ci: update version string in docs

.github/workflows/engine.yml

@@ -22,7 +22,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: jetli/wasm-pack-action@v0.4.0
@@ -30,7 +30,7 @@ jobs:
         run: rustup target add wasm32-unknown-unknown
 
       - name: Add rust-src
-        run: rustup component add rust-src --toolchain nightly-2024-02-06-x86_64-unknown-linux-gnu
+        run: rustup component add rust-src --toolchain nightly-2024-07-18-x86_64-unknown-linux-gnu
       - name: Install binaryen
         run: |
           set -e

.github/workflows/large-tests.yml

@@ -11,7 +11,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - name: nanoGPT Mock

.github/workflows/pypi.yml

@@ -40,7 +40,7 @@ jobs:
 
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
 
@@ -86,7 +86,7 @@ jobs:
 
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
 

.github/workflows/release.yml

@@ -45,7 +45,7 @@ jobs:
     steps:
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - name: Checkout repo
@@ -106,27 +106,27 @@ jobs:
         include:
           - build: windows-msvc
             os: windows-latest
-            rust: nightly-2024-02-06
+            rust: nightly-2024-07-18
             target: x86_64-pc-windows-msvc
           - build: macos
             os: macos-13
-            rust: nightly-2024-02-06
+            rust: nightly-2024-07-18
             target: x86_64-apple-darwin
           - build: macos-aarch64
             os: macos-13
-            rust: nightly-2024-02-06
+            rust: nightly-2024-07-18
             target: aarch64-apple-darwin
           - build: linux-musl
             os: ubuntu-22.04
-            rust: nightly-2024-02-06
+            rust: nightly-2024-07-18
             target: x86_64-unknown-linux-musl
           - build: linux-gnu
             os: ubuntu-22.04
-            rust: nightly-2024-02-06
+            rust: nightly-2024-07-18
             target: x86_64-unknown-linux-gnu
           - build: linux-aarch64
             os: ubuntu-22.04
-            rust: nightly-2024-02-06
+            rust: nightly-2024-07-18
             target: aarch64-unknown-linux-gnu
 
     steps:

.github/workflows/rust.yml

@@ -26,7 +26,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - name: Build
@@ -38,7 +38,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - name: Docs
@@ -50,7 +50,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           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-2024-02-06
+          toolchain: nightly-2024-07-18
           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-2024-02-06
+          toolchain: nightly-2024-07-18
           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-2024-02-06
+          toolchain: nightly-2024-07-18
           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-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -189,7 +189,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           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-2024-02-06-x86_64-unknown-linux-gnu
+        run: rustup component add rust-src --toolchain nightly-2024-07-18-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
@@ -212,7 +212,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -229,13 +229,15 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
         with:
           crate: cargo-nextest
           locked: true
+      # - name: The Worm Mock
+      #   run: cargo nextest run --release --verbose tests::large_mock_::large_tests_5_expects -- --include-ignored
       - name: public outputs and tolerance > 0
         run: cargo nextest run --release --verbose tests::mock_tolerance_public_outputs_ --test-threads 32
       - name: public outputs + batch size == 10
@@ -258,6 +260,8 @@ jobs:
         run: cargo nextest run --release --verbose tests::mock_hashed_input_::t  --test-threads 32
       - name: hashed params
         run: cargo nextest run --release --verbose tests::mock_hashed_params_::t  --test-threads 32
+      - name: hashed params public inputs
+        run: cargo nextest run --release --verbose tests::mock_hashed_params_public_inputs_::t  --test-threads 32
       - name: hashed outputs
         run: cargo nextest run --release --verbose tests::mock_hashed_output_::t  --test-threads 32
       - name: hashed inputs + params + outputs
@@ -286,7 +290,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -304,7 +308,7 @@ jobs:
           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
+        run: rustup component add rust-src --toolchain nightly-2024-07-18-x86_64-unknown-linux-gnu
       - name: Install dependencies for js tests and in-browser-evm-verifier package
         run: |
           pnpm install --frozen-lockfile
@@ -326,7 +330,7 @@ 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 c2233ec9fe61e0920c61c6d779bc707252852037 --profile local --locked anvil --force
+        run: cargo install --git https://github.com/foundry-rs/foundry --rev 62cdea8ff9e6efef011f77e295823b5f2dbeb3a1 --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)
@@ -365,7 +369,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: jetli/wasm-pack-action@v0.4.0
@@ -373,7 +377,7 @@ jobs:
         run: rustup target add wasm32-unknown-unknown
 
       - name: Add rust-src
-        run: rustup component add rust-src --toolchain nightly-2024-02-06-x86_64-unknown-linux-gnu
+        run: rustup component add rust-src --toolchain nightly-2024-07-18-x86_64-unknown-linux-gnu
       - uses: actions/checkout@v3
       - name: Use pnpm 8
         uses: pnpm/action-setup@v2
@@ -439,11 +443,11 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - name: Add rust-src
-        run: rustup component add rust-src --toolchain nightly-2024-02-06-x86_64-unknown-linux-gnu
+        run: rustup component add rust-src --toolchain nightly-2024-07-18-x86_64-unknown-linux-gnu
       - uses: actions/checkout@v3
       - uses: baptiste0928/cargo-install@v1
         with:
@@ -473,7 +477,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -491,7 +495,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -508,7 +512,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -525,7 +529,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -535,7 +539,7 @@ 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 c2233ec9fe61e0920c61c6d779bc707252852037 --profile local --locked anvil --force
+        run: cargo install --git https://github.com/foundry-rs/foundry --rev 62cdea8ff9e6efef011f77e295823b5f2dbeb3a1 --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
 
@@ -546,7 +550,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -566,7 +570,7 @@ jobs:
           python-version: "3.12"
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - name: Install cmake
@@ -576,7 +580,7 @@ jobs:
       - name: Setup Virtual Env and Install python dependencies
         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 c2233ec9fe61e0920c61c6d779bc707252852037 --profile local --locked anvil --force
+        run: cargo install --git https://github.com/foundry-rs/foundry --rev 62cdea8ff9e6efef011f77e295823b5f2dbeb3a1 --locked anvil --force
       - name: Build python ezkl
         run: source .env/bin/activate; unset CONDA_PREFIX; maturin develop --features python-bindings --release
       - name: Run pytest
@@ -592,7 +596,7 @@ jobs:
           python-version: "3.12"
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -641,7 +645,7 @@ jobs:
           python-version: "3.11"
       - uses: actions-rs/toolchain@v1
         with:
-          toolchain: nightly-2024-02-06
+          toolchain: nightly-2024-07-18
           override: true
           components: rustfmt, clippy
       - uses: baptiste0928/cargo-install@v1
@@ -651,7 +655,7 @@ 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 c2233ec9fe61e0920c61c6d779bc707252852037 --profile local --locked anvil --force
+        run: cargo install --git https://github.com/foundry-rs/foundry --rev 62cdea8ff9e6efef011f77e295823b5f2dbeb3a1 --locked anvil --force
       - name: Install pip
         run: python -m ensurepip --upgrade
       - name: Setup Virtual Env and Install python dependencies

Cargo.toml

@@ -39,7 +39,6 @@ snark-verifier = { git = "https://github.com/zkonduit/snark-verifier", branch =
 halo2_solidity_verifier = { git = "https://github.com/alexander-camuto/halo2-solidity-verifier", branch = "main" }
 maybe-rayon = { version = "0.1.1", default_features = false }
 bincode = { version = "1.3.3", default_features = false }
-ark-std = { version = "^0.3.0", default-features = false }
 unzip-n = "0.1.2"
 num = "0.4.1"
 portable-atomic = "1.6.0"
@@ -63,16 +62,13 @@ reqwest = { version = "0.12.4", default-features = false, features = [
 openssl = { version = "0.10.55", features = ["vendored"] }
 tokio-postgres = "0.7.10"
 pg_bigdecimal = "0.1.5"
-futures-util = "0.3.30"
 lazy_static = "1.4.0"
 colored_json = { version = "3.0.1", default_features = false, optional = true }
-plotters = { version = "0.3.0", default_features = false, optional = true }
 regex = { version = "1", default_features = false }
 tokio = { version = "1.35", default_features = false, features = [
     "macros",
     "rt-multi-thread"
 ] }
-tokio-util = { version = "0.7.9", features = ["codec"] }
 pyo3 = { version = "0.21.2", features = [
     "extension-module",
     "abi3-py37",
@@ -83,9 +79,8 @@ pyo3-asyncio = { git = "https://github.com/jopemachine/pyo3-asyncio/", branch="m
     "tokio-runtime",
 ], default_features = false, optional = true }
 pyo3-log = { version = "0.10.0", default_features = false, optional = true }
-tract-onnx = { git = "https://github.com/sonos/tract/", rev = "05ebf550aa9922b221af4635c21a67a8d2af12a9", default_features = false, optional = true }
+tract-onnx = { git = "https://github.com/sonos/tract/", rev = "40c64319291184814d9fea5fdf4fa16f5a4f7116", default_features = false, optional = true }
 tabled = { version = "0.12.0", optional = true }
-
 objc = { version = "0.2.4", optional = true }
 
 
@@ -108,8 +103,10 @@ console_error_panic_hook = "0.1.7"
 wasm-bindgen-console-logger = "0.1.1"
 
 
+[target.'cfg(not(all(target_arch = "wasm32", target_os = "unknown")))'.dev-dependencies]
+criterion = { version = "0.5.1", features = ["html_reports"] }
+
 [dev-dependencies]
-criterion = { version = "0.3", features = ["html_reports"] }
 tempfile = "3.3.0"
 lazy_static = "1.4.0"
 mnist = "0.5"
@@ -180,7 +177,7 @@ required-features = ["ezkl"]
 
 [features]
 web = ["wasm-bindgen-rayon"]
-default = ["ezkl", "mv-lookup", "no-banner"]
+default = ["ezkl", "mv-lookup", "no-banner", "parallel-poly-read"]
 onnx = ["dep:tract-onnx"]
 python-bindings = ["pyo3", "pyo3-log", "pyo3-asyncio"]
 ezkl = [
@@ -194,6 +191,7 @@ ezkl = [
     "colored_json",
     "halo2_proofs/circuit-params",
 ]
+parallel-poly-read = ["halo2_proofs/parallel-poly-read"]
 mv-lookup = [
     "halo2_proofs/mv-lookup",
     "snark-verifier/mv-lookup",
@@ -211,7 +209,7 @@ metal = ["dep:metal", "dep:objc"]
 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" }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2#098ac0ef3b29255e0e2524ecbb4e7e325ae6e7fd", package = "halo2_proofs" }
 
 
 [profile.release]

benches/accum_conv.rs

@@ -72,6 +72,7 @@ impl Circuit<Fr> for MyCircuit {
                         Box::new(PolyOp::Conv {
                             padding: vec![(0, 0)],
                             stride: vec![1; 2],
+                            group: 1,
                         }),
                     )
                     .unwrap();

benches/relu.rs

@@ -2,6 +2,7 @@ use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Through
 use ezkl::circuit::region::RegionCtx;
 use ezkl::circuit::table::Range;
 use ezkl::circuit::{ops::lookup::LookupOp, BaseConfig as Config, CheckMode};
+use ezkl::fieldutils::IntegerRep;
 use ezkl::pfsys::create_proof_circuit;
 use ezkl::pfsys::TranscriptType;
 use ezkl::pfsys::{create_keys, srs::gen_srs};
@@ -84,7 +85,7 @@ fn runrelu(c: &mut Criterion) {
         };
 
         let input: Tensor<Value<Fr>> =
-            Tensor::<i32>::from((0..len).map(|_| rng.gen_range(0..10))).into();
+            Tensor::<IntegerRep>::from((0..len).map(|_| rng.gen_range(0..10))).into();
 
         let circuit = NLCircuit {
             input: ValTensor::from(input.clone()),

docs/python/requirements-docs.txt

@@ -1,4 +1,4 @@
-ezkl==0.0.0
+ezkl==12.0.2
 sphinx
 sphinx-rtd-theme
 sphinxcontrib-napoleon

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '0.0.0'
+release = '12.0.2'
 version = release
 
 

examples/conv2d_mnist/main.rs

@@ -2,8 +2,7 @@ use ezkl::circuit::region::RegionCtx;
 use ezkl::circuit::{
     ops::lookup::LookupOp, ops::poly::PolyOp, BaseConfig as PolyConfig, CheckMode,
 };
-use ezkl::fieldutils;
-use ezkl::fieldutils::i32_to_felt;
+use ezkl::fieldutils::{self, integer_rep_to_felt, IntegerRep};
 use ezkl::tensor::*;
 use halo2_proofs::dev::MockProver;
 use halo2_proofs::poly::commitment::Params;
@@ -42,8 +41,8 @@ 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: i64,
-    const LOOKUP_MAX: i64,
+    const LOOKUP_MIN: IntegerRep,
+    const LOOKUP_MAX: IntegerRep,
     // Convolution
     const KERNEL_HEIGHT: usize,
     const KERNEL_WIDTH: usize,
@@ -66,8 +65,8 @@ struct Config<
 struct MyCircuit<
     const LEN: usize, //LEN = CHOUT x OH x OW flattened
     const CLASSES: usize,
-    const LOOKUP_MIN: i64,
-    const LOOKUP_MAX: i64,
+    const LOOKUP_MIN: IntegerRep,
+    const LOOKUP_MAX: IntegerRep,
     // Convolution
     const KERNEL_HEIGHT: usize,
     const KERNEL_WIDTH: usize,
@@ -90,8 +89,8 @@ struct MyCircuit<
 impl<
         const LEN: usize,
         const CLASSES: usize,
-        const LOOKUP_MIN: i64,
-        const LOOKUP_MAX: i64,
+        const LOOKUP_MIN: IntegerRep,
+        const LOOKUP_MAX: IntegerRep,
         // Convolution
         const KERNEL_HEIGHT: usize,
         const KERNEL_WIDTH: usize,
@@ -205,6 +204,7 @@ where
                     let op = PolyOp::Conv {
                         padding: vec![(PADDING, PADDING); 2],
                         stride: vec![STRIDE; 2],
+                        group: 1,
                     };
                     let x = config
                         .layer_config
@@ -315,7 +315,11 @@ pub fn runconv() {
         .test_set_length(10_000)
         .finalize();
 
-    let mut train_data = Tensor::from(trn_img.iter().map(|x| i32_to_felt::<F>(*x as i32 / 16)));
+    let mut train_data = Tensor::from(
+        trn_img
+            .iter()
+            .map(|x| integer_rep_to_felt::<F>(*x as IntegerRep / 16)),
+    );
     train_data.reshape(&[50_000, 28, 28]).unwrap();
 
     let mut train_labels = Tensor::from(trn_lbl.iter().map(|x| *x as f32));
@@ -343,8 +347,8 @@ pub fn runconv() {
             .map(|fl| {
                 let dx = fl * 32_f32;
                 let rounded = dx.round();
-                let integral: i32 = unsafe { rounded.to_int_unchecked() };
-                fieldutils::i32_to_felt(integral)
+                let integral: IntegerRep = unsafe { rounded.to_int_unchecked() };
+                fieldutils::integer_rep_to_felt(integral)
             }),
     );
 
@@ -355,7 +359,8 @@ pub fn runconv() {
 
     let l0_kernels = l0_kernels.try_into().unwrap();
 
-    let mut l0_bias = Tensor::<F>::from((0..OUT_CHANNELS).map(|_| fieldutils::i32_to_felt(0)));
+    let mut l0_bias =
+        Tensor::<F>::from((0..OUT_CHANNELS).map(|_| fieldutils::integer_rep_to_felt(0)));
     l0_bias.set_visibility(&ezkl::graph::Visibility::Private);
 
     let l0_bias = l0_bias.try_into().unwrap();
@@ -363,8 +368,8 @@ pub fn runconv() {
     let mut l2_biases = Tensor::<F>::from(myparams.biases.into_iter().map(|fl| {
         let dx = fl * 32_f32;
         let rounded = dx.round();
-        let integral: i32 = unsafe { rounded.to_int_unchecked() };
-        fieldutils::i32_to_felt(integral)
+        let integral: IntegerRep = unsafe { rounded.to_int_unchecked() };
+        fieldutils::integer_rep_to_felt(integral)
     }));
     l2_biases.set_visibility(&ezkl::graph::Visibility::Private);
     l2_biases.reshape(&[l2_biases.len(), 1]).unwrap();
@@ -374,8 +379,8 @@ pub fn runconv() {
     let mut l2_weights = Tensor::<F>::from(myparams.weights.into_iter().flatten().map(|fl| {
         let dx = fl * 32_f32;
         let rounded = dx.round();
-        let integral: i32 = unsafe { rounded.to_int_unchecked() };
-        fieldutils::i32_to_felt(integral)
+        let integral: IntegerRep = unsafe { rounded.to_int_unchecked() };
+        fieldutils::integer_rep_to_felt(integral)
     }));
     l2_weights.set_visibility(&ezkl::graph::Visibility::Private);
     l2_weights.reshape(&[CLASSES, LEN]).unwrap();
@@ -401,13 +406,13 @@ pub fn runconv() {
         l2_params: [l2_weights, l2_biases],
     };
 
-    let public_input: Tensor<i32> = vec![
-        -25124i32, -19304, -16668, -4399, -6209, -4548, -2317, -8349, -6117, -23461,
+    let public_input: Tensor<IntegerRep> = vec![
+        -25124, -19304, -16668, -4399, -6209, -4548, -2317, -8349, -6117, -23461,
     ]
     .into_iter()
     .into();
 
-    let pi_inner: Tensor<F> = public_input.map(i32_to_felt::<F>);
+    let pi_inner: Tensor<F> = public_input.map(integer_rep_to_felt::<F>);
 
     println!("MOCK PROVING");
     let now = Instant::now();

examples/mlp_4d_einsum.rs

@@ -2,7 +2,7 @@ use ezkl::circuit::region::RegionCtx;
 use ezkl::circuit::{
     ops::lookup::LookupOp, ops::poly::PolyOp, BaseConfig as PolyConfig, CheckMode,
 };
-use ezkl::fieldutils::i32_to_felt;
+use ezkl::fieldutils::{integer_rep_to_felt, IntegerRep};
 use ezkl::tensor::*;
 use halo2_proofs::dev::MockProver;
 use halo2_proofs::{
@@ -23,8 +23,8 @@ struct MyConfig {
 #[derive(Clone)]
 struct MyCircuit<
     const LEN: usize, //LEN = CHOUT x OH x OW flattened
-    const LOOKUP_MIN: i64,
-    const LOOKUP_MAX: i64,
+    const LOOKUP_MIN: IntegerRep,
+    const LOOKUP_MAX: IntegerRep,
 > {
     // 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: i64, const LOOKUP_MAX: i64> Circuit<F>
+impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRep> Circuit<F>
     for MyCircuit<LEN, LOOKUP_MIN, LOOKUP_MAX>
 {
     type Config = MyConfig;
@@ -215,33 +215,33 @@ pub fn runmlp() {
     #[cfg(not(target_arch = "wasm32"))]
     env_logger::init();
     // parameters
-    let mut l0_kernel: Tensor<F> = Tensor::<i32>::new(
+    let mut l0_kernel: Tensor<F> = Tensor::<IntegerRep>::new(
         Some(&[10, 0, 0, -1, 0, 10, 1, 0, 0, 1, 10, 0, 1, 0, 0, 10]),
         &[4, 4],
     )
     .unwrap()
-    .map(i32_to_felt);
+    .map(integer_rep_to_felt);
     l0_kernel.set_visibility(&ezkl::graph::Visibility::Private);
 
-    let mut l0_bias: Tensor<F> = Tensor::<i32>::new(Some(&[0, 0, 0, 1]), &[4, 1])
+    let mut l0_bias: Tensor<F> = Tensor::<IntegerRep>::new(Some(&[0, 0, 0, 1]), &[4, 1])
         .unwrap()
-        .map(i32_to_felt);
+        .map(integer_rep_to_felt);
     l0_bias.set_visibility(&ezkl::graph::Visibility::Private);
 
-    let mut l2_kernel: Tensor<F> = Tensor::<i32>::new(
+    let mut l2_kernel: Tensor<F> = Tensor::<IntegerRep>::new(
         Some(&[0, 3, 10, -1, 0, 10, 1, 0, 0, 1, 0, 12, 1, -2, 32, 0]),
         &[4, 4],
     )
     .unwrap()
-    .map(i32_to_felt);
+    .map(integer_rep_to_felt);
     l2_kernel.set_visibility(&ezkl::graph::Visibility::Private);
     // input data, with 1 padding to allow for bias
-    let input: Tensor<Value<F>> = Tensor::<i32>::new(Some(&[-30, -21, 11, 40]), &[4, 1])
+    let input: Tensor<Value<F>> = Tensor::<IntegerRep>::new(Some(&[-30, -21, 11, 40]), &[4, 1])
         .unwrap()
         .into();
-    let mut l2_bias: Tensor<F> = Tensor::<i32>::new(Some(&[0, 0, 0, 1]), &[4, 1])
+    let mut l2_bias: Tensor<F> = Tensor::<IntegerRep>::new(Some(&[0, 0, 0, 1]), &[4, 1])
         .unwrap()
-        .map(i32_to_felt);
+        .map(integer_rep_to_felt);
     l2_bias.set_visibility(&ezkl::graph::Visibility::Private);
 
     let circuit = MyCircuit::<4, -8192, 8192> {
@@ -251,12 +251,12 @@ pub fn runmlp() {
         _marker: PhantomData,
     };
 
-    let public_input: Vec<i32> = unsafe {
+    let public_input: Vec<IntegerRep> = unsafe {
         vec![
-            (531f32 / 128f32).round().to_int_unchecked::<i32>(),
-            (103f32 / 128f32).round().to_int_unchecked::<i32>(),
-            (4469f32 / 128f32).round().to_int_unchecked::<i32>(),
-            (2849f32 / 128f32).to_int_unchecked::<i32>(),
+            (531f32 / 128f32).round().to_int_unchecked::<IntegerRep>(),
+            (103f32 / 128f32).round().to_int_unchecked::<IntegerRep>(),
+            (4469f32 / 128f32).round().to_int_unchecked::<IntegerRep>(),
+            (2849f32 / 128f32).to_int_unchecked::<IntegerRep>(),
         ]
     };
 
@@ -265,7 +265,10 @@ pub fn runmlp() {
     let prover = MockProver::run(
         K as u32,
         &circuit,
-        vec![public_input.iter().map(|x| i32_to_felt::<F>(*x)).collect()],
+        vec![public_input
+            .iter()
+            .map(|x| integer_rep_to_felt::<F>(*x))
+            .collect()],
     )
     .unwrap();
     prover.assert_satisfied();

examples/notebooks/svm.ipynb

@@ -39,7 +39,7 @@
                 "import json\n",
                 "import numpy as np\n",
                 "from sklearn.svm import SVC\n",
-                "import sk2torch\n",
+                "from hummingbird.ml import convert\n",
                 "import torch\n",
                 "import ezkl\n",
                 "import os\n",
@@ -59,11 +59,11 @@
                 "# Train an SVM on the data and wrap it in PyTorch.\n",
                 "sk_model = SVC(probability=True)\n",
                 "sk_model.fit(xs, ys)\n",
-                "model = sk2torch.wrap(sk_model)\n",
-                "\n",
+                "model = convert(sk_model, \"torch\").model\n",
                 "\n",
                 "\n",
                 "\n",
+                "model\n",
                 "\n"
             ]
         },
@@ -84,33 +84,6 @@
                 "data_path = os.path.join('input.json')"
             ]
         },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "7f0ca328",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import matplotlib.pyplot as plt\n",
-                "# Create a coordinate grid to compute a vector field on.\n",
-                "spaced = np.linspace(-2, 2, num=25)\n",
-                "grid_xs = torch.tensor([[x, y] for x in spaced for y in spaced], requires_grad=True)\n",
-                "\n",
-                "\n",
-                "# Compute the gradients of the SVM output.\n",
-                "outputs = model.predict_proba(grid_xs)[:, 1]\n",
-                "(input_grads,) = torch.autograd.grad(outputs.sum(), (grid_xs,))\n",
-                "\n",
-                "\n",
-                "# Create a quiver plot of the vector field.\n",
-                "plt.quiver(\n",
-                "    grid_xs[:, 0].detach().numpy(),\n",
-                "    grid_xs[:, 1].detach().numpy(),\n",
-                "    input_grads[:, 0].detach().numpy(),\n",
-                "    input_grads[:, 1].detach().numpy(),\n",
-                ")\n"
-            ]
-        },
         {
             "cell_type": "code",
             "execution_count": null,
@@ -119,14 +92,14 @@
             "outputs": [],
             "source": [
                 "\n",
-                "\n",
+                "spaced = np.linspace(-2, 2, num=25)\n",
+                "grid_xs = torch.tensor([[x, y] for x in spaced for y in spaced], requires_grad=True)\n",
                 "# export to onnx format\n",
                 "# !!!!!!!!!!!!!!!!! This will flash a warning but it is fine !!!!!!!!!!!!!!!!!!!!!\n",
                 "\n",
                 "# Input to the model\n",
                 "shape = xs.shape[1:]\n",
                 "x = grid_xs[0:1]\n",
-                "torch_out = model.predict(x)\n",
                 "# Export the model\n",
                 "torch.onnx.export(model,               # model being run\n",
                 "                  # model input (or a tuple for multiple inputs)\n",
@@ -143,9 +116,7 @@
                 "\n",
                 "d = ((x).detach().numpy()).reshape([-1]).tolist()\n",
                 "\n",
-                "data = dict(input_shapes=[shape],\n",
-                "            input_data=[d],\n",
-                "            output_data=[o.reshape([-1]).tolist() for o in torch_out])\n",
+                "data = dict(input_data=[d])\n",
                 "\n",
                 "# Serialize data into file:\n",
                 "json.dump(data, open(\"input.json\", 'w'))\n"
@@ -167,6 +138,7 @@
         {
             "cell_type": "code",
             "execution_count": null,
+            "id": "0bee4d7f",
             "metadata": {},
             "outputs": [],
             "source": [
@@ -220,7 +192,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 10,
+            "execution_count": null,
             "id": "b1c561a8",
             "metadata": {},
             "outputs": [],
@@ -441,9 +413,9 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.3"
         }
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}

examples/onnx/1l_conv_transpose/settings.json

@@ -0,0 +1 @@
+{"run_args":{"tolerance":{"val":0.0,"scale":1.0},"input_scale":7,"param_scale":7,"scale_rebase_multiplier":10,"lookup_range":[0,0],"logrows":13,"variables":[["batch_size",1]],"input_visibility":"Private","output_visibility":"Public","param_visibility":"Private"},"num_constraints":5619,"total_const_size":513,"model_instance_shapes":[[1,3,10,10]],"model_output_scales":[14],"model_input_scales":[7],"module_sizes":{"kzg":[],"poseidon":[0,[0]],"elgamal":[0,[0]]},"required_lookups":[],"check_mode":"UNSAFE","version":"0.0.0","num_blinding_factors":null}

examples/onnx/smallworm/.gitattributes

@@ -0,0 +1 @@
+network.onnx filter=lfs diff=lfs merge=lfs -text

examples/onnx/smallworm/Readme.md

@@ -0,0 +1,47 @@
+## The worm 
+
+This is an onnx file for a [WormVAE](https://github.com/TuragaLab/wormvae?tab=readme-ov-file) model, which is a VAE / latent-space representation of the C. elegans connectome.
+
+The model "is a large-scale latent variable model with a very high-dimensional latent space
+consisting of voltage dynamics of 300 neurons over 5 minutes of time at the simulation frequency
+of 160 Hz. The generative model for these latent variables is described by stochastic differential
+equations modeling the nonlinear dynamics of the network activity." (see [here](https://openreview.net/pdf?id=CJzi3dRlJE-)). 
+
+In effect this is a generative model for a worm's voltage dynamics, which can be used to generate new worm-like voltage dynamics given previous connectome state.
+
+Using ezkl you can create a zk circuit equivalent to the wormvae model, allowing you to "prove" execution of the worm model. If you're feeling particularly adventurous, you can also use the zk circuit to generate new worm-state that can be verified on chain. 
+
+To do so you'll first want to fetch the files using git-lfs (as the onnx file is too large to be stored in git). 
+
+```bash
+git lfs fetch --all
+```
+
+You'll then want to use the usual ezkl loop to generate the zk circuit. We recommend using fixed visibility for the model parameters, as the model is quite large and this will prune the circuit significantly. 
+
+```bash
+ezkl gen-settings --param-visibility=fixed
+cp input.json calibration.json
+ezkl calibrate-settings
+ezkl compile-circuit
+ezkl gen-witness
+ezkl prove
+```
+
+You might also need to aggregate the proof to get it to fit on chain.
+
+```bash
+ezkl aggregate
+```
+
+You can then create a smart contract that verifies this aggregate proof
+
+```bash
+ezkl create-evm-verifier-aggr
+```
+
+This can then be deployed on the chain of your choice.
+
+
+> Note: the model is large and thus we recommend a machine with at least 512GB of RAM to run the above commands. If you're ever compute constrained you can always use the lilith service to generate the zk circuit. Message us on discord or telegram for more details :)
+

examples/onnx/smallworm/network.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f88c5901d3768ec21e3cf2f2840d255e84fa13c364df86b24d960cca3333769
+size 82095882

examples/onnx/smallworm/settings.json

@@ -0,0 +1 @@
+{"run_args":{"tolerance":{"val":0.0,"scale":1.0},"input_scale":0,"param_scale":6,"scale_rebase_multiplier":1,"lookup_range":[-32768,32768],"logrows":17,"variables":[["batch_size",1]],"input_visibility":"Private","output_visibility":"Public","param_visibility":"Fixed"},"num_constraints":367422820,"total_const_size":365577160,"model_instance_shapes":[[1,300,1200]],"model_output_scales":[6],"model_input_scales":[0,0,0],"module_sizes":{"kzg":[],"poseidon":[0,[0]],"elgamal":[0,[0]]},"required_lookups":[{"Div":{"denom":64.0}},"ReLU",{"Ln":{"scale":64.0}},{"Exp":{"scale":64.0}}],"check_mode":"UNSAFE","version":"0.0.0","num_blinding_factors":null}

rust-toolchain

@@ -1,3 +1,3 @@
 [toolchain]
-channel = "nightly-2024-02-06"
+channel = "nightly-2024-07-18"
 components = ["rustfmt", "clippy"]

src/circuit/ops/chip.rs

@@ -22,7 +22,7 @@ use crate::{
         table::{Range, RangeCheck, Table},
         utils,
     },
-    tensor::{IntoI64, Tensor, TensorType, ValTensor, VarTensor},
+    tensor::{Tensor, TensorType, ValTensor, VarTensor},
 };
 use std::{collections::BTreeMap, marker::PhantomData};
 
@@ -327,7 +327,7 @@ pub struct BaseConfig<F: PrimeField + TensorType + PartialOrd> {
     _marker: PhantomData<F>,
 }
 
-impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> BaseConfig<F> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
     /// Returns a new [BaseConfig] with no inputs, no selectors, and no tables.
     pub fn dummy(col_size: usize, num_inner_cols: usize) -> Self {
         Self {

src/circuit/ops/errors.rs

@@ -1,6 +1,6 @@
 use std::convert::Infallible;
 
-use crate::tensor::TensorError;
+use crate::{fieldutils::IntegerRep, tensor::TensorError};
 use halo2_proofs::plonk::Error as PlonkError;
 use thiserror::Error;
 
@@ -57,7 +57,7 @@ pub enum CircuitError {
     InvalidConversion(#[from] Infallible),
     /// Invalid min/max lookup range
     #[error("invalid min/max lookup range: min: {0}, max: {1}")]
-    InvalidMinMaxRange(i64, i64),
+    InvalidMinMaxRange(IntegerRep, IntegerRep),
     /// Missing product in einsum
     #[error("missing product in einsum")]
     MissingEinsumProduct,
@@ -81,7 +81,7 @@ pub enum CircuitError {
     MissingSelectors(String),
     /// Table lookup error
     #[error("value ({0}) out of range: ({1}, {2})")]
-    TableOOR(i64, i64, i64),
+    TableOOR(IntegerRep, IntegerRep, IntegerRep),
     /// Loookup not configured
     #[error("lookup not configured: {0}")]
     LookupNotConfigured(String),

src/circuit/ops/hybrid.rs

@@ -1,7 +1,7 @@
 use super::*;
 use crate::{
     circuit::{layouts, utils, Tolerance},
-    fieldutils::i64_to_felt,
+    fieldutils::integer_rep_to_felt,
     graph::multiplier_to_scale,
     tensor::{self, Tensor, TensorType, ValTensor},
 };
@@ -71,7 +71,7 @@ pub enum HybridOp {
     },
 }
 
-impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Op<F> for HybridOp {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash > Op<F> for HybridOp {
     ///
     fn requires_homogenous_input_scales(&self) -> Vec<usize> {
         match self {
@@ -184,8 +184,8 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Op<F>
                         config,
                         region,
                         values[..].try_into()?,
-                        i64_to_felt(input_scale.0 as i64),
-                        i64_to_felt(output_scale.0 as i64),
+                        integer_rep_to_felt(input_scale.0 as i128),
+                        integer_rep_to_felt(output_scale.0 as i128),
                     )?
                 } else {
                     layouts::nonlinearity(
@@ -209,7 +209,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Op<F>
                         config,
                         region,
                         values[..].try_into()?,
-                        i64_to_felt(denom.0 as i64),
+                        integer_rep_to_felt(denom.0 as i128),
                     )?
                 } else {
                     layouts::nonlinearity(

src/circuit/ops/lookup.rs

@@ -3,9 +3,9 @@ use serde::{Deserialize, Serialize};
 
 use crate::{
     circuit::{layouts, table::Range, utils},
-    fieldutils::{felt_to_i64, i64_to_felt},
+    fieldutils::{felt_to_integer_rep, integer_rep_to_felt, IntegerRep},
     graph::multiplier_to_scale,
-    tensor::{self, IntoI64, Tensor, TensorError, TensorType},
+    tensor::{self, Tensor, TensorError, TensorType},
 };
 
 use super::Op;
@@ -131,16 +131,16 @@ 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 i64;
+        let range = range as IntegerRep;
         (-range, range)
     }
 
     /// Matches a [Op] to an operation in the `tensor::ops` module.
-    pub(crate) fn f<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64>(
+    pub(crate) fn f<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
         &self,
         x: &[Tensor<F>],
     ) -> Result<ForwardResult<F>, TensorError> {
-        let x = x[0].clone().map(|x| felt_to_i64(x));
+        let x = x[0].clone().map(|x| felt_to_integer_rep(x));
         let res = match &self {
             LookupOp::Abs => Ok(tensor::ops::abs(&x)?),
             LookupOp::Ceil { scale } => Ok(tensor::ops::nonlinearities::ceil(&x, scale.into())),
@@ -227,13 +227,13 @@ impl LookupOp {
             }
         }?;
 
-        let output = res.map(|x| i64_to_felt(x));
+        let output = res.map(|x| integer_rep_to_felt(x));
 
         Ok(ForwardResult { output })
     }
 }
 
-impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Op<F> for LookupOp {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for LookupOp {
     /// Returns a reference to the Any trait.
     fn as_any(&self) -> &dyn Any {
         self

src/circuit/ops/mod.rs

@@ -4,7 +4,7 @@ use serde::{Deserialize, Serialize};
 
 use crate::{
     graph::quantize_tensor,
-    tensor::{self, IntoI64, Tensor, TensorType, ValTensor},
+    tensor::{self, Tensor, TensorType, ValTensor},
 };
 use halo2curves::ff::PrimeField;
 
@@ -31,12 +31,12 @@ pub use errors::CircuitError;
 
 /// 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 + std::hash::Hash + IntoI64> {
+pub struct ForwardResult<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> {
     pub(crate) output: Tensor<F>,
 }
 
 /// A trait representing operations that can be represented as constraints in a circuit.
-pub trait Op<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64>:
+pub trait Op<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>:
     std::fmt::Debug + Send + Sync + Any
 {
     /// Returns a string representation of the operation.
@@ -75,7 +75,7 @@ pub trait Op<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64
     fn as_any(&self) -> &dyn Any;
 }
 
-impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Clone for Box<dyn Op<F>> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Clone for Box<dyn Op<F>> {
     fn clone(&self) -> Self {
         self.clone_dyn()
     }
@@ -142,7 +142,7 @@ pub struct Input {
     pub datum_type: InputType,
 }
 
-impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Op<F> for Input {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Input {
     fn out_scale(&self, _: Vec<crate::Scale>) -> Result<crate::Scale, CircuitError> {
         Ok(self.scale)
     }
@@ -197,7 +197,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Op<F>
 #[derive(Clone, Debug, PartialEq, Eq, Hash, PartialOrd, Ord, Serialize, Deserialize)]
 pub struct Unknown;
 
-impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Op<F> for Unknown {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Unknown {
     fn out_scale(&self, _: Vec<crate::Scale>) -> Result<crate::Scale, CircuitError> {
         Ok(0)
     }
@@ -224,7 +224,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Op<F>
 
 ///
 #[derive(Clone, Debug, Serialize, Deserialize)]
-pub struct Constant<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> {
+pub struct Constant<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> {
     ///
     pub quantized_values: Tensor<F>,
     ///
@@ -234,7 +234,7 @@ pub struct Constant<F: PrimeField + TensorType + PartialOrd + std::hash::Hash +
     pub pre_assigned_val: Option<ValTensor<F>>,
 }
 
-impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Constant<F> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Constant<F> {
     ///
     pub fn new(quantized_values: Tensor<F>, raw_values: Tensor<f32>) -> Self {
         Self {
@@ -267,8 +267,7 @@ impl<
             + PartialOrd
             + std::hash::Hash
             + Serialize
-            + for<'de> Deserialize<'de>
-            + IntoI64,
+            + for<'de> Deserialize<'de>,
     > Op<F> for Constant<F>
 {
     fn as_any(&self) -> &dyn Any {

src/circuit/ops/poly.rs

@@ -33,6 +33,7 @@ pub enum PolyOp {
     Conv {
         padding: Vec<(usize, usize)>,
         stride: Vec<usize>,
+        group: usize,
     },
     Downsample {
         axis: usize,
@@ -43,6 +44,7 @@ pub enum PolyOp {
         padding: Vec<(usize, usize)>,
         output_padding: Vec<usize>,
         stride: Vec<usize>,
+        group: usize,
     },
     Add,
     Sub,
@@ -98,7 +100,7 @@ impl<
             + std::hash::Hash
             + Serialize
             + for<'de> Deserialize<'de>
-            + IntoI64,
+            ,
     > Op<F> for PolyOp
 {
     /// Returns a reference to the Any trait.
@@ -148,17 +150,25 @@ impl<
             PolyOp::Sum { axes } => format!("SUM (axes={:?})", axes),
             PolyOp::Prod { .. } => "PROD".into(),
             PolyOp::Pow(_) => "POW".into(),
-            PolyOp::Conv { stride, padding } => {
-                format!("CONV (stride={:?}, padding={:?})", stride, padding)
+            PolyOp::Conv {
+                stride,
+                padding,
+                group,
+            } => {
+                format!(
+                    "CONV (stride={:?}, padding={:?}, group={})",
+                    stride, padding, group
+                )
             }
             PolyOp::DeConv {
                 stride,
                 padding,
                 output_padding,
+                group,
             } => {
                 format!(
-                    "DECONV (stride={:?}, padding={:?}, output_padding={:?})",
-                    stride, padding, output_padding
+                    "DECONV (stride={:?}, padding={:?}, output_padding={:?}, group={})",
+                    stride, padding, output_padding, group
                 )
             }
             PolyOp::Concat { axis } => format!("CONCAT (axis={})", axis),
@@ -212,9 +222,18 @@ impl<
             PolyOp::Prod { axes, .. } => {
                 layouts::prod_axes(config, region, values[..].try_into()?, axes)?
             }
-            PolyOp::Conv { padding, stride } => {
-                layouts::conv(config, region, values[..].try_into()?, padding, stride)?
-            }
+            PolyOp::Conv {
+                padding,
+                stride,
+                group,
+            } => layouts::conv(
+                config,
+                region,
+                values[..].try_into()?,
+                padding,
+                stride,
+                *group,
+            )?,
             PolyOp::GatherElements { dim, constant_idx } => {
                 if let Some(idx) = constant_idx {
                     tensor::ops::gather_elements(values[0].get_inner_tensor()?, idx, *dim)?.into()
@@ -261,6 +280,7 @@ impl<
                 padding,
                 output_padding,
                 stride,
+                group,
             } => layouts::deconv(
                 config,
                 region,
@@ -268,6 +288,7 @@ impl<
                 padding,
                 output_padding,
                 stride,
+                *group,
             )?,
             PolyOp::Add => layouts::pairwise(config, region, values[..].try_into()?, BaseOp::Add)?,
             PolyOp::Sub => layouts::pairwise(config, region, values[..].try_into()?, BaseOp::Sub)?,

src/circuit/ops/region.rs

@@ -1,5 +1,6 @@
 use crate::{
     circuit::table::Range,
+    fieldutils::IntegerRep,
     tensor::{Tensor, TensorType, ValTensor, ValType, VarTensor},
 };
 #[cfg(not(target_arch = "wasm32"))]
@@ -9,7 +10,8 @@ use halo2_proofs::{
     plonk::{Error, Selector},
 };
 use halo2curves::ff::PrimeField;
-use portable_atomic::AtomicI64 as AtomicInt;
+use itertools::Itertools;
+use maybe_rayon::iter::ParallelExtend;
 use std::{
     cell::RefCell,
     collections::{HashMap, HashSet},
@@ -84,6 +86,78 @@ impl ShuffleIndex {
     }
 }
 
+#[derive(Debug, Clone)]
+/// Some settings for a region to differentiate it across the different phases of proof generation
+pub struct RegionSettings {
+    /// whether we are in witness generation mode
+    pub witness_gen: bool,
+    /// whether we should check range checks for validity
+    pub check_range: bool,
+}
+
+#[allow(unsafe_code)]
+unsafe impl Sync for RegionSettings {}
+#[allow(unsafe_code)]
+unsafe impl Send for RegionSettings {}
+
+impl RegionSettings {
+    /// Create a new region settings
+    pub fn new(witness_gen: bool, check_range: bool) -> RegionSettings {
+        RegionSettings {
+            witness_gen,
+            check_range,
+        }
+    }
+
+    /// Create a new region settings with all true
+    pub fn all_true() -> RegionSettings {
+        RegionSettings {
+            witness_gen: true,
+            check_range: true,
+        }
+    }
+
+    /// Create a new region settings with all false
+    pub fn all_false() -> RegionSettings {
+        RegionSettings {
+            witness_gen: false,
+            check_range: false,
+        }
+    }
+}
+
+#[derive(Debug, Default, Clone)]
+/// Region statistics
+pub struct RegionStatistics {
+    /// the current maximum value of the lookup inputs
+    pub max_lookup_inputs: IntegerRep,
+    /// the current minimum value of the lookup inputs
+    pub min_lookup_inputs: IntegerRep,
+    /// the current maximum value of the range size
+    pub max_range_size: IntegerRep,
+    /// the current set of used lookups
+    pub used_lookups: HashSet<LookupOp>,
+    /// the current set of used range checks
+    pub used_range_checks: HashSet<Range>,
+}
+
+impl RegionStatistics {
+    /// update the statistics with another set of statistics
+    pub fn update(&mut self, other: &RegionStatistics) {
+        self.max_lookup_inputs = self.max_lookup_inputs.max(other.max_lookup_inputs);
+        self.min_lookup_inputs = self.min_lookup_inputs.min(other.min_lookup_inputs);
+        self.max_range_size = self.max_range_size.max(other.max_range_size);
+        self.used_lookups.extend(other.used_lookups.clone());
+        self.used_range_checks
+            .extend(other.used_range_checks.clone());
+    }
+}
+
+#[allow(unsafe_code)]
+unsafe impl Sync for RegionStatistics {}
+#[allow(unsafe_code)]
+unsafe impl Send for RegionStatistics {}
+
 #[derive(Debug)]
 /// A context for a region
 pub struct RegionCtx<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> {
@@ -93,13 +167,8 @@ pub struct RegionCtx<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Ha
     num_inner_cols: usize,
     dynamic_lookup_index: DynamicLookupIndex,
     shuffle_index: ShuffleIndex,
-    used_lookups: HashSet<LookupOp>,
-    used_range_checks: HashSet<Range>,
-    max_lookup_inputs: i64,
-    min_lookup_inputs: i64,
-    max_range_size: i64,
-    witness_gen: bool,
-    check_lookup_range: bool,
+    statistics: RegionStatistics,
+    settings: RegionSettings,
     assigned_constants: ConstantsMap<F>,
 }
 
@@ -151,12 +220,17 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
 
     ///
     pub fn witness_gen(&self) -> bool {
-        self.witness_gen
+        self.settings.witness_gen
     }
 
     ///
-    pub fn check_lookup_range(&self) -> bool {
-        self.check_lookup_range
+    pub fn check_range(&self) -> bool {
+        self.settings.check_range
+    }
+
+    ///
+    pub fn statistics(&self) -> &RegionStatistics {
+        &self.statistics
     }
 
     /// Create a new region context
@@ -171,13 +245,8 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
             linear_coord,
             dynamic_lookup_index: DynamicLookupIndex::default(),
             shuffle_index: ShuffleIndex::default(),
-            used_lookups: HashSet::new(),
-            used_range_checks: HashSet::new(),
-            max_lookup_inputs: 0,
-            min_lookup_inputs: 0,
-            max_range_size: 0,
-            witness_gen: true,
-            check_lookup_range: true,
+            statistics: RegionStatistics::default(),
+            settings: RegionSettings::all_true(),
             assigned_constants: HashMap::new(),
         }
     }
@@ -193,39 +262,12 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         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>>>,
-        row: usize,
-        num_inner_cols: usize,
-        dynamic_lookup_index: DynamicLookupIndex,
-        shuffle_index: ShuffleIndex,
-    ) -> RegionCtx<'a, F> {
-        let linear_coord = row * num_inner_cols;
-        RegionCtx {
-            region,
-            num_inner_cols,
-            linear_coord,
-            row,
-            dynamic_lookup_index,
-            shuffle_index,
-            used_lookups: HashSet::new(),
-            used_range_checks: HashSet::new(),
-            max_lookup_inputs: 0,
-            min_lookup_inputs: 0,
-            max_range_size: 0,
-            witness_gen: false,
-            check_lookup_range: false,
-            assigned_constants: HashMap::new(),
-        }
-    }
 
     /// Create a new region context
     pub fn new_dummy(
         row: usize,
         num_inner_cols: usize,
-        witness_gen: bool,
-        check_lookup_range: bool,
+        settings: RegionSettings,
     ) -> RegionCtx<'a, F> {
         let region = None;
         let linear_coord = row * num_inner_cols;
@@ -237,13 +279,8 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
             row,
             dynamic_lookup_index: DynamicLookupIndex::default(),
             shuffle_index: ShuffleIndex::default(),
-            used_lookups: HashSet::new(),
-            used_range_checks: HashSet::new(),
-            max_lookup_inputs: 0,
-            min_lookup_inputs: 0,
-            max_range_size: 0,
-            witness_gen,
-            check_lookup_range,
+            statistics: RegionStatistics::default(),
+            settings,
             assigned_constants: HashMap::new(),
         }
     }
@@ -253,8 +290,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         row: usize,
         linear_coord: usize,
         num_inner_cols: usize,
-        witness_gen: bool,
-        check_lookup_range: bool,
+        settings: RegionSettings,
     ) -> RegionCtx<'a, F> {
         let region = None;
         RegionCtx {
@@ -264,13 +300,8 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
             row,
             dynamic_lookup_index: DynamicLookupIndex::default(),
             shuffle_index: ShuffleIndex::default(),
-            used_lookups: HashSet::new(),
-            used_range_checks: HashSet::new(),
-            max_lookup_inputs: 0,
-            min_lookup_inputs: 0,
-            max_range_size: 0,
-            witness_gen,
-            check_lookup_range,
+            statistics: RegionStatistics::default(),
+            settings,
             assigned_constants: HashMap::new(),
         }
     }
@@ -321,12 +352,9 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
     ) -> Result<(), CircuitError> {
         let row = AtomicUsize::new(self.row());
         let linear_coord = AtomicUsize::new(self.linear_coord());
-        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 statistics = Arc::new(Mutex::new(self.statistics.clone()));
         let shuffle_index = Arc::new(Mutex::new(self.shuffle_index.clone()));
+        let dynamic_lookup_index = Arc::new(Mutex::new(self.dynamic_lookup_index.clone()));
         let constants = Arc::new(Mutex::new(self.assigned_constants.clone()));
 
         *output = output.par_enum_map(|idx, _| {
@@ -340,8 +368,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
                 starting_offset,
                 starting_linear_coord,
                 self.num_inner_cols,
-                self.witness_gen,
-                self.check_lookup_range,
+                self.settings.clone(),
             );
             let res = inner_loop_function(idx, &mut local_reg);
             // we update the offset and constants
@@ -351,14 +378,9 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
                 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);
             // update the lookups
-            let mut lookups = lookups.lock().unwrap();
-            lookups.extend(local_reg.used_lookups());
-            // update the range checks
-            let mut range_checks = range_checks.lock().unwrap();
-            range_checks.extend(local_reg.used_range_checks());
+            let mut statistics = statistics.lock().unwrap();
+            statistics.update(local_reg.statistics());
             // update the dynamic lookup index
             let mut dynamic_lookup_index = dynamic_lookup_index.lock().unwrap();
             dynamic_lookup_index.update(&local_reg.dynamic_lookup_index);
@@ -372,20 +394,11 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
             res
         })?;
         self.linear_coord = linear_coord.into_inner();
-        #[allow(trivial_numeric_casts)]
-        {
-            self.max_lookup_inputs = max_lookup_inputs.into_inner();
-            self.min_lookup_inputs = min_lookup_inputs.into_inner();
-        }
         self.row = row.into_inner();
-        self.used_lookups = Arc::try_unwrap(lookups)
+        self.statistics = Arc::try_unwrap(statistics)
             .map_err(|e| CircuitError::GetLookupsError(format!("{:?}", e)))?
             .into_inner()
             .map_err(|e| CircuitError::GetLookupsError(format!("{:?}", e)))?;
-        self.used_range_checks = Arc::try_unwrap(range_checks)
-            .map_err(|e| CircuitError::GetRangeChecksError(format!("{:?}", e)))?
-            .into_inner()
-            .map_err(|e| CircuitError::GetRangeChecksError(format!("{:?}", e)))?;
         self.dynamic_lookup_index = Arc::try_unwrap(dynamic_lookup_index)
             .map_err(|e| CircuitError::GetDynamicLookupError(format!("{:?}", e)))?
             .into_inner()
@@ -409,11 +422,11 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
     ) -> Result<(), CircuitError> {
         let (mut min, mut max) = (0, 0);
         for i in inputs {
-            max = max.max(i.get_int_evals()?.into_iter().max().unwrap_or_default());
-            min = min.min(i.get_int_evals()?.into_iter().min().unwrap_or_default());
+            max = max.max(i.int_evals()?.into_iter().max().unwrap_or_default());
+            min = min.min(i.int_evals()?.into_iter().min().unwrap_or_default());
         }
-        self.max_lookup_inputs = self.max_lookup_inputs.max(max);
-        self.min_lookup_inputs = self.min_lookup_inputs.min(min);
+        self.statistics.max_lookup_inputs = self.statistics.max_lookup_inputs.max(max);
+        self.statistics.min_lookup_inputs = self.statistics.min_lookup_inputs.min(min);
         Ok(())
     }
 
@@ -425,7 +438,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
 
         let range_size = (range.1 - range.0).abs();
 
-        self.max_range_size = self.max_range_size.max(range_size);
+        self.statistics.max_range_size = self.statistics.max_range_size.max(range_size);
         Ok(())
     }
 
@@ -440,13 +453,13 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         lookup: LookupOp,
         inputs: &[ValTensor<F>],
     ) -> Result<(), CircuitError> {
-        self.used_lookups.insert(lookup);
+        self.statistics.used_lookups.insert(lookup);
         self.update_max_min_lookup_inputs(inputs)
     }
 
     /// add used range check
     pub fn add_used_range_check(&mut self, range: Range) -> Result<(), CircuitError> {
-        self.used_range_checks.insert(range);
+        self.statistics.used_range_checks.insert(range);
         self.update_max_min_lookup_range(range)
     }
 
@@ -487,27 +500,27 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
 
     /// get used lookups
     pub fn used_lookups(&self) -> HashSet<LookupOp> {
-        self.used_lookups.clone()
+        self.statistics.used_lookups.clone()
     }
 
     /// get used range checks
     pub fn used_range_checks(&self) -> HashSet<Range> {
-        self.used_range_checks.clone()
+        self.statistics.used_range_checks.clone()
     }
 
     /// max lookup inputs
-    pub fn max_lookup_inputs(&self) -> i64 {
-        self.max_lookup_inputs
+    pub fn max_lookup_inputs(&self) -> IntegerRep {
+        self.statistics.max_lookup_inputs
     }
 
     /// min lookup inputs
-    pub fn min_lookup_inputs(&self) -> i64 {
-        self.min_lookup_inputs
+    pub fn min_lookup_inputs(&self) -> IntegerRep {
+        self.statistics.min_lookup_inputs
     }
 
     /// max range check
-    pub fn max_range_size(&self) -> i64 {
-        self.max_range_size
+    pub fn max_range_size(&self) -> IntegerRep {
+        self.statistics.max_range_size
     }
 
     /// Assign a valtensor to a vartensor
@@ -515,18 +528,18 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         &mut self,
         var: &VarTensor,
         values: &ValTensor<F>,
-    ) -> Result<ValTensor<F>, Error> {
+    ) -> Result<ValTensor<F>, CircuitError> {
         if let Some(region) = &self.region {
-            var.assign(
+            Ok(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);
+                self.assigned_constants.par_extend(values_map);
             }
             Ok(values.clone())
         }
@@ -542,18 +555,18 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         &mut self,
         var: &VarTensor,
         values: &ValTensor<F>,
-    ) -> Result<ValTensor<F>, Error> {
+    ) -> Result<ValTensor<F>, CircuitError> {
         if let Some(region) = &self.region {
-            var.assign(
+            Ok(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);
+                self.assigned_constants.par_extend(values_map);
             }
             Ok(values.clone())
         }
@@ -564,7 +577,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         &mut self,
         var: &VarTensor,
         values: &ValTensor<F>,
-    ) -> Result<ValTensor<F>, Error> {
+    ) -> Result<ValTensor<F>, CircuitError> {
         self.assign_dynamic_lookup(var, values)
     }
 
@@ -573,27 +586,24 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         &mut self,
         var: &VarTensor,
         values: &ValTensor<F>,
-        ommissions: &HashSet<&usize>,
-    ) -> Result<ValTensor<F>, Error> {
+        ommissions: &HashSet<usize>,
+    ) -> Result<ValTensor<F>, CircuitError> {
         if let Some(region) = &self.region {
-            var.assign_with_omissions(
+            Ok(var.assign_with_omissions(
                 &mut region.borrow_mut(),
                 self.linear_coord,
                 values,
                 ommissions,
                 &mut self.assigned_constants,
-            )
+            )?)
         } else {
-            let inner_tensor = values.get_inner_tensor().unwrap();
-            let mut values_map = values.create_constants_map();
+            let mut values_clone = values.clone();
+            let mut indices = ommissions.clone().into_iter().collect_vec();
+            values_clone.remove_indices(&mut indices, false)?;
 
-            for o in ommissions {
-                if let ValType::Constant(value) = inner_tensor.get_flat_index(**o) {
-                    values_map.remove(&value);
-                }
-            }
+            let values_map = values.create_constants_map();
 
-            self.assigned_constants.extend(values_map);
+            self.assigned_constants.par_extend(values_map);
 
             Ok(values.clone())
         }

src/circuit/table.rs

@@ -11,17 +11,17 @@ use maybe_rayon::prelude::{IntoParallelIterator, ParallelIterator};
 
 use crate::{
     circuit::CircuitError,
-    fieldutils::i64_to_felt,
-    tensor::{IntoI64, Tensor, TensorType},
+    fieldutils::{integer_rep_to_felt, IntegerRep},
+    tensor::{Tensor, TensorType},
 };
 
 use crate::circuit::lookup::LookupOp;
 
 /// The range of the lookup table.
-pub type Range = (i64, i64);
+pub type Range = (IntegerRep, IntegerRep);
 
 /// The safety factor for the range of the lookup table.
-pub const RANGE_MULTIPLIER: i64 = 2;
+pub const RANGE_MULTIPLIER: IntegerRep = 2;
 /// The safety factor offset for the number of rows in the lookup table.
 pub const RESERVED_BLINDING_ROWS_PAD: usize = 3;
 
@@ -96,21 +96,22 @@ pub struct Table<F: PrimeField> {
     _marker: PhantomData<F>,
 }
 
-impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Table<F> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> 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_i64(input) - self.range.0).abs() / (self.col_size as i64);
+        let chunk = (crate::fieldutils::felt_to_integer_rep(input) - self.range.0).abs()
+            / (self.col_size as IntegerRep);
 
-        i64_to_felt(chunk)
+        integer_rep_to_felt(chunk)
     }
 
     /// get first_element of column
     pub fn get_first_element(&self, chunk: usize) -> (F, F) {
-        let chunk = chunk as i64;
+        let chunk = chunk as IntegerRep;
         // we index from 1 to prevent soundness issues
-        let first_element = i64_to_felt(chunk * (self.col_size as i64) + self.range.0);
+        let first_element =
+            integer_rep_to_felt(chunk * (self.col_size as IntegerRep) + self.range.0);
         let op_f = self
             .nonlinearity
             .f(&[Tensor::from(vec![first_element].into_iter())])
@@ -130,12 +131,12 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Table<
 }
 
 ///
-pub fn num_cols_required(range_len: i64, col_size: usize) -> usize {
+pub fn num_cols_required(range_len: IntegerRep, col_size: usize) -> usize {
     // number of cols needed to store the range
-    (range_len / (col_size as i64)) as usize + 1
+    (range_len / (col_size as IntegerRep)) as usize + 1
 }
 
-impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Table<F> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
     /// Configures the table.
     pub fn configure(
         cs: &mut ConstraintSystem<F>,
@@ -202,7 +203,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> Table<
         let smallest = self.range.0;
         let largest = self.range.1;
 
-        let inputs: Tensor<F> = Tensor::from(smallest..=largest).map(|x| i64_to_felt(x));
+        let inputs: Tensor<F> = Tensor::from(smallest..=largest).map(|x| integer_rep_to_felt(x));
         let evals = self.nonlinearity.f(&[inputs.clone()])?;
         let chunked_inputs = inputs.chunks(self.col_size);
 
@@ -272,12 +273,12 @@ pub struct RangeCheck<F: PrimeField> {
     _marker: PhantomData<F>,
 }
 
-impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> RangeCheck<F> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RangeCheck<F> {
     /// get first_element of column
     pub fn get_first_element(&self, chunk: usize) -> F {
-        let chunk = chunk as i64;
+        let chunk = chunk as IntegerRep;
         // we index from 1 to prevent soundness issues
-        i64_to_felt(chunk * (self.col_size as i64) + self.range.0)
+        integer_rep_to_felt(chunk * (self.col_size as IntegerRep) + self.range.0)
     }
 
     ///
@@ -293,14 +294,14 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> RangeC
     /// 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_i64(input) - self.range.0).abs() / (self.col_size as i64);
+        let chunk = (crate::fieldutils::felt_to_integer_rep(input) - self.range.0).abs()
+            / (self.col_size as IntegerRep);
 
-        i64_to_felt(chunk)
+        integer_rep_to_felt(chunk)
     }
 }
 
-impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> RangeCheck<F> {
+impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RangeCheck<F> {
     /// Configures the table.
     pub fn configure(cs: &mut ConstraintSystem<F>, range: Range, logrows: usize) -> RangeCheck<F> {
         log::debug!("range check range: {:?}", range);
@@ -350,7 +351,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash + IntoI64> RangeC
         let smallest = self.range.0;
         let largest = self.range.1;
 
-        let inputs: Tensor<F> = Tensor::from(smallest..=largest).map(|x| i64_to_felt(x));
+        let inputs: Tensor<F> = Tensor::from(smallest..=largest).map(|x| integer_rep_to_felt(x));
         let chunked_inputs = inputs.chunks(self.col_size);
 
         self.is_assigned = true;

src/circuit/tests.rs

@@ -1050,6 +1050,7 @@ mod conv {
                                 Box::new(PolyOp::Conv {
                                     padding: vec![(1, 1); 2],
                                     stride: vec![2; 2],
+                                    group: 1,
                                 }),
                             )
                             .map_err(|_| Error::Synthesis)
@@ -1200,6 +1201,7 @@ mod conv_col_ultra_overflow {
                                 Box::new(PolyOp::Conv {
                                     padding: vec![(1, 1); 2],
                                     stride: vec![2; 2],
+                                    group: 1,
                                 }),
                             )
                             .map_err(|_| Error::Synthesis)
@@ -1345,6 +1347,7 @@ mod conv_relu_col_ultra_overflow {
                                 Box::new(PolyOp::Conv {
                                     padding: vec![(1, 1); 2],
                                     stride: vec![2; 2],
+                                    group: 1,
                                 }),
                             )
                             .map_err(|_| Error::Synthesis);

src/execute.rs

@@ -1,3 +1,4 @@
+use crate::circuit::region::RegionSettings;
 use crate::circuit::CheckMode;
 #[cfg(not(target_arch = "wasm32"))]
 use crate::commands::CalibrationTarget;
@@ -785,6 +786,8 @@ pub(crate) async fn gen_witness(
 
     let commitment: Commitments = settings.run_args.commitment.into();
 
+    let region_settings = RegionSettings::all_true();
+
     let start_time = Instant::now();
     let witness = if settings.module_requires_polycommit() {
         if get_srs_path(settings.run_args.logrows, srs_path.clone(), commitment).exists() {
@@ -799,8 +802,7 @@ pub(crate) async fn gen_witness(
                         &mut input,
                         vk.as_ref(),
                         Some(&srs),
-                        true,
-                        true,
+                        region_settings,
                     )?
                 }
                 Commitments::IPA => {
@@ -814,8 +816,7 @@ pub(crate) async fn gen_witness(
                         &mut input,
                         vk.as_ref(),
                         Some(&srs),
-                        true,
-                        true,
+                        region_settings,
                     )?
                 }
             }
@@ -825,12 +826,16 @@ pub(crate) async fn gen_witness(
                 &mut input,
                 vk.as_ref(),
                 None,
-                true,
-                true,
+                region_settings,
             )?
         }
     } else {
-        circuit.forward::<KZGCommitmentScheme<Bn256>>(&mut input, vk.as_ref(), None, true, true)?
+        circuit.forward::<KZGCommitmentScheme<Bn256>>(
+            &mut input,
+            vk.as_ref(),
+            None,
+            region_settings,
+        )?
     };
 
     // print each variable tuple (symbol, value) as symbol=value
@@ -1023,6 +1028,8 @@ pub(crate) async fn calibrate(
     use std::collections::HashMap;
     use tabled::Table;
 
+    use crate::fieldutils::IntegerRep;
+
     let data = GraphData::from_path(data)?;
     // load the pre-generated settings
     let settings = GraphSettings::load(&settings_path)?;
@@ -1131,7 +1138,7 @@ pub(crate) async fn calibrate(
             param_scale,
             scale_rebase_multiplier,
             div_rebasing,
-            lookup_range: (i64::MIN, i64::MAX),
+            lookup_range: (IntegerRep::MIN, IntegerRep::MAX),
             ..settings.run_args.clone()
         };
 
@@ -1171,8 +1178,7 @@ pub(crate) async fn calibrate(
                         &mut data.clone(),
                         None,
                         None,
-                        true,
-                        false,
+                        RegionSettings::all_true(),
                     )
                     .map_err(|e| format!("failed to forward: {}", e))?;
 
@@ -1502,10 +1508,10 @@ pub(crate) async fn create_evm_vk(
 #[cfg(not(target_arch = "wasm32"))]
 pub(crate) async fn create_evm_data_attestation(
     settings_path: PathBuf,
-    _sol_code_path: PathBuf,
-    _abi_path: PathBuf,
-    _input: PathBuf,
-    _witness: Option<PathBuf>,
+    sol_code_path: PathBuf,
+    abi_path: PathBuf,
+    input: PathBuf,
+    witness: Option<PathBuf>,
 ) -> Result<String, EZKLError> {
     #[allow(unused_imports)]
     use crate::graph::{DataSource, VarVisibility};
@@ -1517,7 +1523,7 @@ pub(crate) async fn create_evm_data_attestation(
     trace!("params computed");
 
     // if input is not provided, we just instantiate dummy input data
-    let data = GraphData::from_path(_input).unwrap_or(GraphData::new(DataSource::File(vec![])));
+    let data = GraphData::from_path(input).unwrap_or(GraphData::new(DataSource::File(vec![])));
 
     let output_data = if let Some(DataSource::OnChain(source)) = data.output_data {
         if visibility.output.is_private() {
@@ -1552,7 +1558,7 @@ pub(crate) async fn create_evm_data_attestation(
         || settings.run_args.output_visibility == Visibility::KZGCommit
         || settings.run_args.param_visibility == Visibility::KZGCommit
     {
-        let witness = GraphWitness::from_path(_witness.unwrap_or(DEFAULT_WITNESS.into()))?;
+        let witness = GraphWitness::from_path(witness.unwrap_or(DEFAULT_WITNESS.into()))?;
         let commitments = witness.get_polycommitments();
         let proof_first_bytes = get_proof_commitments::<
             KZGCommitmentScheme<Bn256>,
@@ -1566,12 +1572,12 @@ pub(crate) async fn create_evm_data_attestation(
     };
 
     let output = fix_da_sol(input_data, output_data, commitment_bytes)?;
-    let mut f = File::create(_sol_code_path.clone())?;
+    let mut f = File::create(sol_code_path.clone())?;
     let _ = f.write(output.as_bytes());
     // fetch abi of the contract
-    let (abi, _, _) = get_contract_artifacts(_sol_code_path, "DataAttestation", 0).await?;
+    let (abi, _, _) = get_contract_artifacts(sol_code_path, "DataAttestation", 0).await?;
     // save abi to file
-    serde_json::to_writer(std::fs::File::create(_abi_path)?, &abi)?;
+    serde_json::to_writer(std::fs::File::create(abi_path)?, &abi)?;
 
     Ok(String::new())
 }

src/fieldutils.rs

@@ -2,42 +2,21 @@ use halo2_proofs::arithmetic::Field;
 /// Utilities for converting from Halo2 PrimeField types to integers (and vice-versa).
 use halo2curves::ff::PrimeField;
 
-/// Converts an i32 to a PrimeField element.
-pub fn i32_to_felt<F: PrimeField>(x: i32) -> F {
-    if x >= 0 {
-        F::from(x as u64)
-    } else {
-        -F::from(x.unsigned_abs() as u64)
-    }
-}
+/// Integer representation of a PrimeField element.
+pub type IntegerRep = i128;
 
 /// Converts an i64 to a PrimeField element.
-pub fn i64_to_felt<F: PrimeField>(x: i64) -> F {
+pub fn integer_rep_to_felt<F: PrimeField>(x: IntegerRep) -> F {
     if x >= 0 {
         F::from_u128(x as u128)
     } else {
-        -F::from_u128((-x) as u128)
-    }
-}
-
-/// Converts a PrimeField element to an i32.
-pub fn felt_to_i32<F: PrimeField + PartialOrd + Field>(x: F) -> i32 {
-    if x > F::from(i32::MAX as u64) {
-        let rep = (-x).to_repr();
-        let negtmp: &[u8] = rep.as_ref();
-        let lower_32 = u32::from_le_bytes(negtmp[..4].try_into().unwrap());
-        -(lower_32 as i32)
-    } else {
-        let rep = (x).to_repr();
-        let tmp: &[u8] = rep.as_ref();
-        let lower_32 = u32::from_le_bytes(tmp[..4].try_into().unwrap());
-        lower_32 as i32
+        -F::from_u128(x.saturating_neg() as u128)
     }
 }
 
 /// Converts a PrimeField element to an f64.
 pub fn felt_to_f64<F: PrimeField + PartialOrd + Field>(x: F) -> f64 {
-    if x > F::from_u128(i64::MAX as u128) {
+    if x > F::from_u128(IntegerRep::MAX as u128) {
         let rep = (-x).to_repr();
         let negtmp: &[u8] = rep.as_ref();
         let lower_128: u128 = u128::from_le_bytes(negtmp[..16].try_into().unwrap());
@@ -51,17 +30,17 @@ pub fn felt_to_f64<F: PrimeField + PartialOrd + Field>(x: F) -> f64 {
 }
 
 /// Converts a PrimeField element to an i64.
-pub fn felt_to_i64<F: PrimeField + PartialOrd + Field>(x: F) -> i64 {
-    if x > F::from_u128(i64::MAX as u128) {
+pub fn felt_to_integer_rep<F: PrimeField + PartialOrd + Field>(x: F) -> IntegerRep {
+    if x > F::from_u128(IntegerRep::MAX as u128) {
         let rep = (-x).to_repr();
         let negtmp: &[u8] = rep.as_ref();
         let lower_128: u128 = u128::from_le_bytes(negtmp[..16].try_into().unwrap());
-        -(lower_128 as i64)
+        -(lower_128 as IntegerRep)
     } else {
         let rep = (x).to_repr();
         let tmp: &[u8] = rep.as_ref();
         let lower_128: u128 = u128::from_le_bytes(tmp[..16].try_into().unwrap());
-        lower_128 as i64
+        lower_128 as IntegerRep
     }
 }
 
@@ -73,33 +52,24 @@ mod test {
 
     #[test]
     fn test_conv() {
-        let res: F = i32_to_felt(-15i32);
+        let res: F = integer_rep_to_felt(-15);
         assert_eq!(res, -F::from(15));
 
-        let res: F = i32_to_felt(2_i32.pow(17));
+        let res: F = integer_rep_to_felt(2_i128.pow(17));
         assert_eq!(res, F::from(131072));
 
-        let res: F = i64_to_felt(-15i64);
+        let res: F = integer_rep_to_felt(-15);
         assert_eq!(res, -F::from(15));
 
-        let res: F = i64_to_felt(2_i64.pow(17));
+        let res: F = integer_rep_to_felt(2_i128.pow(17));
         assert_eq!(res, F::from(131072));
     }
 
     #[test]
-    fn felttoi32() {
-        for x in -(2i32.pow(16))..(2i32.pow(16)) {
-            let fieldx: F = i32_to_felt::<F>(x);
-            let xf: i32 = felt_to_i32::<F>(fieldx);
-            assert_eq!(x, xf);
-        }
-    }
-
-    #[test]
-    fn felttoi64() {
-        for x in -(2i64.pow(20))..(2i64.pow(20)) {
-            let fieldx: F = i64_to_felt::<F>(x);
-            let xf: i64 = felt_to_i64::<F>(fieldx);
+    fn felttointegerrep() {
+        for x in -(2_i128.pow(16))..(2_i128.pow(16)) {
+            let fieldx: F = integer_rep_to_felt::<F>(x);
+            let xf: i128 = felt_to_integer_rep::<F>(fieldx);
             assert_eq!(x, xf);
         }
     }

src/graph/errors.rs

@@ -50,7 +50,7 @@ pub enum GraphError {
     /// Tract error
     #[cfg(not(all(target_arch = "wasm32", target_os = "unknown")))]
     #[error("[tract] {0}")]
-    TractError(#[from] tract_onnx::tract_core::anyhow::Error),
+    TractError(#[from] tract_onnx::prelude::TractError),
     /// Packing exponent is too large
     #[error("largest packing exponent exceeds max. try reducing the scale")]
     PackingExponent,

src/graph/input.rs

@@ -1,7 +1,7 @@
 use super::errors::GraphError;
 use super::quantize_float;
 use crate::circuit::InputType;
-use crate::fieldutils::i64_to_felt;
+use crate::fieldutils::integer_rep_to_felt;
 #[cfg(not(target_arch = "wasm32"))]
 use crate::graph::postgres::Client;
 #[cfg(not(target_arch = "wasm32"))]
@@ -128,7 +128,7 @@ impl FileSourceInner {
     /// Convert to a field element
     pub fn to_field(&self, scale: crate::Scale) -> Fp {
         match self {
-            FileSourceInner::Float(f) => i64_to_felt(quantize_float(f, 0.0, scale).unwrap()),
+            FileSourceInner::Float(f) => integer_rep_to_felt(quantize_float(f, 0.0, scale).unwrap()),
             FileSourceInner::Bool(f) => {
                 if *f {
                     Fp::one()
@@ -150,7 +150,7 @@ impl FileSourceInner {
                     0.0
                 }
             }
-            FileSourceInner::Field(f) => crate::fieldutils::felt_to_i64(*f) as f64,
+            FileSourceInner::Field(f) => crate::fieldutils::felt_to_integer_rep(*f) as f64,
         }
     }
 }

src/graph/mod.rs

@@ -34,10 +34,10 @@ use self::input::{FileSource, GraphData};
 use self::modules::{GraphModules, ModuleConfigs, ModuleForwardResult, ModuleSizes};
 use crate::circuit::lookup::LookupOp;
 use crate::circuit::modules::ModulePlanner;
-use crate::circuit::region::ConstantsMap;
+use crate::circuit::region::{ConstantsMap, RegionSettings};
 use crate::circuit::table::{num_cols_required, Range, Table, RESERVED_BLINDING_ROWS_PAD};
 use crate::circuit::{CheckMode, InputType};
-use crate::fieldutils::felt_to_f64;
+use crate::fieldutils::{felt_to_f64, IntegerRep};
 use crate::pfsys::PrettyElements;
 use crate::tensor::{Tensor, ValTensor};
 use crate::{RunArgs, EZKL_BUF_CAPACITY};
@@ -69,13 +69,14 @@ pub use vars::*;
 use crate::pfsys::field_to_string;
 
 /// The safety factor for the range of the lookup table.
-pub const RANGE_MULTIPLIER: i64 = 2;
+pub const RANGE_MULTIPLIER: IntegerRep = 2;
 
 /// The maximum number of columns in a lookup table.
 pub const MAX_NUM_LOOKUP_COLS: usize = 12;
 
 /// Max representation of a lookup table input
-pub const MAX_LOOKUP_ABS: i64 = (MAX_NUM_LOOKUP_COLS as i64) * 2_i64.pow(MAX_PUBLIC_SRS);
+pub const MAX_LOOKUP_ABS: IntegerRep =
+    (MAX_NUM_LOOKUP_COLS as IntegerRep) * 2_i128.pow(MAX_PUBLIC_SRS);
 
 #[cfg(not(target_arch = "wasm32"))]
 lazy_static! {
@@ -126,11 +127,11 @@ pub struct GraphWitness {
     /// Any hashes of outputs generated during the forward pass
     pub processed_outputs: Option<ModuleForwardResult>,
     /// max lookup input
-    pub max_lookup_inputs: i64,
+    pub max_lookup_inputs: IntegerRep,
     /// max lookup input
-    pub min_lookup_inputs: i64,
+    pub min_lookup_inputs: IntegerRep,
     /// max range check size
-    pub max_range_size: i64,
+    pub max_range_size: IntegerRep,
 }
 
 impl GraphWitness {
@@ -804,18 +805,26 @@ impl GraphCircuit {
         // the ordering here is important, we want the inputs to come before the outputs
         // as they are configured in that order as Column<Instances>
         let mut public_inputs: Vec<Fp> = vec![];
-        if self.settings().run_args.input_visibility.is_public() {
-            public_inputs.extend(self.graph_witness.inputs.clone().into_iter().flatten())
-        } else if let Some(processed_inputs) = &data.processed_inputs {
+
+        // we first process the inputs
+        if let Some(processed_inputs) = &data.processed_inputs {
             public_inputs.extend(processed_inputs.get_instances().into_iter().flatten());
         }
 
+        // we then process the params
         if let Some(processed_params) = &data.processed_params {
             public_inputs.extend(processed_params.get_instances().into_iter().flatten());
         }
 
+        // if the inputs are public, we add them to the public inputs AFTER the processed params as they are configured in that order as Column<Instances>
+        if self.settings().run_args.input_visibility.is_public() {
+            public_inputs.extend(self.graph_witness.inputs.clone().into_iter().flatten())
+        }
+
+        // if the outputs are public, we add them to the public inputs
         if self.settings().run_args.output_visibility.is_public() {
             public_inputs.extend(self.graph_witness.outputs.clone().into_iter().flatten());
+        // if the outputs are processed, we add the processed outputs to the public inputs
         } else if let Some(processed_outputs) = &data.processed_outputs {
             public_inputs.extend(processed_outputs.get_instances().into_iter().flatten());
         }
@@ -1036,12 +1045,12 @@ impl GraphCircuit {
 
     fn calc_safe_lookup_range(min_max_lookup: Range, lookup_safety_margin: f64) -> Range {
         (
-            (lookup_safety_margin * min_max_lookup.0 as f64).floor() as i64,
-            (lookup_safety_margin * min_max_lookup.1 as f64).ceil() as i64,
+            (lookup_safety_margin * min_max_lookup.0 as f64).floor() as IntegerRep,
+            (lookup_safety_margin * min_max_lookup.1 as f64).ceil() as IntegerRep,
         )
     }
 
-    fn calc_num_cols(range_len: i64, max_logrows: u32) -> usize {
+    fn calc_num_cols(range_len: IntegerRep, max_logrows: u32) -> usize {
         let max_col_size = Table::<Fp>::cal_col_size(max_logrows as usize, RESERVED_BLINDING_ROWS);
         num_cols_required(range_len, max_col_size)
     }
@@ -1049,7 +1058,7 @@ impl GraphCircuit {
     fn table_size_logrows(
         &self,
         safe_lookup_range: Range,
-        max_range_size: i64,
+        max_range_size: IntegerRep,
     ) -> Result<u32, GraphError> {
         // pick the range with the largest absolute size safe_lookup_range or max_range_size
         let safe_range = std::cmp::max(
@@ -1068,7 +1077,7 @@ impl GraphCircuit {
     pub fn calc_min_logrows(
         &mut self,
         min_max_lookup: Range,
-        max_range_size: i64,
+        max_range_size: IntegerRep,
         max_logrows: Option<u32>,
         lookup_safety_margin: f64,
     ) -> Result<(), GraphError> {
@@ -1086,7 +1095,7 @@ impl GraphCircuit {
             (safe_lookup_range.1.saturating_sub(safe_lookup_range.0)).saturating_abs();
         // check if has overflowed max lookup input
 
-        if lookup_size > (MAX_LOOKUP_ABS as f64 / lookup_safety_margin).floor() as i64 {
+        if lookup_size > (MAX_LOOKUP_ABS as f64 / lookup_safety_margin).floor() as IntegerRep {
             return Err(GraphError::LookupRangeTooLarge(
                 lookup_size.unsigned_abs() as usize
             ));
@@ -1166,7 +1175,7 @@ impl GraphCircuit {
         &self,
         k: u32,
         safe_lookup_range: Range,
-        max_range_size: i64,
+        max_range_size: IntegerRep,
     ) -> bool {
         // if num cols is too large then the extended k is too large
         if Self::calc_num_cols(safe_lookup_range.1 - safe_lookup_range.0, k) > MAX_NUM_LOOKUP_COLS
@@ -1224,8 +1233,7 @@ impl GraphCircuit {
         inputs: &mut [Tensor<Fp>],
         vk: Option<&VerifyingKey<G1Affine>>,
         srs: Option<&Scheme::ParamsProver>,
-        witness_gen: bool,
-        check_lookup: bool,
+        region_settings: RegionSettings,
     ) -> Result<GraphWitness, GraphError> {
         let original_inputs = inputs.to_vec();
 
@@ -1274,7 +1282,7 @@ impl GraphCircuit {
 
         let mut model_results =
             self.model()
-                .forward(inputs, &self.settings().run_args, witness_gen, check_lookup)?;
+                .forward(inputs, &self.settings().run_args, region_settings)?;
 
         if visibility.output.requires_processing() {
             let module_outlets = visibility.output.overwrites_inputs();

src/graph/model.rs

@@ -7,10 +7,12 @@ use super::GraphSettings;
 use crate::circuit::hybrid::HybridOp;
 use crate::circuit::region::ConstantsMap;
 use crate::circuit::region::RegionCtx;
+use crate::circuit::region::RegionSettings;
 use crate::circuit::table::Range;
 use crate::circuit::Input;
 use crate::circuit::InputType;
 use crate::circuit::Unknown;
+use crate::fieldutils::IntegerRep;
 use crate::tensor::ValType;
 use crate::{
     circuit::{lookup::LookupOp, BaseConfig as PolyConfig, CheckMode, Op},
@@ -64,11 +66,11 @@ pub struct ForwardResult {
     /// The outputs of the forward pass.
     pub outputs: Vec<Tensor<Fp>>,
     /// The maximum value of any input to a lookup operation.
-    pub max_lookup_inputs: i64,
+    pub max_lookup_inputs: IntegerRep,
     /// The minimum value of any input to a lookup operation.
-    pub min_lookup_inputs: i64,
+    pub min_lookup_inputs: IntegerRep,
     /// The max range check size
-    pub max_range_size: i64,
+    pub max_range_size: IntegerRep,
 }
 
 impl From<DummyPassRes> for ForwardResult {
@@ -116,11 +118,11 @@ pub struct DummyPassRes {
     /// range checks
     pub range_checks: HashSet<Range>,
     /// max lookup inputs
-    pub max_lookup_inputs: i64,
+    pub max_lookup_inputs: IntegerRep,
     /// min lookup inputs
-    pub min_lookup_inputs: i64,
+    pub min_lookup_inputs: IntegerRep,
     /// min range check
-    pub max_range_size: i64,
+    pub max_range_size: IntegerRep,
     /// outputs
     pub outputs: Vec<Tensor<Fp>>,
 }
@@ -545,7 +547,7 @@ impl Model {
             })
             .collect::<Result<Vec<_>, GraphError>>()?;
 
-        let res = self.dummy_layout(run_args, &inputs, false, false)?;
+        let res = self.dummy_layout(run_args, &inputs, RegionSettings::all_false())?;
 
         // if we're using percentage tolerance, we need to add the necessary range check ops for it.
 
@@ -588,14 +590,13 @@ impl Model {
         &self,
         model_inputs: &[Tensor<Fp>],
         run_args: &RunArgs,
-        witness_gen: bool,
-        check_lookup: bool,
+        region_settings: RegionSettings,
     ) -> Result<ForwardResult, GraphError> {
         let valtensor_inputs: Vec<ValTensor<Fp>> = model_inputs
             .iter()
             .map(|x| x.map(|elem| ValType::Value(Value::known(elem))).into())
             .collect();
-        let res = self.dummy_layout(run_args, &valtensor_inputs, witness_gen, check_lookup)?;
+        let res = self.dummy_layout(run_args, &valtensor_inputs, region_settings)?;
         Ok(res.into())
     }
 
@@ -609,9 +610,7 @@ impl Model {
         reader: &mut dyn std::io::Read,
         run_args: &RunArgs,
     ) -> Result<TractResult, GraphError> {
-        use tract_onnx::{
-            tract_core::internal::IntoArcTensor, tract_hir::internal::GenericFactoid,
-        };
+        use tract_onnx::tract_hir::internal::GenericFactoid;
 
         let mut model = tract_onnx::onnx().model_for_read(reader)?;
 
@@ -648,29 +647,11 @@ impl Model {
         }
 
         // Note: do not optimize the model, as the layout will depend on underlying hardware
-        let mut typed_model = model
+        let typed_model = model
             .into_typed()?
             .concretize_dims(&symbol_values)?
             .into_decluttered()?;
 
-        // concretize constants
-        for node in typed_model.eval_order()? {
-            let node = typed_model.node_mut(node);
-            if let Some(op) = node.op_as_mut::<tract_onnx::tract_core::ops::konst::Const>() {
-                if op.0.datum_type() == DatumType::TDim {
-                    // get inner value to Arc<Tensor>
-                    let mut constant = op.0.as_ref().clone();
-                    // Generally a shape or hyperparam
-                    constant
-                        .as_slice_mut::<tract_onnx::prelude::TDim>()?
-                        .iter_mut()
-                        .for_each(|x| *x = x.eval(&symbol_values));
-
-                    op.0 = constant.into_arc_tensor();
-                }
-            }
-        }
-
         Ok((typed_model, symbol_values))
     }
 
@@ -1410,8 +1391,7 @@ impl Model {
         &self,
         run_args: &RunArgs,
         inputs: &[ValTensor<Fp>],
-        witness_gen: bool,
-        check_lookup: bool,
+        region_settings: RegionSettings,
     ) -> Result<DummyPassRes, GraphError> {
         debug!("calculating num of constraints using dummy model layout...");
 
@@ -1430,8 +1410,7 @@ impl Model {
             vars: ModelVars::new_dummy(),
         };
 
-        let mut region =
-            RegionCtx::new_dummy(0, run_args.num_inner_cols, witness_gen, check_lookup);
+        let mut region = RegionCtx::new_dummy(0, run_args.num_inner_cols, region_settings);
 
         let outputs = self.layout_nodes(&mut model_config, &mut region, &mut results)?;
 

src/graph/utilities.rs

@@ -9,6 +9,7 @@ use crate::circuit::lookup::LookupOp;
 #[cfg(not(target_arch = "wasm32"))]
 use crate::circuit::poly::PolyOp;
 use crate::circuit::Op;
+use crate::fieldutils::IntegerRep;
 use crate::tensor::{Tensor, TensorError, TensorType};
 use halo2curves::bn256::Fr as Fp;
 use halo2curves::ff::PrimeField;
@@ -50,16 +51,20 @@ use tract_onnx::tract_hir::{
 /// * `dims` - the dimensionality of the resulting [Tensor].
 /// * `shift` - offset used in the fixed point representation.
 /// * `scale` - `2^scale` used in the fixed point representation.
-pub fn quantize_float(elem: &f64, shift: f64, scale: crate::Scale) -> Result<i64, TensorError> {
+pub fn quantize_float(
+    elem: &f64,
+    shift: f64,
+    scale: crate::Scale,
+) -> Result<IntegerRep, TensorError> {
     let mult = scale_to_multiplier(scale);
-    let max_value = ((i64::MAX as f64 - shift) / mult).round(); // the maximum value that can be represented w/o sig bit truncation
+    let max_value = ((IntegerRep::MAX as f64 - shift) / mult).round(); // the maximum value that can be represented w/o sig bit truncation
 
     if *elem > max_value {
         return Err(TensorError::SigBitTruncationError);
     }
 
     // we parallelize the quantization process as it seems to be quite slow at times
-    let scaled = (mult * *elem + shift).round() as i64;
+    let scaled = (mult * *elem + shift).round() as IntegerRep;
 
     Ok(scaled)
 }
@@ -70,7 +75,7 @@ pub fn quantize_float(elem: &f64, shift: f64, scale: crate::Scale) -> Result<i64
 /// * `scale` - `2^scale` used in the fixed point representation.
 /// * `shift` - offset used in the fixed point representation.
 pub fn dequantize(felt: Fp, scale: crate::Scale, shift: f64) -> f64 {
-    let int_rep = crate::fieldutils::felt_to_i64(felt);
+    let int_rep = crate::fieldutils::felt_to_integer_rep(felt);
     let multiplier = scale_to_multiplier(scale);
     int_rep as f64 / multiplier - shift
 }
@@ -85,6 +90,34 @@ pub fn multiplier_to_scale(mult: f64) -> crate::Scale {
     mult.log2().round() as crate::Scale
 }
 
+#[cfg(not(target_arch = "wasm32"))]
+/// extract padding from a onnx node.
+pub fn extract_padding(
+    pool_spec: &PoolSpec,
+    num_dims: usize,
+) -> Result<Vec<(usize, usize)>, GraphError> {
+    let padding = match &pool_spec.padding {
+        PaddingSpec::Explicit(b, a) | PaddingSpec::ExplicitOnnxPool(b, a, _) => {
+            b.iter().zip(a.iter()).map(|(b, a)| (*b, *a)).collect()
+        }
+        PaddingSpec::Valid => vec![(0, 0); num_dims],
+        _ => {
+            return Err(GraphError::MissingParams("padding".to_string()));
+        }
+    };
+    Ok(padding)
+}
+
+#[cfg(not(target_arch = "wasm32"))]
+/// Extracts the strides from a onnx node.
+pub fn extract_strides(pool_spec: &PoolSpec) -> Result<Vec<usize>, GraphError> {
+    Ok(pool_spec
+        .strides
+        .clone()
+        .ok_or(GraphError::MissingParams("stride".to_string()))?
+        .to_vec())
+}
+
 /// Gets the shape of a onnx node's outlets.
 #[cfg(not(target_arch = "wasm32"))]
 pub fn node_output_shapes(
@@ -255,6 +288,8 @@ pub fn new_op_from_onnx(
         .flat_map(|x| x.out_scales())
         .collect::<Vec<_>>();
 
+    let input_dims = inputs.iter().flat_map(|x| x.out_dims()).collect::<Vec<_>>();
+
     let mut replace_const = |scale: crate::Scale,
                              index: usize,
                              default_op: SupportedOp|
@@ -309,12 +344,9 @@ pub fn new_op_from_onnx(
             }
         }
         "MultiBroadcastTo" => {
-            let op = load_op::<MultiBroadcastTo>(node.op(), idx, node.op().name().to_string())?;
-            let shape = op.shape.clone();
-            let shape = shape
-                .iter()
-                .map(|x| x.to_usize())
-                .collect::<Result<Vec<_>, _>>()?;
+            let _op = load_op::<MultiBroadcastTo>(node.op(), idx, node.op().name().to_string())?;
+            let shapes = node_output_shapes(&node, symbol_values)?;
+            let shape = shapes[0].clone();
             SupportedOp::Linear(PolyOp::MultiBroadcastTo { shape })
         }
 
@@ -1073,18 +1105,8 @@ pub fn new_op_from_onnx(
                 ));
             }
 
-            let stride = pool_spec
-                .strides
-                .clone()
-                .ok_or(GraphError::MissingParams("stride".to_string()))?;
-            let padding = match &pool_spec.padding {
-                PaddingSpec::Explicit(b, a) | PaddingSpec::ExplicitOnnxPool(b, a, _) => {
-                    b.iter().zip(a.iter()).map(|(b, a)| (*b, *a)).collect()
-                }
-                _ => {
-                    return Err(GraphError::MissingParams("padding".to_string()));
-                }
-            };
+            let stride = extract_strides(pool_spec)?;
+            let padding = extract_padding(pool_spec, input_dims[0].len())?;
             let kernel_shape = &pool_spec.kernel_shape;
 
             SupportedOp::Hybrid(HybridOp::MaxPool {
@@ -1151,21 +1173,10 @@ pub fn new_op_from_onnx(
                 ));
             }
 
-            let stride = match conv_node.pool_spec.strides.clone() {
-                Some(s) => s.to_vec(),
-                None => {
-                    return Err(GraphError::MissingParams("strides".to_string()));
-                }
-            };
+            let pool_spec = &conv_node.pool_spec;
 
-            let padding = match &conv_node.pool_spec.padding {
-                PaddingSpec::Explicit(b, a) | PaddingSpec::ExplicitOnnxPool(b, a, _) => {
-                    b.iter().zip(a.iter()).map(|(b, a)| (*b, *a)).collect()
-                }
-                _ => {
-                    return Err(GraphError::MissingParams("padding".to_string()));
-                }
-            };
+            let stride = extract_strides(pool_spec)?;
+            let padding = extract_padding(pool_spec, input_dims[0].len())?;
 
             // if bias exists then rescale it to the input + kernel scale
             if input_scales.len() == 3 {
@@ -1183,7 +1194,13 @@ pub fn new_op_from_onnx(
                 }
             }
 
-            SupportedOp::Linear(PolyOp::Conv { padding, stride })
+            let group = conv_node.group;
+
+            SupportedOp::Linear(PolyOp::Conv {
+                padding,
+                stride,
+                group,
+            })
         }
         "Not" => SupportedOp::Linear(PolyOp::Not),
         "And" => SupportedOp::Linear(PolyOp::And),
@@ -1214,21 +1231,10 @@ pub fn new_op_from_onnx(
                 ));
             }
 
-            let stride = match deconv_node.pool_spec.strides.clone() {
-                Some(s) => s.to_vec(),
-                None => {
-                    return Err(GraphError::MissingParams("strides".to_string()));
-                }
-            };
-            let padding = match &deconv_node.pool_spec.padding {
-                PaddingSpec::Explicit(b, a) | PaddingSpec::ExplicitOnnxPool(b, a, _) => {
-                    b.iter().zip(a.iter()).map(|(b, a)| (*b, *a)).collect()
-                }
-                _ => {
-                    return Err(GraphError::MissingParams("padding".to_string()));
-                }
-            };
+            let pool_spec = &deconv_node.pool_spec;
 
+            let stride = extract_strides(pool_spec)?;
+            let padding = extract_padding(pool_spec, input_dims[0].len())?;
             // if bias exists then rescale it to the input + kernel scale
             if input_scales.len() == 3 {
                 let bias_scale = input_scales[2];
@@ -1249,6 +1255,7 @@ pub fn new_op_from_onnx(
                 padding,
                 output_padding: deconv_node.adjustments.to_vec(),
                 stride,
+                group: deconv_node.group,
             })
         }
         "Downsample" => {
@@ -1339,18 +1346,8 @@ pub fn new_op_from_onnx(
                 ));
             }
 
-            let stride = pool_spec
-                .strides
-                .clone()
-                .ok_or(GraphError::MissingParams("stride".to_string()))?;
-            let padding = match &pool_spec.padding {
-                PaddingSpec::Explicit(b, a) | PaddingSpec::ExplicitOnnxPool(b, a, _) => {
-                    b.iter().zip(a.iter()).map(|(b, a)| (*b, *a)).collect()
-                }
-                _ => {
-                    return Err(GraphError::MissingParams("padding".to_string()));
-                }
-            };
+            let stride = extract_strides(pool_spec)?;
+            let padding = extract_padding(pool_spec, input_dims[0].len())?;
 
             SupportedOp::Hybrid(HybridOp::SumPool {
                 padding,
@@ -1432,7 +1429,7 @@ pub fn quantize_tensor<F: PrimeField + TensorType + PartialOrd>(
     visibility: &Visibility,
 ) -> Result<Tensor<F>, TensorError> {
     let mut value: Tensor<F> = const_value.par_enum_map(|_, x| {
-        Ok::<_, TensorError>(crate::fieldutils::i64_to_felt::<F>(quantize_float(
+        Ok::<_, TensorError>(crate::fieldutils::integer_rep_to_felt::<F>(quantize_float(
             &(x).into(),
             0.0,
             scale,

src/lib.rs

@@ -85,6 +85,7 @@ use std::str::FromStr;
 
 use circuit::{table::Range, CheckMode, Tolerance};
 use clap::Args;
+use fieldutils::IntegerRep;
 use graph::Visibility;
 use halo2_proofs::poly::{
     ipa::commitment::IPACommitmentScheme, kzg::commitment::KZGCommitmentScheme,
@@ -243,7 +244,7 @@ pub struct RunArgs {
     #[arg(long, default_value = "1",  value_hint = clap::ValueHint::Other)]
     pub scale_rebase_multiplier: u32,
     /// The min and max elements in the lookup table input column
-    #[arg(short = 'B', long, value_parser = parse_key_val::<i64, i64>, default_value = "-32768->32768")]
+    #[arg(short = 'B', long, value_parser = parse_key_val::<IntegerRep, IntegerRep>, default_value = "-32768->32768")]
     pub lookup_range: Range,
     /// The log_2 number of rows
     #[arg(short = 'K', long, default_value = "17", value_hint = clap::ValueHint::Other)]

src/python.rs

@@ -6,7 +6,7 @@ use crate::circuit::modules::poseidon::{
 use crate::circuit::modules::Module;
 use crate::circuit::{CheckMode, Tolerance};
 use crate::commands::*;
-use crate::fieldutils::{felt_to_i64, i64_to_felt};
+use crate::fieldutils::{felt_to_integer_rep, integer_rep_to_felt, IntegerRep};
 use crate::graph::modules::POSEIDON_LEN_GRAPH;
 use crate::graph::TestDataSource;
 use crate::graph::{
@@ -331,9 +331,9 @@ fn felt_to_big_endian(felt: PyFelt) -> PyResult<String> {
 #[pyfunction(signature = (
     felt,
 ))]
-fn felt_to_int(felt: PyFelt) -> PyResult<i64> {
+fn felt_to_int(felt: PyFelt) -> PyResult<IntegerRep> {
     let felt = crate::pfsys::string_to_field::<Fr>(&felt);
-    let int_rep = felt_to_i64(felt);
+    let int_rep = felt_to_integer_rep(felt);
     Ok(int_rep)
 }
 
@@ -357,7 +357,7 @@ fn felt_to_int(felt: PyFelt) -> PyResult<i64> {
 ))]
 fn felt_to_float(felt: PyFelt, scale: crate::Scale) -> PyResult<f64> {
     let felt = crate::pfsys::string_to_field::<Fr>(&felt);
-    let int_rep = felt_to_i64(felt);
+    let int_rep = felt_to_integer_rep(felt);
     let multiplier = scale_to_multiplier(scale);
     let float_rep = int_rep as f64 / multiplier;
     Ok(float_rep)
@@ -385,7 +385,7 @@ fn felt_to_float(felt: PyFelt, scale: crate::Scale) -> PyResult<f64> {
 fn float_to_felt(input: f64, scale: crate::Scale) -> PyResult<PyFelt> {
     let int_rep = quantize_float(&input, 0.0, scale)
         .map_err(|_| PyIOError::new_err("Failed to quantize input"))?;
-    let felt = i64_to_felt(int_rep);
+    let felt = integer_rep_to_felt(int_rep);
     Ok(crate::pfsys::field_to_string::<Fr>(&felt))
 }
 

src/tensor/mod.rs

@@ -9,7 +9,7 @@ pub mod var;
 
 pub use errors::TensorError;
 
-use halo2curves::{bn256::Fr, ff::PrimeField};
+use halo2curves::ff::PrimeField;
 use maybe_rayon::{
     prelude::{
         IndexedParallelIterator, IntoParallelRefIterator, IntoParallelRefMutIterator,
@@ -26,7 +26,7 @@ use instant::Instant;
 
 use crate::{
     circuit::utils,
-    fieldutils::{felt_to_i32, felt_to_i64, i32_to_felt, i64_to_felt},
+    fieldutils::{integer_rep_to_felt, IntegerRep},
     graph::Visibility,
 };
 
@@ -108,7 +108,7 @@ impl TensorType for f32 {
         Some(0.0)
     }
 
-    // f32 doesnt impl Ord so we cant just use max like we can for i32, usize.
+    // f32 doesnt impl Ord so we cant just use max like we can for IntegerRep, usize.
     // A comparison between f32s needs to handle NAN values.
     fn tmax(&self, other: &Self) -> Option<Self> {
         match (self.is_nan(), other.is_nan()) {
@@ -131,7 +131,7 @@ impl TensorType for f64 {
         Some(0.0)
     }
 
-    // f32 doesnt impl Ord so we cant just use max like we can for i32, usize.
+    // f32 doesnt impl Ord so we cant just use max like we can for IntegerRep, usize.
     // A comparison between f32s needs to handle NAN values.
     fn tmax(&self, other: &Self) -> Option<Self> {
         match (self.is_nan(), other.is_nan()) {
@@ -150,8 +150,7 @@ impl TensorType for f64 {
 }
 
 tensor_type!(bool, Bool, false, true);
-tensor_type!(i64, Int64, 0, 1);
-tensor_type!(i32, Int32, 0, 1);
+tensor_type!(IntegerRep, IntegerRep, 0, 1);
 tensor_type!(usize, USize, 0, 1);
 tensor_type!((), Empty, (), ());
 tensor_type!(utils::F32, F32, utils::F32(0.0), utils::F32(1.0));
@@ -316,92 +315,6 @@ impl<T: TensorType> DerefMut for Tensor<T> {
         self.inner.deref_mut()
     }
 }
-/// Convert to i64 trait
-pub trait IntoI64 {
-    /// Convert to i64
-    fn into_i64(self) -> i64;
-
-    /// From i64
-    fn from_i64(i: i64) -> Self;
-}
-
-impl IntoI64 for i64 {
-    fn into_i64(self) -> i64 {
-        self
-    }
-    fn from_i64(i: i64) -> i64 {
-        i
-    }
-}
-
-impl IntoI64 for i32 {
-    fn into_i64(self) -> i64 {
-        self as i64
-    }
-    fn from_i64(i: i64) -> Self {
-        i as i32
-    }
-}
-
-impl IntoI64 for usize {
-    fn into_i64(self) -> i64 {
-        self as i64
-    }
-    fn from_i64(i: i64) -> Self {
-        i as usize
-    }
-}
-
-impl IntoI64 for f32 {
-    fn into_i64(self) -> i64 {
-        self as i64
-    }
-    fn from_i64(i: i64) -> Self {
-        i as f32
-    }
-}
-
-impl IntoI64 for f64 {
-    fn into_i64(self) -> i64 {
-        self as i64
-    }
-    fn from_i64(i: i64) -> Self {
-        i as f64
-    }
-}
-
-impl IntoI64 for () {
-    fn into_i64(self) -> i64 {
-        0
-    }
-    fn from_i64(_: i64) -> Self {}
-}
-
-impl IntoI64 for Fr {
-    fn into_i64(self) -> i64 {
-        felt_to_i64(self)
-    }
-    fn from_i64(i: i64) -> Self {
-        i64_to_felt::<Fr>(i)
-    }
-}
-
-impl<F: PrimeField + IntoI64> IntoI64 for Value<F> {
-    fn into_i64(self) -> i64 {
-        let mut res = vec![];
-        self.map(|x| res.push(x.into_i64()));
-
-        if res.is_empty() {
-            0
-        } else {
-            res[0]
-        }
-    }
-
-    fn from_i64(i: i64) -> Self {
-        Value::known(F::from_i64(i))
-    }
-}
 
 impl<T: PartialEq + TensorType> PartialEq for Tensor<T> {
     fn eq(&self, other: &Tensor<T>) -> bool {
@@ -431,42 +344,6 @@ where
     }
 }
 
-impl<F: PrimeField + Clone + TensorType + PartialOrd> From<Tensor<AssignedCell<Assigned<F>, F>>>
-    for Tensor<i32>
-{
-    fn from(value: Tensor<AssignedCell<Assigned<F>, F>>) -> Tensor<i32> {
-        let mut output = Vec::new();
-        value.map(|x| {
-            x.evaluate().value().map(|y| {
-                let e = felt_to_i32(*y);
-                output.push(e);
-                e
-            })
-        });
-        Tensor::new(Some(&output), value.dims()).unwrap()
-    }
-}
-
-impl<F: PrimeField + Clone + TensorType + PartialOrd> From<Tensor<AssignedCell<F, F>>>
-    for Tensor<i32>
-{
-    fn from(value: Tensor<AssignedCell<F, F>>) -> Tensor<i32> {
-        let mut output = Vec::new();
-        value.map(|x| {
-            let mut i = 0;
-            x.value().map(|y| {
-                let e = felt_to_i32(*y);
-                output.push(e);
-                i += 1;
-            });
-            if i == 0 {
-                output.push(0);
-            }
-        });
-        Tensor::new(Some(&output), value.dims()).unwrap()
-    }
-}
-
 impl<F: PrimeField + Clone + TensorType + PartialOrd> From<Tensor<AssignedCell<Assigned<F>, F>>>
     for Tensor<Value<F>>
 {
@@ -479,24 +356,6 @@ impl<F: PrimeField + Clone + TensorType + PartialOrd> From<Tensor<AssignedCell<A
     }
 }
 
-impl<F: PrimeField + TensorType + Clone + PartialOrd> From<Tensor<Value<F>>> for Tensor<i32> {
-    fn from(t: Tensor<Value<F>>) -> Tensor<i32> {
-        let mut output = Vec::new();
-        t.map(|x| {
-            let mut i = 0;
-            x.map(|y| {
-                let e = felt_to_i32(y);
-                output.push(e);
-                i += 1;
-            });
-            if i == 0 {
-                output.push(0);
-            }
-        });
-        Tensor::new(Some(&output), t.dims()).unwrap()
-    }
-}
-
 impl<F: PrimeField + TensorType + Clone + PartialOrd> From<Tensor<Value<F>>>
     for Tensor<Value<Assigned<F>>>
 {
@@ -508,20 +367,10 @@ impl<F: PrimeField + TensorType + Clone + PartialOrd> From<Tensor<Value<F>>>
     }
 }
 
-impl<F: PrimeField + TensorType + Clone> From<Tensor<i32>> for Tensor<Value<F>> {
-    fn from(t: Tensor<i32>) -> Tensor<Value<F>> {
+impl<F: PrimeField + TensorType + Clone> From<Tensor<IntegerRep>> for Tensor<Value<F>> {
+    fn from(t: Tensor<IntegerRep>) -> Tensor<Value<F>> {
         let mut ta: Tensor<Value<F>> =
-            Tensor::from((0..t.len()).map(|i| Value::known(i32_to_felt::<F>(t[i]))));
-        // safe to unwrap as we know the dims are correct
-        ta.reshape(t.dims()).unwrap();
-        ta
-    }
-}
-
-impl<F: PrimeField + TensorType + Clone> From<Tensor<i64>> for Tensor<Value<F>> {
-    fn from(t: Tensor<i64>) -> Tensor<Value<F>> {
-        let mut ta: Tensor<Value<F>> =
-            Tensor::from((0..t.len()).map(|i| Value::known(i64_to_felt::<F>(t[i]))));
+            Tensor::from((0..t.len()).map(|i| Value::known(integer_rep_to_felt::<F>(t[i]))));
         // safe to unwrap as we know the dims are correct
         ta.reshape(t.dims()).unwrap();
         ta
@@ -633,7 +482,8 @@ impl<T: Clone + TensorType> Tensor<T> {
     ///
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let mut a = Tensor::<i32>::new(None, &[3, 3, 3]).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(None, &[3, 3, 3]).unwrap();
     ///
     /// a.set(&[0, 0, 1], 10);
     /// assert_eq!(a[0 + 0 + 1], 10);
@@ -650,7 +500,8 @@ impl<T: Clone + TensorType> Tensor<T> {
     ///
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let mut a = Tensor::<i32>::new(None, &[2, 3, 5]).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(None, &[2, 3, 5]).unwrap();
     ///
     /// a[1*15 + 1*5 + 1] = 5;
     /// assert_eq!(a.get(&[1, 1, 1]), 5);
@@ -664,7 +515,8 @@ impl<T: Clone + TensorType> Tensor<T> {
     ///
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let mut a = Tensor::<i32>::new(None, &[2, 3, 5]).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(None, &[2, 3, 5]).unwrap();
     ///
     /// a[1*15 + 1*5 + 1] = 5;
     /// assert_eq!(a.get(&[1, 1, 1]), 5);
@@ -684,11 +536,12 @@ impl<T: Clone + TensorType> Tensor<T> {
     /// Pad to a length that is divisible by n
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let mut a = Tensor::<i32>::new(Some(&[1,2,3,4,5,6]), &[2, 3]).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6, 0, 0]), &[8]).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1,2,3,4,5,6]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6, 0, 0]), &[8]).unwrap();
     /// assert_eq!(a.pad_to_zero_rem(4, 0).unwrap(), expected);
     ///
-    /// let expected = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6, 0, 0, 0]), &[9]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6, 0, 0, 0]), &[9]).unwrap();
     /// assert_eq!(a.pad_to_zero_rem(9, 0).unwrap(), expected);
     /// ```
     pub fn pad_to_zero_rem(&self, n: usize, pad: T) -> Result<Tensor<T>, TensorError> {
@@ -704,7 +557,8 @@ impl<T: Clone + TensorType> Tensor<T> {
     ///
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let mut a = Tensor::<i32>::new(None, &[2, 3, 5]).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(None, &[2, 3, 5]).unwrap();
     ///
     /// let flat_index = 1*15 + 1*5 + 1;
     /// a[1*15 + 1*5 + 1] = 5;
@@ -731,8 +585,9 @@ impl<T: Clone + TensorType> Tensor<T> {
     /// Get a slice from the Tensor.
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 2, 3]), &[3]).unwrap();
-    /// let mut b = Tensor::<i32>::new(Some(&[1, 2]), &[2]).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3]), &[3]).unwrap();
+    /// let mut b = Tensor::<IntegerRep>::new(Some(&[1, 2]), &[2]).unwrap();
     ///
     /// assert_eq!(a.get_slice(&[0..2]).unwrap(), b);
     /// ```
@@ -782,9 +637,10 @@ impl<T: Clone + TensorType> Tensor<T> {
     /// Set a slice of the Tensor.
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6]), &[2, 3]).unwrap();
-    /// let b = Tensor::<i32>::new(Some(&[1, 2, 3, 1, 2, 3]), &[2, 3]).unwrap();
-    /// a.set_slice(&[1..2], &Tensor::<i32>::new(Some(&[1, 2, 3]), &[1, 3]).unwrap()).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6]), &[2, 3]).unwrap();
+    /// let b = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 1, 2, 3]), &[2, 3]).unwrap();
+    /// a.set_slice(&[1..2], &Tensor::<IntegerRep>::new(Some(&[1, 2, 3]), &[1, 3]).unwrap()).unwrap();
     /// assert_eq!(a, b);
     /// ```
     pub fn set_slice(
@@ -845,6 +701,7 @@ impl<T: Clone + TensorType> Tensor<T> {
     ///
     /// ```
     /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
     /// let a = Tensor::<f32>::new(None, &[3, 3, 3]).unwrap();
     ///
     /// assert_eq!(a.get_index(&[2, 2, 2]), 26);
@@ -868,12 +725,13 @@ impl<T: Clone + TensorType> Tensor<T> {
     ///
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let a = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6]), &[6]).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[1, 2, 3, 3, 4, 5, 5, 6]), &[8]).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6]), &[6]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 3, 4, 5, 5, 6]), &[8]).unwrap();
     /// assert_eq!(a.duplicate_every_n(3, 1, 0).unwrap(), expected);
     /// assert_eq!(a.duplicate_every_n(7, 1, 0).unwrap(), a);
     ///
-    /// let expected = Tensor::<i32>::new(Some(&[1, 1, 2, 3, 3, 4, 5, 5, 6]), &[9]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[1, 1, 2, 3, 3, 4, 5, 5, 6]), &[9]).unwrap();
     /// assert_eq!(a.duplicate_every_n(3, 1, 2).unwrap(), expected);
     ///
     /// ```
@@ -900,8 +758,9 @@ impl<T: Clone + TensorType> Tensor<T> {
     ///
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let a = Tensor::<i32>::new(Some(&[1, 2, 3, 3, 4, 5, 6, 6]), &[8]).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[1, 2, 3, 3, 5, 6, 6]), &[7]).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 3, 4, 5, 6, 6]), &[8]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 3, 5, 6, 6]), &[7]).unwrap();
     /// assert_eq!(a.remove_every_n(4, 1, 0).unwrap(), expected);
     ///
     ///
@@ -935,14 +794,15 @@ impl<T: Clone + TensorType> Tensor<T> {
     /// WARN: assumes indices are in ascending order for speed
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let a = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6]), &[6]).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[1, 2, 3, 6]), &[4]).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6]), &[6]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 6]), &[4]).unwrap();
     /// let mut indices = vec![3, 4];
     /// assert_eq!(a.remove_indices(&mut indices, true).unwrap(), expected);
     ///
     ///
-    /// let a = Tensor::<i32>::new(Some(&[52, -245, 153, 13, -4, -56, -163, 249, -128, -172, 396, 143, 2, -96, 504, -44, -158, -393, 61, 95, 191, 74, 64, -219, 553, 104, 235, 222, 44, -216, 63, -251, 40, -140, 112, -355, 60, 123, 26, -116, -89, -200, -109, 168, 135, -34, -99, -54, 5, -81, 322, 87, 4, -139, 420, 92, -295, -12, 262, -1, 26, -48, 231, 1, -335, 244, 188, -4, 5, -362, 57, -198, -184, -117, 40, 305, 49, 30, -59, -26, -37, 96]), &[82]).unwrap();
-    /// let b = Tensor::<i32>::new(Some(&[52, -245, 153, 13, -4, -56, -163, 249, -128, -172, 396, 143, 2, -96, 504, -44, -158, -393, 61, 95, 191, 74, 64, -219, 553, 104, 235, 222, 44, -216, 63, -251, 40, -140, 112, -355, 60, 123, 26, -116, -89, -200, -109, 168, 135, -34, -99, -54, 5, -81, 322, 87, 4, -139, 420, 92, -295, -12, 262, -1, 26, -48, 231, -335, 244, 188, 5, -362, 57, -198, -184, -117, 40, 305, 49, 30, -59, -26, -37, 96]), &[80]).unwrap();
+    /// let a = Tensor::<IntegerRep>::new(Some(&[52, -245, 153, 13, -4, -56, -163, 249, -128, -172, 396, 143, 2, -96, 504, -44, -158, -393, 61, 95, 191, 74, 64, -219, 553, 104, 235, 222, 44, -216, 63, -251, 40, -140, 112, -355, 60, 123, 26, -116, -89, -200, -109, 168, 135, -34, -99, -54, 5, -81, 322, 87, 4, -139, 420, 92, -295, -12, 262, -1, 26, -48, 231, 1, -335, 244, 188, -4, 5, -362, 57, -198, -184, -117, 40, 305, 49, 30, -59, -26, -37, 96]), &[82]).unwrap();
+    /// let b = Tensor::<IntegerRep>::new(Some(&[52, -245, 153, 13, -4, -56, -163, 249, -128, -172, 396, 143, 2, -96, 504, -44, -158, -393, 61, 95, 191, 74, 64, -219, 553, 104, 235, 222, 44, -216, 63, -251, 40, -140, 112, -355, 60, 123, 26, -116, -89, -200, -109, 168, 135, -34, -99, -54, 5, -81, 322, 87, 4, -139, 420, 92, -295, -12, 262, -1, 26, -48, 231, -335, 244, 188, 5, -362, 57, -198, -184, -117, 40, 305, 49, 30, -59, -26, -37, 96]), &[80]).unwrap();
     /// let mut indices = vec![63, 67];
     /// assert_eq!(a.remove_indices(&mut indices, true).unwrap(), b);
     /// ```
@@ -972,6 +832,7 @@ impl<T: Clone + TensorType> Tensor<T> {
     ///Reshape the tensor
     /// ```
     /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
     /// let mut a = Tensor::<f32>::new(None, &[3, 3, 3]).unwrap();
     /// a.reshape(&[9, 3]);
     /// assert_eq!(a.dims(), &[9, 3]);
@@ -1006,22 +867,23 @@ impl<T: Clone + TensorType> Tensor<T> {
     /// Move axis of the tensor
     /// ```
     /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
     /// let mut a = Tensor::<f32>::new(None, &[3, 3, 3]).unwrap();
     /// let b = a.move_axis(0, 2).unwrap();
     /// assert_eq!(b.dims(), &[3, 3, 3]);
     ///
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6]), &[3, 1, 2]).unwrap();
-    /// let mut expected = Tensor::<i32>::new(Some(&[1, 3, 5, 2, 4, 6]), &[1, 2, 3]).unwrap();
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6]), &[3, 1, 2]).unwrap();
+    /// let mut expected = Tensor::<IntegerRep>::new(Some(&[1, 3, 5, 2, 4, 6]), &[1, 2, 3]).unwrap();
     /// let b = a.move_axis(0, 2).unwrap();
     /// assert_eq!(b, expected);
     ///
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), &[2, 3, 2]).unwrap();
-    /// let mut expected = Tensor::<i32>::new(Some(&[1, 3, 5, 2, 4, 6, 7, 9, 11, 8, 10, 12]), &[2, 2, 3]).unwrap();
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), &[2, 3, 2]).unwrap();
+    /// let mut expected = Tensor::<IntegerRep>::new(Some(&[1, 3, 5, 2, 4, 6, 7, 9, 11, 8, 10, 12]), &[2, 2, 3]).unwrap();
     /// let b = a.move_axis(1, 2).unwrap();
     /// assert_eq!(b, expected);
     ///
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), &[2, 3, 2]).unwrap();
-    /// let mut expected = Tensor::<i32>::new(Some(&[1, 3, 5, 2, 4, 6, 7, 9, 11, 8, 10, 12]), &[2, 2, 3]).unwrap();
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), &[2, 3, 2]).unwrap();
+    /// let mut expected = Tensor::<IntegerRep>::new(Some(&[1, 3, 5, 2, 4, 6, 7, 9, 11, 8, 10, 12]), &[2, 2, 3]).unwrap();
     /// let b = a.move_axis(2, 1).unwrap();
     /// assert_eq!(b, expected);
     /// ```
@@ -1086,22 +948,23 @@ impl<T: Clone + TensorType> Tensor<T> {
     /// Swap axes of the tensor
     /// ```
     /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
     /// let mut a = Tensor::<f32>::new(None, &[3, 3, 3]).unwrap();
     /// let b = a.swap_axes(0, 2).unwrap();
     /// assert_eq!(b.dims(), &[3, 3, 3]);
     ///
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6]), &[3, 1, 2]).unwrap();
-    /// let mut expected = Tensor::<i32>::new(Some(&[1, 3, 5, 2, 4, 6]), &[2, 1, 3]).unwrap();
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6]), &[3, 1, 2]).unwrap();
+    /// let mut expected = Tensor::<IntegerRep>::new(Some(&[1, 3, 5, 2, 4, 6]), &[2, 1, 3]).unwrap();
     /// let b = a.swap_axes(0, 2).unwrap();
     /// assert_eq!(b, expected);
     ///
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), &[2, 3, 2]).unwrap();
-    /// let mut expected = Tensor::<i32>::new(Some(&[1, 3, 5, 2, 4, 6, 7, 9, 11, 8, 10, 12]), &[2, 2, 3]).unwrap();
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), &[2, 3, 2]).unwrap();
+    /// let mut expected = Tensor::<IntegerRep>::new(Some(&[1, 3, 5, 2, 4, 6, 7, 9, 11, 8, 10, 12]), &[2, 2, 3]).unwrap();
     /// let b = a.swap_axes(1, 2).unwrap();
     /// assert_eq!(b, expected);
     ///
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), &[2, 3, 2]).unwrap();
-    /// let mut expected = Tensor::<i32>::new(Some(&[1, 3, 5, 2, 4, 6, 7, 9, 11, 8, 10, 12]), &[2, 2, 3]).unwrap();
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), &[2, 3, 2]).unwrap();
+    /// let mut expected = Tensor::<IntegerRep>::new(Some(&[1, 3, 5, 2, 4, 6, 7, 9, 11, 8, 10, 12]), &[2, 2, 3]).unwrap();
     /// let b = a.swap_axes(2, 1).unwrap();
     /// assert_eq!(b, expected);
     /// ```
@@ -1148,9 +1011,10 @@ impl<T: Clone + TensorType> Tensor<T> {
     /// Broadcasts the tensor to a given shape
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 2, 3]), &[3, 1]).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3]), &[3, 1]).unwrap();
     ///
-    /// let mut expected = Tensor::<i32>::new(Some(&[1, 1, 1, 2, 2, 2, 3, 3, 3]), &[3, 3]).unwrap();
+    /// let mut expected = Tensor::<IntegerRep>::new(Some(&[1, 1, 1, 2, 2, 2, 3, 3, 3]), &[3, 3]).unwrap();
     /// assert_eq!(a.expand(&[3, 3]).unwrap(), expected);
     ///
     /// ```
@@ -1204,6 +1068,7 @@ impl<T: Clone + TensorType> Tensor<T> {
     ///Flatten the tensor shape
     /// ```
     /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
     /// let mut a = Tensor::<f32>::new(None, &[3, 3, 3]).unwrap();
     /// a.flatten();
     /// assert_eq!(a.dims(), &[27]);
@@ -1217,8 +1082,9 @@ impl<T: Clone + TensorType> Tensor<T> {
     /// Maps a function to tensors
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 4]), &[2]).unwrap();
-    /// let mut c = a.map(|x| i32::pow(x,2));
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 4]), &[2]).unwrap();
+    /// let mut c = a.map(|x| IntegerRep::pow(x,2));
     /// assert_eq!(c, Tensor::from([1, 16].into_iter()))
     /// ```
     pub fn map<F: FnMut(T) -> G, G: TensorType>(&self, mut f: F) -> Tensor<G> {
@@ -1231,8 +1097,9 @@ impl<T: Clone + TensorType> Tensor<T> {
     /// Maps a function to tensors and enumerates
     /// ```
     /// use ezkl::tensor::{Tensor, TensorError};
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 4]), &[2]).unwrap();
-    /// let mut c = a.enum_map::<_,_,TensorError>(|i, x| Ok(i32::pow(x + i as i32, 2))).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 4]), &[2]).unwrap();
+    /// let mut c = a.enum_map::<_,_,TensorError>(|i, x| Ok(IntegerRep::pow(x + i as IntegerRep, 2))).unwrap();
     /// assert_eq!(c, Tensor::from([1, 25].into_iter()));
     /// ```
     pub fn enum_map<F: FnMut(usize, T) -> Result<G, E>, G: TensorType, E: Error>(
@@ -1254,8 +1121,9 @@ impl<T: Clone + TensorType> Tensor<T> {
     /// Maps a function to tensors and enumerates in parallel
     /// ```
     /// use ezkl::tensor::{Tensor, TensorError};
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 4]), &[2]).unwrap();
-    /// let mut c = a.par_enum_map::<_,_,TensorError>(|i, x| Ok(i32::pow(x + i as i32, 2))).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 4]), &[2]).unwrap();
+    /// let mut c = a.par_enum_map::<_,_,TensorError>(|i, x| Ok(IntegerRep::pow(x + i as IntegerRep, 2))).unwrap();
     /// assert_eq!(c, Tensor::from([1, 25].into_iter()));
     /// ```
     pub fn par_enum_map<
@@ -1281,11 +1149,37 @@ impl<T: Clone + TensorType> Tensor<T> {
         Ok(t)
     }
 
+    /// Get last elem from Tensor
+    /// ```
+    /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3]), &[3]).unwrap();
+    /// let mut b = Tensor::<IntegerRep>::new(Some(&[3]), &[1]).unwrap();
+    ///
+    /// assert_eq!(a.last().unwrap(), b);
+    /// ```
+    pub fn last(&self) -> Result<Tensor<T>, TensorError>
+    where
+        T: Send + Sync,
+    {
+        let res = match self.inner.last() {
+            Some(e) => e.clone(),
+            None => {
+                return Err(TensorError::DimError(
+                    "Cannot get last element of empty tensor".to_string(),
+                ))
+            }
+        };
+
+        Tensor::new(Some(&[res]), &[1])
+    }
+
     /// Maps a function to tensors and enumerates in parallel
     /// ```
     /// use ezkl::tensor::{Tensor, TensorError};
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 4]), &[2]).unwrap();
-    /// let mut c = a.par_enum_map::<_,_,TensorError>(|i, x| Ok(i32::pow(x + i as i32, 2))).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 4]), &[2]).unwrap();
+    /// let mut c = a.par_enum_map::<_,_,TensorError>(|i, x| Ok(IntegerRep::pow(x + i as IntegerRep, 2))).unwrap();
     /// assert_eq!(c, Tensor::from([1, 25].into_iter()));
     /// ```
     pub fn par_enum_map_mut_filtered<
@@ -1293,7 +1187,7 @@ impl<T: Clone + TensorType> Tensor<T> {
         E: Error + std::marker::Send + std::marker::Sync,
     >(
         &mut self,
-        filter_indices: &std::collections::HashSet<&usize>,
+        filter_indices: &std::collections::HashSet<usize>,
         f: F,
     ) -> Result<(), E>
     where
@@ -1308,103 +1202,13 @@ impl<T: Clone + TensorType> Tensor<T> {
     }
 }
 
-#[cfg(feature = "metal")]
-#[allow(unsafe_code)]
-/// Perform a tensor operation on the GPU using Metal.
-pub fn metal_tensor_op<T: Clone + TensorType + IntoI64 + Send + Sync>(
-    v: &Tensor<T>,
-    w: &Tensor<T>,
-    op: &str,
-) -> Tensor<T> {
-    assert_eq!(v.dims(), w.dims());
-
-    log::trace!("------------------------------------------------");
-
-    let start = Instant::now();
-    let v = v
-        .par_enum_map(|_, x| Ok::<_, TensorError>(x.into_i64()))
-        .unwrap();
-    let w = w
-        .par_enum_map(|_, x| Ok::<_, TensorError>(x.into_i64()))
-        .unwrap();
-    log::trace!("Time to map tensors: {:?}", start.elapsed());
-
-    objc::rc::autoreleasepool(|| {
-        // create function pipeline.
-        // this compiles the function, so a pipline can't be created in performance sensitive code.
-
-        let pipeline = &PIPELINES[op];
-
-        let length = v.len() as u64;
-        let size = length * core::mem::size_of::<i64>() as u64;
-        assert_eq!(v.len(), w.len());
-
-        let start = Instant::now();
-
-        let buffer_a = DEVICE.new_buffer_with_data(
-            unsafe { std::mem::transmute(v.as_ptr()) },
-            size,
-            MTLResourceOptions::StorageModeShared,
-        );
-        let buffer_b = DEVICE.new_buffer_with_data(
-            unsafe { std::mem::transmute(w.as_ptr()) },
-            size,
-            MTLResourceOptions::StorageModeShared,
-        );
-        let buffer_result = DEVICE.new_buffer(
-            size, // the operation will return an array with the same size.
-            MTLResourceOptions::StorageModeShared,
-        );
-
-        log::trace!("Time to load buffers: {:?}", start.elapsed());
-
-        // for sending commands, a command buffer is needed.
-        let start = Instant::now();
-        let command_buffer = QUEUE.new_command_buffer();
-        log::trace!("Time to load command buffer: {:?}", start.elapsed());
-        // to write commands into a buffer an encoder is needed, in our case a compute encoder.
-        let start = Instant::now();
-        let compute_encoder = command_buffer.new_compute_command_encoder();
-        compute_encoder.set_compute_pipeline_state(&pipeline);
-        compute_encoder.set_buffers(
-            0,
-            &[Some(&buffer_a), Some(&buffer_b), Some(&buffer_result)],
-            &[0; 3],
-        );
-        log::trace!("Time to load compute encoder: {:?}", start.elapsed());
-
-        // specify thread count and organization
-        let start = Instant::now();
-        let grid_size = MTLSize::new(length, 1, 1);
-        let threadgroup_size = MTLSize::new(length, 1, 1);
-        compute_encoder.dispatch_threads(grid_size, threadgroup_size);
-        log::trace!("Time to dispatch threads: {:?}", start.elapsed());
-
-        // end encoding and execute commands
-        let start = Instant::now();
-        compute_encoder.end_encoding();
-        command_buffer.commit();
-
-        command_buffer.wait_until_completed();
-        log::trace!("Time to commit: {:?}", start.elapsed());
-
-        let start = Instant::now();
-        let ptr = buffer_result.contents() as *const i64;
-        let len = buffer_result.length() as usize / std::mem::size_of::<i64>();
-        let slice = unsafe { core::slice::from_raw_parts(ptr, len) };
-        let res = Tensor::new(Some(&slice.to_vec()), &v.dims()).unwrap();
-        log::trace!("Time to get result: {:?}", start.elapsed());
-
-        res.map(|x| T::from_i64(x))
-    })
-}
-
 impl<T: Clone + TensorType> Tensor<Tensor<T>> {
     /// Flattens a tensor of tensors
     /// ```
     /// use ezkl::tensor::Tensor;
-    /// let mut a = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6]), &[2, 3]).unwrap();
-    /// let mut b = Tensor::<i32>::new(Some(&[1, 4]), &[2, 1]).unwrap();
+    /// use ezkl::fieldutils::IntegerRep;
+    /// let mut a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6]), &[2, 3]).unwrap();
+    /// let mut b = Tensor::<IntegerRep>::new(Some(&[1, 4]), &[2, 1]).unwrap();
     /// let mut c = Tensor::new(Some(&[a,b]), &[2]).unwrap();
     /// let mut d = c.combine().unwrap();
     /// assert_eq!(d.dims(), &[8]);
@@ -1420,9 +1224,7 @@ impl<T: Clone + TensorType> Tensor<Tensor<T>> {
     }
 }
 
-impl<T: TensorType + Add<Output = T> + std::marker::Send + std::marker::Sync + IntoI64> Add
-    for Tensor<T>
-{
+impl<T: TensorType + Add<Output = T> + std::marker::Send + std::marker::Sync> Add for Tensor<T> {
     type Output = Result<Tensor<T>, TensorError>;
     /// Adds tensors.
     /// # Arguments
@@ -1432,42 +1234,43 @@ impl<T: TensorType + Add<Output = T> + std::marker::Send + std::marker::Sync + I
     /// # Examples
     /// ```
     /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
     /// use std::ops::Add;
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 1, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
-    /// let k = Tensor::<i32>::new(
+    /// let k = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 3, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
     /// let result = x.add(k).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[4, 4, 4, 2, 2, 2]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[4, 4, 4, 2, 2, 2]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     ///
     /// // Now test 1D casting
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 1, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
-    /// let k = Tensor::<i32>::new(
+    /// let k = Tensor::<IntegerRep>::new(
     ///     Some(&[2]),
     ///     &[1]).unwrap();
     /// let result = x.add(k).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[4, 3, 4, 3, 3, 3]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[4, 3, 4, 3, 3, 3]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     ///
     ///
     /// // Now test 2D casting
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 1, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
-    /// let k = Tensor::<i32>::new(
+    /// let k = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 3]),
     ///     &[2]).unwrap();
     /// let result = x.add(k).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[4, 3, 4, 4, 4, 4]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[4, 3, 4, 4, 4, 4]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     /// ```
     fn add(self, rhs: Self) -> Self::Output {
@@ -1501,13 +1304,14 @@ impl<T: TensorType + Neg<Output = T> + std::marker::Send + std::marker::Sync> Ne
     /// # Examples
     /// ```
     /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
     /// use std::ops::Neg;
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///    Some(&[2, 1, 2, 1, 1, 1]),
     ///   &[2, 3],
     /// ).unwrap();
     /// let result = x.neg();
-    /// let expected = Tensor::<i32>::new(Some(&[-2, -1, -2, -1, -1, -1]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[-2, -1, -2, -1, -1, -1]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     /// ```
     fn neg(self) -> Self {
@@ -1520,9 +1324,7 @@ impl<T: TensorType + Neg<Output = T> + std::marker::Send + std::marker::Sync> Ne
     }
 }
 
-impl<T: TensorType + Sub<Output = T> + std::marker::Send + std::marker::Sync + IntoI64> Sub
-    for Tensor<T>
-{
+impl<T: TensorType + Sub<Output = T> + std::marker::Send + std::marker::Sync> Sub for Tensor<T> {
     type Output = Result<Tensor<T>, TensorError>;
     /// Subtracts tensors.
     /// # Arguments
@@ -1532,43 +1334,44 @@ impl<T: TensorType + Sub<Output = T> + std::marker::Send + std::marker::Sync + I
     /// # Examples
     /// ```
     /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
     /// use std::ops::Sub;
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 1, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
-    /// let k = Tensor::<i32>::new(
+    /// let k = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 3, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
     /// let result = x.sub(k).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[0, -2, 0, 0, 0, 0]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[0, -2, 0, 0, 0, 0]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     ///
     /// // Now test 1D sub
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 1, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
-    /// let k = Tensor::<i32>::new(
+    /// let k = Tensor::<IntegerRep>::new(
     ///     Some(&[2]),
     ///     &[1],
     /// ).unwrap();
     /// let result = x.sub(k).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[0, -1, 0, -1, -1, -1]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[0, -1, 0, -1, -1, -1]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     ///
     /// // Now test 2D sub
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 1, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
-    /// let k = Tensor::<i32>::new(
+    /// let k = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 3]),
     ///     &[2],
     /// ).unwrap();
     /// let result = x.sub(k).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[0, -1, 0, -2, -2, -2]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[0, -1, 0, -2, -2, -2]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     /// ```
     fn sub(self, rhs: Self) -> Self::Output {
@@ -1594,9 +1397,7 @@ impl<T: TensorType + Sub<Output = T> + std::marker::Send + std::marker::Sync + I
     }
 }
 
-impl<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::Sync + IntoI64> Mul
-    for Tensor<T>
-{
+impl<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::Sync> Mul for Tensor<T> {
     type Output = Result<Tensor<T>, TensorError>;
     /// Elementwise multiplies tensors.
     /// # Arguments
@@ -1606,41 +1407,42 @@ impl<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::Sync + I
     /// # Examples
     /// ```
     /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
     /// use std::ops::Mul;
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 1, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
-    /// let k = Tensor::<i32>::new(
+    /// let k = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 3, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
     /// let result = x.mul(k).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[4, 3, 4, 1, 1, 1]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[4, 3, 4, 1, 1, 1]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     ///
     /// // Now test 1D mult
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 1, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
-    /// let k = Tensor::<i32>::new(
+    /// let k = Tensor::<IntegerRep>::new(
     ///     Some(&[2]),
     ///     &[1]).unwrap();
     /// let result = x.mul(k).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[4, 2, 4, 2, 2, 2]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[4, 2, 4, 2, 2, 2]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     ///
     /// // Now test 2D mult
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 1, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
-    /// let k = Tensor::<i32>::new(
+    /// let k = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 2]),
     ///     &[2]).unwrap();
     /// let result = x.mul(k).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[4, 2, 4, 2, 2, 2]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[4, 2, 4, 2, 2, 2]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     /// ```
     fn mul(self, rhs: Self) -> Self::Output {
@@ -1666,7 +1468,7 @@ impl<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::Sync + I
     }
 }
 
-impl<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::Sync + IntoI64> Tensor<T> {
+impl<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::Sync> Tensor<T> {
     /// Elementwise raise a tensor to the nth power.
     /// # Arguments
     ///
@@ -1675,13 +1477,14 @@ impl<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::Sync + I
     /// # Examples
     /// ```
     /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
     /// use std::ops::Mul;
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 15, 2, 1, 1, 0]),
     ///     &[2, 3],
     /// ).unwrap();
     /// let result = x.pow(3).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[8, 3375, 8, 1, 1, 0]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[8, 3375, 8, 1, 1, 0]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     /// ```
     pub fn pow(&self, mut exp: u32) -> Result<Self, TensorError> {
@@ -1715,30 +1518,31 @@ impl<T: TensorType + Div<Output = T> + std::marker::Send + std::marker::Sync> Di
     /// # Examples
     /// ```
     /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
     /// use std::ops::Div;
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///     Some(&[4, 1, 4, 1, 1, 4]),
     ///     &[2, 3],
     /// ).unwrap();
-    /// let y = Tensor::<i32>::new(
+    /// let y = Tensor::<IntegerRep>::new(
     ///     Some(&[2, 1, 2, 1, 1, 1]),
     ///     &[2, 3],
     /// ).unwrap();
     /// let result = x.div(y).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[2, 1, 2, 1, 1, 4]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[2, 1, 2, 1, 1, 4]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     ///
     /// // test 1D casting
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///     Some(&[4, 1, 4, 1, 1, 4]),
     ///     &[2, 3],
     /// ).unwrap();
-    /// let y = Tensor::<i32>::new(
+    /// let y = Tensor::<IntegerRep>::new(
     ///     Some(&[2]),
     ///     &[1],
     /// ).unwrap();
     /// let result = x.div(y).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[2, 0, 2, 0, 0, 2]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[2, 0, 2, 0, 0, 2]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     /// ```
     fn div(self, rhs: Self) -> Self::Output {
@@ -1765,17 +1569,18 @@ impl<T: TensorType + Rem<Output = T> + std::marker::Send + std::marker::Sync> Re
     /// # Examples
     /// ```
     /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
     /// use std::ops::Rem;
-    /// let x = Tensor::<i32>::new(
+    /// let x = Tensor::<IntegerRep>::new(
     ///    Some(&[4, 1, 4, 1, 1, 4]),
     ///   &[2, 3],
     /// ).unwrap();
-    /// let y = Tensor::<i32>::new(
+    /// let y = Tensor::<IntegerRep>::new(
     ///    Some(&[2, 1, 2, 1, 1, 1]),
     ///  &[2, 3],
     /// ).unwrap();
     /// let result = x.rem(y).unwrap();
-    /// let expected = Tensor::<i32>::new(Some(&[0, 0, 0, 0, 0, 0]), &[2, 3]).unwrap();
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[0, 0, 0, 0, 0, 0]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     /// ```
     fn rem(self, rhs: Self) -> Self::Output {
@@ -1859,25 +1664,25 @@ mod tests {
 
     #[test]
     fn tensor_clone() {
-        let x = Tensor::<i32>::new(Some(&[1, 2, 3]), &[3]).unwrap();
+        let x = Tensor::<IntegerRep>::new(Some(&[1, 2, 3]), &[3]).unwrap();
         assert_eq!(x, x.clone());
     }
 
     #[test]
     fn tensor_eq() {
-        let a = Tensor::<i32>::new(Some(&[1, 2, 3]), &[3]).unwrap();
-        let mut b = Tensor::<i32>::new(Some(&[1, 2, 3]), &[3, 1]).unwrap();
+        let a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3]), &[3]).unwrap();
+        let mut b = Tensor::<IntegerRep>::new(Some(&[1, 2, 3]), &[3, 1]).unwrap();
         b.reshape(&[3]).unwrap();
-        let c = Tensor::<i32>::new(Some(&[1, 2, 4]), &[3]).unwrap();
-        let d = Tensor::<i32>::new(Some(&[1, 2, 4]), &[3, 1]).unwrap();
+        let c = Tensor::<IntegerRep>::new(Some(&[1, 2, 4]), &[3]).unwrap();
+        let d = Tensor::<IntegerRep>::new(Some(&[1, 2, 4]), &[3, 1]).unwrap();
         assert_eq!(a, b);
         assert_ne!(a, c);
         assert_ne!(a, d);
     }
     #[test]
     fn tensor_slice() {
-        let a = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6]), &[2, 3]).unwrap();
-        let b = Tensor::<i32>::new(Some(&[1, 4]), &[2, 1]).unwrap();
+        let a = Tensor::<IntegerRep>::new(Some(&[1, 2, 3, 4, 5, 6]), &[2, 3]).unwrap();
+        let b = Tensor::<IntegerRep>::new(Some(&[1, 4]), &[2, 1]).unwrap();
         assert_eq!(a.get_slice(&[0..2, 0..1]).unwrap(), b);
     }
 

src/tensor/val.rs

@@ -1,12 +1,12 @@
-use core::{iter::FilterMap, slice::Iter};
-
-use crate::circuit::region::ConstantsMap;
+use crate::{circuit::region::ConstantsMap, fieldutils::felt_to_integer_rep};
+use maybe_rayon::slice::Iter;
 
 use super::{
     ops::{intercalate_values, pad, resize},
     *,
 };
 use halo2_proofs::{arithmetic::Field, circuit::Cell, plonk::Instance};
+use maybe_rayon::iter::{FilterMap, IntoParallelIterator, ParallelIterator};
 
 pub(crate) fn create_constant_tensor<
     F: PrimeField + TensorType + std::marker::Send + std::marker::Sync + PartialOrd,
@@ -54,6 +54,44 @@ pub enum ValType<F: PrimeField + TensorType + std::marker::Send + std::marker::S
     AssignedConstant(AssignedCell<F, F>, F),
 }
 
+impl<F: PrimeField + TensorType + PartialOrd> From<ValType<F>> for IntegerRep {
+    fn from(val: ValType<F>) -> Self {
+        match val {
+            ValType::Value(v) => {
+                let mut output = 0;
+                let mut i = 0;
+                v.map(|y| {
+                    let e = felt_to_integer_rep(y);
+                    output = e;
+                    i += 1;
+                });
+                output
+            }
+            ValType::AssignedValue(v) => {
+                let mut output = 0;
+                let mut i = 0;
+                v.evaluate().map(|y| {
+                    let e = felt_to_integer_rep(y);
+                    output = e;
+                    i += 1;
+                });
+                output
+            }
+            ValType::PrevAssigned(v) | ValType::AssignedConstant(v, ..) => {
+                let mut output = 0;
+                let mut i = 0;
+                v.value().map(|y| {
+                    let e = felt_to_integer_rep(*y);
+                    output = e;
+                    i += 1;
+                });
+                output
+            }
+            ValType::Constant(v) => felt_to_integer_rep(v),
+        }
+    }
+}
+
 impl<F: PrimeField + TensorType + std::marker::Send + std::marker::Sync + PartialOrd> ValType<F> {
     /// Returns the inner cell of the [ValType].
     pub fn cell(&self) -> Option<Cell> {
@@ -121,44 +159,6 @@ impl<F: PrimeField + TensorType + std::marker::Send + std::marker::Sync + Partia
     }
 }
 
-impl<F: PrimeField + TensorType + PartialOrd> From<ValType<F>> for i32 {
-    fn from(val: ValType<F>) -> Self {
-        match val {
-            ValType::Value(v) => {
-                let mut output = 0_i32;
-                let mut i = 0;
-                v.map(|y| {
-                    let e = felt_to_i32(y);
-                    output = e;
-                    i += 1;
-                });
-                output
-            }
-            ValType::AssignedValue(v) => {
-                let mut output = 0_i32;
-                let mut i = 0;
-                v.evaluate().map(|y| {
-                    let e = felt_to_i32(y);
-                    output = e;
-                    i += 1;
-                });
-                output
-            }
-            ValType::PrevAssigned(v) | ValType::AssignedConstant(v, ..) => {
-                let mut output = 0_i32;
-                let mut i = 0;
-                v.value().map(|y| {
-                    let e = felt_to_i32(*y);
-                    output = e;
-                    i += 1;
-                });
-                output
-            }
-            ValType::Constant(v) => felt_to_i32(v),
-        }
-    }
-}
-
 impl<F: PrimeField + TensorType + PartialOrd> From<F> for ValType<F> {
     fn from(t: F) -> ValType<F> {
         ValType::Constant(t)
@@ -317,8 +317,8 @@ impl<F: PrimeField + TensorType + PartialOrd> From<Tensor<AssignedCell<F, F>>> f
 
 impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ValTensor<F> {
     /// Allocate a new [ValTensor::Value] from the given [Tensor] of [i64].
-    pub fn from_i64_tensor(t: Tensor<i64>) -> ValTensor<F> {
-        let inner = t.map(|x| ValType::Value(Value::known(i64_to_felt(x))));
+    pub fn from_integer_rep_tensor(t: Tensor<IntegerRep>) -> ValTensor<F> {
+        let inner = t.map(|x| ValType::Value(Value::known(integer_rep_to_felt(x))));
         inner.into()
     }
 
@@ -460,7 +460,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ValTensor<F> {
         &self,
     ) -> FilterMap<Iter<'_, ValType<F>>, fn(&ValType<F>) -> Option<(F, ValType<F>)>> {
         match self {
-            ValTensor::Value { inner, .. } => inner.iter().filter_map(|x| {
+            ValTensor::Value { inner, .. } => inner.par_iter().filter_map(|x| {
                 if let ValType::Constant(v) = x {
                     Some((*v, x.clone()))
                 } else {
@@ -521,9 +521,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ValTensor<F> {
     }
 
     /// Calls `int_evals` on the inner tensor.
-    pub fn get_int_evals(&self) -> Result<Tensor<i64>, TensorError> {
+    pub fn int_evals(&self) -> Result<Tensor<IntegerRep>, TensorError> {
         // finally convert to vector of integers
-        let mut integer_evals: Vec<i64> = vec![];
+        let mut integer_evals: Vec<IntegerRep> = vec![];
         match self {
             ValTensor::Value {
                 inner: v, dims: _, ..
@@ -531,25 +531,26 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ValTensor<F> {
                 // we have to push to an externally created vector or else vaf.map() returns an evaluation wrapped in Value<> (which we don't want)
                 let _ = v.map(|vaf| match vaf {
                     ValType::Value(v) => v.map(|f| {
-                        integer_evals.push(crate::fieldutils::felt_to_i64(f));
+                        integer_evals.push(crate::fieldutils::felt_to_integer_rep(f));
                     }),
                     ValType::AssignedValue(v) => v.map(|f| {
-                        integer_evals.push(crate::fieldutils::felt_to_i64(f.evaluate()));
+                        integer_evals.push(crate::fieldutils::felt_to_integer_rep(f.evaluate()));
                     }),
                     ValType::PrevAssigned(v) | ValType::AssignedConstant(v, ..) => {
                         v.value_field().map(|f| {
-                            integer_evals.push(crate::fieldutils::felt_to_i64(f.evaluate()));
+                            integer_evals
+                                .push(crate::fieldutils::felt_to_integer_rep(f.evaluate()));
                         })
                     }
                     ValType::Constant(v) => {
-                        integer_evals.push(crate::fieldutils::felt_to_i64(v));
+                        integer_evals.push(crate::fieldutils::felt_to_integer_rep(v));
                         Value::unknown()
                     }
                 });
             }
             _ => return Err(TensorError::WrongMethod),
         };
-        let mut tensor: Tensor<i64> = integer_evals.into_iter().into();
+        let mut tensor: Tensor<IntegerRep> = integer_evals.into_iter().into();
         match tensor.reshape(self.dims()) {
             _ => {}
         };
@@ -573,6 +574,27 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ValTensor<F> {
         Ok(())
     }
 
+    /// Calls `get_slice` on the inner tensor.
+    pub fn last(&self) -> Result<ValTensor<F>, TensorError> {
+        let slice = match self {
+            ValTensor::Value {
+                inner: v,
+                dims: _,
+                scale,
+            } => {
+                let inner = v.last()?;
+                let dims = inner.dims().to_vec();
+                ValTensor::Value {
+                    inner,
+                    dims,
+                    scale: *scale,
+                }
+            }
+            _ => return Err(TensorError::WrongMethod),
+        };
+        Ok(slice)
+    }
+
     /// Calls `get_slice` on the inner tensor.
     pub fn get_slice(&self, indices: &[Range<usize>]) -> Result<ValTensor<F>, TensorError> {
         if indices.iter().map(|x| x.end - x.start).collect::<Vec<_>>() == self.dims() {
@@ -753,43 +775,72 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ValTensor<F> {
         Ok(())
     }
 
-    /// gets constants
-    pub fn get_const_zero_indices(&self) -> Result<Vec<usize>, TensorError> {
+    /// remove constant zero values constants
+    pub fn remove_const_zero_values(&mut self) {
         match self {
-            ValTensor::Value { inner: v, .. } => {
-                let mut indices = vec![];
-                for (i, e) in v.iter().enumerate() {
-                    if let ValType::Constant(r) = e {
-                        if *r == F::ZERO {
-                            indices.push(i);
+            ValTensor::Value { inner: v, dims, .. } => {
+                *v = v
+                    .clone()
+                    .into_par_iter()
+                    .filter_map(|e| {
+                        if let ValType::Constant(r) = e {
+                            if r == F::ZERO {
+                                return None;
+                            }
+                        } else if let ValType::AssignedConstant(_, r) = e {
+                            if r == F::ZERO {
+                                return None;
+                            }
                         }
-                    } else if let ValType::AssignedConstant(_, r) = e {
-                        if *r == F::ZERO {
-                            indices.push(i);
-                        }
-                    }
-                }
-                Ok(indices)
+                        Some(e)
+                    })
+                    .collect();
+                *dims = v.dims().to_vec();
             }
-            ValTensor::Instance { .. } => Ok(vec![]),
+            ValTensor::Instance { .. } => {}
         }
     }
 
     /// gets constants
-    pub fn get_const_indices(&self) -> Result<Vec<usize>, TensorError> {
+    pub fn get_const_zero_indices(&self) -> Vec<usize> {
         match self {
-            ValTensor::Value { inner: v, .. } => {
-                let mut indices = vec![];
-                for (i, e) in v.iter().enumerate() {
-                    if let ValType::Constant(_) = e {
-                        indices.push(i);
-                    } else if let ValType::AssignedConstant(_, _) = e {
-                        indices.push(i);
+            ValTensor::Value { inner: v, .. } => v
+                .par_iter()
+                .enumerate()
+                .filter_map(|(i, e)| {
+                    if let ValType::Constant(r) = e {
+                        if *r == F::ZERO {
+                            return Some(i);
+                        }
+                    } else if let ValType::AssignedConstant(_, r) = e {
+                        if *r == F::ZERO {
+                            return Some(i);
+                        }
                     }
-                }
-                Ok(indices)
-            }
-            ValTensor::Instance { .. } => Ok(vec![]),
+                    None
+                })
+                .collect(),
+            ValTensor::Instance { .. } => vec![],
+        }
+    }
+
+    /// gets constants
+    pub fn get_const_indices(&self) -> Vec<usize> {
+        match self {
+            ValTensor::Value { inner: v, .. } => v
+                .par_iter()
+                .enumerate()
+                .filter_map(|(i, e)| {
+                    if let ValType::Constant(_) = e {
+                        Some(i)
+                    } else if let ValType::AssignedConstant(_, _) = e {
+                        Some(i)
+                    } else {
+                        None
+                    }
+                })
+                .collect(),
+            ValTensor::Instance { .. } => vec![],
         }
     }
 
@@ -952,25 +1003,22 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ValTensor<F> {
     }
     /// A [String] representation of the [ValTensor] for display, for example in showing intermediate values in a computational graph.
     pub fn show(&self) -> String {
-        match self.clone() {
-            ValTensor::Value {
-                inner: v, dims: _, ..
-            } => {
-                let r: Tensor<i32> = v.map(|x| x.into());
-                if r.len() > 10 {
-                    let start = r[..5].to_vec();
-                    let end = r[r.len() - 5..].to_vec();
-                    // print the two split by ... in the middle
-                    format!(
-                        "[{} ... {}]",
-                        start.iter().map(|x| format!("{}", x)).join(", "),
-                        end.iter().map(|x| format!("{}", x)).join(", ")
-                    )
-                } else {
-                    format!("{:?}", r)
-                }
-            }
-            _ => "ValTensor not PrevAssigned".into(),
+        let r = match self.int_evals() {
+            Ok(v) => v,
+            Err(_) => return "ValTensor not PrevAssigned".into(),
+        };
+
+        if r.len() > 10 {
+            let start = r[..5].to_vec();
+            let end = r[r.len() - 5..].to_vec();
+            // print the two split by ... in the middle
+            format!(
+                "[{} ... {}]",
+                start.iter().map(|x| format!("{}", x)).join(", "),
+                end.iter().map(|x| format!("{}", x)).join(", ")
+            )
+        } else {
+            format!("{:?}", r)
         }
     }
 }

src/tensor/var.rs

@@ -319,7 +319,7 @@ impl VarTensor {
         region: &mut Region<F>,
         offset: usize,
         values: &ValTensor<F>,
-        omissions: &HashSet<&usize>,
+        omissions: &HashSet<usize>,
         constants: &mut ConstantsMap<F>,
     ) -> Result<ValTensor<F>, halo2_proofs::plonk::Error> {
         let mut assigned_coord = 0;
@@ -368,7 +368,7 @@ impl VarTensor {
                             .sum::<usize>();
                     let dims = &dims[*idx];
                     // this should never ever fail
-                    let t: Tensor<i32> = Tensor::new(None, dims).unwrap();
+                    let t: Tensor<IntegerRep> = Tensor::new(None, dims).unwrap();
                     Ok(t.enum_map(|coord, _| {
                         let (x, y, z) = self.cartesian_coord(offset + coord);
                         region.assign_advice_from_instance(
@@ -497,7 +497,7 @@ impl VarTensor {
                     let (x, y, z) = self.cartesian_coord(offset + coord * step);
                     if matches!(check_mode, CheckMode::SAFE) && coord > 0 && z == 0 && y == 0 {
                         // assert that duplication occurred correctly
-                        assert_eq!(Into::<i32>::into(k.clone()), Into::<i32>::into(v[coord - 1].clone()));
+                        assert_eq!(Into::<IntegerRep>::into(k.clone()), Into::<IntegerRep>::into(v[coord - 1].clone()));
                     };
 
                     let cell = self.assign_value(region, offset, k.clone(), coord * step, constants)?;
@@ -533,13 +533,14 @@ impl VarTensor {
                 if matches!(check_mode, CheckMode::SAFE) {
                      // during key generation this will be 0 so we use this as a flag to check
                      // TODO: this isn't very safe and would be better to get the phase directly
-                    let is_assigned = !Into::<Tensor<i32>>::into(res.clone().get_inner().unwrap())
+                    let res_evals = res.int_evals().unwrap();
+                    let is_assigned = res_evals
                     .iter()
                     .all(|&x| x == 0);
-                    if is_assigned {
+                    if !is_assigned {
                         assert_eq!(
-                            Into::<Tensor<i32>>::into(values.get_inner().unwrap()),
-                            Into::<Tensor<i32>>::into(res.get_inner().unwrap())
+                           values.int_evals().unwrap(),
+                           res_evals
                     )};
                 }
 

src/wasm.rs

@@ -1,41 +1,52 @@
-use crate::circuit::modules::polycommit::PolyCommitChip;
-use crate::circuit::modules::poseidon::spec::{PoseidonSpec, POSEIDON_RATE, POSEIDON_WIDTH};
-use crate::circuit::modules::poseidon::PoseidonChip;
-use crate::circuit::modules::Module;
-use crate::fieldutils::felt_to_i64;
-use crate::fieldutils::i64_to_felt;
-use crate::graph::modules::POSEIDON_LEN_GRAPH;
-use crate::graph::quantize_float;
-use crate::graph::scale_to_multiplier;
-use crate::graph::{GraphCircuit, GraphSettings};
-use crate::pfsys::create_proof_circuit;
-use crate::pfsys::evm::aggregation_kzg::AggregationCircuit;
-use crate::pfsys::evm::aggregation_kzg::PoseidonTranscript;
-use crate::pfsys::verify_proof_circuit;
-use crate::pfsys::TranscriptType;
-use crate::tensor::TensorType;
-use crate::CheckMode;
-use crate::Commitments;
+use crate::{
+    circuit::{
+        modules::{
+            polycommit::PolyCommitChip,
+            poseidon::{
+                spec::{PoseidonSpec, POSEIDON_RATE, POSEIDON_WIDTH},
+                PoseidonChip,
+            },
+            Module,
+        },
+        region::RegionSettings,
+    },
+    fieldutils::{felt_to_integer_rep, integer_rep_to_felt},
+    graph::{
+        modules::POSEIDON_LEN_GRAPH, quantize_float, scale_to_multiplier, GraphCircuit,
+        GraphSettings,
+    },
+    pfsys::{
+        create_proof_circuit,
+        evm::aggregation_kzg::{AggregationCircuit, PoseidonTranscript},
+        verify_proof_circuit, TranscriptType,
+    },
+    tensor::TensorType,
+    CheckMode, Commitments,
+};
 use console_error_panic_hook;
-use halo2_proofs::plonk::*;
-use halo2_proofs::poly::commitment::{CommitmentScheme, ParamsProver};
-use halo2_proofs::poly::ipa::multiopen::{ProverIPA, VerifierIPA};
-use halo2_proofs::poly::ipa::{
-    commitment::{IPACommitmentScheme, ParamsIPA},
-    strategy::SingleStrategy as IPASingleStrategy,
+use halo2_proofs::{
+    plonk::*,
+    poly::{
+        commitment::{CommitmentScheme, ParamsProver},
+        ipa::{
+            commitment::{IPACommitmentScheme, ParamsIPA},
+            multiopen::{ProverIPA, VerifierIPA},
+            strategy::SingleStrategy as IPASingleStrategy,
+        },
+        kzg::{
+            commitment::{KZGCommitmentScheme, ParamsKZG},
+            multiopen::{ProverSHPLONK, VerifierSHPLONK},
+            strategy::SingleStrategy as KZGSingleStrategy,
+        },
+        VerificationStrategy,
+    },
 };
-use halo2_proofs::poly::kzg::multiopen::ProverSHPLONK;
-use halo2_proofs::poly::kzg::multiopen::VerifierSHPLONK;
-use halo2_proofs::poly::kzg::{
-    commitment::{KZGCommitmentScheme, ParamsKZG},
-    strategy::SingleStrategy as KZGSingleStrategy,
-};
-use halo2_proofs::poly::VerificationStrategy;
 use halo2_solidity_verifier::encode_calldata;
-use halo2curves::bn256::{Bn256, Fr, G1Affine};
-use halo2curves::ff::{FromUniformBytes, PrimeField};
-use snark_verifier::loader::native::NativeLoader;
-use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
+use halo2curves::{
+    bn256::{Bn256, Fr, G1Affine},
+    ff::{FromUniformBytes, PrimeField},
+};
+use snark_verifier::{loader::native::NativeLoader, system::halo2::transcript::evm::EvmTranscript};
 use std::str::FromStr;
 use wasm_bindgen::prelude::*;
 use wasm_bindgen_console_logger::DEFAULT_LOGGER;
@@ -113,7 +124,7 @@ pub fn feltToInt(
     let felt: Fr = serde_json::from_slice(&array[..])
         .map_err(|e| JsError::new(&format!("Failed to deserialize field element: {}", e)))?;
     Ok(wasm_bindgen::Clamped(
-        serde_json::to_vec(&felt_to_i64(felt))
+        serde_json::to_vec(&felt_to_integer_rep(felt))
             .map_err(|e| JsError::new(&format!("Failed to serialize integer: {}", e)))?,
     ))
 }
@@ -127,7 +138,7 @@ pub fn feltToFloat(
 ) -> Result<f64, JsError> {
     let felt: Fr = serde_json::from_slice(&array[..])
         .map_err(|e| JsError::new(&format!("Failed to deserialize field element: {}", e)))?;
-    let int_rep = felt_to_i64(felt);
+    let int_rep = felt_to_integer_rep(felt);
     let multiplier = scale_to_multiplier(scale);
     Ok(int_rep as f64 / multiplier)
 }
@@ -141,7 +152,7 @@ pub fn floatToFelt(
 ) -> Result<wasm_bindgen::Clamped<Vec<u8>>, JsError> {
     let int_rep =
         quantize_float(&input, 0.0, scale).map_err(|e| JsError::new(&format!("{}", e)))?;
-    let felt = i64_to_felt(int_rep);
+    let felt = integer_rep_to_felt(int_rep);
     let vec = crate::pfsys::field_to_string::<halo2curves::bn256::Fr>(&felt);
     Ok(wasm_bindgen::Clamped(serde_json::to_vec(&vec).map_err(
         |e| JsError::new(&format!("Failed to serialize a float to felt{}", e)),
@@ -275,7 +286,7 @@ pub fn genWitness(
         .map_err(|e| JsError::new(&format!("{}", e)))?;
 
     let witness = circuit
-        .forward::<KZGCommitmentScheme<Bn256>>(&mut input, None, None, false, false)
+        .forward::<KZGCommitmentScheme<Bn256>>(&mut input, None, None, RegionSettings::all_false())
         .map_err(|e| JsError::new(&format!("{}", e)))?;
 
     serde_json::to_vec(&witness)

tests/integration_tests.rs

@@ -3,7 +3,7 @@
 mod native_tests {
 
     use ezkl::circuit::Tolerance;
-    use ezkl::fieldutils::{felt_to_i64, i64_to_felt};
+    use ezkl::fieldutils::{felt_to_integer_rep, integer_rep_to_felt, IntegerRep};
     // use ezkl::circuit::table::RESERVED_BLINDING_ROWS_PAD;
     use ezkl::graph::input::{FileSource, FileSourceInner, GraphData};
     use ezkl::graph::{DataSource, GraphSettings, GraphWitness};
@@ -183,12 +183,13 @@ mod native_tests {
 
     const PF_FAILURE_AGGR: &str = "examples/test_failure_aggr_proof.json";
 
-    const LARGE_TESTS: [&str; 5] = [
+    const LARGE_TESTS: [&str; 6] = [
         "self_attention",
         "nanoGPT",
         "multihead_attention",
         "mobilenet",
         "mnist_gan",
+        "smallworm",
     ];
 
     const ACCURACY_CAL_TESTS: [&str; 6] = [
@@ -200,7 +201,7 @@ mod native_tests {
         "1l_tiny_div",
     ];
 
-    const TESTS: [&str; 93] = [
+    const TESTS: [&str; 94] = [
         "1l_mlp", //0
         "1l_slice",
         "1l_concat",
@@ -298,6 +299,7 @@ mod native_tests {
         "1l_lppool",
         "lstm_large",  // 91
         "lstm_medium", // 92
+        "lenet_5",     // 93
     ];
 
     const WASM_TESTS: [&str; 46] = [
@@ -536,7 +538,7 @@ mod native_tests {
             }
         });
 
-            seq!(N in 0..=92 {
+            seq!(N in 0..=93 {
 
             #(#[test_case(TESTS[N])])*
             #[ignore]
@@ -644,6 +646,15 @@ mod native_tests {
                 test_dir.close().unwrap();
             }
 
+            #(#[test_case(TESTS[N])])*
+            fn mock_hashed_params_public_inputs_(test: &str) {
+                crate::native_tests::init_binary();
+                let test_dir = TempDir::new(test).unwrap();
+                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
+                mock(path, test.to_string(), "public", "hashed", "private", 1, "resources", None, 0.0);
+                test_dir.close().unwrap();
+            }
+
             #(#[test_case(TESTS[N])])*
             fn mock_fixed_inputs_(test: &str) {
                 crate::native_tests::init_binary();
@@ -940,7 +951,7 @@ mod native_tests {
 
             });
 
-            seq!(N in 0..=4 {
+            seq!(N in 0..=5 {
 
             #(#[test_case(LARGE_TESTS[N])])*
             #[ignore]
@@ -1411,10 +1422,10 @@ mod native_tests {
                             let perturbation = if v == &halo2curves::bn256::Fr::zero() {
                                 halo2curves::bn256::Fr::zero()
                             } else {
-                                i64_to_felt(
-                                    (felt_to_i64(*v) as f32
+                                integer_rep_to_felt(
+                                    (felt_to_integer_rep(*v) as f32
                                         * (rand::thread_rng().gen_range(-0.01..0.01) * tolerance))
-                                        as i64,
+                                        as IntegerRep,
                                 )
                             };
 
@@ -1434,10 +1445,10 @@ mod native_tests {
                             let perturbation = if v == &halo2curves::bn256::Fr::zero() {
                                 halo2curves::bn256::Fr::from(2)
                             } else {
-                                i64_to_felt(
-                                    (felt_to_i64(*v) as f32
+                                integer_rep_to_felt(
+                                    (felt_to_integer_rep(*v) as f32
                                         * (rand::thread_rng().gen_range(0.02..0.1) * tolerance))
-                                        as i64,
+                                        as IntegerRep,
                                 )
                             };
                             *v + perturbation

tests/python/binding_tests.py

@@ -870,6 +870,7 @@ def get_examples():
         'accuracy',
         'linear_regression',
         "mnist_gan",
+        "smallworm",
     ]
     examples = []
     for subdir, _, _ in os.walk(os.path.join(examples_path, "onnx")):