Update pypi.yml

This reverts commit 20ac99fdbf.

.github/workflows/engine.yml

@@ -47,6 +47,10 @@ jobs:
           curl -L https://github.com/WebAssembly/binaryen/releases/download/version_116/binaryen-version_116-x86_64-linux.tar.gz | tar xzf -
           export PATH=$PATH:$PWD/binaryen-version_116/bin
           wasm-opt --version
+      - name: Build wasm files for both web and nodejs compilation targets
+        run: |
+            wasm-pack build --release --target nodejs --out-dir ./pkg/nodejs . -- -Z build-std="panic_abort,std"
+            wasm-pack build --release --target web --out-dir ./pkg/web . -- -Z build-std="panic_abort,std" --features web
       - name: Create package.json in pkg folder
         shell: bash
         run: |

.github/workflows/pypi-gpu.yml

@@ -43,6 +43,9 @@ jobs:
             sed "s/ezkl/ezkl-gpu/" pyproject.toml.orig >pyproject.toml
             sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" pyproject.toml.orig >pyproject.toml
 
+      - name: rename ezkl.pyi to ezkl-gpu.pyi
+        run: mv ezkl.pyi ezkl-gpu.pyi
+
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -76,7 +79,7 @@ jobs:
           pip install ezkl-gpu --no-index --find-links dist --force-reinstall
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: wheels
           path: dist

.github/workflows/pypi.yml

@@ -73,9 +73,9 @@ jobs:
           python -c "import ezkl"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          name: wheels
+          name: dist-macos-${{ matrix.target }}
           path: dist
 
   windows:
@@ -130,9 +130,9 @@ jobs:
           python -c "import ezkl"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          name: wheels
+          name: dist-windows-${{ matrix.target }}
           path: dist
 
   linux:
@@ -203,9 +203,9 @@ jobs:
           python -c "import ezkl"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          name: wheels
+          name: dist-linux-${{ matrix.target }}
           path: dist
 
   musllinux:
@@ -271,9 +271,9 @@ jobs:
             python3 -c "import ezkl"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          name: wheels
+          name: dist-musllinux-${{ matrix.target }}
           path: dist
 
   musllinux-cross:
@@ -334,9 +334,9 @@ jobs:
             python3 -c "import ezkl"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          name: wheels
+          name: dist-musllinux-${{ matrix.platform.target }}
           path: dist
 
   pypi-publish:
@@ -349,7 +349,9 @@ jobs:
     steps:
       - uses: actions/download-artifact@v3
         with:
-          name: wheels
+          pattern: dist-*
+          merge-multiple: true
+          path: dist
       - name: List Files
         run: ls -R
       

Cargo.lock

@@ -944,7 +944,7 @@ dependencies = [
  "bitflags 2.5.0",
  "cexpr",
  "clang-sys",
- "itertools 0.12.1",
+ "itertools 0.11.0",
  "lazy_static",
  "lazycell",
  "log",
@@ -2397,7 +2397,7 @@ dependencies = [
 [[package]]
 name = "halo2_gadgets"
 version = "0.2.0"
-source = "git+https://github.com/zkonduit/halo2#d7ecad83c7439fa1cb450ee4a89c2d0b45604ceb"
+source = "git+https://github.com/zkonduit/halo2#f441c920be45f8f05d2c06a173d82e8885a5ed4d"
 dependencies = [
  "arrayvec 0.7.4",
  "bitvec",
@@ -2414,7 +2414,7 @@ dependencies = [
 [[package]]
 name = "halo2_proofs"
 version = "0.3.0"
-source = "git+https://github.com/zkonduit/halo2#bf9d0057a82443be48c4779bbe14961c18fb5996#bf9d0057a82443be48c4779bbe14961c18fb5996"
+source = "git+https://github.com/zkonduit/halo2#f441c920be45f8f05d2c06a173d82e8885a5ed4d#f441c920be45f8f05d2c06a173d82e8885a5ed4d"
 dependencies = [
  "bincode",
  "blake2b_simd",
@@ -3139,7 +3139,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "fc2f4eb4bc735547cfed7c0a4922cbd04a4655978c09b54f1f7b228750664c34"
 dependencies = [
  "cfg-if",
- "windows-targets 0.52.6",
+ "windows-targets 0.48.5",
 ]
 
 [[package]]

Cargo.toml

@@ -276,10 +276,11 @@ macos-metal = ["halo2_proofs/macos"]
 ios-metal = ["halo2_proofs/ios"]
 
 [patch.'https://github.com/zkonduit/halo2']
-halo2_proofs = { git = "https://github.com/zkonduit/halo2#bf9d0057a82443be48c4779bbe14961c18fb5996", package = "halo2_proofs" }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2#f441c920be45f8f05d2c06a173d82e8885a5ed4d", package = "halo2_proofs" }
 
 [patch.'https://github.com/zkonduit/halo2#0654e92bdf725fd44d849bfef3643870a8c7d50b']
-halo2_proofs = { git = "https://github.com/zkonduit/halo2#bf9d0057a82443be48c4779bbe14961c18fb5996", package = "halo2_proofs" }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2#f441c920be45f8f05d2c06a173d82e8885a5ed4d", package = "halo2_proofs" }
+
 
 [patch.crates-io]
 uniffi_testing = { git = "https://github.com/ElusAegis/uniffi-rs", branch = "feat/testing-feature-build-fix" }

README.md

@@ -150,6 +150,13 @@ Ezkl is unaudited, beta software undergoing rapid development. There may be bugs
 
 > NOTE: Because operations are quantized when they are converted from an onnx file to a zk-circuit, outputs in python and ezkl may differ slightly. 
 
+
+### Advanced security topics 
+
+Check out `docs/advanced_security` for more advanced information on potential threat vectors. 
+
+
+
 ### no warranty
 
 Copyright (c) 2024 Zkonduit Inc. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

docs/advanced_security/public_commitments.md

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

docs/advanced_security/quantization_backdoors.md

@@ -0,0 +1,22 @@
+# EZKL Security Note: Quantization-Induced Model Backdoors
+
+> Note: this only affects a situation where a party separate to an application's developer has access to the model's weights and can modify them. This is a common scenario in adversarial machine learning research, but can be less common in real-world applications. If you're building your models in house and deploying them yourself, this is less of a concern. If you're building a permisionless system where anyone can submit models, this is more of a concern.
+
+Models processed through EZKL's quantization step can harbor backdoors that are dormant in the original full-precision model but activate during quantization. These backdoors force specific outputs when triggered, with impact varying by application.
+
+Key Factors:
+
+- Larger models increase attack feasibility through more parameter capacity
+- Smaller quantization scales facilitate attacks by allowing greater weight modifications
+- Rebase ratio of 1 enables exploitation of convolutional layer consistency
+
+Limitations: 
+
+- Attack effectiveness depends on calibration settings and internal rescaling operations. 
+- Further research needed on backdoor persistence through witness/proof stages.
+- Can be mitigated by evaluating the quantized model (using `ezkl gen-witness`), rather than relying on the evaluation of the original model. 
+
+References:
+
+1. [Quantization Backdoors to Deep Learning Commercial Frameworks (Ma et al., 2021)](https://arxiv.org/abs/2108.09187)
+2. [Planting Undetectable Backdoors in Machine Learning Models (Goldwasser et al., 2022)](https://arxiv.org/abs/2204.06974)

examples/onnx/integer_div/gen.py

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

examples/onnx/integer_div/input.json

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

src/bindings/python.rs

@@ -8,7 +8,6 @@ use crate::circuit::InputType;
 use crate::circuit::{CheckMode, Tolerance};
 use crate::commands::*;
 use crate::fieldutils::{felt_to_integer_rep, integer_rep_to_felt, IntegerRep};
-use crate::graph::modules::POSEIDON_LEN_GRAPH;
 use crate::graph::TestDataSource;
 use crate::graph::{
     quantize_float, scale_to_multiplier, GraphCircuit, GraphSettings, Model, Visibility,
@@ -578,10 +577,7 @@ fn poseidon_hash(message: Vec<PyFelt>) -> PyResult<Vec<PyFelt>> {
         .map(crate::pfsys::string_to_field::<Fr>)
         .collect::<Vec<_>>();
 
-    let output =
-        PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, POSEIDON_LEN_GRAPH>::run(
-            message.clone(),
-        )
+    let output = PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::run(message.clone())
         .map_err(|_| PyIOError::new_err("Failed to run poseidon"))?;
 
     let hash = output[0]

src/bindings/wasm.rs

@@ -8,10 +8,7 @@ use crate::{
         Module,
     },
     fieldutils::{felt_to_integer_rep, integer_rep_to_felt},
-    graph::{
-        modules::POSEIDON_LEN_GRAPH, quantize_float, scale_to_multiplier, GraphCircuit,
-        GraphSettings,
-    },
+    graph::{quantize_float, scale_to_multiplier, GraphCircuit, GraphSettings},
 };
 use console_error_panic_hook;
 use halo2_proofs::{
@@ -231,10 +228,7 @@ pub fn poseidonHash(
     let message: Vec<Fr> = serde_json::from_slice(&message[..])
         .map_err(|e| JsError::new(&format!("Failed to deserialize message: {}", e)))?;
 
-    let output =
-        PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, POSEIDON_LEN_GRAPH>::run(
-            message.clone(),
-        )
+    let output = PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::run(message.clone())
         .map_err(|e| JsError::new(&format!("{}", e)))?;
 
     Ok(wasm_bindgen::Clamped(serde_json::to_vec(&output).map_err(

src/circuit/modules/poseidon.rs

@@ -8,13 +8,11 @@ pub mod poseidon_params;
 pub mod spec;
 
 // This chip adds a set of advice columns to the gadget Chip to store the inputs of the hash
-use halo2_gadgets::poseidon::{primitives::*, Hash, Pow5Chip, Pow5Config};
-use halo2_proofs::arithmetic::Field;
+use halo2_gadgets::poseidon::{
+    primitives::VariableLength, primitives::*, Hash, Pow5Chip, Pow5Config,
+};
 use halo2_proofs::halo2curves::bn256::Fr as Fp;
 use halo2_proofs::{circuit::*, plonk::*};
-// use maybe_rayon::prelude::{IndexedParallelIterator, IntoParallelRefIterator};
-use maybe_rayon::prelude::ParallelIterator;
-use maybe_rayon::slice::ParallelSlice;
 
 use std::marker::PhantomData;
 
@@ -40,22 +38,17 @@ pub struct PoseidonConfig<const WIDTH: usize, const RATE: usize> {
     pub pow5_config: Pow5Config<Fp, WIDTH, RATE>,
 }
 
-type InputAssignments = (Vec<AssignedCell<Fp, Fp>>, AssignedCell<Fp, Fp>);
+type InputAssignments = Vec<AssignedCell<Fp, Fp>>;
 
 /// PoseidonChip is a wrapper around the Pow5Chip that adds a set of advice columns to the gadget Chip to store the inputs of the hash
 #[derive(Debug, Clone)]
-pub struct PoseidonChip<
-    S: Spec<Fp, WIDTH, RATE> + Sync,
-    const WIDTH: usize,
-    const RATE: usize,
-    const L: usize,
-> {
+pub struct PoseidonChip<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize> {
     config: PoseidonConfig<WIDTH, RATE>,
     _marker: PhantomData<S>,
 }
 
-impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, const L: usize>
-    PoseidonChip<S, WIDTH, RATE, L>
+impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize>
+    PoseidonChip<S, WIDTH, RATE>
 {
     /// Creates a new PoseidonChip
     pub fn configure_with_cols(
@@ -82,8 +75,8 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
     }
 }
 
-impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, const L: usize>
-    PoseidonChip<S, WIDTH, RATE, L>
+impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize>
+    PoseidonChip<S, WIDTH, RATE>
 {
     /// Configuration of the PoseidonChip
     pub fn configure_with_optional_instance(
@@ -113,8 +106,8 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
     }
 }
 
-impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, const L: usize>
-    Module<Fp> for PoseidonChip<S, WIDTH, RATE, L>
+impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize> Module<Fp>
+    for PoseidonChip<S, WIDTH, RATE>
 {
     type Config = PoseidonConfig<WIDTH, RATE>;
     type InputAssignments = InputAssignments;
@@ -183,95 +176,81 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
         let res = layouter.assign_region(
             || "load message",
             |mut region| {
-                let assigned_message: Result<Vec<AssignedCell<Fp, Fp>>, ModuleError> =
-                    match &message {
-                        ValTensor::Value { inner: v, .. } => {
-                            v.iter()
-                                .enumerate()
-                                .map(|(i, value)| {
-                                    let x = i % WIDTH;
-                                    let y = i / WIDTH;
+                let assigned_message: Result<Vec<AssignedCell<Fp, Fp>>, _> = match &message {
+                    ValTensor::Value { inner: v, .. } => v
+                        .iter()
+                        .enumerate()
+                        .map(|(i, value)| {
+                            let x = i % WIDTH;
+                            let y = i / WIDTH;
 
-                                    match value {
-                                        ValType::Value(v) => region
-                                            .assign_advice(
-                                                || format!("load message_{}", i),
-                                                self.config.hash_inputs[x],
-                                                y,
-                                                || *v,
-                                            )
-                                            .map_err(|e| e.into()),
-                                        ValType::PrevAssigned(v)
-                                        | ValType::AssignedConstant(v, ..) => Ok(v.clone()),
-                                        ValType::Constant(f) => {
-                                            if local_constants.contains_key(f) {
-                                                Ok(constants
-                                                    .get(f)
-                                                    .unwrap()
-                                                    .assigned_cell()
-                                                    .ok_or(ModuleError::ConstantNotAssigned)?)
-                                            } else {
-                                                let res = region.assign_advice_from_constant(
-                                                    || format!("load message_{}", i),
-                                                    self.config.hash_inputs[x],
-                                                    y,
-                                                    *f,
-                                                )?;
-
-                                                constants.insert(
-                                                    *f,
-                                                    ValType::AssignedConstant(res.clone(), *f),
-                                                );
-
-                                                Ok(res)
-                                            }
-                                        }
-                                        e => Err(ModuleError::WrongInputType(
-                                            format!("{:?}", e),
-                                            "AssignedValue".to_string(),
-                                        )),
-                                    }
-                                })
-                                .collect()
-                        }
-                        ValTensor::Instance {
-                            dims,
-                            inner: col,
-                            idx,
-                            initial_offset,
-                            ..
-                        } => {
-                            // this should never ever fail
-                            let num_elems = dims[*idx].iter().product::<usize>();
-                            (0..num_elems)
-                                .map(|i| {
-                                    let x = i % WIDTH;
-                                    let y = i / WIDTH;
-                                    region.assign_advice_from_instance(
-                                        || "pub input anchor",
-                                        *col,
-                                        initial_offset + i,
+                            match value {
+                                ValType::Value(v) => region
+                                    .assign_advice(
+                                        || format!("load message_{}", i),
                                         self.config.hash_inputs[x],
                                         y,
+                                        || *v,
                                     )
-                                })
-                                .collect::<Result<Vec<_>, _>>()
-                                .map_err(|e| e.into())
-                        }
-                    };
+                                    .map_err(|e| e.into()),
+                                ValType::PrevAssigned(v) | ValType::AssignedConstant(v, ..) => {
+                                    Ok(v.clone())
+                                }
+                                ValType::Constant(f) => {
+                                    if local_constants.contains_key(f) {
+                                        Ok(constants
+                                            .get(f)
+                                            .unwrap()
+                                            .assigned_cell()
+                                            .ok_or(ModuleError::ConstantNotAssigned)?)
+                                    } else {
+                                        let res = region.assign_advice_from_constant(
+                                            || format!("load message_{}", i),
+                                            self.config.hash_inputs[x],
+                                            y,
+                                            *f,
+                                        )?;
 
-                let offset = message.len() / WIDTH + 1;
+                                        constants
+                                            .insert(*f, ValType::AssignedConstant(res.clone(), *f));
 
-                let zero_val = region
-                    .assign_advice_from_constant(
-                        || "",
-                        self.config.hash_inputs[0],
-                        offset,
-                        Fp::ZERO,
-                    )
-                    .unwrap();
+                                        Ok(res)
+                                    }
+                                }
+                                e => Err(ModuleError::WrongInputType(
+                                    format!("{:?}", e),
+                                    "AssignedValue".to_string(),
+                                )),
+                            }
+                        })
+                        .collect(),
+                    ValTensor::Instance {
+                        dims,
+                        inner: col,
+                        idx,
+                        initial_offset,
+                        ..
+                    } => {
+                        // this should never ever fail
+                        let num_elems = dims[*idx].iter().product::<usize>();
+                        (0..num_elems)
+                            .map(|i| {
+                                let x = i % WIDTH;
+                                let y = i / WIDTH;
+                                region.assign_advice_from_instance(
+                                    || "pub input anchor",
+                                    *col,
+                                    initial_offset + i,
+                                    self.config.hash_inputs[x],
+                                    y,
+                                )
+                            })
+                            .collect::<Result<Vec<_>, _>>()
+                            .map_err(|e| e.into())
+                    }
+                };
 
-                Ok((assigned_message?, zero_val))
+                Ok(assigned_message?)
             },
         );
         log::trace!(
@@ -292,7 +271,7 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
         row_offset: usize,
         constants: &mut ConstantsMap<Fp>,
     ) -> Result<ValTensor<Fp>, ModuleError> {
-        let (mut input_cells, zero_val) = self.layout_inputs(layouter, input, constants)?;
+        let input_cells = self.layout_inputs(layouter, input, constants)?;
 
         // empty hash case
         if input_cells.is_empty() {
@@ -306,52 +285,25 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
 
         let start_time = instant::Instant::now();
 
-        let mut one_iter = false;
-        // do the Tree dance baby
-        while input_cells.len() > 1 || !one_iter {
-            let hashes: Result<Vec<AssignedCell<Fp, Fp>>, ModuleError> = input_cells
-                .chunks(L)
-                .enumerate()
-                .map(|(i, block)| {
-                    let _start_time = instant::Instant::now();
+        let pow5_chip = Pow5Chip::construct(self.config.pow5_config.clone());
+        // initialize the hasher
+        let hasher = Hash::<_, _, S, VariableLength, WIDTH, RATE>::init(
+            pow5_chip,
+            layouter.namespace(|| "block_hasher"),
+        )?;
 
-                    let mut block = block.to_vec();
-                    let remainder = block.len() % L;
-
-                    if remainder != 0 {
-                        block.extend(vec![zero_val.clone(); L - remainder]);
-                    }
-
-                    let pow5_chip = Pow5Chip::construct(self.config.pow5_config.clone());
-                    // initialize the hasher
-                    let hasher = Hash::<_, _, S, ConstantLength<L>, WIDTH, RATE>::init(
-                        pow5_chip,
-                        layouter.namespace(|| "block_hasher"),
-                    )?;
-
-                    let hash = hasher.hash(
-                        layouter.namespace(|| "hash"),
-                        block.to_vec().try_into().map_err(|_| Error::Synthesis)?,
-                    );
-
-                    if i == 0 {
-                        log::trace!("block (L={:?}) took: {:?}", L, _start_time.elapsed());
-                    }
-
-                    hash
-                })
-                .collect::<Result<Vec<_>, _>>()
-                .map_err(|e| e.into());
-
-            log::trace!("hashes (N={:?}) took: {:?}", len, start_time.elapsed());
-            one_iter = true;
-            input_cells = hashes?;
-        }
+        let hash: AssignedCell<Fp, Fp> = hasher.hash(
+            layouter.namespace(|| "hash"),
+            input_cells
+                .to_vec()
+                .try_into()
+                .map_err(|_| Error::Synthesis)?,
+        )?;
 
         let duration = start_time.elapsed();
         log::trace!("layout (N={:?}) took: {:?}", len, duration);
 
-        let result = Tensor::from(input_cells.iter().map(|e| ValType::from(e.clone())));
+        let result = Tensor::from(vec![ValType::from(hash.clone())].into_iter());
 
         let output = match result[0].clone() {
             ValType::PrevAssigned(v) => v,
@@ -390,69 +342,59 @@ impl<S: Spec<Fp, WIDTH, RATE> + Sync, const WIDTH: usize, const RATE: usize, con
 
     ///
     fn run(message: Vec<Fp>) -> Result<Vec<Vec<Fp>>, ModuleError> {
-        let mut hash_inputs = message;
-
-        let len = hash_inputs.len();
+        let len = message.len();
+        if len == 0 {
+            return Ok(vec![vec![]]);
+        }
 
         let start_time = instant::Instant::now();
 
-        let mut one_iter = false;
-        // do the Tree dance baby
-        while hash_inputs.len() > 1 || !one_iter {
-            let hashes: Vec<Fp> = hash_inputs
-                .par_chunks(L)
-                .map(|block| {
-                    let mut block = block.to_vec();
-                    let remainder = block.len() % L;
-
-                    if remainder != 0 {
-                        block.extend(vec![Fp::ZERO; L - remainder].iter());
-                    }
-
-                    let block_len = block.len();
-
-                    let message = block
-                        .try_into()
-                        .map_err(|_| ModuleError::InputWrongLength(block_len))?;
-
-                    Ok(halo2_gadgets::poseidon::primitives::Hash::<
-                        _,
-                        S,
-                        ConstantLength<L>,
-                        { WIDTH },
-                        { RATE },
-                    >::init()
-                    .hash(message))
-                })
-                .collect::<Result<Vec<_>, ModuleError>>()?;
-            one_iter = true;
-            hash_inputs = hashes;
-        }
+        let hash = halo2_gadgets::poseidon::primitives::Hash::<
+            _,
+            S,
+            VariableLength,
+            { WIDTH },
+            { RATE },
+        >::init()
+        .hash(message);
 
         let duration = start_time.elapsed();
         log::trace!("run (N={:?}) took: {:?}", len, duration);
 
-        Ok(vec![hash_inputs])
+        Ok(vec![vec![hash]])
     }
 
-    fn num_rows(mut input_len: usize) -> usize {
+    fn num_rows(input_len: usize) -> usize {
         // this was determined by running the circuit and looking at the number of constraints
         // in the test called hash_for_a_range_of_input_sizes, then regressing in python to find the slope
-        let fixed_cost: usize = 41 * L;
+        // import numpy as np
+        // from scipy import stats
 
-        let mut num_rows = 0;
+        // x = np.array([32, 64, 96, 128, 160, 192])
+        // y = np.array([1298, 2594, 3890, 5186, 6482, 7778])
 
-        loop {
-            // the number of times the input_len is divisible by L
-            let num_chunks = input_len / L + 1;
-            num_rows += num_chunks * fixed_cost;
-            if num_chunks == 1 {
-                break;
-            }
-            input_len = num_chunks;
-        }
+        // slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
 
-        num_rows
+        // print(f"slope: {slope}")
+        // print(f"intercept: {intercept}")
+        // print(f"R^2: {r_value**2}")
+
+        // # Predict for any x
+        // def predict(x):
+        // return slope * x + intercept
+
+        // # Test prediction
+        // test_x = 256
+        // print(f"Predicted value for x={test_x}: {predict(test_x)}")
+        // our output:
+        // slope: 40.5
+        // intercept: 2.0
+        // R^2: 1.0
+        // Predicted value for x=256: 10370.0
+        let fixed_cost: usize = 41 * input_len;
+
+        // the cost of the hash function is linear with the number of inputs
+        fixed_cost + 2
     }
 }
 
@@ -479,12 +421,12 @@ mod tests {
     const RATE: usize = POSEIDON_RATE;
     const R: usize = 240;
 
-    struct HashCircuit<S: Spec<Fp, WIDTH, RATE>, const L: usize> {
+    struct HashCircuit<S: Spec<Fp, WIDTH, RATE>> {
         message: ValTensor<Fp>,
         _spec: PhantomData<S>,
     }
 
-    impl<S: Spec<Fp, WIDTH, RATE>, const L: usize> Circuit<Fp> for HashCircuit<S, L> {
+    impl<S: Spec<Fp, WIDTH, RATE>> Circuit<Fp> for HashCircuit<S> {
         type Config = PoseidonConfig<WIDTH, RATE>;
         type FloorPlanner = ModulePlanner;
         type Params = ();
@@ -500,7 +442,7 @@ mod tests {
         }
 
         fn configure(meta: &mut ConstraintSystem<Fp>) -> PoseidonConfig<WIDTH, RATE> {
-            PoseidonChip::<PoseidonSpec, WIDTH, RATE, L>::configure(meta, ())
+            PoseidonChip::<PoseidonSpec, WIDTH, RATE>::configure(meta, ())
         }
 
         fn synthesize(
@@ -508,7 +450,7 @@ mod tests {
             config: PoseidonConfig<WIDTH, RATE>,
             mut layouter: impl Layouter<Fp>,
         ) -> Result<(), Error> {
-            let chip: PoseidonChip<PoseidonSpec, WIDTH, RATE, L> = PoseidonChip::new(config);
+            let chip: PoseidonChip<PoseidonSpec, WIDTH, RATE> = PoseidonChip::new(config);
             chip.layout(
                 &mut layouter,
                 &[self.message.clone()],
@@ -523,15 +465,15 @@ mod tests {
     #[test]
     fn poseidon_hash_empty() {
         let message = [];
-        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE, 2>::run(message.to_vec()).unwrap();
+        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.to_vec()).unwrap();
         let mut message: Tensor<ValType<Fp>> =
             message.into_iter().map(|m| Value::known(m).into()).into();
         let k = 9;
-        let circuit = HashCircuit::<PoseidonSpec, 2> {
+        let circuit = HashCircuit::<PoseidonSpec> {
             message: message.into(),
             _spec: PhantomData,
         };
-        let prover = halo2_proofs::dev::MockProver::run(k, &circuit, output).unwrap();
+        let prover = halo2_proofs::dev::MockProver::run(k, &circuit, vec![vec![]]).unwrap();
         assert_eq!(prover.verify(), Ok(()))
     }
 
@@ -540,13 +482,13 @@ mod tests {
         let rng = rand::rngs::OsRng;
 
         let message = [Fp::random(rng), Fp::random(rng)];
-        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE, 2>::run(message.to_vec()).unwrap();
+        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.to_vec()).unwrap();
 
         let mut message: Tensor<ValType<Fp>> =
             message.into_iter().map(|m| Value::known(m).into()).into();
 
         let k = 9;
-        let circuit = HashCircuit::<PoseidonSpec, 2> {
+        let circuit = HashCircuit::<PoseidonSpec> {
             message: message.into(),
             _spec: PhantomData,
         };
@@ -559,13 +501,13 @@ mod tests {
         let rng = rand::rngs::OsRng;
 
         let message = [Fp::random(rng), Fp::random(rng), Fp::random(rng)];
-        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE, 3>::run(message.to_vec()).unwrap();
+        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.to_vec()).unwrap();
 
         let mut message: Tensor<ValType<Fp>> =
             message.into_iter().map(|m| Value::known(m).into()).into();
 
         let k = 9;
-        let circuit = HashCircuit::<PoseidonSpec, 3> {
+        let circuit = HashCircuit::<PoseidonSpec> {
             message: message.into(),
             _spec: PhantomData,
         };
@@ -581,23 +523,21 @@ mod tests {
         #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
         env_logger::init();
 
-        {
-            let i = 32;
+        for i in (32..128).step_by(32) {
             // print a bunch of new lines
-            println!(
+            log::info!(
                 "i is {} -------------------------------------------------",
                 i
             );
 
             let message: Vec<Fp> = (0..i).map(|_| Fp::random(rng)).collect::<Vec<_>>();
-            let output =
-                PoseidonChip::<PoseidonSpec, WIDTH, RATE, 32>::run(message.clone()).unwrap();
+            let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.clone()).unwrap();
 
             let mut message: Tensor<ValType<Fp>> =
                 message.into_iter().map(|m| Value::known(m).into()).into();
 
             let k = 17;
-            let circuit = HashCircuit::<PoseidonSpec, 32> {
+            let circuit = HashCircuit::<PoseidonSpec> {
                 message: message.into(),
                 _spec: PhantomData,
             };
@@ -614,13 +554,13 @@ mod tests {
 
         let mut message: Vec<Fp> = (0..2048).map(|_| Fp::random(rng)).collect::<Vec<_>>();
 
-        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE, 25>::run(message.clone()).unwrap();
+        let output = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(message.clone()).unwrap();
 
         let mut message: Tensor<ValType<Fp>> =
             message.into_iter().map(|m| Value::known(m).into()).into();
 
         let k = 17;
-        let circuit = HashCircuit::<PoseidonSpec, 25> {
+        let circuit = HashCircuit::<PoseidonSpec> {
             message: message.into(),
             _spec: PhantomData,
         };

src/circuit/ops/layouts.rs

@@ -157,7 +157,9 @@ pub(crate) fn div<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     // implicitly check if the prover provided output is within range
     let claimed_output = identity(config, region, &[claimed_output], true)?;
     // check if x is too large only if the decomp would support overflow in the previous op
-    if (IntegerRep::MAX).abs() < ((region.base() as i128).pow(region.legs() as u32)) - 1 {
+    if F::from_u128(IntegerRep::MAX as u128)
+        < F::from_u128(region.base() as u128).pow([region.legs() as u64]) - F::ONE
+    {
         // here we decompose and extract the sign of the input
         let sign = sign(config, region, &[claimed_output.clone()])?;
 
@@ -254,7 +256,9 @@ pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     )?;
 
     // check if x is too large only if the decomp would support overflow in the previous op
-    if (IntegerRep::MAX).abs() < ((region.base() as i128).pow(region.legs() as u32)) - 1 {
+    if F::from_u128(IntegerRep::MAX as u128)
+        < F::from_u128(region.base() as u128).pow([region.legs() as u64]) - F::ONE
+    {
         // here we decompose and extract the sign of the input
         let sign = sign(config, region, &[masked_output.clone()])?;
         let abs_value = pairwise(
@@ -2652,9 +2656,9 @@ pub fn mean_of_squares_axes<F: PrimeField + TensorType + PartialOrd + std::hash:
     let squared = pow(config, region, values, 2)?;
     let sum_squared = sum_axes(config, region, &[squared], axes)?;
 
-    let dividand: usize = values[0].len() / sum_squared.len();
+    let dividend: usize = values[0].len() / sum_squared.len();
 
-    let mean_squared = div(config, region, &[sum_squared], F::from(dividand as u64))?;
+    let mean_squared = div(config, region, &[sum_squared], F::from(dividend as u64))?;
     Ok(mean_squared)
 }
 

src/circuit/tests.rs

@@ -1999,7 +1999,7 @@ mod add_with_overflow_and_poseidon {
             let base = BaseConfig::configure(cs, &[a, b], &output, CheckMode::SAFE);
             VarTensor::constant_cols(cs, K, 2, false);
 
-            let poseidon = PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::configure(cs, ());
+            let poseidon = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::configure(cs, ());
 
             MyCircuitConfig { base, poseidon }
         }
@@ -2009,7 +2009,7 @@ mod add_with_overflow_and_poseidon {
             mut config: Self::Config,
             mut layouter: impl Layouter<Fr>,
         ) -> Result<(), Error> {
-            let poseidon_chip: PoseidonChip<PoseidonSpec, WIDTH, RATE, WIDTH> =
+            let poseidon_chip: PoseidonChip<PoseidonSpec, WIDTH, RATE> =
                 PoseidonChip::new(config.poseidon.clone());
 
             let assigned_inputs_a =
@@ -2044,11 +2044,9 @@ mod add_with_overflow_and_poseidon {
         let b = (0..LEN)
             .map(|i| halo2curves::bn256::Fr::from(i as u64 + 1))
             .collect::<Vec<_>>();
-        let commitment_a =
-            PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::run(a.clone()).unwrap()[0][0];
+        let commitment_a = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(a.clone()).unwrap()[0][0];
 
-        let commitment_b =
-            PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::run(b.clone()).unwrap()[0][0];
+        let commitment_b = PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(b.clone()).unwrap()[0][0];
 
         // parameters
         let a = Tensor::from(a.into_iter().map(Value::known));
@@ -2070,13 +2068,11 @@ mod add_with_overflow_and_poseidon {
         let b = (0..LEN)
             .map(|i| halo2curves::bn256::Fr::from(i as u64 + 1))
             .collect::<Vec<_>>();
-        let commitment_a = PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::run(a.clone())
-            .unwrap()[0][0]
-            + Fr::one();
+        let commitment_a =
+            PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(a.clone()).unwrap()[0][0] + Fr::one();
 
-        let commitment_b = PoseidonChip::<PoseidonSpec, WIDTH, RATE, WIDTH>::run(b.clone())
-            .unwrap()[0][0]
-            + Fr::one();
+        let commitment_b =
+            PoseidonChip::<PoseidonSpec, WIDTH, RATE>::run(b.clone()).unwrap()[0][0] + Fr::one();
 
         // parameters
         let a = Tensor::from(a.into_iter().map(Value::known));

src/graph/modules.rs

@@ -14,14 +14,11 @@ use serde::{Deserialize, Serialize};
 use super::errors::GraphError;
 use super::{VarVisibility, Visibility};
 
-/// poseidon len to hash in tree
-pub const POSEIDON_LEN_GRAPH: usize = 32;
 /// Poseidon number of instances
 pub const POSEIDON_INSTANCES: usize = 1;
 
 /// Poseidon module type
-pub type ModulePoseidon =
-    PoseidonChip<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, POSEIDON_LEN_GRAPH>;
+pub type ModulePoseidon = PoseidonChip<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>;
 /// Poseidon module config
 pub type ModulePoseidonConfig = PoseidonConfig<POSEIDON_WIDTH, POSEIDON_RATE>;
 

src/graph/utilities.rs

@@ -274,11 +274,9 @@ pub fn new_op_from_onnx(
     symbol_values: &SymbolValues,
     run_args: &crate::RunArgs,
 ) -> Result<(SupportedOp, Vec<usize>), GraphError> {
-    use std::f64::consts::E;
-
-    use tract_onnx::tract_core::ops::array::Trilu;
-
     use crate::circuit::InputType;
+    use std::f64::consts::E;
+    use tract_onnx::tract_core::ops::array::Trilu;
 
     let input_scales = inputs
         .iter()
@@ -1274,9 +1272,19 @@ pub fn new_op_from_onnx(
                     // get the non constant index
                     let denom = c.raw_values[0];
 
-                    SupportedOp::Hybrid(HybridOp::Div {
+                    let op = SupportedOp::Hybrid(HybridOp::Div {
                         denom: denom.into(),
-                    })
+                    });
+
+                    // if the input is scale 0 we re up to the max scale
+                    if input_scales[0] == 0 {
+                        SupportedOp::Rescaled(Rescaled {
+                            inner: Box::new(op),
+                            scale: vec![(0, scale_to_multiplier(scales.get_max()) as u128)],
+                        })
+                    } else {
+                        op
+                    }
                 } else {
                     return Err(GraphError::MisformedParams(
                         "only support non zero divisors of size 1".to_string(),

tests/integration_tests.rs

@@ -206,7 +206,7 @@ mod native_tests {
         "1l_tiny_div",
     ];
 
-    const TESTS: [&str; 98] = [
+    const TESTS: [&str; 99] = [
         "1l_mlp",     //0
         "1l_slice",   //1
         "1l_concat",  //2
@@ -309,6 +309,7 @@ mod native_tests {
         "log",         // 95
         "exp",         // 96
         "general_exp", // 97
+        "integer_div", // 98
     ];
 
     const WASM_TESTS: [&str; 46] = [
@@ -547,7 +548,7 @@ mod native_tests {
             }
         });
 
-            seq!(N in 0..=97 {
+            seq!(N in 0..=98 {
 
             #(#[test_case(TESTS[N])])*
             #[ignore]

tests/python/binding_tests.py

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

tests/wasm.rs

@@ -11,7 +11,6 @@ mod wasm32 {
     use ezkl::circuit::modules::poseidon::spec::{PoseidonSpec, POSEIDON_RATE, POSEIDON_WIDTH};
     use ezkl::circuit::modules::poseidon::PoseidonChip;
     use ezkl::circuit::modules::Module;
-    use ezkl::graph::modules::POSEIDON_LEN_GRAPH;
     use ezkl::graph::GraphCircuit;
     use ezkl::graph::{GraphSettings, GraphWitness};
     use ezkl::pfsys;
@@ -227,11 +226,9 @@ mod wasm32 {
         let hash: Vec<Vec<Fr>> = serde_json::from_slice(&hash[..]).unwrap();
 
         let reference_hash =
-            PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, POSEIDON_LEN_GRAPH>::run(
-                message.clone(),
-            )
-            .map_err(|_| "failed")
-            .unwrap();
+            PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::run(message.clone())
+                .map_err(|_| "failed")
+                .unwrap();
 
         assert_eq!(hash, reference_hash)
     }