cap inf

docs/advanced_security/quantization_backdoors.md

@@ -1,52 +1,20 @@
-# EZKL Security Note: Quantization-Activated Model Backdoors
+# EZKL Security Note: Quantization-Induced Model Backdoors
 
-## Model backdoors and provenance
+> 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.
 
-Machine learning models inherently suffer from robustness issues, which can lead to various
-kinds of attacks, from backdoors to evasion attacks. These vulnerabilities are a direct byproductof how machine learning models learn and cannot be remediated.
+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.
 
-We say a model has a backdoor whenever a specific attacker-chosen trigger in the input leads
-to the model misbehaving. For instance, if we have an image classifier discriminating cats from dogs, the ability to turn any image of a cat into an image classified as a dog by changing a specific pixel pattern constitutes a backdoor.
+Key Factors:
 
-Backdoors can be introduced using many different vectors. An attacker can introduce a
-backdoor using traditional security vulnerabilities. For instance, they could directly alter the file containing model weights or dynamically hack the Python code of the model. In addition, backdoors can be introduced by the training data through a process known as poisoning. In this case, an attacker adds malicious data points to the dataset before the model is trained so that the model learns to associate the backdoor trigger with the intended misbehavior.
+- 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
 
-All these vectors constitute a whole range of provenance challenges, as any component of an
-AI system can virtually be an entrypoint for a backdoor. Although provenance is already a
-concern with traditional code, the issue is exacerbated with AI, as retraining a model is
-cost-prohibitive. It is thus impractical to translate the “recompile it yourself” thinking to AI.
+Limitations: 
 
-## Quantization activated backdoors
-
-Backdoors are a generic concern in AI that is outside the scope of EZKL. However, EZKL may
-activate a specific subset of backdoors. Several academic papers have demonstrated the
-possibility, both in theory and in practice, of implanting undetectable and inactive backdoors in a full precision model that can be reactivated by quantization.
-
-An external attacker may trick the user of an application running EZKL into loading a model
-containing a quantization backdoor. This backdoor is active in the resulting model and circuit but not in the full-precision model supplied to EZKL, compromising the integrity of the target application and the resulting proof.
-
-### When is this a concern for me as a user?
-
-Any untrusted component in your AI stack may be a backdoor vector. In practice, the most
-sensitive parts include:
-
-- Datasets downloaded from the web or containing crowdsourced data
-- Models downloaded from the web even after finetuning
-- Untrusted software dependencies (well-known frameworks such as PyTorch can typically
-be considered trusted)
-- Any component loaded through an unsafe serialization format, such as Pickle.
-Because backdoors are inherent to ML and cannot be eliminated, reviewing the provenance of
-these sensitive components is especially important.
-
-### Responsibilities of the user and EZKL
-
-As EZKL cannot prevent backdoored models from being used, it is the responsibility of the user to review the provenance of all the components in their AI stack to ensure that no backdoor could have been implanted. EZKL shall not be held responsible for misleading prediction proofs resulting from using a backdoored model or for any harm caused to a system or its users due to a misbehaving model.
-
-### Limitations:
-
-- Attack effectiveness depends on calibration settings and internal rescaling operations.
+- Attack effectiveness depends on calibration settings and internal rescaling operations. 
 - Further research needed on backdoor persistence through witness/proof stages.
-- Can be mitigated by evaluating the quantized model (using `ezkl gen-witness`), rather than relying on the evaluation of the original model in pytorch or onnx-runtime as difference in evaluation could reveal a backdoor.
+- Can be mitigated by evaluating the quantized model (using `ezkl gen-witness`), rather than relying on the evaluation of the original model. 
 
 References:
 

src/circuit/ops/base.rs

@@ -21,10 +21,7 @@ pub enum BaseOp {
 
 /// Matches a [BaseOp] to an operation over inputs
 impl BaseOp {
-    /// forward func for non-accumulating operations
-    /// # Panics
-    /// Panics if called on an accumulating operation
-    /// # Examples
+    /// forward func
     pub fn nonaccum_f<
         T: TensorType + Add<Output = T> + Sub<Output = T> + Mul<Output = T> + Neg<Output = T>,
     >(
@@ -40,9 +37,7 @@ impl BaseOp {
         }
     }
 
-    /// forward func for accumulating operations
-    /// # Panics
-    /// Panics if called on a non-accumulating operation
+    /// forward func
     pub fn accum_f<
         T: TensorType + Add<Output = T> + Sub<Output = T> + Mul<Output = T> + Neg<Output = T>,
     >(

src/circuit/ops/mod.rs

@@ -159,8 +159,6 @@ impl std::str::FromStr for InputType {
 
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 impl From<DatumType> for InputType {
-    /// # Panics
-    /// Panics if the datum type is not supported
     fn from(datum_type: DatumType) -> Self {
         match datum_type {
             DatumType::Bool => InputType::Bool,
@@ -303,7 +301,8 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Constant<F> {
             Some(v) => v,
             None => return Err(CircuitError::UnsetVisibility),
         };
-        self.quantized_values = quantize_tensor(self.raw_values.clone(), new_scale, &visibility)?;
+        self.quantized_values =
+            quantize_tensor(self.raw_values.clone(), new_scale, &visibility, true)?;
         Ok(())
     }
 

src/circuit/ops/poly.rs

@@ -49,7 +49,7 @@ pub enum PolyOp {
     },
     Downsample {
         axis: usize,
-        stride: isize,
+        stride: usize,
         modulo: usize,
     },
     DeConv {
@@ -108,8 +108,13 @@ pub enum PolyOp {
 }
 
 impl<
-    F: PrimeField + TensorType + PartialOrd + std::hash::Hash + Serialize + for<'de> Deserialize<'de>,
-> Op<F> for PolyOp
+        F: PrimeField
+            + TensorType
+            + PartialOrd
+            + std::hash::Hash
+            + Serialize
+            + for<'de> Deserialize<'de>,
+    > Op<F> for PolyOp
 {
     /// Returns a reference to the Any trait.
     fn as_any(&self) -> &dyn Any {
@@ -183,8 +188,7 @@ impl<
             } => {
                 format!(
                     "DECONV (stride={:?}, padding={:?}, output_padding={:?}, group={}, data_format={:?}, kernel_format={:?})",
-                    stride, padding, output_padding, group, data_format, kernel_format
-                )
+                    stride, padding, output_padding, group, data_format, kernel_format)
             }
             PolyOp::Concat { axis } => format!("CONCAT (axis={})", axis),
             PolyOp::Slice { axis, start, end } => {

src/graph/node.rs

@@ -14,14 +14,14 @@ use super::VarScales;
 use super::Visibility;
 
 // Import operation types for different circuit components
-use crate::circuit::hybrid::HybridOp;
-use crate::circuit::lookup::LookupOp;
-use crate::circuit::poly::PolyOp;
 use crate::circuit::CircuitError;
 use crate::circuit::Constant;
 use crate::circuit::Input;
 use crate::circuit::Op;
 use crate::circuit::Unknown;
+use crate::circuit::hybrid::HybridOp;
+use crate::circuit::lookup::LookupOp;
+use crate::circuit::poly::PolyOp;
 
 // Import graph error types for EZKL
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
@@ -740,7 +740,7 @@ fn rescale_const_with_single_use(
         if scale_max > &current_scale {
             let raw_values = constant.raw_values.clone();
             constant.quantized_values =
-                super::quantize_tensor(raw_values, *scale_max, param_visibility)?;
+                super::quantize_tensor(raw_values, *scale_max, param_visibility, true)?;
         }
     }
 

src/graph/utilities.rs

@@ -68,6 +68,33 @@ pub fn quantize_float(
     Ok(scaled)
 }
 
+/// Quantizes an iterable of f64 to a [Tensor] of IntegerRep using a fixed point representation.
+/// NAN gets mapped to 0. And values that exceed the max are capped.
+/// Arguments
+///
+/// * `elem` - the element to quantize.
+/// * `shift` - offset used in the fixed point representation.
+/// * `scale` - `2^scale` used in the fixed point representation.
+pub fn quantize_float_and_cap(
+    elem: &f64,
+    shift: f64,
+    scale: crate::Scale,
+) -> Result<IntegerRep, TensorError> {
+    let mult = scale_to_multiplier(scale);
+    let max_value = ((IntegerRep::MAX as f64 - shift) / mult).round(); // the maximum value that can be represented w/o sig bit truncation
+
+    if *elem > max_value {
+        return Ok(IntegerRep::MAX);
+    } else if *elem < -max_value {
+        return Ok(IntegerRep::MIN);
+    }
+
+    // we parallelize the quantization process as it seems to be quite slow at times
+    let scaled = (mult * *elem + shift).round() as IntegerRep;
+
+    Ok(scaled)
+}
+
 /// Dequantizes a field element to a f64 using a fixed point representation.
 /// Arguments
 /// * `felt` - the field element to dequantize.
@@ -379,7 +406,7 @@ pub fn new_op_from_onnx(
             let range = (start..end).step_by(delta).collect::<Vec<_>>();
             let raw_value = range.iter().map(|x| *x as f32).collect::<Tensor<_>>();
             // Quantize the raw value (integers)
-            let quantized_value = quantize_tensor(raw_value.clone(), 0, &Visibility::Fixed)?;
+            let quantized_value = quantize_tensor(raw_value.clone(), 0, &Visibility::Fixed, false)?;
 
             let c = crate::circuit::ops::Constant::new(
                 quantized_value,
@@ -711,6 +738,7 @@ pub fn new_op_from_onnx(
                 raw_value.clone(),
                 constant_scale,
                 &run_args.param_visibility,
+                true,
             )?;
             let c = crate::circuit::ops::Constant::new(
                 quantized_value,
@@ -1398,7 +1426,7 @@ pub fn new_op_from_onnx(
 
             SupportedOp::Linear(PolyOp::Downsample {
                 axis: downsample_node.axis,
-                stride: downsample_node.stride,
+                stride: downsample_node.stride as usize,
                 modulo: downsample_node.modulo,
             })
         }
@@ -1550,13 +1578,20 @@ pub fn quantize_tensor<F: PrimeField + TensorType + PartialOrd>(
     const_value: Tensor<f32>,
     scale: crate::Scale,
     visibility: &Visibility,
+    cap: bool,
 ) -> Result<Tensor<F>, TensorError> {
     let mut value: Tensor<F> = const_value.par_enum_map(|_, x| {
-        Ok::<_, TensorError>(crate::fieldutils::integer_rep_to_felt::<F>(quantize_float(
-            &(x).into(),
-            0.0,
-            scale,
-        )?))
+        if cap {
+            Ok::<F, TensorError>(crate::fieldutils::integer_rep_to_felt::<F>(
+                quantize_float_and_cap(&(x).into(), 0.0, scale)?,
+            ))
+        } else {
+            Ok(crate::fieldutils::integer_rep_to_felt::<F>(quantize_float(
+                &(x).into(),
+                0.0,
+                scale,
+            )?))
+        }
     })?;
 
     value.set_scale(scale);
@@ -1644,7 +1679,7 @@ pub mod tests {
         let reference: Tensor<Fp> = (0..10).map(|x| x.into()).into();
         let scale = 0;
         let visibility = &Visibility::Public;
-        let quantized: Tensor<Fp> = quantize_tensor(tensor, scale, visibility).unwrap();
+        let quantized: Tensor<Fp> = quantize_tensor(tensor, scale, visibility, false).unwrap();
         assert_eq!(quantized.len(), 10);
         assert_eq!(quantized, reference);
     }

src/pfsys/mod.rs

@@ -17,16 +17,16 @@ use crate::{Commitments, EZKL_BUF_CAPACITY, EZKL_KEY_FORMAT};
 use clap::ValueEnum;
 use halo2_proofs::circuit::Value;
 use halo2_proofs::plonk::{
-    Circuit, ProvingKey, VerifyingKey, create_proof, keygen_pk, keygen_vk_custom, verify_proof,
+    create_proof, keygen_pk, keygen_vk_custom, verify_proof, Circuit, ProvingKey, VerifyingKey,
 };
-use halo2_proofs::poly::VerificationStrategy;
 use halo2_proofs::poly::commitment::{CommitmentScheme, Params, ParamsProver, Prover, Verifier};
 use halo2_proofs::poly::ipa::commitment::IPACommitmentScheme;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
+use halo2_proofs::poly::VerificationStrategy;
 use halo2_proofs::transcript::{EncodedChallenge, TranscriptReadBuffer, TranscriptWriterBuffer};
-use halo2curves::CurveAffine;
 use halo2curves::ff::{FromUniformBytes, PrimeField, WithSmallOrderMulGroup};
 use halo2curves::serde::SerdeObject;
+use halo2curves::CurveAffine;
 use instant::Instant;
 use log::{debug, info, trace};
 #[cfg(not(feature = "det-prove"))]
@@ -51,9 +51,6 @@ use pyo3::types::PyDictMethods;
 
 use halo2curves::bn256::{Bn256, Fr, G1Affine};
 
-/// Converts a string to a `SerdeFormat`.
-/// # Panics
-/// Panics if the provided `s` is not a valid `SerdeFormat` (i.e. not one of "processed", "raw-bytes-unchecked", or "raw-bytes").
 fn serde_format_from_str(s: &str) -> halo2_proofs::SerdeFormat {
     match s {
         "processed" => halo2_proofs::SerdeFormat::Processed,
@@ -324,7 +321,7 @@ where
 }
 
 #[cfg(feature = "python-bindings")]
-use pyo3::{PyObject, Python, ToPyObject, types::PyDict};
+use pyo3::{types::PyDict, PyObject, Python, ToPyObject};
 #[cfg(feature = "python-bindings")]
 impl<F: PrimeField + SerdeObject + Serialize, C: CurveAffine + Serialize> ToPyObject for Snark<F, C>
 where
@@ -348,9 +345,9 @@ where
 }
 
 impl<
-    F: PrimeField + SerdeObject + Serialize + FromUniformBytes<64> + DeserializeOwned,
-    C: CurveAffine + Serialize + DeserializeOwned,
-> Snark<F, C>
+        F: PrimeField + SerdeObject + Serialize + FromUniformBytes<64> + DeserializeOwned,
+        C: CurveAffine + Serialize + DeserializeOwned,
+    > Snark<F, C>
 where
     C::Scalar: Serialize + DeserializeOwned,
     C::ScalarExt: Serialize + DeserializeOwned,

src/tensor/mod.rs

@@ -926,9 +926,6 @@ impl<T: Clone + TensorType> Tensor<T> {
                 ));
             }
             self.dims = vec![];
-        }
-        if self.dims() == &[0] && new_dims.iter().product::<usize>() == 1 {
-            self.dims = Vec::from(new_dims);
         } else {
             let product = if new_dims != [0] {
                 new_dims.iter().product::<usize>()
@@ -1107,10 +1104,6 @@ impl<T: Clone + TensorType> Tensor<T> {
             let mut output = self.clone();
             output.reshape(shape)?;
             return Ok(output);
-        } else if self.dims() == &[0] && shape.iter().product::<usize>() == 1 {
-            let mut output = self.clone();
-            output.reshape(shape)?;
-            return Ok(output);
         }
 
         if self.dims().len() > shape.len() {
@@ -1774,7 +1767,6 @@ pub fn get_broadcasted_shape(
     }
 }
 ////////////////////////
-///
 
 /// The shape of data for some operations
 #[derive(Clone, Debug, Serialize, Deserialize, PartialEq, Default, Copy)]

src/tensor/ops.rs

@@ -535,101 +535,30 @@ pub fn mult<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::S
 /// let result = downsample(&x, 1, 2, 2).unwrap();
 /// let expected = Tensor::<IntegerRep>::new(Some(&[3, 6]), &[2, 1]).unwrap();
 /// assert_eq!(result, expected);
-/// let x = Tensor::<IntegerRep>::new(
-///     Some(&[1, 2, 3, 4, 5, 6]),
-///     &[2, 3],
-/// ).unwrap();
-///
-/// // Test case 1: Negative stride along dimension 0
-/// // This should flip the order along dimension 0
-/// let result = downsample(&x, 0, -1, 0).unwrap();
-/// let expected = Tensor::<IntegerRep>::new(
-///     Some(&[4, 5, 6, 1, 2, 3]), // Flipped order of rows
-///     &[2, 3]
-/// ).unwrap();
-/// assert_eq!(result, expected);
-///
-/// // Test case 2: Negative stride along dimension 1
-/// // This should flip the order along dimension 1
-/// let result = downsample(&x, 1, -1, 0).unwrap();
-/// let expected = Tensor::<IntegerRep>::new(
-///     Some(&[3, 2, 1, 6, 5, 4]), // Flipped order of columns
-///     &[2, 3]
-/// ).unwrap();
-/// assert_eq!(result, expected);
-///
-/// // Test case 3: Negative stride with stride magnitude > 1
-/// // This should both skip and flip
-/// let result = downsample(&x, 1, -2, 0).unwrap();
-/// let expected = Tensor::<IntegerRep>::new(
-///     Some(&[3, 1, 6, 4]), // Take every 2nd element in reverse
-///     &[2, 2]
-/// ).unwrap();
-/// assert_eq!(result, expected);
-///
-/// // Test case 4: Negative stride with non-zero modulo
-/// // This should start at (size - 1 - modulo) and reverse
-/// let result = downsample(&x, 1, -2, 1).unwrap();
-/// let expected = Tensor::<IntegerRep>::new(
-///     Some(&[2, 5]), // Start at second element from end, take every 2nd in reverse
-///     &[2, 1]
-/// ).unwrap();
-/// assert_eq!(result, expected);
-///
-/// // Create a larger test case for more complex downsampling
-/// let y = Tensor::<IntegerRep>::new(
-///     Some(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]),
-///     &[3, 4],
-/// ).unwrap();
-///
-/// // Test case 5: Negative stride with modulo on larger tensor
-/// let result = downsample(&y, 1, -2, 1).unwrap();
-/// let expected = Tensor::<IntegerRep>::new(
-///     Some(&[3, 1, 7, 5, 11, 9]), // Start at one after reverse, take every 2nd
-///     &[3, 2]
-/// ).unwrap();
-/// assert_eq!(result, expected);
-/// ```
 pub fn downsample<T: TensorType + Send + Sync>(
     input: &Tensor<T>,
     dim: usize,
-    stride: isize, // Changed from usize to isize to support negative strides
+    stride: usize,
     modulo: usize,
 ) -> Result<Tensor<T>, TensorError> {
-    // Handle negative stride case
-    if stride == 0 {
-        return Err(TensorError::DimMismatch(
-            "downsample stride cannot be zero".to_string(),
-        ));
-    }
-
-    let stride_abs = stride.unsigned_abs();
     let mut output_shape = input.dims().to_vec();
+    // now downsample along axis dim offset by modulo, rounding up (+1 if remaidner is non-zero)
+    let remainder = (input.dims()[dim] - modulo) % stride;
+    let div = (input.dims()[dim] - modulo) / stride;
+    output_shape[dim] = div + (remainder > 0) as usize;
+    let mut output = Tensor::<T>::new(None, &output_shape)?;
 
-    if modulo >= input.dims()[dim] {
+    if modulo > input.dims()[dim] {
         return Err(TensorError::DimMismatch("downsample".to_string()));
     }
 
-    // Calculate output shape based on the absolute value of stride
-    let remainder = (input.dims()[dim] - modulo) % stride_abs;
-    let div = (input.dims()[dim] - modulo) / stride_abs;
-    output_shape[dim] = div + (remainder > 0) as usize;
-
-    let mut output = Tensor::<T>::new(None, &output_shape)?;
-
-    // Calculate indices based on stride direction
+    // now downsample along axis dim offset by modulo
     let indices = (0..output_shape.len())
         .map(|i| {
             if i == dim {
                 let mut index = vec![0; output_shape[i]];
-                for (j, idx) in index.iter_mut().enumerate() {
-                    if stride > 0 {
-                        // Positive stride: move forward from modulo
-                        *idx = j * stride_abs + modulo;
-                    } else {
-                        // Negative stride: move backward from (size - 1 - modulo)
-                        *idx = (input.dims()[dim] - 1 - modulo) - j * stride_abs;
-                    }
+                for (i, idx) in index.iter_mut().enumerate() {
+                    *idx = i * stride + modulo;
                 }
                 index
             } else {
@@ -2346,11 +2275,7 @@ pub mod nonlinearities {
     pub fn recip(a: &Tensor<IntegerRep>, input_scale: f64, out_scale: f64) -> Tensor<IntegerRep> {
         a.par_enum_map(|_, a_i| {
             let rescaled = (a_i as f64) / input_scale;
-            let denom = if rescaled == 0_f64 {
-                (1_f64) / (rescaled + f64::EPSILON)
-            } else {
-                (1_f64) / (rescaled)
-            };
+            let denom = (1_f64) / (rescaled + f64::EPSILON);
             let d_inv_x = out_scale * denom;
             Ok::<_, TensorError>(d_inv_x.round() as IntegerRep)
         })

src/tensor/val.rs

@@ -1342,11 +1342,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ValTensor<F> {
     /// Gets the total number of elements in the tensor
     pub fn len(&self) -> usize {
         match self {
-            ValTensor::Value { dims, inner, .. } => {
+            ValTensor::Value { dims, .. } => {
                 if !dims.is_empty() && (dims != &[0]) {
                     dims.iter().product::<usize>()
-                } else if dims.is_empty() {
-                    inner.inner.len()
                 } else {
                     0
                 }

src/tensor/var.rs

@@ -2,7 +2,7 @@ use std::collections::HashSet;
 
 use log::{debug, error, warn};
 
-use crate::circuit::{CheckMode, region::ConstantsMap};
+use crate::circuit::{region::ConstantsMap, CheckMode};
 
 use super::*;
 /// A wrapper around Halo2's Column types that represents a tensor of variables in the circuit.
@@ -403,10 +403,7 @@ impl VarTensor {
         let mut assigned_coord = 0;
         let mut res: ValTensor<F> = match values {
             ValTensor::Instance { .. } => {
-                error!(
-                    "assignment with omissions is not supported on instance columns. increase K if you require more rows."
-                );
-                Err(halo2_proofs::plonk::Error::Synthesis)
+                unimplemented!("cannot assign instance to advice columns with omissions")
             }
             ValTensor::Value { inner: v, .. } => Ok::<ValTensor<F>, halo2_proofs::plonk::Error>(
                 v.enum_map(|coord, k| {
@@ -572,13 +569,8 @@ impl VarTensor {
         constants: &mut ConstantsMap<F>,
     ) -> Result<(ValTensor<F>, usize), halo2_proofs::plonk::Error> {
         match values {
-            ValTensor::Instance { .. } => {
-                error!(
-                    "duplication is not supported on instance columns. increase K if you require more rows."
-                );
-                Err(halo2_proofs::plonk::Error::Synthesis)
-            }
-            ValTensor::Value { inner: v, dims, .. } => {
+            ValTensor::Instance { .. } => unimplemented!("duplication is not supported on instance columns. increase K if you require more rows."),
+            ValTensor::Value { inner: v, dims , ..} => {
                 let duplication_freq = if single_inner_col {
                     self.col_size()
                 } else {
@@ -591,20 +583,21 @@ impl VarTensor {
                     self.num_inner_cols()
                 };
 
-                let duplication_offset = if single_inner_col { row } else { offset };
+                let duplication_offset = if single_inner_col {
+                    row
+                } else {
+                    offset
+                };
+
 
                 // duplicates every nth element to adjust for column overflow
-                let mut res: ValTensor<F> = v
-                    .duplicate_every_n(duplication_freq, num_repeats, duplication_offset)
-                    .unwrap()
-                    .into();
+                let mut res: ValTensor<F> = v.duplicate_every_n(duplication_freq, num_repeats, duplication_offset).unwrap().into();
 
                 let constants_map = res.create_constants_map();
                 constants.extend(constants_map);
 
                 let total_used_len = res.len();
-                res.remove_every_n(duplication_freq, num_repeats, duplication_offset)
-                    .unwrap();
+                res.remove_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();
 
                 res.reshape(dims).unwrap();
                 res.set_scale(values.scale());
@@ -634,13 +627,9 @@ impl VarTensor {
         constants: &mut ConstantsMap<F>,
     ) -> Result<(ValTensor<F>, usize), halo2_proofs::plonk::Error> {
         match values {
-            ValTensor::Instance { .. } => {
-                error!(
-                    "duplication is not supported on instance columns. increase K if you require more rows."
-                );
-                Err(halo2_proofs::plonk::Error::Synthesis)
-            }
-            ValTensor::Value { inner: v, dims, .. } => {
+            ValTensor::Instance { .. } => unimplemented!("duplication is not supported on instance columns. increase K if you require more rows."),
+            ValTensor::Value { inner: v, dims , ..} => {
+
                 let duplication_freq = self.block_size();
 
                 let num_repeats = self.num_inner_cols();
@@ -648,31 +637,17 @@ impl VarTensor {
                 let duplication_offset = offset;
 
                 // duplicates every nth element to adjust for column overflow
-                let v = v
-                    .duplicate_every_n(duplication_freq, num_repeats, duplication_offset)
-                    .map_err(|e| {
-                        error!("Error duplicating values: {:?}", e);
-                        halo2_proofs::plonk::Error::Synthesis
-                    })?;
+                let v = v.duplicate_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();
                 let mut res: ValTensor<F> = {
                     v.enum_map(|coord, k| {
-                        let cell =
-                            self.assign_value(region, offset, k.clone(), coord, constants)?;
-                        Ok::<_, halo2_proofs::plonk::Error>(cell)
-                    })?
-                    .into()
-                };
-                let total_used_len = res.len();
-                res.remove_every_n(duplication_freq, num_repeats, duplication_offset)
-                    .map_err(|e| {
-                        error!("Error duplicating values: {:?}", e);
-                        halo2_proofs::plonk::Error::Synthesis
-                    })?;
+                    let cell = self.assign_value(region, offset, k.clone(), coord, constants)?;
+                    Ok::<_, halo2_proofs::plonk::Error>(cell)
 
-                res.reshape(dims).map_err(|e| {
-                    error!("Error duplicating values: {:?}", e);
-                    halo2_proofs::plonk::Error::Synthesis
-                })?;
+                })?.into()};
+                let total_used_len = res.len();
+                res.remove_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();
+
+                res.reshape(dims).unwrap();
                 res.set_scale(values.scale());
 
                 Ok((res, total_used_len))
@@ -706,71 +681,61 @@ impl VarTensor {
         let mut prev_cell = None;
 
         match values {
-            ValTensor::Instance { .. } => {
-                error!(
-                    "duplication is not supported on instance columns. increase K if you require more rows."
-                );
-                Err(halo2_proofs::plonk::Error::Synthesis)
-            }
-            ValTensor::Value { inner: v, dims, .. } => {
+            ValTensor::Instance { .. } => unimplemented!("duplication is not supported on instance columns. increase K if you require more rows."),
+            ValTensor::Value { inner: v, dims , ..} => {
+
                 let duplication_freq = self.col_size();
                 let num_repeats = 1;
                 let duplication_offset = row;
 
                 // duplicates every nth element to adjust for column overflow
-                let v = v
-                    .duplicate_every_n(duplication_freq, num_repeats, duplication_offset)
-                    .unwrap();
-                let mut res: ValTensor<F> = v
-                    .enum_map(|coord, k| {
-                        let step = self.num_inner_cols();
+                let v = v.duplicate_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();
+                let mut res: ValTensor<F> =
+                    v.enum_map(|coord, k| {
 
-                        let (x, y, z) = self.cartesian_coord(offset + coord * step);
-                        if matches!(check_mode, CheckMode::SAFE) && coord > 0 && z == 0 && y == 0 {
-                            // assert that duplication occurred correctly
-                            assert_eq!(
-                                Into::<IntegerRep>::into(k.clone()),
-                                Into::<IntegerRep>::into(v[coord - 1].clone())
-                            );
-                        };
+                    let step = self.num_inner_cols();
 
-                        let cell =
-                            self.assign_value(region, offset, k.clone(), coord * step, constants)?;
+                    let (x, y, z) = self.cartesian_coord(offset + coord * step);
+                    if matches!(check_mode, CheckMode::SAFE) && coord > 0 && z == 0 && y == 0 {
+                        // assert that duplication occurred correctly
+                        assert_eq!(Into::<IntegerRep>::into(k.clone()), Into::<IntegerRep>::into(v[coord - 1].clone()));
+                    };
 
-                        let at_end_of_column = z == duplication_freq - 1;
-                        let at_beginning_of_column = z == 0;
+                    let cell = self.assign_value(region, offset, k.clone(), coord * step, constants)?;
 
-                        if at_end_of_column {
-                            // if we are at the end of the column, we need to copy the cell to the next column
-                            prev_cell = Some(cell.clone());
-                        } else if coord > 0 && at_beginning_of_column {
-                            if let Some(prev_cell) = prev_cell.as_ref() {
-                                let cell = if let Some(cell) = cell.cell() {
-                                    cell
-                                } else {
-                                    error!("Error getting cell: {:?}", (x, y));
-                                    return Err(halo2_proofs::plonk::Error::Synthesis);
-                                };
-                                let prev_cell = if let Some(prev_cell) = prev_cell.cell() {
-                                    prev_cell
-                                } else {
-                                    error!("Error getting prev cell: {:?}", (x, y));
-                                    return Err(halo2_proofs::plonk::Error::Synthesis);
-                                };
-                                region.constrain_equal(prev_cell, cell)?;
+                    let at_end_of_column = z == duplication_freq - 1;
+                    let at_beginning_of_column = z == 0;
+
+                    if at_end_of_column {
+                        // if we are at the end of the column, we need to copy the cell to the next column
+                        prev_cell = Some(cell.clone());
+                    } else if coord > 0 && at_beginning_of_column  {
+                        if let Some(prev_cell) = prev_cell.as_ref() {
+                            let cell = if let Some(cell) = cell.cell() {
+                                cell
                             } else {
-                                error!("Previous cell was not set");
+                                error!("Error getting cell: {:?}", (x,y));
                                 return Err(halo2_proofs::plonk::Error::Synthesis);
-                            }
+                            };
+                            let prev_cell = if let Some(prev_cell) = prev_cell.cell() {
+                                prev_cell
+                            } else {
+                                error!("Error getting prev cell: {:?}", (x,y));
+                                return Err(halo2_proofs::plonk::Error::Synthesis);
+                            };
+                            region.constrain_equal(prev_cell,cell)?;
+                        } else {
+                            error!("Previous cell was not set");
+                            return Err(halo2_proofs::plonk::Error::Synthesis);
                         }
+                    }
 
-                        Ok(cell)
-                    })?
-                    .into();
+                    Ok(cell)
+
+                })?.into();
 
                 let total_used_len = res.len();
-                res.remove_every_n(duplication_freq, num_repeats, duplication_offset)
-                    .unwrap();
+                res.remove_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();
 
                 res.reshape(dims).unwrap();
                 res.set_scale(values.scale());
@@ -806,30 +771,21 @@ impl VarTensor {
                 VarTensor::Advice { inner: advices, .. } => {
                     ValType::PrevAssigned(region.assign_advice(|| "k", advices[x][y], z, || v)?)
                 }
-                _ => {
-                    error!("VarTensor was not initialized");
-                    return Err(halo2_proofs::plonk::Error::Synthesis);
-                }
+                _ => unimplemented!(),
             },
             // Handle copying previously assigned value
             ValType::PrevAssigned(v) => match &self {
                 VarTensor::Advice { inner: advices, .. } => {
                     ValType::PrevAssigned(v.copy_advice(|| "k", region, advices[x][y], z)?)
                 }
-                _ => {
-                    error!("VarTensor was not initialized");
-                    return Err(halo2_proofs::plonk::Error::Synthesis);
-                }
+                _ => unimplemented!(),
             },
             // Handle copying previously assigned constant
             ValType::AssignedConstant(v, val) => match &self {
                 VarTensor::Advice { inner: advices, .. } => {
                     ValType::AssignedConstant(v.copy_advice(|| "k", region, advices[x][y], z)?, val)
                 }
-                _ => {
-                    error!("VarTensor was not initialized");
-                    return Err(halo2_proofs::plonk::Error::Synthesis);
-                }
+                _ => unimplemented!(),
             },
             // Handle assigning evaluated value
             ValType::AssignedValue(v) => match &self {
@@ -838,10 +794,7 @@ impl VarTensor {
                         .assign_advice(|| "k", advices[x][y], z, || v)?
                         .evaluate(),
                 ),
-                _ => {
-                    error!("VarTensor was not initialized");
-                    return Err(halo2_proofs::plonk::Error::Synthesis);
-                }
+                _ => unimplemented!(),
             },
             // Handle constant value assignment with caching
             ValType::Constant(v) => {

tests/integration_tests.rs

@@ -1,12 +1,12 @@
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 #[cfg(test)]
 mod native_tests {
-
+    
     // use ezkl::circuit::table::RESERVED_BLINDING_ROWS_PAD;
-    use ezkl::Commitments;
     use ezkl::graph::input::{FileSource, FileSourceInner, GraphData};
     use ezkl::graph::{DataSource, GraphSettings, GraphWitness};
     use ezkl::pfsys::Snark;
+    use ezkl::Commitments;
     use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
     use halo2curves::bn256::Bn256;
     use lazy_static::lazy_static;
@@ -522,7 +522,7 @@ mod native_tests {
             use crate::native_tests::run_js_tests;
             use crate::native_tests::render_circuit;
             use crate::native_tests::model_serialization_different_binaries;
-
+            
             use tempdir::TempDir;
             use ezkl::Commitments;
 
@@ -2293,12 +2293,7 @@ mod native_tests {
                 .expect("failed to execute process");
 
             if status.success() {
-                log::error!(
-                    "Verification unexpectedly succeeded for modified proof {}. Flipped bit {} in byte {}",
-                    i,
-                    random_bit,
-                    random_byte
-                );
+                log::error!("Verification unexpectedly succeeded for modified proof {}. Flipped bit {} in byte {}", i, random_bit, random_byte);
             }
 
             assert!(