ci: update version string in docs

docs/python/requirements-docs.txt

@@ -1,4 +1,4 @@
-ezkl==15.2.0
+ezkl==15.3.0
 sphinx
 sphinx-rtd-theme
 sphinxcontrib-napoleon

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '15.2.0'
+release = '15.3.0'
 version = release
 
 

examples/onnx/rounding_ops/input.json

@@ -1 +1,148 @@
-{"input_shapes": [[3, 2, 3], [3, 2, 3], [3, 2, 3], [3, 2, 3]], "input_data": [[0.6261028051376343, 0.49872446060180664, -0.04514765739440918, 0.5936200618743896, 0.9271858930587769, 0.6688600778579712, -0.20331168174743652, -0.7016235589981079, 0.025863051414489746, -0.19426143169403076, 0.9827852249145508, 0.4897397756576538, 0.2992602586746216, 0.7011144161224365, 0.9278832674026489, 0.5943725109100342, -0.573331356048584, 0.3675816059112549], [0.7803324460983276, -0.9616303443908691, 0.6070173978805542, -0.028337717056274414, -0.5080242156982422, -0.9280107021331787, 0.6150380373001099, 0.3865993022918701, -0.43668973445892334, 0.17152702808380127, 0.5144252777099609, -0.28881049156188965, 0.8932310342788696, 0.059034109115600586, 0.6865451335906982, 0.009820222854614258, 0.23011493682861328, -0.9492779970169067], [-0.21352827548980713, -0.16015326976776123, -0.38964390754699707, 0.13464701175689697, -0.8814496994018555, 0.5037975311279297, -0.804405927658081, 0.9858957529067993, 0.19567716121673584, 0.9777265787124634, 0.6151977777481079, 0.568595290184021, 0.10584986209869385, -0.8975653648376465, 0.6235959529876709, -0.547879695892334, 0.9289869070053101, 0.7567293643951416]], "output_data": [[1.0, 0.0, -0.0, 1.0, 1.0, 1.0, -0.0, -1.0, 0.0, -0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, -1.0, 0.0], [0.0, -1.0, 0.0, -1.0, -1.0, -1.0, 0.0, 0.0, -1.0, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0], [-0.0, -0.0, -0.0, 1.0, -0.0, 1.0, -0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.0, 1.0, -0.0, 1.0, 1.0]]}
+{
+    "input_shapes": [
+        [
+            3,
+            2,
+            3
+        ],
+        [
+            3,
+            2,
+            3
+        ],
+        [
+            3,
+            2,
+            3
+        ],
+        [
+            3,
+            2,
+            3
+        ]
+    ],
+    "input_data": [
+        [
+            0.5,
+            1.5,
+            -0.04514765739440918,
+            0.5936200618743896,
+            0.9271858930587769,
+            0.6688600778579712,
+            -0.20331168174743652,
+            -0.7016235589981079,
+            0.025863051414489746,
+            -0.19426143169403076,
+            0.9827852249145508,
+            0.4897397756576538,
+            -1.5,
+            -0.5,
+            0.9278832674026489,
+            0.5943725109100342,
+            -0.573331356048584,
+            0.3675816059112549
+        ],
+        [
+            0.7803324460983276,
+            -0.9616303443908691,
+            0.6070173978805542,
+            -0.028337717056274414,
+            -0.5080242156982422,
+            -0.9280107021331787,
+            0.6150380373001099,
+            0.3865993022918701,
+            -0.43668973445892334,
+            0.17152702808380127,
+            0.5144252777099609,
+            -0.28881049156188965,
+            0.8932310342788696,
+            0.059034109115600586,
+            0.6865451335906982,
+            0.009820222854614258,
+            0.23011493682861328,
+            -0.9492779970169067
+        ],
+        [
+            -0.21352827548980713,
+            -0.16015326976776123,
+            -0.38964390754699707,
+            0.13464701175689697,
+            -0.8814496994018555,
+            0.5037975311279297,
+            -0.804405927658081,
+            0.9858957529067993,
+            0.19567716121673584,
+            0.9777265787124634,
+            0.6151977777481079,
+            0.568595290184021,
+            0.10584986209869385,
+            -0.8975653648376465,
+            0.6235959529876709,
+            -0.547879695892334,
+            0.9289869070053101,
+            0.7567293643951416
+        ]
+    ],
+    "output_data": [
+        [
+            1.0,
+            0.0,
+            -0.0,
+            1.0,
+            1.0,
+            1.0,
+            -0.0,
+            -1.0,
+            0.0,
+            -0.0,
+            1.0,
+            0.0,
+            0.0,
+            1.0,
+            1.0,
+            1.0,
+            -1.0,
+            0.0
+        ],
+        [
+            0.0,
+            -1.0,
+            0.0,
+            -1.0,
+            -1.0,
+            -1.0,
+            0.0,
+            0.0,
+            -1.0,
+            0.0,
+            0.0,
+            -1.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            -1.0
+        ],
+        [
+            -0.0,
+            -0.0,
+            -0.0,
+            1.0,
+            -0.0,
+            1.0,
+            -0.0,
+            1.0,
+            1.0,
+            1.0,
+            1.0,
+            1.0,
+            1.0,
+            -0.0,
+            1.0,
+            -0.0,
+            1.0,
+            1.0
+        ]
+    ]
+}

src/circuit/ops/hybrid.rs

@@ -13,6 +13,10 @@ use serde::{Deserialize, Serialize};
 /// An enum representing the operations that consist of both lookups and arithmetic operations.
 #[derive(Clone, Debug, Serialize, Deserialize)]
 pub enum HybridOp {
+    RoundHalfToEven {
+        scale: utils::F32,
+        legs: usize,
+    },
     Ceil {
         scale: utils::F32,
         legs: usize,
@@ -108,9 +112,13 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
 
     fn as_string(&self) -> String {
         match self {
+            HybridOp::RoundHalfToEven { scale, legs } => {
+                format!("ROUND_HALF_TO_EVEN(scale={}, legs={})", scale, legs)
+            }
             HybridOp::Ceil { scale, legs } => format!("CEIL(scale={}, legs={})", scale, legs),
             HybridOp::Floor { scale, legs } => format!("FLOOR(scale={}, legs={})", scale, legs),
             HybridOp::Round { scale, legs } => format!("ROUND(scale={}, legs={})", scale, legs),
+
             HybridOp::Max => format!("MAX"),
             HybridOp::Min => format!("MIN"),
             HybridOp::Recip {
@@ -181,6 +189,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
         values: &[ValTensor<F>],
     ) -> Result<Option<ValTensor<F>>, CircuitError> {
         Ok(Some(match self {
+            HybridOp::RoundHalfToEven { scale, legs } => {
+                layouts::round_half_to_even(config, region, values[..].try_into()?, *scale, *legs)?
+            }
             HybridOp::Ceil { scale, legs } => {
                 layouts::ceil(config, region, values[..].try_into()?, *scale, *legs)?
             }

src/circuit/ops/layouts.rs

@@ -4654,6 +4654,155 @@ pub fn round<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     )
 }
 
+/// round half to even layout
+/// # Arguments
+/// * `config` - BaseConfig
+/// * `region` - RegionCtx
+/// * `values` - &[ValTensor<F>; 1]
+/// * `scale` - utils::F32
+/// * `legs` - usize
+/// # Returns
+/// * ValTensor<F>
+/// # Example
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::fieldutils::IntegerRep;
+/// use ezkl::circuit::ops::layouts::round;
+/// use ezkl::tensor::val::ValTensor;
+/// use halo2curves::bn256::Fr as Fp;
+/// use ezkl::circuit::region::RegionCtx;
+/// use ezkl::circuit::region::RegionSettings;
+/// use ezkl::circuit::BaseConfig;
+/// let dummy_config = BaseConfig::dummy(12, 2);
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
+/// Some(&[3, -2, 3, 1]),
+/// &[1, 1, 2, 2],
+/// ).unwrap());
+/// let result = round::<Fp>(&dummy_config, &mut dummy_region, &[x], 4.0.into(), 2).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[4, -4, 4, 0]), &[1, 1, 2, 2]).unwrap();
+/// assert_eq!(result.int_evals().unwrap(), expected);
+/// ```
+///
+pub fn round_half_to_even<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    values: &[ValTensor<F>; 1],
+    scale: utils::F32,
+    legs: usize,
+) -> Result<ValTensor<F>, CircuitError> {
+    // decompose with base scale and then set the last element to zero
+    let decomposition = decompose(config, region, values, &(scale.0 as usize), &legs)?;
+    // set the last element to zero and then recompose, we don't actually need to assign here
+    // as this will automatically be assigned in the recompose function and uses the constant caching of RegionCtx
+    let zero = ValType::Constant(F::ZERO);
+
+    // if scale is not exactly divisible by 2 we warn
+    if scale.0 % 2.0 != 0.0 {
+        log::warn!("Scale is not exactly divisible by 2.0, rounding may not be accurate");
+    }
+
+    let midway_point: ValTensor<F> = create_constant_tensor(
+        integer_rep_to_felt((scale.0 / 2.0).round() as IntegerRep),
+        1,
+    );
+    let assigned_midway_point = region.assign(&config.custom_gates.inputs[1], &midway_point)?;
+
+    region.increment(1);
+
+    let dims = decomposition.dims().to_vec();
+    let first_dims = decomposition.dims().to_vec()[..decomposition.dims().len() - 1].to_vec();
+
+    let mut incremented_tensor = Tensor::new(None, &first_dims)?;
+
+    let cartesian_coord = first_dims
+        .iter()
+        .map(|x| 0..*x)
+        .multi_cartesian_product()
+        .collect::<Vec<_>>();
+
+    let inner_loop_function =
+        |i: usize, region: &mut RegionCtx<F>| -> Result<Tensor<ValType<F>>, CircuitError> {
+            let coord = cartesian_coord[i].clone();
+            let slice = coord.iter().map(|x| *x..*x + 1).collect::<Vec<_>>();
+            let mut sliced_input = decomposition.get_slice(&slice)?;
+            sliced_input.flatten();
+            let last_elem = sliced_input.last()?;
+
+            let penultimate_elem =
+                sliced_input.get_slice(&[sliced_input.len() - 2..sliced_input.len() - 1])?;
+
+            let is_equal_to_midway = equals(
+                config,
+                region,
+                &[last_elem.clone(), assigned_midway_point.clone()],
+            )?;
+            // penultimate_elem is equal to midway point and even, do nothing
+            let is_odd = nonlinearity(
+                config,
+                region,
+                &[penultimate_elem.clone()],
+                &LookupOp::IsOdd,
+            )?;
+
+            let is_odd_and_equal_to_midway = and(
+                config,
+                region,
+                &[is_odd.clone(), is_equal_to_midway.clone()],
+            )?;
+
+            let is_greater_than_midway = greater(
+                config,
+                region,
+                &[last_elem.clone(), assigned_midway_point.clone()],
+            )?;
+
+            // if the number is equal to midway point and odd increment, or if it is is_greater_than_midway
+            let is_odd_and_equal_to_midway_or_greater_than_midway = or(
+                config,
+                region,
+                &[
+                    is_odd_and_equal_to_midway.clone(),
+                    is_greater_than_midway.clone(),
+                ],
+            )?;
+
+            // increment the penultimate element
+            let incremented_elem = pairwise(
+                config,
+                region,
+                &[
+                    sliced_input.get_slice(&[sliced_input.len() - 2..sliced_input.len() - 1])?,
+                    is_odd_and_equal_to_midway_or_greater_than_midway.clone(),
+                ],
+                BaseOp::Add,
+            )?;
+
+            let mut inner_tensor = sliced_input.get_inner_tensor()?.clone();
+            inner_tensor[sliced_input.len() - 2] =
+                incremented_elem.get_inner_tensor()?.clone()[0].clone();
+
+            // set the last elem to zero
+            inner_tensor[sliced_input.len() - 1] = zero.clone();
+
+            Ok(inner_tensor.clone())
+        };
+
+    region.update_max_min_lookup_inputs_force(0, scale.0 as IntegerRep)?;
+
+    region.apply_in_loop(&mut incremented_tensor, inner_loop_function)?;
+
+    let mut incremented_tensor = incremented_tensor.combine()?;
+    incremented_tensor.reshape(&dims)?;
+
+    recompose(
+        config,
+        region,
+        &[incremented_tensor.into()],
+        &(scale.0 as usize),
+    )
+}
+
 pub(crate) fn recompose<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,

src/circuit/ops/lookup.rs

@@ -4,7 +4,6 @@ use serde::{Deserialize, Serialize};
 use crate::{
     circuit::{layouts, table::Range, utils},
     fieldutils::{felt_to_integer_rep, integer_rep_to_felt, IntegerRep},
-    graph::multiplier_to_scale,
     tensor::{self, Tensor, TensorError, TensorType},
 };
 
@@ -16,8 +15,7 @@ use halo2curves::ff::PrimeField;
 #[derive(Clone, Debug, PartialEq, Eq, Hash, PartialOrd, Ord, Deserialize, Serialize)]
 pub enum LookupOp {
     Div { denom: utils::F32 },
-    Cast { scale: utils::F32 },
-    RoundHalfToEven { scale: utils::F32 },
+    IsOdd,
     Sqrt { scale: utils::F32 },
     Rsqrt { scale: utils::F32 },
     Sigmoid { scale: utils::F32 },
@@ -51,10 +49,9 @@ impl LookupOp {
     /// as path
     pub fn as_path(&self) -> String {
         match self {
-            LookupOp::RoundHalfToEven { scale } => format!("round_half_to_even_{}", scale),
             LookupOp::Pow { scale, a } => format!("pow_{}_{}", scale, a),
+            LookupOp::IsOdd => "is_odd".to_string(),
             LookupOp::Div { denom } => format!("div_{}", denom),
-            LookupOp::Cast { scale } => format!("cast_{}", scale),
             LookupOp::Sigmoid { scale } => format!("sigmoid_{}", scale),
             LookupOp::Sqrt { scale } => format!("sqrt_{}", scale),
             LookupOp::Rsqrt { scale } => format!("rsqrt_{}", scale),
@@ -85,18 +82,13 @@ impl LookupOp {
         let x = x[0].clone().map(|x| felt_to_integer_rep(x));
         let res =
             match &self {
-                LookupOp::RoundHalfToEven { scale } => Ok::<_, TensorError>(
-                    tensor::ops::nonlinearities::round_half_to_even(&x, scale.into()),
-                ),
+                LookupOp::IsOdd => Ok::<_, TensorError>(tensor::ops::nonlinearities::is_odd(&x)),
                 LookupOp::Pow { scale, a } => Ok::<_, TensorError>(
                     tensor::ops::nonlinearities::pow(&x, scale.0.into(), a.0.into()),
                 ),
                 LookupOp::Div { denom } => Ok::<_, TensorError>(
                     tensor::ops::nonlinearities::const_div(&x, f32::from(*denom).into()),
                 ),
-                LookupOp::Cast { scale } => Ok::<_, TensorError>(
-                    tensor::ops::nonlinearities::const_div(&x, f32::from(*scale).into()),
-                ),
                 LookupOp::Sigmoid { scale } => {
                     Ok::<_, TensorError>(tensor::ops::nonlinearities::sigmoid(&x, scale.into()))
                 }
@@ -171,10 +163,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Lookup
     /// Returns the name of the operation
     fn as_string(&self) -> String {
         match self {
-            LookupOp::RoundHalfToEven { scale } => format!("ROUND_HALF_TO_EVEN(scale={})", scale),
+            LookupOp::IsOdd => "IS_ODD".to_string(),
             LookupOp::Pow { a, scale } => format!("POW(scale={}, exponent={})", scale, a),
             LookupOp::Div { denom, .. } => format!("DIV(denom={})", denom),
-            LookupOp::Cast { scale } => format!("CAST(scale={})", scale),
             LookupOp::Ln { scale } => format!("LN(scale={})", scale),
             LookupOp::Sigmoid { scale } => format!("SIGMOID(scale={})", scale),
             LookupOp::Sqrt { scale } => format!("SQRT(scale={})", scale),
@@ -214,10 +205,6 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Lookup
     /// Returns the scale of the output of the operation.
     fn out_scale(&self, inputs_scale: Vec<crate::Scale>) -> Result<crate::Scale, CircuitError> {
         let scale = match self {
-            LookupOp::Cast { scale } => {
-                let in_scale = inputs_scale[0];
-                in_scale + multiplier_to_scale(1. / scale.0 as f64)
-            }
             _ => inputs_scale[0],
         };
         Ok(scale)

src/circuit/ops/region.rs

@@ -474,6 +474,17 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         Ok(())
     }
 
+    /// Update the max and min forcefully 
+    pub fn update_max_min_lookup_inputs_force(
+        &mut self,
+        min: IntegerRep,
+        max: IntegerRep,
+    ) -> Result<(), CircuitError> {
+        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(())
+    }
+
     /// Update the max and min from inputs
     pub fn update_max_min_lookup_range(&mut self, range: Range) -> Result<(), CircuitError> {
         if range.0 > range.1 {

src/circuit/table.rs

@@ -150,12 +150,16 @@ pub fn num_cols_required(range_len: IntegerRep, col_size: usize) -> usize {
 }
 
 impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
+    /// get largest element represented by the range
+    pub fn largest(&self) -> IntegerRep {
+        self.range.0 + (self.col_size * self.table_inputs.len() - 1) as IntegerRep
+    }
     fn name(&self) -> String {
         format!(
             "{}_{}_{}",
             self.nonlinearity.as_path(),
             self.range.0,
-            self.range.1
+            self.largest()
         )
     }
     /// Configures the table.
@@ -222,7 +226,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
         }
 
         let smallest = self.range.0;
-        let largest = self.range.1;
+        let largest = self.largest();
 
         let gen_table = || -> Result<(Tensor<F>, Tensor<F>), crate::tensor::TensorError> {
             let inputs = Tensor::from(smallest..=largest)
@@ -291,6 +295,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
 
                                 row_offset += chunk_idx * self.col_size;
                                 let (x, y) = self.cartesian_coord(row_offset);
+
                                 if !preassigned_input {
                                     table.assign_cell(
                                         || format!("nl_i_col row {}", row_offset),

src/graph/utilities.rs

@@ -803,7 +803,7 @@ pub fn new_op_from_onnx(
             }
         }
         "Recip" => {
-            let in_scale = inputs[0].out_scales()[0];
+            let in_scale = input_scales[0];
             let max_scale = std::cmp::max(scales.get_max(), in_scale);
             // If the input scale is larger than the params scale
             SupportedOp::Hybrid(HybridOp::Recip {
@@ -837,61 +837,61 @@ pub fn new_op_from_onnx(
         "Abs" => SupportedOp::Linear(PolyOp::Abs),
         "Neg" => SupportedOp::Linear(PolyOp::Neg),
         "HardSwish" => SupportedOp::Nonlinear(LookupOp::HardSwish {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Sigmoid" => SupportedOp::Nonlinear(LookupOp::Sigmoid {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Sqrt" => SupportedOp::Nonlinear(LookupOp::Sqrt {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Rsqrt" => SupportedOp::Nonlinear(LookupOp::Rsqrt {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Exp" => SupportedOp::Nonlinear(LookupOp::Exp {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Ln" => SupportedOp::Nonlinear(LookupOp::Ln {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Sin" => SupportedOp::Nonlinear(LookupOp::Sin {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Cos" => SupportedOp::Nonlinear(LookupOp::Cos {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Tan" => SupportedOp::Nonlinear(LookupOp::Tan {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Asin" => SupportedOp::Nonlinear(LookupOp::ASin {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Acos" => SupportedOp::Nonlinear(LookupOp::ACos {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Atan" => SupportedOp::Nonlinear(LookupOp::ATan {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Sinh" => SupportedOp::Nonlinear(LookupOp::Sinh {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Cosh" => SupportedOp::Nonlinear(LookupOp::Cosh {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Tanh" => SupportedOp::Nonlinear(LookupOp::Tanh {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Asinh" => SupportedOp::Nonlinear(LookupOp::ASinh {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Acosh" => SupportedOp::Nonlinear(LookupOp::ACosh {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Atanh" => SupportedOp::Nonlinear(LookupOp::ATanh {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Erf" => SupportedOp::Nonlinear(LookupOp::Erf {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Source" => {
             let dt = node.outputs[0].fact.datum_type;
@@ -935,11 +935,9 @@ pub fn new_op_from_onnx(
                         replace_const(
                             0,
                             0,
-                            SupportedOp::Nonlinear(LookupOp::Cast {
-                                scale: crate::circuit::utils::F32(scale_to_multiplier(
-                                    input_scales[0],
-                                )
-                                    as f32),
+                            SupportedOp::Hybrid(HybridOp::Floor {
+                                scale: scale_to_multiplier(input_scales[0]).into(),
+                                legs: run_args.decomp_legs,
                             }),
                         )?
                     } else {
@@ -1045,7 +1043,7 @@ pub fn new_op_from_onnx(
                 }
             };
 
-            let in_scale = inputs[0].out_scales()[0];
+            let in_scale = input_scales[0];
             let max_scale = std::cmp::max(scales.get_max(), in_scale);
 
             SupportedOp::Hybrid(HybridOp::Softmax {
@@ -1084,19 +1082,20 @@ pub fn new_op_from_onnx(
             })
         }
         "Ceil" => SupportedOp::Hybrid(HybridOp::Ceil {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
             legs: run_args.decomp_legs,
         }),
         "Floor" => SupportedOp::Hybrid(HybridOp::Floor {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
             legs: run_args.decomp_legs,
         }),
         "Round" => SupportedOp::Hybrid(HybridOp::Round {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
             legs: run_args.decomp_legs,
         }),
-        "RoundHalfToEven" => SupportedOp::Nonlinear(LookupOp::RoundHalfToEven {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+        "RoundHalfToEven" => SupportedOp::Hybrid(HybridOp::RoundHalfToEven {
+            scale: scale_to_multiplier(input_scales[0]).into(),
+            legs: run_args.decomp_legs,
         }),
         "Sign" => SupportedOp::Linear(PolyOp::Sign),
         "Pow" => {
@@ -1116,7 +1115,7 @@ pub fn new_op_from_onnx(
                     SupportedOp::Linear(PolyOp::Pow(exponent as u32))
                 } else {
                     SupportedOp::Nonlinear(LookupOp::Pow {
-                        scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+                        scale: scale_to_multiplier(input_scales[0]).into(),
                         a: crate::circuit::utils::F32(exponent),
                     })
                 }

src/tensor/ops.rs

@@ -1474,6 +1474,32 @@ pub mod nonlinearities {
         .unwrap()
     }
 
+    /// Checks if a tensor's elements are odd
+    /// # Arguments
+    /// * `a` - Tensor
+    /// * `scale` - Single value
+    /// # Examples
+    /// ```
+    /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
+    /// use ezkl::tensor::ops::nonlinearities::is_odd;
+    /// let x = Tensor::<IntegerRep>::new(
+    ///   Some(&[2, 15, 2, 1, 1, 0]),
+    /// &[2, 3],
+    /// ).unwrap();
+    ///
+    /// let result = is_odd(&x);
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[0, 1, 0, 1, 1, 0]), &[2, 3]).unwrap();
+    /// assert_eq!(result, expected);
+    /// ```
+    pub fn is_odd(a: &Tensor<IntegerRep>) -> Tensor<IntegerRep> {
+        a.par_enum_map(|_, a_i| {
+            let rounded = if a_i % 2 == 0 { 0 } else { 1 };
+            Ok::<_, TensorError>(rounded)
+        })
+        .unwrap()
+    }
+
     /// Elementwise applies sigmoid to a tensor of integers.
     /// # Arguments
     ///