feat: lookupless sqrt and rsqrt (#867 )

chore: version proof and witness (#865 )
feat: bounded lookup log argument (#864 )
2026-01-13 16:27:59 -05:00 · 2024-11-10 15:56:38 +00:00 · 2024-11-08 02:55:35 +00:00 · 2024-11-07 12:16:55 +00:00 · 2024-11-01 00:51:15 -04:00
29 changed files with 1215 additions and 374 deletions
--- a/.github/workflows/rust.yml
+++ b/.github/workflows/rust.yml
@@ -240,6 +240,8 @@ jobs:
          locked: true
      # - name: The Worm Mock
      #   run: cargo nextest run --release --verbose tests::large_mock_::large_tests_5_expects -- --include-ignored
+      - name: public outputs and bounded lookup log
+        run: cargo nextest run --release --verbose tests::mock_bounded_lookup_log --test-threads 32
      - name: public outputs and tolerance > 0
        run: cargo nextest run --release --verbose tests::mock_tolerance_public_outputs_ --test-threads 32
      - name: public outputs + batch size == 10
--- a/examples/notebooks/data_attest.ipynb
+++ b/examples/notebooks/data_attest.ipynb
@@ -592,7 +592,7 @@
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
-            "version": "3.12.2"
+            "version": "3.12.5"
        },
        "orig_nbformat": 4
    },
--- a/examples/notebooks/data_attest_hashed.ipynb
+++ b/examples/notebooks/data_attest_hashed.ipynb
@@ -648,10 +648,10 @@
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.7"
        },
        "orig_nbformat": 4
    },
    "nbformat": 4,
    "nbformat_minor": 2
-}
+}
--- a/examples/notebooks/logistic_regression.ipynb
+++ b/examples/notebooks/logistic_regression.ipynb
@@ -271,7 +271,7 @@
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
-            "version": "3.12.2"
+            "version": "3.12.7"
        }
    },
    "nbformat": 4,
--- a/examples/notebooks/simple_demo_aggregated_proofs.ipynb
+++ b/examples/notebooks/simple_demo_aggregated_proofs.ipynb
@@ -171,7 +171,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -328,7 +328,7 @@
        },
        {
            "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 26,
            "id": "171702d3",
            "metadata": {},
            "outputs": [],
@@ -348,7 +348,7 @@
        },
        {
            "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 27,
            "id": "671dfdd5",
            "metadata": {},
            "outputs": [],
@@ -364,7 +364,7 @@
        },
        {
            "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 28,
            "id": "50eba2f4",
            "metadata": {},
            "outputs": [],
@@ -399,9 +399,9 @@
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.7"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
-}
+}
--- a/examples/onnx/log/gen.py
+++ b/examples/onnx/log/gen.py
@@ -0,0 +1,42 @@
+from torch import nn
+import torch
+import json
+import numpy as np
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+
+    def forward(self, x):          
+        m = torch.log(x)
+        
+        return m 
+
+
+circuit = MyModel()
+
+x = torch.empty(1, 8).uniform_(0, 3)
+
+out = circuit(x)
+
+print(out)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))
--- a/examples/onnx/log/input.json
+++ b/examples/onnx/log/input.json
@@ -0,0 +1 @@
+{"input_data": [[1.9252371788024902, 1.8418371677398682, 0.8400403261184692, 2.083845853805542, 0.9760497808456421, 0.6940176486968994, 0.015579521656036377, 2.2689192295074463]]}
--- a/examples/onnx/log/network.onnx
+++ b/examples/onnx/log/network.onnx
@@ -0,0 +1,14 @@
+pytorch2.2.2:o
+
+inputoutput/Log"Log
+main_graphZ!
+input
+
+
+batch_size
+b"
+output
+
+
+batch_size
+B
--- a/examples/onnx/rounding_ops/input.json
+++ b/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
+        ]
+    ]
+}
--- a/src/bindings/python.rs
+++ b/src/bindings/python.rs
@@ -180,9 +180,6 @@ struct PyRunArgs {
    /// list[tuple[str, int]]: Hand-written parser for graph variables, eg. batch_size=1
    pub variables: Vec<(String, usize)>,
    #[pyo3(get, set)]
-    /// bool: Rebase the scale using lookup table for division instead of using a range check
-    pub div_rebasing: bool,
-    #[pyo3(get, set)]
    /// bool: Should constants with 0.0 fraction be rebased to scale 0
    pub rebase_frac_zero_constants: bool,
    #[pyo3(get, set)]
@@ -197,6 +194,9 @@ struct PyRunArgs {
    /// int: The number of legs used for decomposition
    #[pyo3(get, set)]
    pub decomp_legs: usize,
+    /// bool: Should the circuit use unbounded lookups for log
+    #[pyo3(get, set)]
+    pub bounded_log_lookup: bool,
 }

 /// default instantiation of PyRunArgs
@@ -212,6 +212,7 @@ impl PyRunArgs {
 impl From<PyRunArgs> for RunArgs {
    fn from(py_run_args: PyRunArgs) -> Self {
        RunArgs {
+            bounded_log_lookup: py_run_args.bounded_log_lookup,
            tolerance: Tolerance::from(py_run_args.tolerance),
            input_scale: py_run_args.input_scale,
            param_scale: py_run_args.param_scale,
@@ -223,7 +224,6 @@ impl From<PyRunArgs> for RunArgs {
            output_visibility: py_run_args.output_visibility,
            param_visibility: py_run_args.param_visibility,
            variables: py_run_args.variables,
-            div_rebasing: py_run_args.div_rebasing,
            rebase_frac_zero_constants: py_run_args.rebase_frac_zero_constants,
            check_mode: py_run_args.check_mode,
            commitment: Some(py_run_args.commitment.into()),
@@ -236,6 +236,7 @@ impl From<PyRunArgs> for RunArgs {
 impl Into<PyRunArgs> for RunArgs {
    fn into(self) -> PyRunArgs {
        PyRunArgs {
+            bounded_log_lookup: self.bounded_log_lookup,
            tolerance: self.tolerance.val,
            input_scale: self.input_scale,
            param_scale: self.param_scale,
@@ -247,7 +248,6 @@ impl Into<PyRunArgs> for RunArgs {
            output_visibility: self.output_visibility,
            param_visibility: self.param_visibility,
            variables: self.variables,
-            div_rebasing: self.div_rebasing,
            rebase_frac_zero_constants: self.rebase_frac_zero_constants,
            check_mode: self.check_mode,
            commitment: self.commitment.into(),
@@ -873,8 +873,6 @@ fn gen_settings(
 /// max_logrows: int
 ///     Optional max logrows to use for calibration
 ///
-/// only_range_check_rebase: bool
-///     Check ranges when rebasing
 ///
 /// Returns
 /// -------
@@ -889,7 +887,6 @@ fn gen_settings(
    scales = None,
    scale_rebase_multiplier = DEFAULT_SCALE_REBASE_MULTIPLIERS.split(",").map(|x| x.parse().unwrap()).collect(),
    max_logrows = None,
-    only_range_check_rebase = DEFAULT_ONLY_RANGE_CHECK_REBASE.parse().unwrap(),
 ))]
 fn calibrate_settings(
    py: Python,
@@ -901,7 +898,6 @@ fn calibrate_settings(
    scales: Option<Vec<crate::Scale>>,
    scale_rebase_multiplier: Vec<u32>,
    max_logrows: Option<u32>,
-    only_range_check_rebase: bool,
 ) -> PyResult<Bound<'_, PyAny>> {
    pyo3_asyncio::tokio::future_into_py(py, async move {
        crate::execute::calibrate(
@@ -912,7 +908,6 @@ fn calibrate_settings(
            lookup_safety_margin,
            scales,
            scale_rebase_multiplier,
-            only_range_check_rebase,
            max_logrows,
        )
        .await
--- a/src/circuit/ops/hybrid.rs
+++ b/src/circuit/ops/hybrid.rs
@@ -13,6 +13,20 @@ 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 {
+    Ln {
+        scale: utils::F32,
+    },
+    Rsqrt {
+        input_scale: utils::F32,
+        output_scale: utils::F32,
+    },
+    Sqrt {
+        scale: utils::F32,
+    },
+    RoundHalfToEven {
+        scale: utils::F32,
+        legs: usize,
+    },
    Ceil {
        scale: utils::F32,
        legs: usize,
@@ -31,7 +45,6 @@ pub enum HybridOp {
    },
    Div {
        denom: utils::F32,
-        use_range_check_for_int: bool,
    },
    ReduceMax {
        axes: Vec<usize>,
@@ -108,11 +121,24 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid

    fn as_string(&self) -> String {
        match self {
+            HybridOp::Rsqrt {
+                input_scale,
+                output_scale,
+            } => format!(
+                "RSQRT (input_scale={}, output_scale={})",
+                input_scale, output_scale
+            ),
+            HybridOp::Sqrt { scale } => format!("SQRT(scale={})", scale),
+            HybridOp::Ln { scale } => format!("LN(scale={})", scale),
+            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::Max => "MAX".to_string(),
+            HybridOp::Min => "MIN".to_string(),
            HybridOp::Recip {
                input_scale,
                output_scale,
@@ -120,13 +146,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                "RECIP (input_scale={}, output_scale={})",
                input_scale, output_scale
            ),
-            HybridOp::Div {
-                denom,
-                use_range_check_for_int,
-            } => format!(
-                "DIV (denom={}, use_range_check_for_int={})",
-                denom, use_range_check_for_int
-            ),
+            HybridOp::Div { denom } => format!("DIV (denom={})", denom),
            HybridOp::SumPool {
                padding,
                stride,
@@ -181,6 +201,23 @@ 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::Rsqrt {
+                input_scale,
+                output_scale,
+            } => layouts::rsqrt(
+                config,
+                region,
+                values[..].try_into()?,
+                *input_scale,
+                *output_scale,
+            )?,
+            HybridOp::Sqrt { scale } => {
+                layouts::sqrt(config, region, values[..].try_into()?, *scale)?
+            }
+            HybridOp::Ln { scale } => layouts::ln(config, region, values[..].try_into()?, *scale)?,
+            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)?
            }
@@ -216,13 +253,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                integer_rep_to_felt(input_scale.0 as i128),
                integer_rep_to_felt(output_scale.0 as i128),
            )?,
-            HybridOp::Div {
-                denom,
-                use_range_check_for_int,
-                ..
-            } => {
-                if denom.0.fract() == 0.0 && *use_range_check_for_int {
-                    layouts::loop_div(
+            HybridOp::Div { denom, .. } => {
+                if denom.0.fract() == 0.0 {
+                    layouts::div(
                        config,
                        region,
                        values[..].try_into()?,
@@ -313,9 +346,18 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
            | HybridOp::ReduceArgMax { .. }
            | HybridOp::OneHot { .. }
            | HybridOp::ReduceArgMin { .. } => 0,
-            HybridOp::Softmax { output_scale, .. } | HybridOp::Recip { output_scale, .. } => {
+
+            HybridOp::Recip { output_scale, .. } | HybridOp::Rsqrt { output_scale, .. } => {
                multiplier_to_scale(output_scale.0 as f64)
            }
+            HybridOp::Softmax {
+                output_scale,
+                input_scale,
+                ..
+            } => multiplier_to_scale((output_scale.0 * input_scale.0) as f64),
+            HybridOp::Ln {
+                scale: output_scale,
+            } => 4 * multiplier_to_scale(output_scale.0 as f64),
            _ => in_scales[0],
        };
        Ok(scale)
--- a/src/circuit/ops/layouts.rs
+++ b/src/circuit/ops/layouts.rs
@@ -29,41 +29,96 @@ use crate::{
 use super::*;
 use crate::circuit::ops::lookup::LookupOp;

-/// Same as div but splits the division into N parts
-pub(crate) fn loop_div<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+/// Calculate the L1 distance between two tensors.
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::fieldutils::IntegerRep;
+/// use ezkl::circuit::ops::layouts::l1_distance;
+/// use halo2curves::bn256::Fr as Fp;
+/// use ezkl::circuit::region::RegionCtx;
+/// use ezkl::circuit::region::RegionSettings;
+/// use ezkl::circuit::BaseConfig;
+/// use ezkl::tensor::ValTensor;
+/// 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(&[1, 2, 3, 2, 3, 4, 3, 4, 5]),
+/// &[3, 3],
+/// ).unwrap());
+/// let k = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
+///   Some(&[1, 2, 3, 1, 2, 3, 1, 2, 3]),
+/// &[3, 3],
+/// ).unwrap());
+/// let result = l1_distance::<Fp>(&dummy_config, &mut dummy_region, &[x, k]).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[0, 0, 0, 1, 1, 1, 2, 2, 2]), &[3, 3]).unwrap();
+/// assert_eq!(result.int_evals().unwrap(), expected);
+/// ```
+pub fn l1_distance<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
    config: &BaseConfig<F>,
    region: &mut RegionCtx<F>,
-    value: &[ValTensor<F>; 1],
-    divisor: F,
+    values: &[ValTensor<F>; 2],
 ) -> Result<ValTensor<F>, CircuitError> {
-    if divisor == F::ONE {
-        return Ok(value[0].clone());
-    }
+    let diff = pairwise(config, region, values, BaseOp::Sub)?;
+    let abs_diff = abs(config, region, &[diff])?;

-    // if integer val is divisible by 2, we can use a faster method and div > F::S
-    let mut divisor = divisor;
-    let mut num_parts = 1;
+    Ok(abs_diff)
+}

-    while felt_to_integer_rep(divisor) % 2 == 0
-        && felt_to_integer_rep(divisor) > (2_i128.pow(F::S - 4))
-    {
-        divisor = integer_rep_to_felt(felt_to_integer_rep(divisor) / 2);
-        num_parts += 1;
-    }
+/// Determines if from a set of 3 tensors the 1st is closest to a reference tensor.
+/// should only be used in the context of a monotonic function like the product used in the division, recipe, and sqrt arguments;
+/// or the increasing powers of 2 in the ln argument. Which is used to construct a convex error function.
+fn optimum_convex_function<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    x: &ValTensor<F>,
+    f: impl Fn(&BaseConfig<F>, &mut RegionCtx<F>, &ValTensor<F>) -> Result<ValTensor<F>, CircuitError>,
+) -> Result<(), CircuitError> {
+    let one = create_constant_tensor(F::from(1), 1);

-    let output = div(config, region, value, divisor)?;
-    if num_parts == 1 {
-        return Ok(output);
-    }
+    let f_x = f(config, region, x)?;

-    let divisor_int = 2_i128.pow(num_parts - 1);
-    let divisor_felt = integer_rep_to_felt(divisor_int);
-    if divisor_int <= 2_i128.pow(F::S - 3) {
-        div(config, region, &[output], divisor_felt)
-    } else {
-        // keep splitting the divisor until it satisfies the condition
-        loop_div(config, region, &[output], divisor_felt)
-    }
+    let x_plus_1 = pairwise(config, region, &[x.clone(), one.clone()], BaseOp::Add)?;
+    let f_x_plus_1 = f(config, region, &x_plus_1)?;
+
+    let x_minus_1 = pairwise(config, region, &[x.clone(), one.clone()], BaseOp::Sub)?;
+    let f_x_minus_1 = f(config, region, &x_minus_1)?;
+
+    // because the function is convex, the result should be the minimum of the three
+    // not that we offset the x by 1 to get the next value
+    // f(x) <= f(x+1) and f(x) <= f(x-1)
+    // the result is 1 if the function is optimal solely because of the convexity of the function
+    // the distances can be equal but this is only possible if f(x) and f(x+1) are both optimal (or f(x) and f(x-1)).
+    let f_x_is_opt_rhs = less_equal(config, region, &[f_x.clone(), f_x_plus_1.clone()])?;
+    let f_x_is_opt_lhs = less_equal(config, region, &[f_x.clone(), f_x_minus_1.clone()])?;
+
+    let is_opt = and(config, region, &[f_x_is_opt_lhs, f_x_is_opt_rhs])?;
+
+    let mut comparison_unit = create_constant_tensor(F::ONE, is_opt.len());
+    comparison_unit.reshape(is_opt.dims())?;
+
+    // assert that the result is 1
+    enforce_equality(config, region, &[is_opt, comparison_unit])?;
+
+    Ok(())
+}
+
+/// Err is less than some constant
+pub fn diff_less_than<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    values: &[ValTensor<F>; 2],
+    constant: F,
+) -> Result<(), CircuitError> {
+    let distance = l1_distance(config, region, values)?;
+
+    let constant = create_constant_tensor(constant, 1);
+    let is_less = less(config, region, &[distance.clone(), constant.clone()])?;
+
+    // assert the result is 1
+    let comparison_unit = create_constant_tensor(F::ONE, is_less.len());
+    enforce_equality(config, region, &[is_less, comparison_unit])?;
+
+    Ok(())
 }

 /// Div accumulated layout
@@ -80,13 +135,8 @@ pub(crate) fn div<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
    let input = value[0].clone();
    let input_dims = input.dims();

-    let range_check_bracket = felt_to_integer_rep(div) / 2;
-
    let divisor = create_constant_tensor(div, 1);

-    let divisor = region.assign(&config.custom_gates.inputs[1], &divisor)?;
-    region.increment(divisor.len());
-
    let is_assigned = !input.any_unknowns()? && !divisor.any_unknowns()?;

    let mut claimed_output: ValTensor<F> = if is_assigned {
@@ -117,19 +167,7 @@ pub(crate) fn div<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
        BaseOp::Mult,
    )?;

-    let diff_with_input = pairwise(
-        config,
-        region,
-        &[product.clone(), input.clone()],
-        BaseOp::Sub,
-    )?;
-
-    range_check(
-        config,
-        region,
-        &[diff_with_input],
-        &(-range_check_bracket, range_check_bracket),
-    )?;
+    diff_less_than(config, region, &[input.clone(), product.clone()], div)?;

    Ok(claimed_output)
 }
@@ -145,19 +183,7 @@ pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
    let input = value[0].clone();
    let input_dims = input.dims();

-    let integer_input_scale = felt_to_integer_rep(input_scale);
-    let integer_output_scale = felt_to_integer_rep(output_scale);
-
-    // range_check_bracket is min of input_scale * output_scale and 2^F::S - 3
-    let range_check_len = std::cmp::min(integer_output_scale, 2_i128.pow(F::S - 4));
-
-    let input_scale_ratio = if range_check_len > 0 {
-        integer_rep_to_felt(integer_input_scale * integer_output_scale / range_check_len)
-    } else {
-        F::ONE
-    };
-
-    let range_check_bracket = range_check_len / 2;
+    let unit_scale = create_constant_tensor(output_scale * input_scale, 1);

    let is_assigned = !input.any_unknowns()?;

@@ -183,25 +209,22 @@ pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
    let claimed_output = region.assign(&config.custom_gates.output, &claimed_output)?;
    region.increment(claimed_output.len());

-    // this is now of scale 2 * scale
-    let product = pairwise(
-        config,
-        region,
-        &[claimed_output.clone(), input.clone()],
-        BaseOp::Mult,
-    )?;
-
    // divide by input_scale
-    let rebased_div = loop_div(config, region, &[product], input_scale_ratio)?;
-
    let zero_inverse_val =
        tensor::ops::nonlinearities::zero_recip(felt_to_integer_rep(output_scale) as f64)[0];
    let zero_inverse = create_constant_tensor(integer_rep_to_felt(zero_inverse_val), 1);

    let equal_zero_mask = equals_zero(config, region, &[input.clone()])?;
-
+    let not_equal_zero_mask = not(config, region, &[equal_zero_mask.clone()])?;
    let equal_inverse_mask = equals(config, region, &[claimed_output.clone(), zero_inverse])?;

+    let masked_unit_scale = pairwise(
+        config,
+        region,
+        &[unit_scale.clone(), not_equal_zero_mask.clone()],
+        BaseOp::Mult,
+    )?;
+
    // assert the two masks are equal
    enforce_equality(
        config,
@@ -209,24 +232,135 @@ pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
        &[equal_zero_mask.clone(), equal_inverse_mask],
    )?;

-    let unit_scale = create_constant_tensor(integer_rep_to_felt(range_check_len), 1);
+    let err_func = |config: &BaseConfig<F>,
+                    region: &mut RegionCtx<F>,
+                    x: &ValTensor<F>|
+     -> Result<ValTensor<F>, CircuitError> {
+        let product = pairwise(config, region, &[x.clone(), input.clone()], BaseOp::Mult)?;

-    let unit_mask = pairwise(config, region, &[equal_zero_mask, unit_scale], BaseOp::Mult)?;
+        let distance = l1_distance(
+            config,
+            region,
+            &[product.clone(), masked_unit_scale.clone()],
+        )?;
+        Ok(distance)
+    };

-    // now add the unit mask to the rebased_div
-    let rebased_offset_div = pairwise(config, region, &[rebased_div, unit_mask], BaseOp::Add)?;
-
-    // at most the error should be in the original unit scale's range
-    range_check(
-        config,
-        region,
-        &[rebased_offset_div],
-        &(range_check_bracket, 3 * range_check_bracket),
-    )?;
+    optimum_convex_function(config, region, &claimed_output, err_func)?;

    Ok(claimed_output)
 }

+/// Square root accumulated layout
+/// # Example
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::fieldutils::IntegerRep;
+/// use ezkl::circuit::ops::layouts::sqrt;
+/// use halo2curves::bn256::Fr as Fp;
+/// use ezkl::circuit::region::RegionCtx;
+/// use ezkl::circuit::region::RegionSettings;
+/// use ezkl::circuit::BaseConfig;
+/// use ezkl::tensor::ValTensor;
+/// 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(&[1, 2, 3, 2, 3, 4, 3, 4, 9]),
+///    &[3, 3],
+/// ).unwrap());
+/// let result = sqrt::<Fp>(&dummy_config, &mut dummy_region, &[x], 1.0.into()).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[1, 1, 2, 1, 2, 2, 2, 2, 3]), &[3, 3]).unwrap();
+/// assert_eq!(result.int_evals().unwrap(), expected);
+/// ```
+pub fn sqrt<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    value: &[ValTensor<F>; 1],
+    input_scale: utils::F32,
+) -> Result<ValTensor<F>, CircuitError> {
+    let input = value[0].clone();
+    let input_dims = input.dims();
+
+    let unit_scale = create_constant_tensor(integer_rep_to_felt(input_scale.0 as IntegerRep), 1);
+
+    let is_assigned = !input.any_unknowns()?;
+
+    let mut claimed_output: ValTensor<F> = if is_assigned {
+        let input_evals = input.int_evals()?;
+        tensor::ops::nonlinearities::sqrt(&input_evals, input_scale.0 as f64)
+            .par_iter()
+            .map(|x| Value::known(integer_rep_to_felt(*x)))
+            .collect::<Tensor<Value<F>>>()
+            .into()
+    } else {
+        Tensor::new(
+            Some(&vec![Value::<F>::unknown(); input.len()]),
+            &[input.len()],
+        )?
+        .into()
+    };
+    claimed_output.reshape(input_dims)?;
+    let claimed_output = region.assign(&config.custom_gates.output, &claimed_output)?;
+    region.increment(claimed_output.len());
+
+    // force the output to be positive or zero
+    let claimed_output = abs(config, region, &[claimed_output.clone()])?;
+
+    // rescaled input
+    let rescaled_input = pairwise(config, region, &[input.clone(), unit_scale], BaseOp::Mult)?;
+
+    let err_func = |config: &BaseConfig<F>,
+                    region: &mut RegionCtx<F>,
+                    x: &ValTensor<F>|
+     -> Result<ValTensor<F>, CircuitError> {
+        let product = pairwise(config, region, &[x.clone(), x.clone()], BaseOp::Mult)?;
+        let distance = l1_distance(config, region, &[product.clone(), rescaled_input.clone()])?;
+        Ok(distance)
+    };
+
+    optimum_convex_function(config, region, &claimed_output, err_func)?;
+
+    Ok(claimed_output)
+}
+
+/// Reciprocal square root accumulated layout
+/// # Example
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::fieldutils::IntegerRep;
+/// use ezkl::circuit::ops::layouts::rsqrt;
+/// use halo2curves::bn256::Fr as Fp;
+/// use ezkl::circuit::region::RegionCtx;
+/// use ezkl::circuit::region::RegionSettings;
+/// use ezkl::circuit::BaseConfig;
+/// use ezkl::tensor::ValTensor;
+/// 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(&[1, 2, 3, 2, 3, 4, 3, 4, 5]),
+/// &[3, 3],
+/// ).unwrap());
+/// let result = rsqrt::<Fp>(&dummy_config, &mut dummy_region, &[x], 1.0.into(), 1.0.into()).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[1, 1, 1, 1, 1, 1, 1, 1, 1]), &[3, 3]).unwrap();
+/// assert_eq!(result.int_evals().unwrap(), expected);
+/// ```
+pub fn rsqrt<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    value: &[ValTensor<F>; 1],
+    input_scale: utils::F32,
+    output_scale: utils::F32,
+) -> Result<ValTensor<F>, CircuitError> {
+    let sqrt = sqrt(config, region, value, input_scale)?;
+
+    let felt_output_scale = integer_rep_to_felt(output_scale.0 as IntegerRep);
+    let felt_input_scale = integer_rep_to_felt(input_scale.0 as IntegerRep);
+
+    let recip = recip(config, region, &[sqrt], felt_input_scale, felt_output_scale)?;
+
+    Ok(recip)
+}
+
 /// Dot product of two tensors.
 /// ```
 /// use ezkl::tensor::Tensor;
@@ -1805,6 +1939,10 @@ pub fn sum<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
    region: &mut RegionCtx<F>,
    values: &[ValTensor<F>; 1],
 ) -> Result<ValTensor<F>, CircuitError> {
+    if values[0].len() == 1 {
+        return Ok(values[0].clone());
+    }
+
    region.flush()?;
    // time this entire function run
    let global_start = instant::Instant::now();
@@ -3102,7 +3240,7 @@ pub fn sumpool<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
    last_elem.reshape(&[&[batch_size, image_channels], shape].concat())?;

    if normalized {
-        last_elem = loop_div(config, region, &[last_elem], F::from(kernel_len as u64))?;
+        last_elem = div(config, region, &[last_elem], F::from(kernel_len as u64))?;
    }
    Ok(last_elem)
 }
@@ -4507,6 +4645,293 @@ pub fn ceil<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
    )
 }

+/// integer ln layout
+/// # Arguments
+/// * `config` - BaseConfig
+/// * `region` - RegionCtx
+/// * `values` - &[ValTensor<F>; 1]
+/// * `scale` - utils::F32
+/// # Returns
+/// * ValTensor<F>
+/// # Example
+///
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::fieldutils::IntegerRep;
+/// use ezkl::circuit::ops::layouts::ln;
+/// 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 = ln::<Fp>(&dummy_config, &mut dummy_region, &[x], 2.0.into()).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[4, 0, 4, -8]), &[1, 1, 2, 2]).unwrap();
+/// assert_eq!(result.int_evals().unwrap(), expected);
+///
+/// ```
+pub fn ln<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    values: &[ValTensor<F>; 1],
+    scale: utils::F32,
+) -> Result<ValTensor<F>, CircuitError> {
+    // first generate the claimed val
+
+    let mut input = values[0].clone();
+    let scale_as_felt = integer_rep_to_felt(scale.0.round() as IntegerRep);
+
+    let triple_scaled_as_felt_tensor =
+        create_constant_tensor(scale_as_felt * scale_as_felt * scale_as_felt, 1);
+
+    // natural ln is log2(x) * ln(2)
+    let ln2 = utils::F32::from(2.0_f32.ln());
+    // now create a constant tensor for ln2 with scale
+    let ln2_tensor: ValTensor<F> = create_constant_tensor(
+        integer_rep_to_felt((ln2.0 * scale.0).round() as IntegerRep),
+        1,
+    );
+    let unit = create_constant_tensor(integer_rep_to_felt(1), 1);
+    let negative_one = create_constant_tensor(integer_rep_to_felt(-1), 1);
+
+    // 2. assign the image
+    if !input.all_prev_assigned() {
+        input = region.assign(&config.custom_gates.inputs[0], &input)?;
+        // don't need to increment because the claimed output is assigned to output and incremented accordingly
+    }
+
+    let is_assigned = !input.any_unknowns()?;
+
+    let mut claimed_output: ValTensor<F> = if is_assigned {
+        let input_evals = input.int_evals()?;
+        // returns an integer with the base 2 logarithm
+        tensor::ops::nonlinearities::ilog2(&input_evals.clone(), scale.0 as f64)
+            .par_iter()
+            .map(|x| Value::known(integer_rep_to_felt(*x)))
+            .collect::<Tensor<Value<F>>>()
+            .into()
+    } else {
+        Tensor::new(
+            Some(&vec![Value::<F>::unknown(); input.len()]),
+            &[input.len()],
+        )?
+        .into()
+    };
+    claimed_output.reshape(input.dims())?;
+    region.assign(&config.custom_gates.output, &claimed_output)?;
+    region.increment(claimed_output.len());
+
+    let pow2_of_claimed_output = nonlinearity(
+        config,
+        region,
+        &[claimed_output.clone()],
+        &LookupOp::PowersOfTwo { scale },
+    )?;
+
+    let num_bits = (std::mem::size_of::<IntegerRep>() * 8) as IntegerRep;
+
+    region.update_max_min_lookup_inputs_force(-num_bits, num_bits)?;
+
+    // now subtract 1 from the claimed output
+    let claimed_output_minus_one = pairwise(
+        config,
+        region,
+        &[claimed_output.clone(), unit.clone()],
+        BaseOp::Sub,
+    )?;
+
+    // now add 1 to the claimed output
+    let claimed_output_plus_one = pairwise(
+        config,
+        region,
+        &[claimed_output.clone(), unit.clone()],
+        BaseOp::Add,
+    )?;
+
+    // prior power of 2 is less than claimed output
+    let prior_pow2 = nonlinearity(
+        config,
+        region,
+        &[claimed_output_minus_one],
+        &LookupOp::PowersOfTwo { scale },
+    )?;
+
+    // next power of 2 is greater than claimed output
+    let next_pow2 = nonlinearity(
+        config,
+        region,
+        &[claimed_output_plus_one],
+        &LookupOp::PowersOfTwo { scale },
+    )?;
+
+    let distance_to_claimed = pairwise(
+        config,
+        region,
+        &[input.clone(), pow2_of_claimed_output.clone()],
+        BaseOp::Sub,
+    )?;
+
+    let abs_distance_to_claimed = abs(config, region, &[distance_to_claimed.clone()])?;
+
+    let abs_distance_to_next_pow2 =
+        l1_distance(config, region, &[input.clone(), next_pow2.clone()])?;
+
+    let abs_distance_to_prior_pow2 =
+        l1_distance(config, region, &[input.clone(), prior_pow2.clone()])?;
+
+    // because we round up this can be equal
+    let is_closest_to_0: ValTensor<F> = less(
+        config,
+        region,
+        &[
+            abs_distance_to_claimed.clone(),
+            abs_distance_to_next_pow2.clone(),
+        ],
+    )?;
+
+    let is_closest_to_1 = less(
+        config,
+        region,
+        &[
+            abs_distance_to_claimed.clone(),
+            abs_distance_to_prior_pow2.clone(),
+        ],
+    )?;
+
+    let is_closest = and(
+        config,
+        region,
+        &[is_closest_to_0.clone(), is_closest_to_1.clone()],
+    )?;
+
+    let mut comparison_unit = create_constant_tensor(integer_rep_to_felt(1), is_closest.len());
+    comparison_unit.reshape(is_closest.dims())?;
+    let assigned_unit = region.assign(&config.custom_gates.inputs[1], &comparison_unit)?;
+
+    enforce_equality(config, region, &[is_closest, assigned_unit])?;
+
+    // get a linear interpolation now
+
+    let sign_of_distance_to_claimed = sign(config, region, &[distance_to_claimed.clone()])?;
+    let sign_of_distance_to_claimed_is_negative = equals(
+        config,
+        region,
+        &[sign_of_distance_to_claimed.clone(), negative_one.clone()],
+    )?;
+
+    let sign_of_distance_to_claimed_is_positive = not(
+        config,
+        region,
+        &[sign_of_distance_to_claimed_is_negative.clone()],
+    )?;
+
+    let pow2_prior_to_claimed_distance = pairwise(
+        config,
+        region,
+        &[pow2_of_claimed_output.clone(), prior_pow2.clone()],
+        BaseOp::Sub,
+    )?;
+
+    let pow2_next_to_claimed_distance = pairwise(
+        config,
+        region,
+        &[next_pow2.clone(), pow2_of_claimed_output.clone()],
+        BaseOp::Sub,
+    )?;
+
+    let recip_pow2_prior_to_claimed_distance = recip(
+        config,
+        region,
+        &[pow2_prior_to_claimed_distance],
+        scale_as_felt,
+        scale_as_felt * scale_as_felt,
+    )?;
+
+    let interpolated_distance = pairwise(
+        config,
+        region,
+        &[
+            recip_pow2_prior_to_claimed_distance.clone(),
+            distance_to_claimed.clone(),
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let gated_prior_interpolated_distance = pairwise(
+        config,
+        region,
+        &[
+            interpolated_distance.clone(),
+            sign_of_distance_to_claimed_is_negative.clone(),
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let recip_next_to_claimed_distance = recip(
+        config,
+        region,
+        &[pow2_next_to_claimed_distance],
+        scale_as_felt,
+        scale_as_felt * scale_as_felt,
+    )?;
+
+    let interpolated_distance_next = pairwise(
+        config,
+        region,
+        &[
+            recip_next_to_claimed_distance.clone(),
+            distance_to_claimed.clone(),
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let gated_next_interpolated_distance = pairwise(
+        config,
+        region,
+        &[
+            interpolated_distance_next.clone(),
+            sign_of_distance_to_claimed_is_positive.clone(),
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let scaled_claimed_output = pairwise(
+        config,
+        region,
+        &[claimed_output.clone(), triple_scaled_as_felt_tensor],
+        BaseOp::Mult,
+    )?;
+
+    let claimed_output = pairwise(
+        config,
+        region,
+        &[
+            scaled_claimed_output.clone(),
+            gated_prior_interpolated_distance.clone(),
+        ],
+        BaseOp::Add,
+    )?;
+
+    let claimed_output = pairwise(
+        config,
+        region,
+        &[
+            claimed_output.clone(),
+            gated_next_interpolated_distance.clone(),
+        ],
+        BaseOp::Add,
+    )?;
+
+    // now multiply the claimed output by ln2
+    pairwise(config, region, &[claimed_output, ln2_tensor], BaseOp::Mult)
+}
+
 /// round layout
 /// # Arguments
 /// * `config` - BaseConfig
@@ -4551,11 +4976,7 @@ pub fn round<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
    let zero = ValType::Constant(F::ZERO);

    let one = create_constant_tensor(integer_rep_to_felt(1), 1);
-    let assigned_one = region.assign(&config.custom_gates.inputs[1], &one)?;
    let negative_one = create_constant_tensor(integer_rep_to_felt(-1), 1);
-    let assigned_negative_one = region.assign(&config.custom_gates.output, &negative_one)?;
-
-    region.increment(1);

    // if scale is not exactly divisible by 2 we warn
    if scale.0 % 2.0 != 0.0 {
@@ -4588,8 +5009,8 @@ pub fn round<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
            let last_elem = sliced_input.last()?;

            let sign = sliced_input.first()?;
-            let is_positive = equals(config, region, &[sign.clone(), assigned_one.clone()])?;
-            let is_negative = equals(config, region, &[sign, assigned_negative_one.clone()])?;
+            let is_positive = equals(config, region, &[sign.clone(), one.clone()])?;
+            let is_negative = equals(config, region, &[sign, negative_one.clone()])?;

            let is_greater_than_midway = greater_equal(
                config,
@@ -4654,6 +5075,146 @@ 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 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(), 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(), 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>,
@@ -5019,11 +5580,8 @@ pub(crate) fn percent<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>
        input_felt_scale,
        output_felt_scale,
    )?;
-    // product of num * (1 / denom) = 2*output_scale
-    let percent = pairwise(config, region, &[input, inv_denom], BaseOp::Mult)?;
-
-    // rebase the percent to 2x the scale
-    loop_div(config, region, &[percent], input_felt_scale)
+    // product of num * (1 / denom) = input_scale * output_scale
+    pairwise(config, region, &[input, inv_denom], BaseOp::Mult)
 }

 /// Applies softmax
@@ -5047,7 +5605,7 @@ pub(crate) fn percent<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>
 /// ).unwrap());
 /// let result = softmax::<Fp>(&dummy_config, &mut dummy_region, &[x], 128.0.into(), (128.0 * 128.0).into()).unwrap();
 /// // doubles the scale of the input
-/// let expected = Tensor::<IntegerRep>::new(Some(&[2734, 2734, 2756, 2734, 2734, 2691]), &[2, 3]).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[350012, 350012, 352768, 350012, 350012, 344500]), &[2, 3]).unwrap();
 /// assert_eq!(result.int_evals().unwrap(), expected);
 /// ```
 pub fn softmax<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
@@ -5127,17 +5685,8 @@ pub fn range_check_percent<F: PrimeField + TensorType + PartialOrd + std::hash::
    let int_scale = scale.0 as IntegerRep;
    // felt scale
    let felt_scale = integer_rep_to_felt(int_scale);
-    // range check len capped at 2^(S-3) and make it divisible 2
-    let range_check_bracket = std::cmp::min(
-        utils::F32(scale.0),
-        utils::F32(2_f32.powf((F::S - 5) as f32)),
-    )
-    .0;
-
-    let range_check_bracket_int = range_check_bracket as IntegerRep;
-
    // input scale ratio we multiply by tol such that in the new scale range_check_len represents tol percent
-    let input_scale_ratio = ((scale.0.powf(2.0) / range_check_bracket) * tol) as IntegerRep / 2 * 2;
+    let input_scale_ratio = (scale.0 * tol) as IntegerRep / 2 * 2;

    let recip = recip(
        config,
@@ -5153,7 +5702,7 @@ pub fn range_check_percent<F: PrimeField + TensorType + PartialOrd + std::hash::
    let product = pairwise(config, region, &[diff, recip], BaseOp::Mult)?;

    log::debug!("product: {}", product.show());
-    let rebased_product = loop_div(
+    let rebased_product = div(
        config,
        region,
        &[product],
@@ -5162,10 +5711,5 @@ pub fn range_check_percent<F: PrimeField + TensorType + PartialOrd + std::hash::
    log::debug!("rebased_product: {}", rebased_product.show());

    // check that it is within the tolerance range
-    range_check(
-        config,
-        region,
-        &[rebased_product],
-        &(-range_check_bracket_int, range_check_bracket_int),
-    )
+    range_check(config, region, &[rebased_product], &(-int_scale, int_scale))
 }
--- a/src/circuit/ops/lookup.rs
+++ b/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},
 };

@@ -16,12 +15,10 @@ 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 },
-    Sqrt { scale: utils::F32 },
-    Rsqrt { scale: utils::F32 },
-    Sigmoid { scale: utils::F32 },
+    IsOdd,
+    PowersOfTwo { scale: utils::F32 },
    Ln { scale: utils::F32 },
+    Sigmoid { scale: utils::F32 },
    Exp { scale: utils::F32 },
    Cos { scale: utils::F32 },
    ACos { scale: utils::F32 },
@@ -51,16 +48,14 @@ 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::Ln { scale } => format!("ln_{}", scale),
+            LookupOp::PowersOfTwo { scale } => format!("pow2_{}", scale),
+            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),
            LookupOp::Erf { scale } => format!("erf_{}", scale),
            LookupOp::Exp { scale } => format!("exp_{}", scale),
-            LookupOp::Ln { scale } => format!("ln_{}", scale),
            LookupOp::Cos { scale } => format!("cos_{}", scale),
            LookupOp::ACos { scale } => format!("acos_{}", scale),
            LookupOp::Cosh { scale } => format!("cosh_{}", scale),
@@ -85,36 +80,28 @@ 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::Ln { scale } => {
+                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ln(&x, scale.into()))
+                }
+                LookupOp::PowersOfTwo { scale } => {
+                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ipow2(&x, scale.0.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()))
                }
-                LookupOp::Sqrt { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::sqrt(&x, scale.into()))
-                }
-                LookupOp::Rsqrt { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::rsqrt(&x, scale.into()))
-                }
                LookupOp::Erf { scale } => {
                    Ok::<_, TensorError>(tensor::ops::nonlinearities::erffunc(&x, scale.into()))
                }
                LookupOp::Exp { scale } => {
                    Ok::<_, TensorError>(tensor::ops::nonlinearities::exp(&x, scale.into()))
                }
-                LookupOp::Ln { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ln(&x, scale.into()))
-                }
                LookupOp::Cos { scale } => {
                    Ok::<_, TensorError>(tensor::ops::nonlinearities::cos(&x, scale.into()))
                }
@@ -171,15 +158,13 @@ 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::Ln { scale } => format!("LN(scale={})", scale),
+            LookupOp::PowersOfTwo { scale } => format!("POWERS_OF_TWO(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),
            LookupOp::Erf { scale } => format!("ERF(scale={})", scale),
-            LookupOp::Rsqrt { scale } => format!("RSQRT(scale={})", scale),
            LookupOp::Exp { scale } => format!("EXP(scale={})", scale),
            LookupOp::Tan { scale } => format!("TAN(scale={})", scale),
            LookupOp::ATan { scale } => format!("ATAN(scale={})", scale),
@@ -213,13 +198,7 @@ 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],
-        };
+        let scale = inputs_scale[0];
        Ok(scale)
    }

--- a/src/circuit/ops/region.rs
+++ b/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 {
--- a/src/circuit/table.rs
+++ b/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),
--- a/src/commands.rs
+++ b/src/commands.rs
@@ -333,18 +333,6 @@ impl<'source> FromPyObject<'source> for ContractType {
        }
    }
 }
-// not wasm
-use lazy_static::lazy_static;
-
-// if CARGO VERSION is 0.0.0 replace with "source - no compatibility guaranteed"
-lazy_static! {
-    /// The version of the ezkl library
-    pub static ref VERSION: &'static str =  if env!("CARGO_PKG_VERSION") == "0.0.0" {
-       "source - no compatibility guaranteed"
-    } else {
-        env!("CARGO_PKG_VERSION")
-    };
-}

 /// Get the styles for the CLI
 pub fn get_styles() -> clap::builder::Styles {
@@ -395,7 +383,7 @@ pub fn print_completions<G: Generator>(gen: G, cmd: &mut Command) {
 #[allow(missing_docs)]
 #[derive(Parser, Debug, Clone)]
 #[command(author, about, long_about = None)]
-#[clap(version = *VERSION, styles = get_styles(), trailing_var_arg = true)]
+#[clap(version = crate::version(), styles = get_styles(), trailing_var_arg = true)]
 pub struct Cli {
    /// If provided, outputs the completion file for given shell
    #[clap(long = "generate", value_parser)]
@@ -486,9 +474,6 @@ pub enum Commands {
        /// max logrows to use for calibration, 26 is the max public SRS size
        #[arg(long, value_hint = clap::ValueHint::Other)]
        max_logrows: Option<u32>,
-        // whether to only range check rebases (instead of trying both range check and lookup)
-        #[arg(long, default_value = DEFAULT_ONLY_RANGE_CHECK_REBASE, action = clap::ArgAction::SetTrue)]
-        only_range_check_rebase: Option<bool>,
    },

    /// Generates a dummy SRS
--- a/src/execute.rs
+++ b/src/execute.rs
@@ -140,7 +140,6 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
            scales,
            scale_rebase_multiplier,
            max_logrows,
-            only_range_check_rebase,
        } => calibrate(
            model.unwrap_or(DEFAULT_MODEL.into()),
            data.unwrap_or(DEFAULT_DATA.into()),
@@ -149,7 +148,6 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
            lookup_safety_margin,
            scales,
            scale_rebase_multiplier,
-            only_range_check_rebase.unwrap_or(DEFAULT_ONLY_RANGE_CHECK_REBASE.parse().unwrap()),
            max_logrows,
        )
        .await
@@ -671,10 +669,10 @@ pub(crate) async fn get_srs_cmd(
            let srs_uri = format!("{}{}", PUBLIC_SRS_URL, k);
            let mut reader = Cursor::new(fetch_srs(&srs_uri).await?);
            // check the SRS
-                let pb = init_spinner();
-                pb.set_message("Validating SRS (this may take a while) ...");
+            let pb = init_spinner();
+            pb.set_message("Validating SRS (this may take a while) ...");
            let params = ParamsKZG::<Bn256>::read(&mut reader)?;
-                pb.finish_with_message("SRS validated.");
+            pb.finish_with_message("SRS validated.");

            info!("Saving SRS to disk...");
            let computed_srs_path = get_srs_path(k, srs_path.clone(), commitment);
@@ -682,7 +680,10 @@ pub(crate) async fn get_srs_cmd(
            let mut buffer = BufWriter::with_capacity(*EZKL_BUF_CAPACITY, &mut file);
            params.write(&mut buffer)?;

-            info!("Saved SRS to {}.", computed_srs_path.as_os_str().to_str().unwrap_or("disk"));
+            info!(
+                "Saved SRS to {}.",
+                computed_srs_path.as_os_str().to_str().unwrap_or("disk")
+            );

            info!("SRS downloaded");
        } else {
@@ -728,7 +729,7 @@ pub(crate) async fn gen_witness(
        None
    };

-        let mut input = circuit.load_graph_input(&data).await?;
+    let mut input = circuit.load_graph_input(&data).await?;
    #[cfg(any(not(feature = "ezkl"), target_arch = "wasm32"))]
    let mut input = circuit.load_graph_input(&data)?;

@@ -968,7 +969,6 @@ pub(crate) async fn calibrate(
    lookup_safety_margin: f64,
    scales: Option<Vec<crate::Scale>>,
    scale_rebase_multiplier: Vec<u32>,
-    only_range_check_rebase: bool,
    max_logrows: Option<u32>,
 ) -> Result<GraphSettings, EZKLError> {
    use log::error;
@@ -1004,12 +1004,6 @@ pub(crate) async fn calibrate(
        (11..14).collect::<Vec<crate::Scale>>()
    };

-    let div_rebasing = if only_range_check_rebase {
-        vec![false]
-    } else {
-        vec![true, false]
-    };
-
    let mut found_params: Vec<GraphSettings> = vec![];

    // 2 x 2 grid
@@ -1047,12 +1041,6 @@ pub(crate) async fn calibrate(
        .map(|(a, b)| (*a, *b))
        .collect::<Vec<((crate::Scale, crate::Scale), u32)>>();

-    let range_grid = range_grid
-        .iter()
-        .cartesian_product(div_rebasing.iter())
-        .map(|(a, b)| (*a, *b))
-        .collect::<Vec<(((crate::Scale, crate::Scale), u32), bool)>>();
-
    let mut forward_pass_res = HashMap::new();

    let pb = init_bar(range_grid.len() as u64);
@@ -1061,30 +1049,23 @@ pub(crate) async fn calibrate(
    let mut num_failed = 0;
    let mut num_passed = 0;

-    for (((input_scale, param_scale), scale_rebase_multiplier), div_rebasing) in range_grid {
+    for ((input_scale, param_scale), scale_rebase_multiplier) in range_grid {
        pb.set_message(format!(
-            "i-scale: {}, p-scale: {}, rebase-(x): {}, div-rebase: {}, fail: {}, pass: {}",
+            "i-scale: {}, p-scale: {}, rebase-(x): {}, fail: {}, pass: {}",
            input_scale.to_string().blue(),
            param_scale.to_string().blue(),
-            scale_rebase_multiplier.to_string().blue(),
-            div_rebasing.to_string().yellow(),
+            scale_rebase_multiplier.to_string().yellow(),
            num_failed.to_string().red(),
            num_passed.to_string().green()
        ));

-        let key = (
-            input_scale,
-            param_scale,
-            scale_rebase_multiplier,
-            div_rebasing,
-        );
+        let key = (input_scale, param_scale, scale_rebase_multiplier);
        forward_pass_res.insert(key, vec![]);

        let local_run_args = RunArgs {
            input_scale,
            param_scale,
            scale_rebase_multiplier,
-            div_rebasing,
            lookup_range: (IntegerRep::MIN, IntegerRep::MAX),
            ..settings.run_args.clone()
        };
@@ -1188,7 +1169,6 @@ pub(crate) async fn calibrate(
            let found_run_args = RunArgs {
                input_scale: new_settings.run_args.input_scale,
                param_scale: new_settings.run_args.param_scale,
-                div_rebasing: new_settings.run_args.div_rebasing,
                lookup_range: new_settings.run_args.lookup_range,
                logrows: new_settings.run_args.logrows,
                scale_rebase_multiplier: new_settings.run_args.scale_rebase_multiplier,
@@ -1296,7 +1276,6 @@ pub(crate) async fn calibrate(
            best_params.run_args.input_scale,
            best_params.run_args.param_scale,
            best_params.run_args.scale_rebase_multiplier,
-            best_params.run_args.div_rebasing,
        ))
        .ok_or("no params found")?
        .iter()
@@ -2022,7 +2001,7 @@ pub(crate) fn mock_aggregate(
        }
    }
    // proof aggregation
-        let pb = {
+    let pb = {
        let pb = init_spinner();
        pb.set_message("Aggregating (may take a while)...");
        pb
@@ -2033,7 +2012,7 @@ pub(crate) fn mock_aggregate(
    let prover = halo2_proofs::dev::MockProver::run(logrows, &circuit, vec![circuit.instances()])
        .map_err(|e| ExecutionError::MockProverError(e.to_string()))?;
    prover.verify().map_err(ExecutionError::VerifyError)?;
-        pb.finish_with_message("Done.");
+    pb.finish_with_message("Done.");
    Ok(String::new())
 }

@@ -2127,7 +2106,7 @@ pub(crate) fn aggregate(
    }

    // proof aggregation
-        let pb = {
+    let pb = {
        let pb = init_spinner();
        pb.set_message("Aggregating (may take a while)...");
        pb
@@ -2276,7 +2255,7 @@ pub(crate) fn aggregate(
    );
    snark.save(&proof_path)?;

-        pb.finish_with_message("Done.");
+    pb.finish_with_message("Done.");

    Ok(snark)
 }
--- a/src/graph/mod.rs
+++ b/src/graph/mod.rs
@@ -133,6 +133,8 @@ pub struct GraphWitness {
    pub min_lookup_inputs: IntegerRep,
    /// max range check size
    pub max_range_size: IntegerRep,
+    /// (optional) version of ezkl used
+    pub version: Option<String>,
 }

 impl GraphWitness {
@@ -161,6 +163,7 @@ impl GraphWitness {
            max_lookup_inputs: 0,
            min_lookup_inputs: 0,
            max_range_size: 0,
+            version: None,
        }
    }

@@ -1350,6 +1353,7 @@ impl GraphCircuit {
            max_lookup_inputs: model_results.max_lookup_inputs,
            min_lookup_inputs: model_results.min_lookup_inputs,
            max_range_size: model_results.max_range_size,
+            version: Some(crate::version().to_string()),
        };

        witness.generate_rescaled_elements(
--- a/src/graph/model.rs
+++ b/src/graph/model.rs
@@ -915,20 +915,9 @@ impl Model {
                        if scales.contains_key(&i) {
                            let scale_diff = n.out_scale - scales[&i];
                            n.opkind = if scale_diff > 0 {
-                                RebaseScale::rebase(
-                                    n.opkind,
-                                    scales[&i],
-                                    n.out_scale,
-                                    1,
-                                    run_args.div_rebasing,
-                                )
+                                RebaseScale::rebase(n.opkind, scales[&i], n.out_scale, 1)
                            } else {
-                                RebaseScale::rebase_up(
-                                    n.opkind,
-                                    scales[&i],
-                                    n.out_scale,
-                                    run_args.div_rebasing,
-                                )
+                                RebaseScale::rebase_up(n.opkind, scales[&i], n.out_scale)
                            };
                            n.out_scale = scales[&i];
                        }
--- a/src/graph/node.rs
+++ b/src/graph/node.rs
@@ -120,7 +120,6 @@ impl RebaseScale {
        global_scale: crate::Scale,
        op_out_scale: crate::Scale,
        scale_rebase_multiplier: u32,
-        div_rebasing: bool,
    ) -> SupportedOp {
        if (op_out_scale > (global_scale * scale_rebase_multiplier as i32))
            && !inner.is_constant()
@@ -137,7 +136,6 @@ impl RebaseScale {
                    multiplier,
                    rebase_op: HybridOp::Div {
                        denom: crate::circuit::utils::F32((multiplier) as f32),
-                        use_range_check_for_int: !div_rebasing,
                    },
                    original_scale: op.original_scale,
                })
@@ -148,7 +146,6 @@ impl RebaseScale {
                    multiplier,
                    rebase_op: HybridOp::Div {
                        denom: crate::circuit::utils::F32(multiplier as f32),
-                        use_range_check_for_int: !div_rebasing,
                    },
                    original_scale: op_out_scale,
                })
@@ -163,7 +160,6 @@ impl RebaseScale {
        inner: SupportedOp,
        target_scale: crate::Scale,
        op_out_scale: crate::Scale,
-        div_rebasing: bool,
    ) -> SupportedOp {
        if (op_out_scale < (target_scale)) && !inner.is_constant() && !inner.is_input() {
            let multiplier = scale_to_multiplier(op_out_scale - target_scale);
@@ -176,7 +172,6 @@ impl RebaseScale {
                    original_scale: op.original_scale,
                    rebase_op: HybridOp::Div {
                        denom: crate::circuit::utils::F32((multiplier) as f32),
-                        use_range_check_for_int: !div_rebasing,
                    },
                })
            } else {
@@ -187,7 +182,6 @@ impl RebaseScale {
                    original_scale: op_out_scale,
                    rebase_op: HybridOp::Div {
                        denom: crate::circuit::utils::F32(multiplier as f32),
-                        use_range_check_for_int: !div_rebasing,
                    },
                })
            }
@@ -595,13 +589,7 @@ impl Node {
        let mut out_scale = opkind.out_scale(in_scales.clone())?;
        // rescale the inputs if necessary to get consistent fixed points, we select the largest scale (highest precision)
        let global_scale = scales.get_max();
-        opkind = RebaseScale::rebase(
-            opkind,
-            global_scale,
-            out_scale,
-            scales.rebase_multiplier,
-            run_args.div_rebasing,
-        );
+        opkind = RebaseScale::rebase(opkind, global_scale, out_scale, scales.rebase_multiplier);

        out_scale = opkind.out_scale(in_scales)?;

--- a/src/graph/utilities.rs
+++ b/src/graph/utilities.rs
@@ -764,7 +764,7 @@ pub fn new_op_from_onnx(
                .collect::<Vec<_>>();

            if inputs.len() == 2 {
-                if const_inputs.len() > 0 {
+                if !const_inputs.is_empty() {
                    let const_idx = const_inputs[0];
                    let boxed_op = inputs[const_idx].opkind();
                    let unit = if let Some(c) = extract_const_raw_values(boxed_op) {
@@ -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 +837,75 @@ 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(),
-        }),
-        "Rsqrt" => SupportedOp::Nonlinear(LookupOp::Rsqrt {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+        "Sqrt" => SupportedOp::Hybrid(HybridOp::Sqrt {
+            scale: scale_to_multiplier(input_scales[0]).into(),
        }),
+        "Rsqrt" => {
+            let in_scale = input_scales[0];
+            let max_scale = std::cmp::max(scales.get_max(), in_scale);
+            SupportedOp::Hybrid(HybridOp::Rsqrt {
+                input_scale: (scale_to_multiplier(in_scale) as f32).into(),
+                output_scale: (scale_to_multiplier(max_scale) as f32).into(),
+            })
+        }
        "Exp" => SupportedOp::Nonlinear(LookupOp::Exp {
-            scale: scale_to_multiplier(inputs[0].out_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(),
        }),
+        "Ln" => {
+            if run_args.bounded_log_lookup {
+                SupportedOp::Hybrid(HybridOp::Ln {
+                    scale: scale_to_multiplier(input_scales[0]).into(),
+                })
+            } else {
+                SupportedOp::Nonlinear(LookupOp::Ln {
+                    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 +949,9 @@ 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 +1057,7 @@ 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 +1096,20 @@ 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 +1129,7 @@ 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),
                    })
                }
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -108,6 +108,18 @@ use serde::{Deserialize, Serialize};
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use tosubcommand::ToFlags;

+// if CARGO VERSION is 0.0.0 replace with "source - no compatibility guaranteed"
+/// The version of the ezkl library
+const VERSION: &str = env!("CARGO_PKG_VERSION");
+
+/// Get the version of the library
+pub fn version() -> &'static str {
+    match VERSION {
+        "0.0.0" => "source - no compatibility guaranteed",
+        _ => VERSION,
+    }
+}
+
 /// Bindings managment
 #[cfg(any(
    feature = "ios-bindings",
@@ -297,8 +309,6 @@ pub struct RunArgs {
        all(feature = "ezkl", not(target_arch = "wasm32")),
        arg(long, default_value = "false")
    )]
-    /// Rebase the scale using lookup table for division instead of using a range check
-    pub div_rebasing: bool,
    /// Should constants with 0.0 fraction be rebased to scale 0
    #[cfg_attr(
        all(feature = "ezkl", not(target_arch = "wasm32")),
@@ -317,11 +327,18 @@ pub struct RunArgs {
    #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "2", value_hint = clap::ValueHint::Other))]
    /// the number of legs used for decompositions
    pub decomp_legs: usize,
+    #[cfg_attr(
+        all(feature = "ezkl", not(target_arch = "wasm32")),
+        arg(long, default_value = "false")
+    )]
+    /// use unbounded lookup for the log
+    pub bounded_log_lookup: bool,
 }

 impl Default for RunArgs {
    fn default() -> Self {
        Self {
+            bounded_log_lookup: false,
            tolerance: Tolerance::default(),
            input_scale: 7,
            param_scale: 7,
@@ -333,7 +350,6 @@ impl Default for RunArgs {
            input_visibility: Visibility::Private,
            output_visibility: Visibility::Public,
            param_visibility: Visibility::Private,
-            div_rebasing: false,
            rebase_frac_zero_constants: false,
            check_mode: CheckMode::UNSAFE,
            commitment: None,
--- a/src/pfsys/mod.rs
+++ b/src/pfsys/mod.rs
@@ -59,10 +59,7 @@ fn serde_format_from_str(s: &str) -> halo2_proofs::SerdeFormat {

 #[allow(missing_docs)]
 #[derive(Copy, Clone, Default, Debug, PartialEq, Eq, Deserialize, Serialize, PartialOrd)]
-#[cfg_attr(
-    all(feature = "ezkl", not(target_arch = "wasm32")),
-    derive(ValueEnum)
-)]
+#[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), derive(ValueEnum))]
 pub enum ProofType {
    #[default]
    Single,
@@ -134,10 +131,7 @@ impl<'source> pyo3::FromPyObject<'source> for ProofType {

 #[allow(missing_docs)]
 #[derive(Copy, Clone, Debug, PartialEq, Eq, Deserialize, Serialize)]
-#[cfg_attr(
-    all(feature = "ezkl", not(target_arch = "wasm32")),
-    derive(ValueEnum)
-)]
+#[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), derive(ValueEnum))]
 pub enum StrategyType {
    Single,
    Accum,
@@ -203,10 +197,7 @@ pub enum PfSysError {

 #[allow(missing_docs)]
 #[derive(Default, Copy, Clone, Debug, PartialEq, Eq, Deserialize, Serialize, PartialOrd)]
-#[cfg_attr(
-    all(feature = "ezkl", not(target_arch = "wasm32")),
-    derive(ValueEnum)
-)]
+#[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), derive(ValueEnum))]
 pub enum TranscriptType {
    Poseidon,
    #[default]
@@ -324,6 +315,8 @@ where
    pub timestamp: Option<u128>,
    /// commitment
    pub commitment: Option<Commitments>,
+    /// (optional) version of ezkl used to generate the proof
+    version: Option<String>,
 }

 #[cfg(feature = "python-bindings")]
@@ -385,6 +378,7 @@ where
                    .as_millis(),
            ),
            commitment,
+            version: Some(crate::version().to_string()),
        }
    }

@@ -920,6 +914,7 @@ mod tests {
            pretty_public_inputs: None,
            timestamp: None,
            commitment: None,
+            version: None,
        };

        snark
--- a/src/tensor/ops.rs
+++ b/src/tensor/ops.rs
@@ -1474,6 +1474,93 @@ 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()
+    }
+
+    /// Powers of 2
+    /// # Arguments
+    /// * `a` - Tensor
+    /// * `scale` - Single value
+    /// # Examples
+    /// ```
+    /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
+    /// use ezkl::tensor::ops::nonlinearities::ipow2;
+    /// let x = Tensor::<IntegerRep>::new(
+    ///  Some(&[2, 15, 2, 1, 1, 0]),
+    /// &[2, 3],
+    /// ).unwrap();
+    /// let result = ipow2(&x, 1.0);
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[4, 32768, 4, 2, 2, 1]), &[2, 3]).unwrap();
+    /// assert_eq!(result, expected);
+    /// ```
+    pub fn ipow2(a: &Tensor<IntegerRep>, scale_output: f64) -> Tensor<IntegerRep> {
+        a.par_enum_map(|_, a_i| {
+            let kix = a_i as f64;
+            let kix = scale_output * (2.0_f64).powf(kix);
+            let rounded = kix.round();
+            Ok::<_, TensorError>(rounded as IntegerRep)
+        })
+        .unwrap()
+    }
+
+    /// Elementwise applies ln base 2 to a tensor of integers.
+    /// # Arguments
+    /// * `a` - Tensor
+    /// * `scale_input` - Single value
+    /// ```
+    /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
+    /// use ezkl::tensor::ops::nonlinearities::ilog2;
+    /// let x = Tensor::<IntegerRep>::new(
+    ///    Some(&[2, 15, 2, 1, 1, 2]),
+    /// &[2, 3],
+    /// ).unwrap();
+    /// let result = ilog2(&x, 1.0);
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[1, 4, 1, 0, 0, 1]), &[2, 3]).unwrap();
+    /// assert_eq!(result, expected);
+    /// ```
+    pub fn ilog2(a: &Tensor<IntegerRep>, scale_input: f64) -> Tensor<IntegerRep> {
+        a.par_enum_map(|_, a_i| {
+            let kix = (a_i as f64) / scale_input;
+            let log = (kix).log2();
+            let floor = log.floor();
+            let ceil = log.ceil();
+            let floor_dist = ((2.0_f64).powf(floor) - kix).abs();
+            let ceil_dist = (kix - (2.0_f64).powf(ceil)).abs();
+
+            if floor_dist < ceil_dist {
+                Ok::<_, TensorError>(floor as IntegerRep)
+            } else {
+                Ok::<_, TensorError>(ceil as IntegerRep)
+            }
+        })
+        .unwrap()
+    }
+
    /// Elementwise applies sigmoid to a tensor of integers.
    /// # Arguments
    ///
@@ -1602,12 +1689,11 @@ pub mod nonlinearities {
        .unwrap()
    }

-    /// Elementwise applies exponential to a tensor of integers.
+    /// Elementwise applies ln to a tensor of integers.
    /// # Arguments
    ///
    /// * `a` - Tensor
    /// * `scale_input` - Single value
-    /// * `scale_output` - Single value
    /// # Examples
    /// ```
    /// use ezkl::tensor::Tensor;
--- a/tests/assets/model.compiled
+++ b/tests/assets/model.compiled
--- a/tests/assets/proof.json
+++ b/tests/assets/proof.json
--- a/tests/assets/settings.json
+++ b/tests/assets/settings.json
@@ -27,7 +27,8 @@
        "check_mode": "UNSAFE",
        "commitment": "KZG",
        "decomp_base": 128,
-        "decomp_legs": 2
+        "decomp_legs": 2,
+        "bounded_log_lookup": false
    },
    "num_rows": 46,
    "total_assignments": 92,
--- a/tests/assets/witness.json
+++ b/tests/assets/witness.json
@@ -1 +1 @@
-{"inputs":[["0200000000000000000000000000000000000000000000000000000000000000","0100000000000000000000000000000000000000000000000000000000000000","0100000000000000000000000000000000000000000000000000000000000000"]],"pretty_elements":{"rescaled_inputs":[["2","1","1"]],"inputs":[["0x0000000000000000000000000000000000000000000000000000000000000002","0x0000000000000000000000000000000000000000000000000000000000000001","0x0000000000000000000000000000000000000000000000000000000000000001"]],"processed_inputs":[],"processed_params":[],"processed_outputs":[],"rescaled_outputs":[["0","0","0","0"]],"outputs":[["0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000"]]},"outputs":[["0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000"]],"processed_inputs":null,"processed_params":null,"processed_outputs":null,"max_lookup_inputs":0,"min_lookup_inputs":0,"max_range_size":127}
+{"inputs":[["0200000000000000000000000000000000000000000000000000000000000000","0100000000000000000000000000000000000000000000000000000000000000","0100000000000000000000000000000000000000000000000000000000000000"]],"pretty_elements":{"rescaled_inputs":[["2","1","1"]],"inputs":[["0x0000000000000000000000000000000000000000000000000000000000000002","0x0000000000000000000000000000000000000000000000000000000000000001","0x0000000000000000000000000000000000000000000000000000000000000001"]],"processed_inputs":[],"processed_params":[],"processed_outputs":[],"rescaled_outputs":[["0","0","0","0"]],"outputs":[["0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000"]]},"outputs":[["0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000"]],"processed_inputs":null,"processed_params":null,"processed_outputs":null,"max_lookup_inputs":0,"min_lookup_inputs":0,"max_range_size":127,"version":"source - no compatibility guaranteed"}
--- a/tests/integration_tests.rs
+++ b/tests/integration_tests.rs
@@ -205,7 +205,7 @@ mod native_tests {
        "1l_tiny_div",
    ];

-    const TESTS: [&str; 95] = [
+    const TESTS: [&str; 96] = [
        "1l_mlp", //0
        "1l_slice",
        "1l_concat",
@@ -305,6 +305,7 @@ mod native_tests {
        "lstm_medium", // 92
        "lenet_5",     // 93
        "rsqrt",       // 94
+        "log",         // 95
    ];

    const WASM_TESTS: [&str; 46] = [
@@ -538,12 +539,12 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "fixed", "public", 1, "accuracy", None, 0.0);
+                mock(path, test.to_string(), "public", "fixed", "public", 1, "accuracy", None, 0.0, false);
                test_dir.close().unwrap();
            }
        });

-            seq!(N in 0..=94 {
+            seq!(N in 0..=95 {

            #(#[test_case(TESTS[N])])*
            #[ignore]
@@ -555,15 +556,7 @@ mod native_tests {
                test_dir.close().unwrap();
            }

-            #(#[test_case(TESTS[N])])*
-            fn accuracy_measurement_div_rebase_(test: &str) {
-                crate::native_tests::init_binary();
-                crate::native_tests::setup_py_env();
-                let test_dir = TempDir::new(test).unwrap();
-                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                accuracy_measurement(path, test.to_string(), "private", "private", "public", 1, "accuracy", 2.6, true);
-                test_dir.close().unwrap();
-            }
+

            #(#[test_case(TESTS[N])])*
            fn accuracy_measurement_public_outputs_(test: &str) {
@@ -571,7 +564,7 @@ mod native_tests {
                crate::native_tests::setup_py_env();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                accuracy_measurement(path, test.to_string(), "private", "private", "public", 1, "accuracy", 2.6, false);
+                accuracy_measurement(path, test.to_string(), "private", "private", "public", 1, "accuracy", 2.6);
                test_dir.close().unwrap();
            }

@@ -581,7 +574,7 @@ mod native_tests {
                crate::native_tests::setup_py_env();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                accuracy_measurement(path, test.to_string(), "private", "fixed", "private", 1, "accuracy", 2.6 , false);
+                accuracy_measurement(path, test.to_string(), "private", "fixed", "private", 1, "accuracy", 2.6 );
                test_dir.close().unwrap();
            }

@@ -591,7 +584,7 @@ mod native_tests {
                crate::native_tests::setup_py_env();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                accuracy_measurement(path, test.to_string(), "public", "private", "private", 1, "accuracy", 2.6, false);
+                accuracy_measurement(path, test.to_string(), "public", "private", "private", 1, "accuracy", 2.6);
                test_dir.close().unwrap();
            }

@@ -602,7 +595,7 @@ mod native_tests {
                crate::native_tests::setup_py_env();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                accuracy_measurement(path, test.to_string(), "private", "private", "public", 1, "resources", 3.1, false);
+                accuracy_measurement(path, test.to_string(), "private", "private", "public", 1, "resources", 3.1);
                test_dir.close().unwrap();
            }

@@ -611,7 +604,17 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, 0.0, false);
+                test_dir.close().unwrap();
+            }
+
+
+            #(#[test_case(TESTS[N])])*
+            fn mock_bounded_lookup_log(test: &str) {
+                crate::native_tests::init_binary();
+                let test_dir = TempDir::new(test).unwrap();
+                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
+                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, 0.0, true);
                test_dir.close().unwrap();
            }

@@ -622,7 +625,7 @@ mod native_tests {
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
                // gen random number between 0.0 and 1.0
                let tolerance = rand::thread_rng().gen_range(0.0..1.0) * 100.0;
-                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, tolerance);
+                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, tolerance, false);
                test_dir.close().unwrap();
            }

@@ -637,7 +640,7 @@ mod native_tests {
                    let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
                    let large_batch_dir = &format!("large_batches_{}", test);
                    crate::native_tests::mk_data_batches_(path, test, &large_batch_dir, 10);
-                    mock(path, large_batch_dir.to_string(), "private", "private", "public", 10, "resources", None, 0.0);
+                    mock(path, large_batch_dir.to_string(), "private", "private", "public", 10, "resources", None, 0.0, false);
                    test_dir.close().unwrap();
                }
            }
@@ -647,7 +650,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "private", "private", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "public", "private", "private", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -656,7 +659,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "hashed", "private", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "public", "hashed", "private", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -665,7 +668,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "fixed", "private", "private", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "fixed", "private", "private", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -674,7 +677,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "private", "private", "fixed", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "private", "private", "fixed", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -683,7 +686,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "private", "fixed", "private", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "private", "fixed", "private", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -692,7 +695,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "hashed", "private", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "hashed", "private", "public", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -701,7 +704,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "polycommit", "private", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "polycommit", "private", "public", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -711,7 +714,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "private", "hashed", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "private", "hashed", "public", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -721,7 +724,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "private", "polycommit", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "private", "polycommit", "public", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -730,7 +733,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "private", "hashed", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "public", "private", "hashed", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -740,7 +743,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "private", "polycommit", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "public", "private", "polycommit", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -749,7 +752,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "fixed", "hashed", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "public", "fixed", "hashed", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -759,7 +762,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "polycommit", "hashed", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "public", "polycommit", "hashed", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -769,7 +772,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "polycommit", "polycommit", "polycommit", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "polycommit", "polycommit", "polycommit", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -779,7 +782,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "hashed", "private", "hashed", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "hashed", "private", "hashed", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -789,7 +792,7 @@ mod native_tests {
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
                // needs an extra row for the large model
-                mock(path, test.to_string(),"hashed", "hashed", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(),"hashed", "hashed", "public", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -799,7 +802,7 @@ mod native_tests {
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
                // needs an extra row for the large model
-                mock(path, test.to_string(),"hashed", "hashed", "hashed", 1, "resources", None, 0.0);
+                mock(path, test.to_string(),"hashed", "hashed", "hashed", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }

@@ -976,7 +979,7 @@ mod native_tests {
                crate::native_tests::init_binary();
                let test_dir = TempDir::new(test).unwrap();
                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "private", "fixed", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "private", "fixed", "public", 1, "resources", None, 0.0, false);
                test_dir.close().unwrap();
            }
        });
@@ -1453,6 +1456,7 @@ mod native_tests {
        cal_target: &str,
        scales_to_use: Option<Vec<u32>>,
        tolerance: f32,
+        bounded_lookup_log: bool,
    ) {
        let mut tolerance = tolerance;
        gen_circuit_settings_and_witness(
@@ -1465,10 +1469,10 @@ mod native_tests {
            cal_target,
            scales_to_use,
            2,
-            false,
            &mut tolerance,
            Commitments::KZG,
            2,
+            bounded_lookup_log,
        );

        if tolerance > 0.0 {
@@ -1606,10 +1610,10 @@ mod native_tests {
        cal_target: &str,
        scales_to_use: Option<Vec<u32>>,
        num_inner_columns: usize,
-        div_rebasing: bool,
        tolerance: &mut f32,
        commitment: Commitments,
        lookup_safety_margin: usize,
+        bounded_lookup_log: bool,
    ) {
        let mut args = vec![
            "gen-settings".to_string(),
@@ -1628,9 +1632,9 @@ mod native_tests {
            format!("--commitment={}", commitment),
        ];

-        if div_rebasing {
-            args.push("--div-rebasing".to_string());
-        };
+        if bounded_lookup_log {
+            args.push("--bounded-log-lookup".to_string());
+        }

        let status = Command::new(format!("{}/release/ezkl", *CARGO_TARGET_DIR))
            .args(args)
@@ -1730,7 +1734,6 @@ mod native_tests {
        batch_size: usize,
        cal_target: &str,
        target_perc: f32,
-        div_rebasing: bool,
    ) {
        gen_circuit_settings_and_witness(
            test_dir,
@@ -1742,10 +1745,10 @@ mod native_tests {
            cal_target,
            None,
            2,
-            div_rebasing,
            &mut 0.0,
            Commitments::KZG,
            2,
+            false,
        );

        println!(
@@ -2026,10 +2029,10 @@ mod native_tests {
            target_str,
            scales_to_use,
            num_inner_columns,
-            false,
            &mut 0.0,
            commitment,
            lookup_safety_margin,
+            false,
        );

        let settings_path = format!("{}/{}/settings.json", test_dir, example_name);
@@ -2458,10 +2461,10 @@ mod native_tests {
            // we need the accuracy
            Some(vec![4]),
            1,
-            false,
            &mut 0.0,
            Commitments::KZG,
            2,
+            false,
        );

        let model_path = format!("{}/{}/network.compiled", test_dir, example_name);
Author	SHA1	Message	Date
dante	523c77c912	feat: lookupless sqrt and rsqrt (#867 )	2024-11-10 15:56:38 +00:00
dante	948e5cd4b9	chore: version proof and witness (#865 )	2024-11-08 02:55:35 +00:00
dante	00155e585f	feat: bounded lookup log argument (#864 )	2024-11-07 12:16:55 +00:00
dante	0876faa12c	feat: bounded lookup round half to even (#863 )	2024-11-01 00:51:15 -04:00
				`@@ -0,0 +1 @@`
				`{"input_data": [[1.9252371788024902, 1.8418371677398682, 0.8400403261184692, 2.083845853805542, 0.9760497808456421, 0.6940176486968994, 0.015579521656036377, 2.2689192295074463]]}`