7 @@
       },
       "outputs": [],
       "source": [
+        "import getpass\n",
         "# make an input.json file from the df above\n",
         "input_filename = os.path.join('input.json')\n",
         "\n",
@@ -183,9 +244,9 @@
         "  \"host\": \"localhost\",\n",
         "  # make sure you replace this with your own username\n",
         "  \"user\": getpass.getuser(),\n",
-        "  \"dbname\": \"e2pg\",\n",
+        "  \"dbname\": \"shovel\",\n",
         "  \"password\": \"\",\n",
-        "  \"query\": \"SELECT value FROM erc20_transfers ORDER BY block_number DESC LIMIT 5\",\n",
+        "  \"query\": \"SELECT v FROM usdc ORDER BY block_num DESC LIMIT 5\",\n",
         "  \"port\": \"5432\",\n",
         "})\n",
         "\n",
@@ -194,7 +255,7 @@
         "\n",
         "\n",
         "    # Serialize data into file:\n",
-        "json.dump( pg_input_file, open(input_filename, 'w' ))\n"
+        "json.dump(pg_input_file, open(input_filename, 'w' ))\n"
       ]
     },
     {
@@ -210,9 +271,9 @@
         "  \"host\": \"localhost\",\n",
         "  # make sure you replace this with your own username\n",
         "  \"user\": getpass.getuser(),\n",
-        "  \"dbname\": \"e2pg\",\n",
+        "  \"dbname\": \"shovel\",\n",
         "  \"password\": \"\",\n",
-        "  \"query\": \"SELECT value FROM erc20_transfers ORDER BY block_number DESC LIMIT 20\",\n",
+        "  \"query\": \"SELECT v FROM usdc ORDER BY block_num DESC LIMIT 20\",\n",
         "  \"port\": \"5432\",\n",
         "})\n",
         "\n",
@@ -229,22 +290,6 @@
         "**EZKL Workflow**"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "onnx_filename = os.path.join('lol.onnx')\n",
-        "compiled_filename = os.path.join('lol.compiled')\n",
-        "settings_filename = os.path.join('settings.json')\n",
-        "\n",
-        "ezkl.gen_settings(onnx_filename, settings_filename)\n",
-        "\n",
-        "ezkl.calibrate_settings(\n",
-        "    input_filename, onnx_filename, settings_filename, \"resources\")"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -253,10 +298,21 @@
       },
       "outputs": [],
       "source": [
-        "# setup kzg params\n",
-        "params_path = os.path.join('kzg.params')\n",
+        "import subprocess\n",
+        "import os\n",
         "\n",
-        "res = ezkl.get_srs(params_path, settings_filename)"
+        "onnx_filename = os.path.join('lol.onnx')\n",
+        "compiled_filename = os.path.join('lol.compiled')\n",
+        "settings_filename = os.path.join('settings.json')\n",
+        "\n",
+        "# Generate settings using ezkl\n",
+        "res = ezkl.gen_settings(onnx_filename, settings_filename)\n",
+        "\n",
+        "assert res == True\n",
+        "\n",
+        "res = ezkl.calibrate_settings(input_filename, onnx_filename, settings_filename, \"resources\")\n",
+        "\n",
+        "assert res == True"
       ]
     },
     {
@@ -306,16 +362,13 @@
       "source": [
         "pk_path = os.path.join('test.pk')\n",
         "vk_path = os.path.join('test.vk')\n",
-        "params_path = os.path.join('kzg.params')\n",
         "\n",
         "\n",
         "# setup the proof\n",
         "res = ezkl.setup(\n",
         "        compiled_filename,\n",
         "        vk_path,\n",
-        "        pk_path,\n",
-        "        params_path,\n",
-        "        settings_filename,\n",
+        "        pk_path\n",
         "    )\n",
         "\n",
         "assert res == True\n",
@@ -331,11 +384,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "\n",
         "witness_path = \"witness.json\"\n",
         "\n",
-        "res = ezkl.gen_witness(input_filename, compiled_filename, witness_path)\n",
-        "assert os.path.isfile(witness_path)"
+        "# generate the witness\n",
+        "res = ezkl.gen_witness(\n",
+        "        input_filename,\n",
+        "        compiled_filename,\n",
+        "        witness_path\n",
+        "    )\n"
       ]
     },
     {
@@ -360,73 +416,14 @@
         "        compiled_filename,\n",
         "        pk_path,\n",
         "        proof_path,\n",
-        "        params_path,\n",
-        "        \"single\",\n",
+        "        \"single\"\n",
         "    )\n",
         "\n",
         "\n",
+        "print(\"proved\")\n",
+        "\n",
         "assert os.path.isfile(proof_path)\n",
-        "\n",
-        "# verify\n",
-        "res = ezkl.verify(\n",
-        "        proof_path,\n",
-        "        settings_filename,\n",
-        "        vk_path,\n",
-        "        params_path,\n",
-        "    )\n",
-        "\n",
-        "assert res == True\n",
-        "print(\"verified\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "W7tAa-DFAtvS"
-      },
-      "source": [
-        "# Part 2 (Using the ZK Computational Graph Onchain!)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8Ym91kaVAIB6"
-      },
-      "source": [
-        "**Now How Do We Do It Onchain?????**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 339
-        },
-        "id": "fodkNgwS70FM",
-        "outputId": "827b5efd-f74f-44de-c114-861b3a86daf2"
-      },
-      "outputs": [],
-      "source": [
-        "# first we need to create evm verifier\n",
-        "print(vk_path)\n",
-        "print(params_path)\n",
-        "print(settings_filename)\n",
-        "\n",
-        "\n",
-        "abi_path = 'test.abi'\n",
-        "sol_code_path = 'test.sol'\n",
-        "\n",
-        "res = ezkl.create_evm_verifier(\n",
-        "        vk_path,\n",
-        "        params_path,\n",
-        "        settings_filename,\n",
-        "        sol_code_path,\n",
-        "        abi_path,\n",
-        "    )\n",
-        "assert res == True"
+        "\n"
       ]
     },
     {
@@ -435,51 +432,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Make sure anvil is running locally first\n",
-        "# run with $ anvil -p 3030\n",
-        "# we use the default anvil node here\n",
-        "import json\n",
-        "\n",
-        "address_path = os.path.join(\"address.json\")\n",
-        "\n",
-        "res = ezkl.deploy_evm(\n",
-        "    address_path,\n",
-        "    sol_code_path,\n",
-        "    'http://127.0.0.1:3030'\n",
-        ")\n",
-        "\n",
-        "assert res == True\n",
-        "\n",
-        "with open(address_path, 'r') as file:\n",
-        "    addr = file.read().rstrip()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# read the address from addr_path\n",
-        "addr = None\n",
-        "with open(address_path, 'r') as f:\n",
-        "    addr = f.read()\n",
-        "\n",
-        "res = ezkl.verify_evm(\n",
-        "    addr,\n",
-        "    proof_path,\n",
-        "    \"http://127.0.0.1:3030\"\n",
-        ")\n",
-        "assert res == True"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "os.system(\"killall -9 e2pg\");"
+        "# kill all shovel process \n",
+        "os.system(\"pkill -f shovel\")"
       ]
     }
   ],
@@ -501,7 +455,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.12.2"
     }
   },
   "nbformat": 4,

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/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/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/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@ezkljs/verify",
-  "version": "0.0.0",
+  "version": "v10.4.2",
   "publishConfig": {
     "access": "public"
   },
@@ -17,19 +17,19 @@
     "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": "pnpm run clean && pnpm run build:commonjs && pnpm run build:esm"
+    "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"
+    "@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": "10.4.2",
+    "ethers": "6.7.1",
+    "json-bigint": "1.0.0"
   },
   "devDependencies": {
     "@types/node": "^20.8.3",

in-browser-evm-verifier/pnpm-lock.yaml

@@ -6,34 +6,34 @@ settings:
 
 dependencies:
   '@ethereumjs/common':
-    specifier: ^4.0.0
+    specifier: 4.0.0
     version: 4.0.0
   '@ethereumjs/evm':
-    specifier: ^2.0.0
+    specifier: 2.0.0
     version: 2.0.0
   '@ethereumjs/statemanager':
-    specifier: ^2.0.0
+    specifier: 2.0.0
     version: 2.0.0
   '@ethereumjs/tx':
-    specifier: ^5.0.0
+    specifier: 5.0.0
     version: 5.0.0
   '@ethereumjs/util':
-    specifier: ^9.0.0
+    specifier: 9.0.0
     version: 9.0.0
   '@ethereumjs/vm':
-    specifier: ^7.0.0
+    specifier: 7.0.0
     version: 7.0.0
   '@ethersproject/abi':
-    specifier: ^5.7.0
+    specifier: 5.7.0
     version: 5.7.0
   '@ezkljs/engine':
-    specifier: ^9.4.4
-    version: 9.4.4
+    specifier: "10.4.2"
+    version: "10.4.2"
   ethers:
-    specifier: ^6.7.1
+    specifier: 6.7.1
     version: 6.7.1
   json-bigint:
-    specifier: ^1.0.0
+    specifier: 1.0.0
     version: 1.0.0
 
 devDependencies:
@@ -397,8 +397,8 @@ packages:
       '@ethersproject/strings': 5.7.0
     dev: false
 
-  /@ezkljs/engine@9.4.4:
-    resolution: {integrity: sha512-kNsTmDQa8mIiQ6yjJmBMwVgAAxh4nfs4NCtnewJifonyA8Mfhs+teXwwW8WhERRDoQPUofKO2pT8BPvV/XGIDA==}
+  /@ezkljs/engine@10.4.2:
+    resolution: {integrity: "sha512-1GNB4vChbaQ1ALcYbEbM/AFoh4QWtswpzGCO/g9wL8Ep6NegM2gQP/uWICU7Utl0Lj1DncXomD7PUhFSXhtx8A=="}
     dependencies:
       '@types/json-bigint': 1.0.2
       json-bigint: 1.0.0

install_ezkl_cli.sh

@@ -36,7 +36,7 @@ if  [ "$(which ezkl)s" != "s" ] && [ "$(which ezkl)" != "$EZKL_DIR/ezkl" ] ; the
     exit 1
 fi
 
-if [[ ":$PATH:" != *":${EZKl_DIR}:"* ]]; then
+if [[ ":$PATH:" != *":${EZKL_DIR}:"* ]]; then
     # Add the ezkl directory to the path and ensure the old PATH variables remain.
     echo >> $PROFILE && echo "export PATH=\"\$PATH:$EZKL_DIR\"" >> $PROFILE
 fi

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["maturin>=0.14,<0.15"]
+requires = ["maturin>=1.0,<2.0"]
 build-backend = "maturin"
 
 [tool.pytest.ini_options]

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==1.5.1
+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/circuit/modules/mod.rs

@@ -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

@@ -4,6 +4,8 @@ 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, CommitmentScheme, Params};
@@ -13,6 +15,7 @@ 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;
@@ -41,12 +44,11 @@ impl PolyCommitChip {
     /// Commit to the message using the KZG commitment scheme
     pub fn commit<Scheme: CommitmentScheme<Scalar = Fp, Curve = G1Affine>>(
         message: Vec<Scheme::Scalar>,
-        degree: u32,
         num_unusable_rows: u32,
         params: &Scheme::ParamsProver,
     ) -> Vec<G1Affine> {
         let k = params.k();
-        let domain = halo2_proofs::poly::EvaluationDomain::new(degree, k);
+        let domain = halo2_proofs::poly::EvaluationDomain::new(2, k);
         let n = 2_u64.pow(k) - num_unusable_rows as u64;
         let num_poly = (message.len() / n as usize) + 1;
         let mut poly = vec![domain.empty_lagrange(); num_poly];
@@ -107,6 +109,7 @@ impl Module<Fp> for PolyCommitChip {
         &self,
         _: &mut impl Layouter<Fp>,
         _: &[ValTensor<Fp>],
+        _: &mut ConstantsMap<Fp>,
     ) -> Result<Self::InputAssignments, Error> {
         Ok(())
     }
@@ -119,11 +122,24 @@ impl Module<Fp> for PolyCommitChip {
         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(
             || "PolyCommit",
-            |mut region| self.config.inputs.assign(&mut region, 0, &input[0]),
+            |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)
+            },
         )
     }
 
@@ -184,7 +200,12 @@ mod tests {
             mut layouter: impl Layouter<Fp>,
         ) -> Result<(), Error> {
             let polycommit_chip = PolyCommitChip::new(config);
-            polycommit_chip.layout(&mut layouter, &[self.message.clone()], 0);
+            polycommit_chip.layout(
+                &mut layouter,
+                &[self.message.clone()],
+                0,
+                &mut HashMap::new(),
+            );
 
             Ok(())
         }

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

@@ -24,7 +24,7 @@ use crate::{
         table::{Range, RangeCheck, Table},
         utils,
     },
-    tensor::{Tensor, TensorType, ValTensor, VarTensor},
+    tensor::{IntoI64, Tensor, TensorType, ValTensor, VarTensor},
 };
 use std::{collections::BTreeMap, error::Error, marker::PhantomData};
 
@@ -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 + IntoI64> 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 {
@@ -956,20 +956,6 @@ impl<F: PrimeField + TensorType + PartialOrd> BaseConfig<F> {
         values: &[ValTensor<F>],
         op: Box<dyn Op<F>>,
     ) -> Result<Option<ValTensor<F>>, Box<dyn Error>> {
-        let res = op.layout(self, region, values)?;
-
-        if matches!(&self.check_mode, CheckMode::SAFE) && !region.is_dummy() {
-            if let Some(claimed_output) = &res {
-                // during key generation this will be unknown vals so we use this as a flag to check
-                let mut is_assigned = !claimed_output.any_unknowns()?;
-                for val in values.iter() {
-                    is_assigned = is_assigned && !val.any_unknowns()?;
-                }
-                if is_assigned {
-                    op.safe_mode_check(claimed_output, values)?;
-                }
-            }
-        };
-        Ok(res)
+        op.layout(self, region, values)
     }
 }

src/circuit/ops/hybrid.rs

@@ -1,9 +1,9 @@
 use super::*;
 use crate::{
     circuit::{layouts, utils, Tolerance},
-    fieldutils::{felt_to_i128, i128_to_felt},
+    fieldutils::i64_to_felt,
     graph::multiplier_to_scale,
-    tensor::{self, Tensor, TensorError, TensorType, ValTensor},
+    tensor::{self, Tensor, TensorType, ValTensor},
 };
 use halo2curves::ff::PrimeField;
 use serde::{Deserialize, Serialize};
@@ -29,15 +29,15 @@ pub enum HybridOp {
         dim: usize,
     },
     SumPool {
-        padding: [(usize, usize); 2],
-        stride: (usize, usize),
-        kernel_shape: (usize, usize),
+        padding: Vec<(usize, usize)>,
+        stride: Vec<usize>,
+        kernel_shape: Vec<usize>,
         normalized: bool,
     },
-    MaxPool2d {
-        padding: [(usize, usize); 2],
-        stride: (usize, usize),
-        pool_dims: (usize, usize),
+    MaxPool {
+        padding: Vec<(usize, usize)>,
+        stride: Vec<usize>,
+        pool_dims: Vec<usize>,
     },
     ReduceMin {
         axes: Vec<usize>,
@@ -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 + IntoI64> Op<F> for HybridOp {
     ///
     fn requires_homogenous_input_scales(&self) -> Vec<usize> {
         match self {
@@ -84,86 +85,6 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for HybridOp {
     fn as_any(&self) -> &dyn Any {
         self
     }
-    /// Matches a [Op] to an operation in the `tensor::ops` module.
-    fn f(&self, inputs: &[Tensor<F>]) -> Result<ForwardResult<F>, TensorError> {
-        let x = inputs[0].clone().map(|x| felt_to_i128(x));
-
-        let res = match &self {
-            HybridOp::ReduceMax { axes, .. } => tensor::ops::max_axes(&x, axes)?,
-            HybridOp::ReduceMin { axes, .. } => tensor::ops::min_axes(&x, axes)?,
-            HybridOp::Div { denom, .. } => {
-                crate::tensor::ops::nonlinearities::const_div(&x, denom.0 as f64)
-            }
-            HybridOp::Recip {
-                input_scale,
-                output_scale,
-                ..
-            } => crate::tensor::ops::nonlinearities::recip(
-                &x,
-                input_scale.0 as f64,
-                output_scale.0 as f64,
-            ),
-            HybridOp::ReduceArgMax { dim } => tensor::ops::argmax_axes(&x, *dim)?,
-            HybridOp::ReduceArgMin { dim } => tensor::ops::argmin_axes(&x, *dim)?,
-            HybridOp::Gather { dim, constant_idx } => {
-                if let Some(idx) = constant_idx {
-                    tensor::ops::gather(&x, idx, *dim)?
-                } else {
-                    let y = inputs[1].clone().map(|x| felt_to_i128(x));
-                    tensor::ops::gather(&x, &y.map(|x| x as usize), *dim)?
-                }
-            }
-            HybridOp::OneHot { dim, num_classes } => {
-                tensor::ops::one_hot(&x, *num_classes, *dim)?.clone()
-            }
-
-            HybridOp::TopK { dim, k, largest } => tensor::ops::topk_axes(&x, *k, *dim, *largest)?,
-            HybridOp::MaxPool2d {
-                padding,
-                stride,
-                pool_dims,
-                ..
-            } => tensor::ops::max_pool2d(&x, padding, stride, pool_dims)?,
-            HybridOp::SumPool {
-                padding,
-                stride,
-                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::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)
-            }
-            HybridOp::Greater => {
-                let y = inputs[1].clone().map(|x| felt_to_i128(x));
-                tensor::ops::greater(&x, &y)?
-            }
-            HybridOp::GreaterEqual => {
-                let y = inputs[1].clone().map(|x| felt_to_i128(x));
-                tensor::ops::greater_equal(&x, &y)?
-            }
-            HybridOp::Less => {
-                let y = inputs[1].clone().map(|x| felt_to_i128(x));
-                tensor::ops::less(&x, &y)?
-            }
-            HybridOp::LessEqual => {
-                let y = inputs[1].clone().map(|x| felt_to_i128(x));
-                tensor::ops::less_equal(&x, &y)?
-            }
-            HybridOp::Equals => {
-                let y = inputs[1].clone().map(|x| felt_to_i128(x));
-                tensor::ops::equals(&x, &y)?
-            }
-        };
-
-        // convert back to felt
-        let output = res.map(|x| i128_to_felt(x));
-
-        Ok(ForwardResult { output })
-    }
 
     fn as_string(&self) -> String {
         match self {
@@ -193,18 +114,25 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for HybridOp {
             ),
             HybridOp::ReduceMax { axes } => format!("REDUCEMAX (axes={:?})", axes),
             HybridOp::ReduceArgMax { dim } => format!("REDUCEARGMAX (dim={})", dim),
-            HybridOp::MaxPool2d {
+            HybridOp::MaxPool {
                 padding,
                 stride,
                 pool_dims,
             } => format!(
-                "MAXPOOL2D (padding={:?}, stride={:?}, pool_dims={:?})",
+                "MaxPool (padding={:?}, stride={:?}, pool_dims={:?})",
                 padding, stride, pool_dims
             ),
             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(),
@@ -238,9 +166,9 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for HybridOp {
                 config,
                 region,
                 values[..].try_into()?,
-                *padding,
-                *stride,
-                *kernel_shape,
+                padding,
+                stride,
+                kernel_shape,
                 *normalized,
             )?,
             HybridOp::Recip {
@@ -256,8 +184,8 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for HybridOp {
                         config,
                         region,
                         values[..].try_into()?,
-                        i128_to_felt(input_scale.0 as i128),
-                        i128_to_felt(output_scale.0 as i128),
+                        i64_to_felt(input_scale.0 as i64),
+                        i64_to_felt(output_scale.0 as i64),
                     )?
                 } else {
                     layouts::nonlinearity(
@@ -281,7 +209,7 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for HybridOp {
                         config,
                         region,
                         values[..].try_into()?,
-                        i128_to_felt(denom.0 as i128),
+                        i64_to_felt(denom.0 as i64),
                     )?
                 } else {
                     layouts::nonlinearity(
@@ -300,17 +228,17 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for HybridOp {
                 }
             }
 
-            HybridOp::MaxPool2d {
+            HybridOp::MaxPool {
                 padding,
                 stride,
                 pool_dims,
-            } => layouts::max_pool2d(
+            } => layouts::max_pool(
                 config,
                 region,
                 values[..].try_into()?,
-                *padding,
-                *stride,
-                *pool_dims,
+                padding,
+                stride,
+                pool_dims,
             )?,
             HybridOp::ReduceMax { axes } => {
                 layouts::max_axes(config, region, values[..].try_into()?, axes)?
@@ -324,9 +252,18 @@ 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 +296,9 @@ 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/lookup.rs

@@ -4,9 +4,9 @@ use std::error::Error;
 
 use crate::{
     circuit::{layouts, table::Range, utils},
-    fieldutils::{felt_to_i128, i128_to_felt},
+    fieldutils::{felt_to_i64, i64_to_felt},
     graph::multiplier_to_scale,
-    tensor::{self, Tensor, TensorError, TensorType},
+    tensor::{self, IntoI64, Tensor, TensorError, TensorType},
 };
 
 use super::Op;
@@ -123,25 +123,25 @@ pub enum LookupOp {
         scale: utils::F32,
         a: utils::F32,
     },
+    HardSwish {
+        scale: utils::F32,
+    },
 }
 
 impl LookupOp {
     /// Returns the range of values that can be represented by the table
     pub fn bit_range(max_len: usize) -> Range {
         let range = (max_len - 1) as f64 / 2_f64;
-        let range = range as i128;
+        let range = range as i64;
         (-range, range)
     }
-}
 
-impl<F: PrimeField + TensorType + PartialOrd> Op<F> for LookupOp {
-    /// Returns a reference to the Any trait.
-    fn as_any(&self) -> &dyn Any {
-        self
-    }
     /// Matches a [Op] to an operation in the `tensor::ops` module.
-    fn f(&self, x: &[Tensor<F>]) -> Result<ForwardResult<F>, TensorError> {
-        let x = x[0].clone().map(|x| felt_to_i128(x));
+    pub(crate) fn f<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64>(
+        &self,
+        x: &[Tensor<F>],
+    ) -> Result<ForwardResult<F>, TensorError> {
+        let x = x[0].clone().map(|x| felt_to_i64(x));
         let res = match &self {
             LookupOp::Abs => Ok(tensor::ops::abs(&x)?),
             LookupOp::Ceil { scale } => Ok(tensor::ops::nonlinearities::ceil(&x, scale.into())),
@@ -223,12 +223,22 @@ 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));
+        let output = res.map(|x| i64_to_felt(x));
 
         Ok(ForwardResult { output })
     }
+}
+
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Op<F> for LookupOp {
+    /// Returns a reference to the Any trait.
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
 
     /// Returns the name of the operation
     fn as_string(&self) -> String {
@@ -276,6 +286,7 @@ 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

@@ -4,7 +4,7 @@ use serde::{Deserialize, Serialize};
 
 use crate::{
     graph::quantize_tensor,
-    tensor::{self, Tensor, TensorError, TensorType, ValTensor},
+    tensor::{self, IntoI64, Tensor, TensorType, ValTensor},
 };
 use halo2curves::ff::PrimeField;
 
@@ -27,14 +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 + IntoI64> {
     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 {
-    /// Matches a [Op] to an operation in the `tensor::ops` module.
-    fn f(&self, x: &[Tensor<F>]) -> Result<ForwardResult<F>, TensorError>;
+pub trait Op<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64>:
+    std::fmt::Debug + Send + Sync + Any
+{
     /// Returns a string representation of the operation.
     fn as_string(&self) -> String;
 
@@ -69,36 +69,9 @@ pub trait Op<F: PrimeField + TensorType + PartialOrd>: std::fmt::Debug + Send +
 
     /// Returns a reference to the Any trait.
     fn as_any(&self) -> &dyn Any;
-
-    /// Safe mode output checl
-    fn safe_mode_check(
-        &self,
-        claimed_output: &ValTensor<F>,
-        original_values: &[ValTensor<F>],
-    ) -> Result<(), TensorError> {
-        let felt_evals = original_values
-            .iter()
-            .map(|v| {
-                let mut evals = v.get_felt_evals().map_err(|_| TensorError::FeltError)?;
-                evals.reshape(v.dims())?;
-                Ok(evals)
-            })
-            .collect::<Result<Vec<_>, _>>()?;
-
-        let ref_op: Tensor<F> = self.f(&felt_evals)?.output;
-
-        let mut output = claimed_output
-            .get_felt_evals()
-            .map_err(|_| TensorError::FeltError)?;
-        output.reshape(claimed_output.dims())?;
-
-        assert_eq!(output, ref_op);
-
-        Ok(())
-    }
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> Clone for Box<dyn Op<F>> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Clone for Box<dyn Op<F>> {
     fn clone(&self) -> Self {
         self.clone_dyn()
     }
@@ -149,8 +122,8 @@ impl InputType {
                 *input = T::from_f64(f64_input).unwrap();
             }
             InputType::Int | InputType::TDim => {
-                let int_input = input.clone().to_i128().unwrap();
-                *input = T::from_i128(int_input).unwrap();
+                let int_input = input.clone().to_i64().unwrap();
+                *input = T::from_i64(int_input).unwrap();
             }
         }
     }
@@ -165,7 +138,7 @@ pub struct Input {
     pub datum_type: InputType,
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> Op<F> for Input {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Op<F> for Input {
     fn out_scale(&self, _: Vec<crate::Scale>) -> Result<crate::Scale, Box<dyn Error>> {
         Ok(self.scale)
     }
@@ -174,12 +147,6 @@ impl<F: PrimeField + TensorType + PartialOrd> Op<F> for Input {
         self
     }
 
-    fn f(&self, x: &[Tensor<F>]) -> Result<ForwardResult<F>, TensorError> {
-        Ok(ForwardResult {
-            output: x[0].clone(),
-        })
-    }
-
     fn as_string(&self) -> String {
         "Input".into()
     }
@@ -226,16 +193,13 @@ 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 + IntoI64> Op<F> for Unknown {
     fn out_scale(&self, _: Vec<crate::Scale>) -> Result<crate::Scale, Box<dyn Error>> {
         Ok(0)
     }
     fn as_any(&self) -> &dyn Any {
         self
     }
-    fn f(&self, _: &[Tensor<F>]) -> Result<ForwardResult<F>, TensorError> {
-        Err(TensorError::WrongMethod)
-    }
 
     fn as_string(&self) -> String {
         "Unknown".into()
@@ -256,7 +220,7 @@ 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 + IntoI64> {
     ///
     pub quantized_values: Tensor<F>,
     ///
@@ -266,7 +230,7 @@ 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 + IntoI64> Constant<F> {
     ///
     pub fn new(quantized_values: Tensor<F>, raw_values: Tensor<f32>) -> Self {
         Self {
@@ -293,17 +257,19 @@ 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>
+            + IntoI64,
+    > Op<F> for Constant<F>
 {
     fn as_any(&self) -> &dyn Any {
         self
     }
-    fn f(&self, _: &[Tensor<F>]) -> Result<ForwardResult<F>, TensorError> {
-        let output = self.quantized_values.clone();
-
-        Ok(ForwardResult { output })
-    }
 
     fn as_string(&self) -> String {
         format!("CONST (scale={})", self.quantized_values.scale().unwrap())

src/circuit/ops/poly.rs

@@ -1,6 +1,5 @@
 use crate::{
     circuit::layouts,
-    fieldutils::felt_to_i128,
     tensor::{self, Tensor, TensorError},
 };
 
@@ -32,8 +31,8 @@ pub enum PolyOp {
         equation: String,
     },
     Conv {
-        padding: [(usize, usize); 2],
-        stride: (usize, usize),
+        padding: Vec<(usize, usize)>,
+        stride: Vec<usize>,
     },
     Downsample {
         axis: usize,
@@ -41,9 +40,9 @@ pub enum PolyOp {
         modulo: usize,
     },
     DeConv {
-        padding: [(usize, usize); 2],
-        output_padding: (usize, usize),
-        stride: (usize, usize),
+        padding: Vec<(usize, usize)>,
+        output_padding: Vec<usize>,
+        stride: Vec<usize>,
     },
     Add,
     Sub,
@@ -58,10 +57,13 @@ pub enum PolyOp {
         destination: usize,
     },
     Flatten(Vec<usize>),
-    Pad([(usize, usize); 2]),
+    Pad(Vec<(usize, usize)>),
     Sum {
         axes: Vec<usize>,
     },
+    MeanOfSquares {
+        axes: Vec<usize>,
+    },
     Prod {
         axes: Vec<usize>,
         len_prod: usize,
@@ -83,10 +85,21 @@ 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>
+            + IntoI64,
+    > Op<F> for PolyOp
 {
     /// Returns a reference to the Any trait.
     fn as_any(&self) -> &dyn Any {
@@ -95,10 +108,28 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
 
     fn as_string(&self) -> String {
         match &self {
-            PolyOp::GatherElements { dim, .. } => format!("GATHERELEMENTS (dim={})", dim),
-            PolyOp::GatherND { batch_dims, .. } => format!("GATHERND (batch_dims={})", batch_dims),
-            PolyOp::ScatterElements { dim, .. } => format!("SCATTERELEMENTS (dim={})", dim),
-            PolyOp::ScatterND { .. } => "SCATTERND".into(),
+            PolyOp::GatherElements { dim, constant_idx } => format!(
+                "GATHERELEMENTS (dim={}, constant_idx{})",
+                dim,
+                constant_idx.is_some()
+            ),
+            PolyOp::GatherND {
+                batch_dims,
+                indices,
+            } => format!(
+                "GATHERND (batch_dims={}, constant_idx{})",
+                batch_dims,
+                indices.is_some()
+            ),
+            PolyOp::MeanOfSquares { axes } => format!("MEANOFSQUARES (axes={:?})", axes),
+            PolyOp::ScatterElements { dim, constant_idx } => format!(
+                "SCATTERELEMENTS (dim={}, constant_idx{})",
+                dim,
+                constant_idx.is_some()
+            ),
+            PolyOp::ScatterND { constant_idx } => {
+                format!("SCATTERND (constant_idx={})", constant_idx.is_some())
+            }
             PolyOp::MultiBroadcastTo { shape } => format!("MULTIBROADCASTTO (shape={:?})", shape),
             PolyOp::MoveAxis { .. } => "MOVEAXIS".into(),
             PolyOp::Downsample { .. } => "DOWNSAMPLE".into(),
@@ -110,15 +141,26 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
             }
             PolyOp::Reshape(shape) => format!("RESHAPE (shape={:?})", shape),
             PolyOp::Flatten(_) => "FLATTEN".into(),
-            PolyOp::Pad(_) => "PAD".into(),
+            PolyOp::Pad(pads) => format!("PAD (pads={:?})", pads),
             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(),
-            PolyOp::DeConv { .. } => "DECONV".into(),
+            PolyOp::Conv { stride, padding } => {
+                format!("CONV (stride={:?}, padding={:?})", stride, padding)
+            }
+            PolyOp::DeConv {
+                stride,
+                padding,
+                output_padding,
+            } => {
+                format!(
+                    "DECONV (stride={:?}, padding={:?}, output_padding={:?})",
+                    stride, padding, output_padding
+                )
+            }
             PolyOp::Concat { axis } => format!("CONCAT (axis={})", axis),
             PolyOp::Slice { axis, start, end } => {
                 format!("SLICE (axis={}, start={}, end={})", axis, start, end)
@@ -128,148 +170,10 @@ 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),
         }
     }
 
-    /// Matches a [Op] to an operation in the `tensor::ops` module.
-    fn f(&self, inputs: &[Tensor<F>]) -> Result<ForwardResult<F>, TensorError> {
-        let mut inputs = inputs.to_vec();
-        let res = match &self {
-            PolyOp::MultiBroadcastTo { shape } => {
-                if 1 != inputs.len() {
-                    return Err(TensorError::DimMismatch(
-                        "multibroadcastto inputs".to_string(),
-                    ));
-                }
-                inputs[0].expand(shape)
-            }
-            PolyOp::And => tensor::ops::and(&inputs[0], &inputs[1]),
-            PolyOp::Or => tensor::ops::or(&inputs[0], &inputs[1]),
-            PolyOp::Xor => tensor::ops::xor(&inputs[0], &inputs[1]),
-            PolyOp::Not => tensor::ops::not(&inputs[0]),
-            PolyOp::Downsample {
-                axis,
-                stride,
-                modulo,
-            } => tensor::ops::downsample(&inputs[0], *axis, *stride, *modulo),
-            PolyOp::Resize { scale_factor } => tensor::ops::resize(&inputs[0], scale_factor),
-            PolyOp::Iff => tensor::ops::iff(&inputs[0], &inputs[1], &inputs[2]),
-            PolyOp::Einsum { equation } => tensor::ops::einsum(equation, &inputs),
-            PolyOp::Identity { .. } => Ok(inputs[0].clone()),
-            PolyOp::Reshape(new_dims) => {
-                let mut t = inputs[0].clone();
-                t.reshape(new_dims)?;
-                Ok(t)
-            }
-            PolyOp::MoveAxis {
-                source,
-                destination,
-            } => inputs[0].move_axis(*source, *destination),
-            PolyOp::Flatten(new_dims) => {
-                let mut t = inputs[0].clone();
-                t.reshape(new_dims)?;
-                Ok(t)
-            }
-            PolyOp::Pad(p) => {
-                if 1 != inputs.len() {
-                    return Err(TensorError::DimMismatch("pad inputs".to_string()));
-                }
-                tensor::ops::pad(&inputs[0], *p)
-            }
-            PolyOp::Add => tensor::ops::add(&inputs),
-            PolyOp::Neg => tensor::ops::neg(&inputs[0]),
-            PolyOp::Sub => tensor::ops::sub(&inputs),
-            PolyOp::Mult => tensor::ops::mult(&inputs),
-            PolyOp::Conv { padding, stride } => tensor::ops::conv(&inputs, *padding, *stride),
-            PolyOp::DeConv {
-                padding,
-                output_padding,
-                stride,
-            } => tensor::ops::deconv(&inputs, *padding, *output_padding, *stride),
-            PolyOp::Pow(u) => {
-                if 1 != inputs.len() {
-                    return Err(TensorError::DimMismatch("pow inputs".to_string()));
-                }
-                inputs[0].pow(*u)
-            }
-            PolyOp::Sum { axes } => {
-                if 1 != inputs.len() {
-                    return Err(TensorError::DimMismatch("sum inputs".to_string()));
-                }
-                tensor::ops::sum_axes(&inputs[0], axes)
-            }
-            PolyOp::Prod { axes, .. } => {
-                if 1 != inputs.len() {
-                    return Err(TensorError::DimMismatch("prod inputs".to_string()));
-                }
-                tensor::ops::prod_axes(&inputs[0], axes)
-            }
-            PolyOp::Concat { axis } => {
-                tensor::ops::concat(&inputs.iter().collect::<Vec<_>>(), *axis)
-            }
-            PolyOp::Slice { axis, start, end } => {
-                if 1 != inputs.len() {
-                    return Err(TensorError::DimMismatch("slice inputs".to_string()));
-                }
-                tensor::ops::slice(&inputs[0], axis, start, end)
-            }
-            PolyOp::GatherElements { dim, constant_idx } => {
-                let x = inputs[0].clone();
-                let y = if let Some(idx) = constant_idx {
-                    idx.clone()
-                } else {
-                    inputs[1].clone().map(|x| felt_to_i128(x) as usize)
-                };
-                tensor::ops::gather_elements(&x, &y, *dim)
-            }
-            PolyOp::GatherND {
-                indices,
-                batch_dims,
-            } => {
-                let x = inputs[0].clone();
-                let y = if let Some(idx) = indices {
-                    idx.clone()
-                } else {
-                    inputs[1].clone().map(|x| felt_to_i128(x) as usize)
-                };
-                tensor::ops::gather_nd(&x, &y, *batch_dims)
-            }
-            PolyOp::ScatterElements { dim, constant_idx } => {
-                let x = inputs[0].clone();
-
-                let idx = if let Some(idx) = constant_idx {
-                    idx.clone()
-                } else {
-                    inputs[1].clone().map(|x| felt_to_i128(x) as usize)
-                };
-
-                let src = if constant_idx.is_some() {
-                    inputs[1].clone()
-                } else {
-                    inputs[2].clone()
-                };
-                tensor::ops::scatter(&x, &idx, &src, *dim)
-            }
-
-            PolyOp::ScatterND { constant_idx } => {
-                let x = inputs[0].clone();
-                let idx = if let Some(idx) = constant_idx {
-                    idx.clone()
-                } else {
-                    inputs[1].clone().map(|x| felt_to_i128(x) as usize)
-                };
-                let src = if constant_idx.is_some() {
-                    inputs[1].clone()
-                } else {
-                    inputs[2].clone()
-                };
-                tensor::ops::scatter_nd(&x, &idx, &src)
-            }
-        }?;
-
-        Ok(ForwardResult { output: res })
-    }
-
     fn layout(
         &self,
         config: &mut crate::circuit::BaseConfig<F>,
@@ -280,6 +184,9 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
             PolyOp::MultiBroadcastTo { shape } => {
                 layouts::expand(config, region, values[..].try_into()?, shape)?
             }
+            PolyOp::MeanOfSquares { axes } => {
+                layouts::mean_of_squares_axes(config, region, values[..].try_into()?, axes)?
+            }
             PolyOp::Xor => layouts::xor(config, region, values[..].try_into()?)?,
             PolyOp::Or => layouts::or(config, region, values[..].try_into()?)?,
             PolyOp::And => layouts::and(config, region, values[..].try_into()?)?,
@@ -306,7 +213,7 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
                 layouts::prod_axes(config, region, values[..].try_into()?, axes)?
             }
             PolyOp::Conv { padding, stride } => {
-                layouts::conv(config, region, values[..].try_into()?, *padding, *stride)?
+                layouts::conv(config, region, values[..].try_into()?, padding, stride)?
             }
             PolyOp::GatherElements { dim, constant_idx } => {
                 if let Some(idx) = constant_idx {
@@ -358,9 +265,9 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
                 config,
                 region,
                 values[..].try_into()?,
-                *padding,
-                *output_padding,
-                *stride,
+                padding,
+                output_padding,
+                stride,
             )?,
             PolyOp::Add => layouts::pairwise(config, region, values[..].try_into()?, BaseOp::Add)?,
             PolyOp::Sub => layouts::pairwise(config, region, values[..].try_into()?, BaseOp::Sub)?,
@@ -376,7 +283,7 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
                     )));
                 }
                 let mut input = values[0].clone();
-                input.pad(*p)?;
+                input.pad(p.clone(), 0)?;
                 input
             }
             PolyOp::Pow(exp) => layouts::pow(config, region, values[..].try_into()?, *exp)?,
@@ -384,11 +291,15 @@ 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)?
+            }
         }))
     }
 
     fn out_scale(&self, in_scales: Vec<crate::Scale>) -> Result<crate::Scale, Box<dyn Error>> {
         let scale = match self {
+            PolyOp::MeanOfSquares { .. } => 2 * in_scales[0],
             PolyOp::Xor | PolyOp::Or | PolyOp::And | PolyOp::Not => 0,
             PolyOp::Iff => in_scales[1],
             PolyOp::Einsum { .. } => {

src/circuit/ops/region.rs

@@ -2,24 +2,28 @@ use crate::{
     circuit::table::Range,
     tensor::{Tensor, TensorError, TensorType, ValTensor, ValType, VarTensor},
 };
+#[cfg(not(target_arch = "wasm32"))]
+use colored::Colorize;
 use halo2_proofs::{
     circuit::Region,
     plonk::{Error, Selector},
 };
 use halo2curves::ff::PrimeField;
+use portable_atomic::AtomicI64 as AtomicInt;
 use std::{
     cell::RefCell,
-    collections::HashSet,
+    collections::{HashMap, HashSet},
     sync::{
         atomic::{AtomicUsize, Ordering},
         Arc, Mutex,
     },
 };
 
-use portable_atomic::AtomicI128 as AtomicInt;
-
 use super::lookup::LookupOp;
 
+/// Constants map
+pub type ConstantsMap<F> = HashMap<F, ValType<F>>;
+
 /// Dynamic lookup index
 #[derive(Clone, Debug, Default)]
 pub struct DynamicLookupIndex {
@@ -120,26 +124,47 @@ impl From<Box<dyn std::error::Error>> for RegionError {
 
 #[derive(Debug)]
 /// A context for a region
-pub struct RegionCtx<'a, F: PrimeField + TensorType + PartialOrd> {
+pub struct RegionCtx<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> {
     region: Option<RefCell<Region<'a, F>>>,
     row: usize,
     linear_coord: usize,
     num_inner_cols: usize,
-    total_constants: usize,
     dynamic_lookup_index: DynamicLookupIndex,
     shuffle_index: ShuffleIndex,
     used_lookups: HashSet<LookupOp>,
     used_range_checks: HashSet<Range>,
-    max_lookup_inputs: i128,
-    min_lookup_inputs: i128,
-    max_range_size: i128,
-    throw_range_check_error: bool,
+    max_lookup_inputs: i64,
+    min_lookup_inputs: i64,
+    max_range_size: i64,
+    witness_gen: bool,
+    check_lookup_range: bool,
+    assigned_constants: ConstantsMap<F>,
 }
 
-impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
+impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a, F> {
+    #[cfg(not(target_arch = "wasm32"))]
     ///
-    pub fn increment_total_constants(&mut self, n: usize) {
-        self.total_constants += n;
+    pub fn debug_report(&self) {
+        log::debug!(
+            "(rows={}, coord={}, constants={}, max_lookup_inputs={}, min_lookup_inputs={}, max_range_size={}, dynamic_lookup_col_coord={}, shuffle_col_coord={})",
+            self.row().to_string().blue(),
+            self.linear_coord().to_string().yellow(),
+            self.total_constants().to_string().red(),
+            self.max_lookup_inputs().to_string().green(),
+            self.min_lookup_inputs().to_string().green(),
+            self.max_range_size().to_string().green(),
+            self.dynamic_lookup_col_coord().to_string().green(),
+            self.shuffle_col_coord().to_string().green());
+    }
+
+    ///
+    pub fn assigned_constants(&self) -> &ConstantsMap<F> {
+        &self.assigned_constants
+    }
+
+    ///
+    pub fn update_constants(&mut self, constants: ConstantsMap<F>) {
+        self.assigned_constants.extend(constants);
     }
 
     ///
@@ -163,8 +188,13 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
     }
 
     ///
-    pub fn throw_range_check_error(&self) -> bool {
-        self.throw_range_check_error
+    pub fn witness_gen(&self) -> bool {
+        self.witness_gen
+    }
+
+    ///
+    pub fn check_lookup_range(&self) -> bool {
+        self.check_lookup_range
     }
 
     /// Create a new region context
@@ -177,7 +207,6 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
             num_inner_cols,
             row,
             linear_coord,
-            total_constants: 0,
             dynamic_lookup_index: DynamicLookupIndex::default(),
             shuffle_index: ShuffleIndex::default(),
             used_lookups: HashSet::new(),
@@ -185,9 +214,23 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
             max_lookup_inputs: 0,
             min_lookup_inputs: 0,
             max_range_size: 0,
-            throw_range_check_error: false,
+            witness_gen: true,
+            check_lookup_range: true,
+            assigned_constants: HashMap::new(),
         }
     }
+
+    /// Create a new region context
+    pub fn new_with_constants(
+        region: Region<'a, F>,
+        row: usize,
+        num_inner_cols: usize,
+        constants: ConstantsMap<F>,
+    ) -> RegionCtx<'a, F> {
+        let mut new_self = Self::new(region, row, num_inner_cols);
+        new_self.assigned_constants = constants;
+        new_self
+    }
     /// Create a new region context from a wrapped region
     pub fn from_wrapped_region(
         region: Option<RefCell<Region<'a, F>>>,
@@ -202,7 +245,6 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
             num_inner_cols,
             linear_coord,
             row,
-            total_constants: 0,
             dynamic_lookup_index,
             shuffle_index,
             used_lookups: HashSet::new(),
@@ -210,7 +252,9 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
             max_lookup_inputs: 0,
             min_lookup_inputs: 0,
             max_range_size: 0,
-            throw_range_check_error: false,
+            witness_gen: false,
+            check_lookup_range: false,
+            assigned_constants: HashMap::new(),
         }
     }
 
@@ -218,7 +262,8 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
     pub fn new_dummy(
         row: usize,
         num_inner_cols: usize,
-        throw_range_check_error: bool,
+        witness_gen: bool,
+        check_lookup_range: bool,
     ) -> RegionCtx<'a, F> {
         let region = None;
         let linear_coord = row * num_inner_cols;
@@ -228,7 +273,6 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
             num_inner_cols,
             linear_coord,
             row,
-            total_constants: 0,
             dynamic_lookup_index: DynamicLookupIndex::default(),
             shuffle_index: ShuffleIndex::default(),
             used_lookups: HashSet::new(),
@@ -236,17 +280,19 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
             max_lookup_inputs: 0,
             min_lookup_inputs: 0,
             max_range_size: 0,
-            throw_range_check_error,
+            witness_gen,
+            check_lookup_range,
+            assigned_constants: HashMap::new(),
         }
     }
 
     /// Create a new region context
-    pub fn new_dummy_with_constants(
+    pub fn new_dummy_with_linear_coord(
         row: usize,
         linear_coord: usize,
-        total_constants: usize,
         num_inner_cols: usize,
-        throw_range_check_error: bool,
+        witness_gen: bool,
+        check_lookup_range: bool,
     ) -> RegionCtx<'a, F> {
         let region = None;
         RegionCtx {
@@ -254,7 +300,6 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
             num_inner_cols,
             linear_coord,
             row,
-            total_constants,
             dynamic_lookup_index: DynamicLookupIndex::default(),
             shuffle_index: ShuffleIndex::default(),
             used_lookups: HashSet::new(),
@@ -262,7 +307,9 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
             max_lookup_inputs: 0,
             min_lookup_inputs: 0,
             max_range_size: 0,
-            throw_range_check_error,
+            witness_gen,
+            check_lookup_range,
+            assigned_constants: HashMap::new(),
         }
     }
 
@@ -312,29 +359,28 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
     ) -> Result<(), RegionError> {
         let row = AtomicUsize::new(self.row());
         let linear_coord = AtomicUsize::new(self.linear_coord());
-        let constants = AtomicUsize::new(self.total_constants());
         let max_lookup_inputs = AtomicInt::new(self.max_lookup_inputs());
         let min_lookup_inputs = AtomicInt::new(self.min_lookup_inputs());
         let lookups = Arc::new(Mutex::new(self.used_lookups.clone()));
         let range_checks = Arc::new(Mutex::new(self.used_range_checks.clone()));
         let dynamic_lookup_index = Arc::new(Mutex::new(self.dynamic_lookup_index.clone()));
         let shuffle_index = Arc::new(Mutex::new(self.shuffle_index.clone()));
+        let constants = Arc::new(Mutex::new(self.assigned_constants.clone()));
 
         *output = output
             .par_enum_map(|idx, _| {
                 // we kick off the loop with the current offset
                 let starting_offset = row.load(Ordering::SeqCst);
                 let starting_linear_coord = linear_coord.load(Ordering::SeqCst);
-                let starting_constants = constants.load(Ordering::SeqCst);
                 // get inner value of the locked lookups
 
                 // we need to make sure that the region is not shared between threads
-                let mut local_reg = Self::new_dummy_with_constants(
+                let mut local_reg = Self::new_dummy_with_linear_coord(
                     starting_offset,
                     starting_linear_coord,
-                    starting_constants,
                     self.num_inner_cols,
-                    self.throw_range_check_error,
+                    self.witness_gen,
+                    self.check_lookup_range,
                 );
                 let res = inner_loop_function(idx, &mut local_reg);
                 // we update the offset and constants
@@ -343,10 +389,6 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
                     local_reg.linear_coord() - starting_linear_coord,
                     Ordering::SeqCst,
                 );
-                constants.fetch_add(
-                    local_reg.total_constants() - starting_constants,
-                    Ordering::SeqCst,
-                );
 
                 max_lookup_inputs.fetch_max(local_reg.max_lookup_inputs(), Ordering::SeqCst);
                 min_lookup_inputs.fetch_min(local_reg.min_lookup_inputs(), Ordering::SeqCst);
@@ -362,11 +404,13 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
                 // update the shuffle index
                 let mut shuffle_index = shuffle_index.lock().unwrap();
                 shuffle_index.update(&local_reg.shuffle_index);
+                // update the constants
+                let mut constants = constants.lock().unwrap();
+                constants.extend(local_reg.assigned_constants);
 
                 res
             })
             .map_err(|e| RegionError::from(format!("dummy_loop: {:?}", e)))?;
-        self.total_constants = constants.into_inner();
         self.linear_coord = linear_coord.into_inner();
         #[allow(trivial_numeric_casts)]
         {
@@ -410,6 +454,14 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
             .map_err(|e| {
                 RegionError::from(format!("dummy_loop: failed to get shuffle index: {:?}", e))
             })?;
+        self.assigned_constants = Arc::try_unwrap(constants)
+            .map_err(|e| {
+                RegionError::from(format!("dummy_loop: failed to get constants: {:?}", e))
+            })?
+            .into_inner()
+            .map_err(|e| {
+                RegionError::from(format!("dummy_loop: failed to get constants: {:?}", e))
+            })?;
 
         Ok(())
     }
@@ -435,7 +487,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
         range: Range,
     ) -> Result<(), Box<dyn std::error::Error>> {
         if range.0 > range.1 {
-            return Err("update_max_min_lookup_range: invalid range".into());
+            return Err(format!("update_max_min_lookup_range: invalid range {:?}", range).into());
         }
 
         let range_size = (range.1 - range.0).abs();
@@ -477,7 +529,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
 
     /// Get the total number of constants
     pub fn total_constants(&self) -> usize {
-        self.total_constants
+        self.assigned_constants.len()
     }
 
     /// Get the dynamic lookup index
@@ -511,40 +563,38 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
     }
 
     /// max lookup inputs
-    pub fn max_lookup_inputs(&self) -> i128 {
+    pub fn max_lookup_inputs(&self) -> i64 {
         self.max_lookup_inputs
     }
 
     /// min lookup inputs
-    pub fn min_lookup_inputs(&self) -> i128 {
+    pub fn min_lookup_inputs(&self) -> i64 {
         self.min_lookup_inputs
     }
 
     /// max range check
-    pub fn max_range_size(&self) -> i128 {
+    pub fn max_range_size(&self) -> i64 {
         self.max_range_size
     }
 
-    /// Assign a constant value
-    pub fn assign_constant(&mut self, var: &VarTensor, value: F) -> Result<ValType<F>, Error> {
-        self.total_constants += 1;
-        if let Some(region) = &self.region {
-            let cell = var.assign_constant(&mut region.borrow_mut(), self.linear_coord, value)?;
-            Ok(cell.into())
-        } else {
-            Ok(value.into())
-        }
-    }
     /// Assign a valtensor to a vartensor
     pub fn assign(
         &mut self,
         var: &VarTensor,
         values: &ValTensor<F>,
     ) -> Result<ValTensor<F>, Error> {
-        self.total_constants += values.num_constants();
         if let Some(region) = &self.region {
-            var.assign(&mut region.borrow_mut(), self.linear_coord, values)
+            var.assign(
+                &mut region.borrow_mut(),
+                self.linear_coord,
+                values,
+                &mut self.assigned_constants,
+            )
         } else {
+            if !values.is_instance() {
+                let values_map = values.create_constants_map_iterator();
+                self.assigned_constants.extend(values_map);
+            }
             Ok(values.clone())
         }
     }
@@ -560,14 +610,18 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
         var: &VarTensor,
         values: &ValTensor<F>,
     ) -> Result<ValTensor<F>, Error> {
-        self.total_constants += values.num_constants();
         if let Some(region) = &self.region {
             var.assign(
                 &mut region.borrow_mut(),
                 self.combined_dynamic_shuffle_coord(),
                 values,
+                &mut self.assigned_constants,
             )
         } else {
+            if !values.is_instance() {
+                let values_map = values.create_constants_map_iterator();
+                self.assigned_constants.extend(values_map);
+            }
             Ok(values.clone())
         }
     }
@@ -594,13 +648,20 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
                 self.linear_coord,
                 values,
                 ommissions,
+                &mut self.assigned_constants,
             )
         } else {
-            self.total_constants += values.num_constants();
             let inner_tensor = values.get_inner_tensor().unwrap();
+            let mut values_map = values.create_constants_map();
+
             for o in ommissions {
-                self.total_constants -= inner_tensor.get_flat_index(**o).is_constant() as usize;
+                if let ValType::Constant(value) = inner_tensor.get_flat_index(**o) {
+                    values_map.remove(&value);
+                }
             }
+
+            self.assigned_constants.extend(values_map);
+
             Ok(values.clone())
         }
     }
@@ -615,24 +676,24 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
     ) -> Result<(ValTensor<F>, usize), Error> {
         if let Some(region) = &self.region {
             // duplicates every nth element to adjust for column overflow
-            let (res, len, total_assigned_constants) = var.assign_with_duplication(
+            let (res, len) = var.assign_with_duplication(
                 &mut region.borrow_mut(),
                 self.row,
                 self.linear_coord,
                 values,
                 check_mode,
                 single_inner_col,
+                &mut self.assigned_constants,
             )?;
-            self.total_constants += total_assigned_constants;
             Ok((res, len))
         } else {
-            let (_, len, total_assigned_constants) = var.dummy_assign_with_duplication(
+            let (_, len) = var.dummy_assign_with_duplication(
                 self.row,
                 self.linear_coord,
                 values,
                 single_inner_col,
+                &mut self.assigned_constants,
             )?;
-            self.total_constants += total_assigned_constants;
             Ok((values.clone(), len))
         }
     }
@@ -699,9 +760,4 @@ impl<'a, F: PrimeField + TensorType + PartialOrd> RegionCtx<'a, F> {
         }
         Ok(())
     }
-
-    /// increment constants
-    pub fn increment_constants(&mut self, n: usize) {
-        self.total_constants += n
-    }
 }

src/circuit/table.rs

@@ -6,24 +6,22 @@ use halo2_proofs::{
     circuit::{Layouter, Value},
     plonk::{ConstraintSystem, Expression, TableColumn},
 };
-use log::warn;
+use log::{debug, warn};
 use maybe_rayon::prelude::{IntoParallelIterator, ParallelIterator};
 
 use crate::{
     circuit::CircuitError,
-    fieldutils::i128_to_felt,
-    tensor::{Tensor, TensorType},
+    fieldutils::i64_to_felt,
+    tensor::{IntoI64, Tensor, TensorType},
 };
 
 use crate::circuit::lookup::LookupOp;
 
-use super::Op;
-
 /// The range of the lookup table.
-pub type Range = (i128, i128);
+pub type Range = (i64, i64);
 
 /// The safety factor for the range of the lookup table.
-pub const RANGE_MULTIPLIER: i128 = 2;
+pub const RANGE_MULTIPLIER: i64 = 2;
 /// The safety factor offset for the number of rows in the lookup table.
 pub const RESERVED_BLINDING_ROWS_PAD: usize = 3;
 
@@ -98,26 +96,25 @@ pub struct Table<F: PrimeField> {
     _marker: PhantomData<F>,
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> Table<F> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Table<F> {
     /// get column index given input
     pub fn get_col_index(&self, input: F) -> F {
         //    range is split up into chunks of size col_size, find the chunk that input is in
         let chunk =
-            (crate::fieldutils::felt_to_i128(input) - self.range.0).abs() / (self.col_size as i128);
+            (crate::fieldutils::felt_to_i64(input) - self.range.0).abs() / (self.col_size as i64);
 
-        i128_to_felt(chunk)
+        i64_to_felt(chunk)
     }
 
     /// get first_element of column
     pub fn get_first_element(&self, chunk: usize) -> (F, F) {
-        let chunk = chunk as i128;
+        let chunk = chunk as i64;
         // we index from 1 to prevent soundness issues
-        let first_element = i128_to_felt(chunk * (self.col_size as i128) + self.range.0);
-        let op_f = Op::<F>::f(
-            &self.nonlinearity,
-            &[Tensor::from(vec![first_element].into_iter())],
-        )
-        .unwrap();
+        let first_element = i64_to_felt(chunk * (self.col_size as i64) + self.range.0);
+        let op_f = self
+            .nonlinearity
+            .f(&[Tensor::from(vec![first_element].into_iter())])
+            .unwrap();
         (first_element, op_f.output[0])
     }
 
@@ -133,12 +130,12 @@ impl<F: PrimeField + TensorType + PartialOrd> Table<F> {
 }
 
 ///
-pub fn num_cols_required(range_len: i128, col_size: usize) -> usize {
+pub fn num_cols_required(range_len: i64, col_size: usize) -> usize {
     // number of cols needed to store the range
-    (range_len / (col_size as i128)) as usize + 1
+    (range_len / (col_size as i64)) as usize + 1
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> Table<F> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Table<F> {
     /// Configures the table.
     pub fn configure(
         cs: &mut ConstraintSystem<F>,
@@ -152,7 +149,7 @@ impl<F: PrimeField + TensorType + PartialOrd> Table<F> {
         // number of cols needed to store the range
         let num_cols = num_cols_required((range.1 - range.0).abs(), col_size);
 
-        log::debug!("table range: {:?}", range);
+        debug!("table range: {:?}", range);
 
         let table_inputs = preexisting_inputs.unwrap_or_else(|| {
             let mut cols = vec![];
@@ -205,8 +202,8 @@ impl<F: PrimeField + TensorType + PartialOrd> Table<F> {
         let smallest = self.range.0;
         let largest = self.range.1;
 
-        let inputs = Tensor::from(smallest..=largest).map(|x| i128_to_felt(x));
-        let evals = Op::<F>::f(&self.nonlinearity, &[inputs.clone()])?;
+        let inputs: Tensor<F> = Tensor::from(smallest..=largest).map(|x| i64_to_felt(x));
+        let evals = self.nonlinearity.f(&[inputs.clone()])?;
         let chunked_inputs = inputs.chunks(self.col_size);
 
         self.is_assigned = true;
@@ -275,12 +272,12 @@ pub struct RangeCheck<F: PrimeField> {
     _marker: PhantomData<F>,
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> RangeCheck<F> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> RangeCheck<F> {
     /// get first_element of column
     pub fn get_first_element(&self, chunk: usize) -> F {
-        let chunk = chunk as i128;
+        let chunk = chunk as i64;
         // we index from 1 to prevent soundness issues
-        i128_to_felt(chunk * (self.col_size as i128) + self.range.0)
+        i64_to_felt(chunk * (self.col_size as i64) + self.range.0)
     }
 
     ///
@@ -297,13 +294,13 @@ impl<F: PrimeField + TensorType + PartialOrd> RangeCheck<F> {
     pub fn get_col_index(&self, input: F) -> F {
         //    range is split up into chunks of size col_size, find the chunk that input is in
         let chunk =
-            (crate::fieldutils::felt_to_i128(input) - self.range.0).abs() / (self.col_size as i128);
+            (crate::fieldutils::felt_to_i64(input) - self.range.0).abs() / (self.col_size as i64);
 
-        i128_to_felt(chunk)
+        i64_to_felt(chunk)
     }
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> RangeCheck<F> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> RangeCheck<F> {
     /// Configures the table.
     pub fn configure(cs: &mut ConstraintSystem<F>, range: Range, logrows: usize) -> RangeCheck<F> {
         log::debug!("range check range: {:?}", range);
@@ -353,7 +350,7 @@ impl<F: PrimeField + TensorType + PartialOrd> RangeCheck<F> {
         let smallest = self.range.0;
         let largest = self.range.1;
 
-        let inputs: Tensor<F> = Tensor::from(smallest..=largest).map(|x| i128_to_felt(x));
+        let inputs: Tensor<F> = Tensor::from(smallest..=largest).map(|x| i64_to_felt(x));
         let chunked_inputs = inputs.chunks(self.col_size);
 
         self.is_assigned = true;

src/circuit/tests.rs

@@ -1048,8 +1048,8 @@ mod conv {
                                 &mut region,
                                 &self.inputs,
                                 Box::new(PolyOp::Conv {
-                                    padding: [(1, 1); 2],
-                                    stride: (2, 2),
+                                    padding: vec![(1, 1); 2],
+                                    stride: vec![2; 2],
                                 }),
                             )
                             .map_err(|_| Error::Synthesis)
@@ -1198,8 +1198,8 @@ mod conv_col_ultra_overflow {
                                 &mut region,
                                 &[self.image.clone(), self.kernel.clone()],
                                 Box::new(PolyOp::Conv {
-                                    padding: [(1, 1); 2],
-                                    stride: (2, 2),
+                                    padding: vec![(1, 1); 2],
+                                    stride: vec![2; 2],
                                 }),
                             )
                             .map_err(|_| Error::Synthesis)
@@ -1343,8 +1343,8 @@ mod conv_relu_col_ultra_overflow {
                                 &mut region,
                                 &[self.image.clone(), self.kernel.clone()],
                                 Box::new(PolyOp::Conv {
-                                    padding: [(1, 1); 2],
-                                    stride: (2, 2),
+                                    padding: vec![(1, 1); 2],
+                                    stride: vec![2; 2],
                                 }),
                             )
                             .map_err(|_| Error::Synthesis);
@@ -1911,6 +1911,8 @@ mod add_with_overflow {
 
 #[cfg(test)]
 mod add_with_overflow_and_poseidon {
+    use std::collections::HashMap;
+
     use halo2curves::bn256::Fr;
 
     use crate::circuit::modules::{
@@ -1969,8 +1971,10 @@ mod add_with_overflow_and_poseidon {
             let poseidon_chip: PoseidonChip<PoseidonSpec, WIDTH, RATE, WIDTH> =
                 PoseidonChip::new(config.poseidon.clone());
 
-            let assigned_inputs_a = poseidon_chip.layout(&mut layouter, &self.inputs[0..1], 0)?;
-            let assigned_inputs_b = poseidon_chip.layout(&mut layouter, &self.inputs[1..2], 1)?;
+            let assigned_inputs_a =
+                poseidon_chip.layout(&mut layouter, &self.inputs[0..1], 0, &mut HashMap::new())?;
+            let assigned_inputs_b =
+                poseidon_chip.layout(&mut layouter, &self.inputs[1..2], 1, &mut HashMap::new())?;
 
             layouter.assign_region(|| "_new_module", |_| Ok(()))?;
 

src/commands.rs

@@ -345,7 +345,7 @@ pub enum Commands {
         target: CalibrationTarget,
         /// the lookup safety margin to use for calibration. if the max lookup is 2^k, then the max lookup will be 2^k * lookup_safety_margin. larger = safer but slower
         #[arg(long, default_value = DEFAULT_LOOKUP_SAFETY_MARGIN)]
-        lookup_safety_margin: i128,
+        lookup_safety_margin: i64,
         /// Optional scales to specifically try for calibration. Example, --scales 0,4
         #[arg(long, value_delimiter = ',', allow_hyphen_values = true)]
         scales: Option<Vec<crate::Scale>>,
@@ -444,7 +444,7 @@ pub enum Commands {
         disable_selector_compression: bool,
         /// commitment used
         #[arg(long, default_value = DEFAULT_COMMITMENT)]
-        commitment: Commitments,
+        commitment: Option<Commitments>,
     },
     /// Aggregates proofs :)
     Aggregate {
@@ -479,7 +479,7 @@ pub enum Commands {
         split_proofs: bool,
         /// commitment used
         #[arg(long, default_value = DEFAULT_COMMITMENT)]
-        commitment: Commitments,
+        commitment: Option<Commitments>,
     },
     /// Compiles a circuit from onnx to a simplified graph (einsum + other ops) and parameters as sets of field elements
     CompileCircuit {
@@ -726,7 +726,7 @@ pub enum Commands {
         logrows: u32,
         /// commitment
         #[arg(long, default_value = DEFAULT_COMMITMENT)]
-        commitment: Commitments,
+        commitment: Option<Commitments>,
     },
     #[cfg(not(target_arch = "wasm32"))]
     /// Deploys an evm verifier that is generated by ezkl