feat: bounded lookup log argument (#864)

* Improve clarity of an info!() message

* Replace references to EZKL_REPO_PATH in `--help' output

Command `--help' messages aren't meant to be unduly verbose; we can
write them for common/simple use cases. We continue to support
EZKL_REPO_PATH for users who need it, for example to support
containerized server use cases.

To be clear, by default, EZKL_REPO_PATH = $HOME/.ezkl

Cargo.toml

@@ -169,7 +169,7 @@ harness = false
 
 
 [[bench]]
-name = "relu"
+name = "sigmoid"
 harness = false
 
 [[bench]]
@@ -177,12 +177,12 @@ name = "relu_lookupless"
 harness = false
 
 [[bench]]
-name = "accum_matmul_relu"
+name = "accum_matmul_sigmoid"
 harness = false
 
 
 [[bench]]
-name = "accum_matmul_relu_overflow"
+name = "accum_matmul_sigmoid_overflow"
 harness = false
 
 [[bin]]

benches/accum_matmul_sigmoid.rs

@@ -64,7 +64,7 @@ impl Circuit<Fr> for MyCircuit {
                 &a,
                 BITS,
                 K,
-                &LookupOp::LeakyReLU { slope: 0.0.into() },
+                &LookupOp::Sigmoid { scale: 1.0.into() },
             )
             .unwrap();
 
@@ -93,7 +93,7 @@ impl Circuit<Fr> for MyCircuit {
                     .layout(
                         &mut region,
                         &[output.unwrap()],
-                        Box::new(LookupOp::LeakyReLU { slope: 0.0.into() }),
+                        Box::new(LookupOp::Sigmoid { scale: 1.0.into() }),
                     )
                     .unwrap();
                 Ok(())

benches/accum_matmul_sigmoid_overflow.rs

@@ -65,7 +65,7 @@ impl Circuit<Fr> for MyCircuit {
                 &a,
                 BITS,
                 k,
-                &LookupOp::LeakyReLU { slope: 0.0.into() },
+                &LookupOp::Sigmoid { scale: 1.0.into() },
             )
             .unwrap();
 
@@ -94,7 +94,7 @@ impl Circuit<Fr> for MyCircuit {
                     .layout(
                         &mut region,
                         &[output.unwrap()],
-                        Box::new(LookupOp::LeakyReLU { slope: 0.0.into() }),
+                        Box::new(LookupOp::Sigmoid { scale: 1.0.into() }),
                     )
                     .unwrap();
                 Ok(())

benches/relu_lookupless.rs

@@ -68,7 +68,14 @@ impl Circuit<Fr> for NLCircuit {
             |region| {
                 let mut region = RegionCtx::new(region, 0, 1, 1024, 2);
                 config
-                    .layout(&mut region, &[self.input.clone()], Box::new(PolyOp::ReLU))
+                    .layout(
+                        &mut region,
+                        &[self.input.clone()],
+                        Box::new(PolyOp::LeakyReLU {
+                            slope: 0.0.into(),
+                            scale: 1,
+                        }),
+                    )
                     .unwrap();
                 Ok(())
             },

benches/sigmoid.rs

@@ -42,7 +42,7 @@ impl Circuit<Fr> for NLCircuit {
                 .map(|_| VarTensor::new_advice(cs, K, 1, LEN))
                 .collect::<Vec<_>>();
 
-            let nl = LookupOp::LeakyReLU { slope: 0.0.into() };
+            let nl = LookupOp::Sigmoid { scale: 1.0.into() };
 
             let mut config = Config::default();
 
@@ -68,7 +68,7 @@ impl Circuit<Fr> for NLCircuit {
                     .layout(
                         &mut region,
                         &[self.input.clone()],
-                        Box::new(LookupOp::LeakyReLU { slope: 0.0.into() }),
+                        Box::new(LookupOp::Sigmoid { scale: 1.0.into() }),
                     )
                     .unwrap();
                 Ok(())

examples/conv2d_mnist/main.rs

@@ -146,6 +146,8 @@ where
         let params = VarTensor::new_advice(cs, K, NUM_INNER_COLS, LEN);
         let output = VarTensor::new_advice(cs, K, NUM_INNER_COLS, LEN);
 
+        let _constant = VarTensor::constant_cols(cs, K, LEN, false);
+
         println!("INPUT COL {:#?}", input);
 
         let mut layer_config = PolyConfig::configure(
@@ -156,15 +158,11 @@ where
         );
 
         layer_config
-            .configure_lookup(
-                cs,
-                &input,
-                &output,
-                &params,
-                (LOOKUP_MIN, LOOKUP_MAX),
-                K,
-                &LookupOp::LeakyReLU { slope: 0.0.into() },
-            )
+            .configure_range_check(cs, &input, &params, (-1, 1), K)
+            .unwrap();
+
+        layer_config
+            .configure_range_check(cs, &input, &params, (0, 1023), K)
             .unwrap();
 
         layer_config
@@ -195,6 +193,11 @@ where
     ) -> Result<(), Error> {
         config.layer_config.layout_tables(&mut layouter).unwrap();
 
+        config
+            .layer_config
+            .layout_range_checks(&mut layouter)
+            .unwrap();
+
         let x = layouter
             .assign_region(
                 || "mlp_4d",
@@ -224,7 +227,10 @@ where
                         .layout(
                             &mut region,
                             &[x.unwrap()],
-                            Box::new(LookupOp::LeakyReLU { slope: 0.0.into() }),
+                            Box::new(PolyOp::LeakyReLU {
+                                slope: 0.0.into(),
+                                scale: 1,
+                            }),
                         )
                         .unwrap();
 

examples/mlp_4d_einsum.rs

@@ -53,6 +53,10 @@ impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRe
         let output = VarTensor::new_advice(cs, K, 1, LEN);
         // tells the config layer to add an affine op to the circuit gate
 
+        let _constant = VarTensor::constant_cols(cs, K, LEN, false);
+
+        println!("INPUT COL {:#?}", input);
+
         let mut layer_config = PolyConfig::<F>::configure(
             cs,
             &[input.clone(), params.clone()],
@@ -60,17 +64,12 @@ impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRe
             CheckMode::SAFE,
         );
 
-        // sets up a new ReLU table and resuses it for l1 and l3 non linearities
         layer_config
-            .configure_lookup(
-                cs,
-                &input,
-                &output,
-                &params,
-                (LOOKUP_MIN, LOOKUP_MAX),
-                K,
-                &LookupOp::LeakyReLU { slope: 0.0.into() },
-            )
+            .configure_range_check(cs, &input, &params, (-1, 1), K)
+            .unwrap();
+
+        layer_config
+            .configure_range_check(cs, &input, &params, (0, 1023), K)
             .unwrap();
 
         // sets up a new ReLU table and resuses it for l1 and l3 non linearities
@@ -104,6 +103,11 @@ impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRe
     ) -> Result<(), Error> {
         config.layer_config.layout_tables(&mut layouter).unwrap();
 
+        config
+            .layer_config
+            .layout_range_checks(&mut layouter)
+            .unwrap();
+
         let x = layouter
             .assign_region(
                 || "mlp_4d",
@@ -144,7 +148,10 @@ impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRe
                         .layout(
                             &mut region,
                             &[x],
-                            Box::new(LookupOp::LeakyReLU { slope: 0.0.into() }),
+                            Box::new(PolyOp::LeakyReLU {
+                                scale: 1,
+                                slope: 0.0.into(),
+                            }),
                         )
                         .unwrap()
                         .unwrap();
@@ -184,7 +191,10 @@ impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRe
                         .layout(
                             &mut region,
                             &[x],
-                            Box::new(LookupOp::LeakyReLU { slope: 0.0.into() }),
+                            Box::new(PolyOp::LeakyReLU {
+                                scale: 1,
+                                slope: 0.0.into(),
+                            }),
                         )
                         .unwrap();
                     println!("6");

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
     },

examples/notebooks/data_attest_hashed.ipynb

@@ -648,10 +648,10 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.5"
         },
         "orig_nbformat": 4
     },
     "nbformat": 4,
     "nbformat_minor": 2
-}
+}

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,

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'))

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]]}

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

examples/onnx/rounding_ops/gen.py

@@ -21,9 +21,9 @@ def main():
     torch_model = Circuit()
     # Input to the model
     shape = [3, 2, 3]
-    w = 0.1*torch.rand(1, *shape, requires_grad=True)
-    x = 0.1*torch.rand(1, *shape, requires_grad=True)
-    y = 0.1*torch.rand(1, *shape, requires_grad=True)
+    w = 2 * torch.rand(1, *shape, requires_grad=True) - 1
+    x = 2 * torch.rand(1, *shape, requires_grad=True) - 1
+    y = 2 * torch.rand(1, *shape, requires_grad=True) - 1
     torch_out = torch_model(w, x, y)
     # Export the model
     torch.onnx.export(torch_model,               # model being run

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": [[0.0025284828152507544, 0.04976580664515495, 0.025840921327471733, 0.0829394981265068, 0.09595223516225815, 0.08764562010765076, 0.06308566778898239, 0.062386948615312576, 0.08090643584728241, 0.09267748892307281, 0.07428313046693802, 0.08987367898225784, 0.005716216750442982, 0.0666426345705986, 0.012837404385209084, 0.05769496038556099, 0.05761152133345604, 0.08006472885608673], [0.007834953255951405, 0.011380612850189209, 0.08560049533843994, 0.022283583879470825, 0.07879520952701569, 0.04422441124916077, 0.030812596902251244, 0.006081616971641779, 0.011045408435165882, 0.08776585012674332, 0.044985152781009674, 0.015603715553879738, 0.07923348993062973, 0.04872611165046692, 0.0036642670165747404, 0.05142095685005188, 0.0963878259062767, 0.03225792199373245], [0.09952805936336517, 0.002214533044025302, 0.011696457862854004, 0.022422820329666138, 0.04151459410786629, 0.027647346258163452, 0.011919880285859108, 0.006539052817970514, 0.06569185107946396, 0.034328874200582504, 0.0032284557819366455, 0.004105025436729193, 0.022395813837647438, 0.07135921716690063, 0.07882415503263474, 0.09764843434095383, 0.05335796996951103, 0.0525360181927681]], "output_data": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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
+        ]
+    ]
+}

examples/onnx/rounding_ops/network.onnx

@@ -1,10 +1,11 @@
-pytorch2.0.1:â
+pytorch2.2.2:ă
 
 
woutput_w/Round"Round
 
 
xoutput_x/Floor"Floor
 
-
youtput_y/Ceil"Ceil	torch_jitZ%
+
youtput_y/Ceil"Ceil
+main_graphZ%
 
w 
 

 

examples/onnx/rsqrt/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):         
+        # reciprocal sqrt 
+        m = 1 / torch.sqrt(x)
+        return m 
+
+
+circuit = MyModel()
+
+x = torch.empty(1, 8).uniform_(0, 1)
+
+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'))

examples/onnx/rsqrt/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.8590779900550842, 0.4029041528701782, 0.6507361531257629, 0.9782488942146301, 0.37392884492874146, 0.6867020726203918, 0.11407750844955444, 0.362740159034729]]}

examples/onnx/rsqrt/network.onnx

@@ -0,0 +1,17 @@
+pytorch2.2.2:Ź

+$
+input/Sqrt_output_0/Sqrt"Sqrt
+1
+/Sqrt_output_0output/Reciprocal"
+Reciprocal
+main_graphZ!
+input
+

+
+batch_size
+b"
+output
+

+
+batch_size
+B

src/bindings/python.rs

@@ -197,6 +197,9 @@ 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 +215,7 @@ 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,
@@ -236,6 +240,7 @@ 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,

src/circuit/ops/errors.rs

@@ -94,4 +94,7 @@ pub enum CircuitError {
     #[error("[io] {0}")]
     /// IO error
     IoError(#[from] std::io::Error),
+    /// Invalid scale
+    #[error("negative scale for an op that requires positive inputs {0}")]
+    NegativeScale(String),
 }

src/circuit/ops/hybrid.rs

@@ -13,10 +13,29 @@ 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,
+    },
+
+    RoundHalfToEven {
+        scale: utils::F32,
+        legs: usize,
+    },
+    Ceil {
+        scale: utils::F32,
+        legs: usize,
+    },
+    Floor {
+        scale: utils::F32,
+        legs: usize,
+    },
+    Round {
+        scale: utils::F32,
+        legs: usize,
+    },
     Recip {
         input_scale: utils::F32,
         output_scale: utils::F32,
-        use_range_check_for_int: bool,
     },
     Div {
         denom: utils::F32,
@@ -45,6 +64,8 @@ pub enum HybridOp {
     ReduceArgMin {
         dim: usize,
     },
+    Max,
+    Min,
     Softmax {
         input_scale: utils::F32,
         output_scale: utils::F32,
@@ -79,6 +100,8 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
             | HybridOp::Less { .. }
             | HybridOp::Equals { .. }
             | HybridOp::GreaterEqual { .. }
+            | HybridOp::Max
+            | HybridOp::Min
             | HybridOp::LessEqual { .. } => {
                 vec![0, 1]
             }
@@ -93,13 +116,22 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
 
     fn as_string(&self) -> String {
         match self {
+            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::Recip {
                 input_scale,
                 output_scale,
-                use_range_check_for_int,
             } => format!(
-                "RECIP (input_scale={}, output_scale={}, use_range_check_for_int={})",
-                input_scale, output_scale, use_range_check_for_int
+                "RECIP (input_scale={}, output_scale={})",
+                input_scale, output_scale
             ),
             HybridOp::Div {
                 denom,
@@ -162,6 +194,21 @@ 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::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)?
+            }
+            HybridOp::Floor { scale, legs } => {
+                layouts::floor(config, region, values[..].try_into()?, *scale, *legs)?
+            }
+            HybridOp::Round { scale, legs } => {
+                layouts::round(config, region, values[..].try_into()?, *scale, *legs)?
+            }
+            HybridOp::Max => layouts::max_comp(config, region, values[..].try_into()?)?,
+            HybridOp::Min => layouts::min_comp(config, region, values[..].try_into()?)?,
             HybridOp::SumPool {
                 padding,
                 stride,
@@ -179,31 +226,13 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
             HybridOp::Recip {
                 input_scale,
                 output_scale,
-                use_range_check_for_int,
-            } => {
-                if input_scale.0.fract() == 0.0
-                    && output_scale.0.fract() == 0.0
-                    && *use_range_check_for_int
-                {
-                    layouts::recip(
-                        config,
-                        region,
-                        values[..].try_into()?,
-                        integer_rep_to_felt(input_scale.0 as i128),
-                        integer_rep_to_felt(output_scale.0 as i128),
-                    )?
-                } else {
-                    layouts::nonlinearity(
-                        config,
-                        region,
-                        values.try_into()?,
-                        &LookupOp::Recip {
-                            input_scale: *input_scale,
-                            output_scale: *output_scale,
-                        },
-                    )?
-                }
-            }
+            } => layouts::recip(
+                config,
+                region,
+                values[..].try_into()?,
+                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,
@@ -304,6 +333,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
             HybridOp::Softmax { output_scale, .. } | HybridOp::Recip { output_scale, .. } => {
                 multiplier_to_scale(output_scale.0 as f64)
             }
+            HybridOp::Ln {
+                scale: output_scale,
+            } => 4 * multiplier_to_scale(output_scale.0 as f64),
             _ => in_scales[0],
         };
         Ok(scale)

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},
 };
 
@@ -15,101 +14,29 @@ use halo2curves::ff::PrimeField;
 /// An enum representing the operations that can be used to express more complex operations via accumulation
 #[derive(Clone, Debug, PartialEq, Eq, Hash, PartialOrd, Ord, Deserialize, Serialize)]
 pub enum LookupOp {
-    Div {
-        denom: utils::F32,
-    },
-    Cast {
-        scale: utils::F32,
-    },
-    Max {
-        scale: utils::F32,
-        a: utils::F32,
-    },
-    Min {
-        scale: utils::F32,
-        a: utils::F32,
-    },
-    Ceil {
-        scale: utils::F32,
-    },
-    Floor {
-        scale: utils::F32,
-    },
-    Round {
-        scale: utils::F32,
-    },
-    RoundHalfToEven {
-        scale: utils::F32,
-    },
-    Sqrt {
-        scale: utils::F32,
-    },
-    Rsqrt {
-        scale: utils::F32,
-    },
-    Recip {
-        input_scale: utils::F32,
-        output_scale: utils::F32,
-    },
-    LeakyReLU {
-        slope: utils::F32,
-    },
-    Sigmoid {
-        scale: utils::F32,
-    },
-    Ln {
-        scale: utils::F32,
-    },
-    Exp {
-        scale: utils::F32,
-    },
-    Cos {
-        scale: utils::F32,
-    },
-    ACos {
-        scale: utils::F32,
-    },
-    Cosh {
-        scale: utils::F32,
-    },
-    ACosh {
-        scale: utils::F32,
-    },
-    Sin {
-        scale: utils::F32,
-    },
-    ASin {
-        scale: utils::F32,
-    },
-    Sinh {
-        scale: utils::F32,
-    },
-    ASinh {
-        scale: utils::F32,
-    },
-    Tan {
-        scale: utils::F32,
-    },
-    ATan {
-        scale: utils::F32,
-    },
-    Tanh {
-        scale: utils::F32,
-    },
-    ATanh {
-        scale: utils::F32,
-    },
-    Erf {
-        scale: utils::F32,
-    },
-    KroneckerDelta,
-    Pow {
-        scale: utils::F32,
-        a: utils::F32,
-    },
-    HardSwish {
-        scale: utils::F32,
-    },
+    Div { denom: utils::F32 },
+    IsOdd,
+    PowersOfTwo { scale: utils::F32 },
+    Ln { scale: utils::F32 },
+    Sqrt { scale: utils::F32 },
+    Rsqrt { scale: utils::F32 },
+    Sigmoid { scale: utils::F32 },
+    Exp { scale: utils::F32 },
+    Cos { scale: utils::F32 },
+    ACos { scale: utils::F32 },
+    Cosh { scale: utils::F32 },
+    ACosh { scale: utils::F32 },
+    Sin { scale: utils::F32 },
+    ASin { scale: utils::F32 },
+    Sinh { scale: utils::F32 },
+    ASinh { scale: utils::F32 },
+    Tan { scale: utils::F32 },
+    ATan { scale: utils::F32 },
+    Tanh { scale: utils::F32 },
+    ATanh { scale: utils::F32 },
+    Erf { scale: utils::F32 },
+    Pow { scale: utils::F32, a: utils::F32 },
+    HardSwish { scale: utils::F32 },
 }
 
 impl LookupOp {
@@ -123,27 +50,16 @@ impl LookupOp {
     /// as path
     pub fn as_path(&self) -> String {
         match self {
-            LookupOp::Ceil { scale } => format!("ceil_{}", scale),
-            LookupOp::Floor { scale } => format!("floor_{}", scale),
-            LookupOp::Round { scale } => format!("round_{}", scale),
-            LookupOp::RoundHalfToEven { scale } => format!("round_half_to_even_{}", scale),
             LookupOp::Pow { scale, a } => format!("pow_{}_{}", scale, a),
-            LookupOp::KroneckerDelta => "kronecker_delta".into(),
-            LookupOp::Max { scale, a } => format!("max_{}_{}", scale, a),
-            LookupOp::Min { scale, a } => format!("min_{}_{}", 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::Recip {
-                input_scale,
-                output_scale,
-            } => format!("recip_{}_{}", input_scale, output_scale),
-            LookupOp::LeakyReLU { slope: a } => format!("leaky_relu_{}", a),
             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),
@@ -168,47 +84,19 @@ impl LookupOp {
         let x = x[0].clone().map(|x| felt_to_integer_rep(x));
         let res =
             match &self {
-                LookupOp::Ceil { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ceil(&x, scale.into()))
+                LookupOp::Ln { scale } => {
+                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ln(&x, scale.into()))
                 }
-                LookupOp::Floor { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::floor(&x, scale.into()))
+                LookupOp::PowersOfTwo { scale } => {
+                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ipow2(&x, scale.0.into()))
                 }
-                LookupOp::Round { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::round(&x, scale.into()))
-                }
-                LookupOp::RoundHalfToEven { scale } => Ok::<_, TensorError>(
-                    tensor::ops::nonlinearities::round_half_to_even(&x, scale.into()),
-                ),
+                LookupOp::IsOdd => Ok::<_, TensorError>(tensor::ops::nonlinearities::is_odd(&x)),
                 LookupOp::Pow { scale, a } => Ok::<_, TensorError>(
                     tensor::ops::nonlinearities::pow(&x, scale.0.into(), a.0.into()),
                 ),
-                LookupOp::KroneckerDelta => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::kronecker_delta(&x))
-                }
-                LookupOp::Max { scale, a } => Ok::<_, TensorError>(
-                    tensor::ops::nonlinearities::max(&x, scale.0.into(), a.0.into()),
-                ),
-                LookupOp::Min { scale, a } => Ok::<_, TensorError>(
-                    tensor::ops::nonlinearities::min(&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::Recip {
-                    input_scale,
-                    output_scale,
-                } => Ok::<_, TensorError>(tensor::ops::nonlinearities::recip(
-                    &x,
-                    input_scale.into(),
-                    output_scale.into(),
-                )),
-                LookupOp::LeakyReLU { slope: a } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::leakyrelu(&x, a.0.into()))
-                }
                 LookupOp::Sigmoid { scale } => {
                     Ok::<_, TensorError>(tensor::ops::nonlinearities::sigmoid(&x, scale.into()))
                 }
@@ -224,9 +112,6 @@ impl LookupOp {
                 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()))
                 }
@@ -283,25 +168,11 @@ 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::Ceil { scale } => format!("CEIL(scale={})", scale),
-            LookupOp::Floor { scale } => format!("FLOOR(scale={})", scale),
-            LookupOp::Round { scale } => format!("ROUND(scale={})", scale),
-            LookupOp::RoundHalfToEven { scale } => format!("ROUND_HALF_TO_EVEN(scale={})", scale),
-            LookupOp::Pow { a, scale } => format!("POW(scale={}, exponent={})", scale, a),
-            LookupOp::KroneckerDelta => "K_DELTA".into(),
-            LookupOp::Max { scale, a } => format!("MAX(scale={}, a={})", scale, a),
-            LookupOp::Min { scale, a } => format!("MIN(scale={}, a={})", scale, a),
-            LookupOp::Recip {
-                input_scale,
-                output_scale,
-            } => format!(
-                "RECIP(input_scale={}, output_scale={})",
-                input_scale, output_scale
-            ),
-            LookupOp::Div { denom, .. } => format!("DIV(denom={})", denom),
-            LookupOp::Cast { scale } => format!("CAST(scale={})", scale),
             LookupOp::Ln { scale } => format!("LN(scale={})", scale),
-            LookupOp::LeakyReLU { slope: a } => format!("L_RELU(slope={})", a),
+            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::Sigmoid { scale } => format!("SIGMOID(scale={})", scale),
             LookupOp::Sqrt { scale } => format!("SQRT(scale={})", scale),
             LookupOp::Erf { scale } => format!("ERF(scale={})", scale),
@@ -340,12 +211,6 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Lookup
     /// Returns the scale of the output of the operation.
     fn out_scale(&self, inputs_scale: Vec<crate::Scale>) -> Result<crate::Scale, CircuitError> {
         let scale = match self {
-            LookupOp::Cast { scale } => {
-                let in_scale = inputs_scale[0];
-                in_scale + multiplier_to_scale(1. / scale.0 as f64)
-            }
-            LookupOp::Recip { output_scale, .. } => multiplier_to_scale(output_scale.into()),
-            LookupOp::KroneckerDelta => 0,
             _ => inputs_scale[0],
         };
         Ok(scale)

src/circuit/ops/poly.rs

@@ -1,5 +1,8 @@
 use crate::{
-    circuit::layouts,
+    circuit::{
+        layouts,
+        utils::{self, F32},
+    },
     tensor::{self, Tensor, TensorError},
 };
 
@@ -9,9 +12,12 @@ use super::{base::BaseOp, *};
 /// An enum representing the operations that can be expressed as arithmetic (non lookup) operations.
 #[derive(Clone, Debug, Serialize, Deserialize)]
 pub enum PolyOp {
-    ReLU,
     Abs,
     Sign,
+    LeakyReLU {
+        slope: utils::F32,
+        scale: i32,
+    },
     GatherElements {
         dim: usize,
         constant_idx: Option<Tensor<usize>>,
@@ -112,9 +118,9 @@ impl<
 
     fn as_string(&self) -> String {
         match &self {
+            PolyOp::LeakyReLU { slope: a, .. } => format!("LEAKYRELU (slope={})", a),
             PolyOp::Abs => "ABS".to_string(),
             PolyOp::Sign => "SIGN".to_string(),
-            PolyOp::ReLU => "RELU".to_string(),
             PolyOp::GatherElements { dim, constant_idx } => format!(
                 "GATHERELEMENTS (dim={}, constant_idx{})",
                 dim,
@@ -198,7 +204,9 @@ impl<
         Ok(Some(match self {
             PolyOp::Abs => layouts::abs(config, region, values[..].try_into()?)?,
             PolyOp::Sign => layouts::sign(config, region, values[..].try_into()?)?,
-            PolyOp::ReLU => layouts::relu(config, region, values[..].try_into()?)?,
+            PolyOp::LeakyReLU { slope, scale } => {
+                layouts::leaky_relu(config, region, values[..].try_into()?, slope, scale)?
+            }
             PolyOp::MultiBroadcastTo { shape } => {
                 layouts::expand(config, region, values[..].try_into()?, shape)?
             }
@@ -329,6 +337,12 @@ impl<
 
     fn out_scale(&self, in_scales: Vec<crate::Scale>) -> Result<crate::Scale, CircuitError> {
         let scale = match self {
+            // this corresponds to the relu operation
+            PolyOp::LeakyReLU {
+                slope: F32(0.0), ..
+            } => in_scales[0],
+            // this corresponds to the leaky relu operation with a slope which induces a change in scale
+            PolyOp::LeakyReLU { scale, .. } => in_scales[0] + *scale,
             PolyOp::MeanOfSquares { .. } => 2 * in_scales[0],
             PolyOp::Xor | PolyOp::Or | PolyOp::And | PolyOp::Not => 0,
             PolyOp::Iff => in_scales[1],

src/circuit/ops/region.rs

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

src/circuit/table.rs

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

src/circuit/tests.rs

@@ -1379,7 +1379,10 @@ mod conv_relu_col_ultra_overflow {
                             .layout(
                                 &mut region,
                                 &[output.unwrap().unwrap()],
-                                Box::new(PolyOp::ReLU),
+                                Box::new(PolyOp::LeakyReLU {
+                                    slope: 0.0.into(),
+                                    scale: 1,
+                                }),
                             )
                             .unwrap();
                         Ok(())
@@ -2347,7 +2350,14 @@ mod matmul_relu {
                         .unwrap();
                     let _output = config
                         .base_config
-                        .layout(&mut region, &[output.unwrap()], Box::new(PolyOp::ReLU))
+                        .layout(
+                            &mut region,
+                            &[output.unwrap()],
+                            Box::new(PolyOp::LeakyReLU {
+                                slope: 0.0.into(),
+                                scale: 1,
+                            }),
+                        )
                         .unwrap();
                     Ok(())
                 },
@@ -2439,7 +2449,14 @@ mod relu {
                     |region| {
                         let mut region = RegionCtx::new(region, 0, 1, 2, 2);
                         Ok(config
-                            .layout(&mut region, &[self.input.clone()], Box::new(PolyOp::ReLU))
+                            .layout(
+                                &mut region,
+                                &[self.input.clone()],
+                                Box::new(PolyOp::LeakyReLU {
+                                    slope: 0.0.into(),
+                                    scale: 1,
+                                }),
+                            )
                             .unwrap())
                     },
                 )
@@ -2482,11 +2499,11 @@ mod lookup_ultra_overflow {
     use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 
     #[derive(Clone)]
-    struct ReLUCircuit<F: PrimeField + TensorType + PartialOrd> {
+    struct SigmoidCircuit<F: PrimeField + TensorType + PartialOrd> {
         pub input: ValTensor<F>,
     }
 
-    impl Circuit<F> for ReLUCircuit<F> {
+    impl Circuit<F> for SigmoidCircuit<F> {
         type Config = BaseConfig<F>;
         type FloorPlanner = SimpleFloorPlanner;
         type Params = TestParams;
@@ -2500,7 +2517,7 @@ mod lookup_ultra_overflow {
                 .map(|_| VarTensor::new_advice(cs, 4, 1, 3))
                 .collect::<Vec<_>>();
 
-            let nl = LookupOp::LeakyReLU { slope: 0.0.into() };
+            let nl = LookupOp::Sigmoid { scale: 1.0.into() };
 
             let mut config = BaseConfig::default();
 
@@ -2533,7 +2550,7 @@ mod lookup_ultra_overflow {
                             .layout(
                                 &mut region,
                                 &[self.input.clone()],
-                                Box::new(LookupOp::LeakyReLU { slope: 0.0.into() }),
+                                Box::new(LookupOp::Sigmoid { scale: 1.0.into() }),
                             )
                             .map_err(|_| Error::Synthesis)
                     },
@@ -2546,13 +2563,13 @@ mod lookup_ultra_overflow {
 
     #[test]
     #[ignore]
-    fn relucircuit() {
+    fn sigmoidcircuit() {
         // get some logs fam
         crate::logger::init_logger();
         // parameters
         let a = Tensor::from((0..4).map(|i| Value::known(F::from(i + 1))));
 
-        let circuit = ReLUCircuit::<F> {
+        let circuit = SigmoidCircuit::<F> {
             input: ValTensor::from(a),
         };
 
@@ -2562,7 +2579,7 @@ mod lookup_ultra_overflow {
 
         let pk = crate::pfsys::create_keys::<
             halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme<halo2curves::bn256::Bn256>,
-            ReLUCircuit<F>,
+            SigmoidCircuit<F>,
         >(&circuit, &params, true)
         .unwrap();
 

src/commands.rs

@@ -508,7 +508,7 @@ pub enum Commands {
         /// Gets an SRS from a circuit settings file.
     #[command(name = "get-srs")]
     GetSrs {
-        /// The path to output the desired srs file, if set to None will save to $EZKL_REPO_PATH/srs
+        /// The path to output the desired srs file, if set to None will save to ~/.ezkl/srs
         #[arg(long, default_value = None, value_hint = clap::ValueHint::FilePath)]
         srs_path: Option<PathBuf>,
         /// Path to the circuit settings .json file to read in logrows from. Overriden by logrows if specified.
@@ -555,7 +555,7 @@ pub enum Commands {
         /// The path to save the proving key to
         #[arg(long, default_value = DEFAULT_PK_AGGREGATED, value_hint = clap::ValueHint::FilePath)]
         pk_path: Option<PathBuf>,
-        /// The path to SRS, if None will use $EZKL_REPO_PATH/srs/kzg{logrows}.srs
+        /// The path to SRS, if None will use ~/.ezkl/srs/kzg{logrows}.srs
         #[arg(long, value_hint = clap::ValueHint::FilePath)]
         srs_path: Option<PathBuf>,
         /// logrows used for aggregation circuit
@@ -582,7 +582,7 @@ pub enum Commands {
         /// The path to output the proof file to
         #[arg(long, default_value = DEFAULT_PROOF_AGGREGATED, value_hint = clap::ValueHint::FilePath)]
         proof_path: Option<PathBuf>,
-        /// The path to SRS, if None will use $EZKL_REPO_PATH/srs/kzg{logrows}.srs
+        /// The path to SRS, if None will use ~/.ezkl/srs/kzg{logrows}.srs
         #[arg(long)]
         srs_path: Option<PathBuf>,
         #[arg(
@@ -624,7 +624,7 @@ pub enum Commands {
         /// The path to the compiled model file (generated using the compile-circuit command)
         #[arg(short = 'M', long, default_value = DEFAULT_COMPILED_CIRCUIT, value_hint = clap::ValueHint::FilePath)]
         compiled_circuit: Option<PathBuf>,
-        /// The path to SRS, if None will use $EZKL_REPO_PATH/srs/kzg{logrows}.srs
+        /// The path to SRS, if None will use ~/.ezkl/srs/kzg{logrows}.srs
         #[arg(long, value_hint = clap::ValueHint::FilePath)]
         srs_path: Option<PathBuf>,
         /// The path to output the verification key file to
@@ -701,7 +701,7 @@ pub enum Commands {
         /// The path to output the proof file to
         #[arg(long, default_value = DEFAULT_PROOF, value_hint = clap::ValueHint::FilePath)]
         proof_path: Option<PathBuf>,
-        /// The path to SRS, if None will use $EZKL_REPO_PATH/srs/kzg{logrows}.srs
+        /// The path to SRS, if None will use ~/.ezkl/srs/kzg{logrows}.srs
         #[arg(long, value_hint = clap::ValueHint::FilePath)]
         srs_path: Option<PathBuf>,
         #[arg(
@@ -733,7 +733,7 @@ pub enum Commands {
         /// Creates an Evm verifier for a single proof
     #[command(name = "create-evm-verifier")]
     CreateEvmVerifier {
-        /// The path to SRS, if None will use $EZKL_REPO_PATH/srs/kzg{logrows}.srs
+        /// The path to SRS, if None will use ~/.ezkl/srs/kzg{logrows}.srs
         #[arg(long, value_hint = clap::ValueHint::FilePath)]
         srs_path: Option<PathBuf>,
         /// The path to load circuit settings .json file from (generated using the gen-settings command)
@@ -755,7 +755,7 @@ pub enum Commands {
         /// Creates an Evm verifier artifact for a single proof to be used by the reusable verifier
     #[command(name = "create-evm-vka")]
     CreateEvmVKArtifact {
-        /// The path to SRS, if None will use $EZKL_REPO_PATH/srs/kzg{logrows}.srs
+        /// The path to SRS, if None will use ~/.ezkl/srs/kzg{logrows}.srs
         #[arg(long, value_hint = clap::ValueHint::FilePath)]
         srs_path: Option<PathBuf>,
         /// The path to load circuit settings .json file from (generated using the gen-settings command)
@@ -798,7 +798,7 @@ pub enum Commands {
         /// Creates an Evm verifier for an aggregate proof
     #[command(name = "create-evm-verifier-aggr")]
     CreateEvmVerifierAggr {
-        /// The path to SRS, if None will use $EZKL_REPO_PATH/srs/kzg{logrows}.srs
+        /// The path to SRS, if None will use ~/.ezkl/srs/kzg{logrows}.srs
         #[arg(long, value_hint = clap::ValueHint::FilePath)]
         srs_path: Option<PathBuf>,
         /// The path to load the desired verification key file
@@ -831,7 +831,7 @@ pub enum Commands {
         /// The path to the verification key file (generated using the setup command)
         #[arg(long, default_value = DEFAULT_VK, value_hint = clap::ValueHint::FilePath)]
         vk_path: Option<PathBuf>,
-        /// The path to SRS, if None will use $EZKL_REPO_PATH/srs/kzg{logrows}.srs
+        /// The path to SRS, if None will use ~/.ezkl/srs/kzg{logrows}.srs
         #[arg(long, value_hint = clap::ValueHint::FilePath)]
         srs_path: Option<PathBuf>,
         /// Reduce SRS logrows to the number of instances rather than the number of logrows used for proofs (only works if the srs were generated in the same ceremony)
@@ -849,7 +849,7 @@ pub enum Commands {
         /// reduced srs
         #[arg(long, default_value = DEFAULT_USE_REDUCED_SRS_FOR_VERIFICATION, action = clap::ArgAction::SetTrue)]
         reduced_srs: Option<bool>,
-        /// The path to SRS, if None will use $EZKL_REPO_PATH/srs/kzg{logrows}.srs
+        /// The path to SRS, if None will use ~/.ezkl/srs/kzg{logrows}.srs
         #[arg(long, value_hint = clap::ValueHint::FilePath)]
         srs_path: Option<PathBuf>,
         /// logrows used for aggregation circuit

src/execute.rs

@@ -677,11 +677,12 @@ pub(crate) async fn get_srs_cmd(
                 pb.finish_with_message("SRS validated.");
 
             info!("Saving SRS to disk...");
-            let mut file = std::fs::File::create(get_srs_path(k, srs_path.clone(), commitment))?;
+            let computed_srs_path = get_srs_path(k, srs_path.clone(), commitment);
+            let mut file = std::fs::File::create(&computed_srs_path)?;
             let mut buffer = BufWriter::with_capacity(*EZKL_BUF_CAPACITY, &mut file);
             params.write(&mut buffer)?;
 
-            info!("Saved SRS to disk.");
+            info!("Saved SRS to {}.", computed_srs_path.as_os_str().to_str().unwrap_or("disk"));
 
             info!("SRS downloaded");
         } else {

src/graph/utilities.rs

@@ -763,93 +763,52 @@ pub fn new_op_from_onnx(
                 .map(|(i, _)| i)
                 .collect::<Vec<_>>();
 
-            if const_inputs.len() != 1 {
-                return Err(GraphError::OpMismatch(idx, "Max".to_string()));
-            }
-
-            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) {
-                if c.len() == 1 {
-                    c[0]
-                } else {
-                    return Err(GraphError::InvalidDims(idx, "max".to_string()));
-                }
-            } else {
-                return Err(GraphError::OpMismatch(idx, "Max".to_string()));
-            };
-
             if inputs.len() == 2 {
-                if let Some(node) = inputs.get_mut(const_idx) {
-                    node.decrement_use();
-                    deleted_indices.push(const_idx);
-                }
-                if unit == 0. {
-                    SupportedOp::Linear(PolyOp::ReLU)
+                if const_inputs.len() > 0 {
+                    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) {
+                        if c.len() == 1 {
+                            c[0]
+                        } else {
+                            return Err(GraphError::InvalidDims(idx, "max".to_string()));
+                        }
+                    } else {
+                        return Err(GraphError::OpMismatch(idx, "Max".to_string()));
+                    };
+                    if unit == 0. {
+                        if let Some(node) = inputs.get_mut(const_idx) {
+                            node.decrement_use();
+                            deleted_indices.push(const_idx);
+                        }
+                        SupportedOp::Linear(PolyOp::LeakyReLU {
+                            slope: 0.0.into(),
+                            scale: 1,
+                        })
+                    } else {
+                        SupportedOp::Hybrid(HybridOp::Max)
+                    }
                 } else {
-                    // get the non-constant index
-                    let non_const_idx = if const_idx == 0 { 1 } else { 0 };
-                    SupportedOp::Nonlinear(LookupOp::Max {
-                        scale: scale_to_multiplier(inputs[non_const_idx].out_scales()[0]).into(),
-                        a: crate::circuit::utils::F32(unit),
-                    })
+                    SupportedOp::Hybrid(HybridOp::Max)
                 }
             } else {
                 return Err(GraphError::InvalidDims(idx, "max".to_string()));
             }
         }
         "Min" => {
-            // Extract the min value
-            // first find the input that is a constant
-            // and then extract the value
-            let const_inputs = inputs
-                .iter()
-                .enumerate()
-                .filter(|(_, n)| n.is_constant())
-                .map(|(i, _)| i)
-                .collect::<Vec<_>>();
-
-            if const_inputs.len() != 1 {
-                return Err(GraphError::OpMismatch(idx, "Min".to_string()));
-            }
-
-            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) {
-                if c.len() == 1 {
-                    c[0]
-                } else {
-                    return Err(GraphError::InvalidDims(idx, "min".to_string()));
-                }
-            } else {
-                return Err(GraphError::OpMismatch(idx, "Min".to_string()));
-            };
-
             if inputs.len() == 2 {
-                if let Some(node) = inputs.get_mut(const_idx) {
-                    node.decrement_use();
-                    deleted_indices.push(const_idx);
-                }
-
-                // get the non-constant index
-                let non_const_idx = if const_idx == 0 { 1 } else { 0 };
-
-                SupportedOp::Nonlinear(LookupOp::Min {
-                    scale: scale_to_multiplier(inputs[non_const_idx].out_scales()[0]).into(),
-                    a: crate::circuit::utils::F32(unit),
-                })
+                SupportedOp::Hybrid(HybridOp::Min)
             } else {
                 return Err(GraphError::InvalidDims(idx, "min".to_string()));
             }
         }
         "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 {
                 input_scale: (scale_to_multiplier(in_scale) as f32).into(),
                 output_scale: (scale_to_multiplier(max_scale) as f32).into(),
-                use_range_check_for_int: true,
             })
         }
 
@@ -864,8 +823,9 @@ pub fn new_op_from_onnx(
                 }
             };
 
-            SupportedOp::Nonlinear(LookupOp::LeakyReLU {
+            SupportedOp::Linear(PolyOp::LeakyReLU {
                 slope: crate::circuit::utils::F32(leaky_op.alpha),
+                scale: scales.params,
             })
         }
         "Scan" => {
@@ -877,61 +837,70 @@ pub fn new_op_from_onnx(
         "Abs" => SupportedOp::Linear(PolyOp::Abs),
         "Neg" => SupportedOp::Linear(PolyOp::Neg),
         "HardSwish" => SupportedOp::Nonlinear(LookupOp::HardSwish {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Sigmoid" => SupportedOp::Nonlinear(LookupOp::Sigmoid {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Sqrt" => SupportedOp::Nonlinear(LookupOp::Sqrt {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Rsqrt" => SupportedOp::Nonlinear(LookupOp::Rsqrt {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Exp" => SupportedOp::Nonlinear(LookupOp::Exp {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
-        }),
-        "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;
@@ -975,11 +944,9 @@ 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 {
@@ -1085,7 +1052,7 @@ 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 {
@@ -1123,17 +1090,21 @@ pub fn new_op_from_onnx(
                 pool_dims: kernel_shape.to_vec(),
             })
         }
-        "Ceil" => SupportedOp::Nonlinear(LookupOp::Ceil {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+        "Ceil" => SupportedOp::Hybrid(HybridOp::Ceil {
+            scale: scale_to_multiplier(input_scales[0]).into(),
+            legs: run_args.decomp_legs,
         }),
-        "Floor" => SupportedOp::Nonlinear(LookupOp::Floor {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+        "Floor" => SupportedOp::Hybrid(HybridOp::Floor {
+            scale: scale_to_multiplier(input_scales[0]).into(),
+            legs: run_args.decomp_legs,
         }),
-        "Round" => SupportedOp::Nonlinear(LookupOp::Round {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+        "Round" => SupportedOp::Hybrid(HybridOp::Round {
+            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" => {
@@ -1146,10 +1117,17 @@ pub fn new_op_from_onnx(
                 if c.raw_values.len() > 1 {
                     unimplemented!("only support scalar pow")
                 }
-                SupportedOp::Nonlinear(LookupOp::Pow {
-                    scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
-                    a: crate::circuit::utils::F32(c.raw_values[0]),
-                })
+
+                let exponent = c.raw_values[0];
+
+                if exponent.fract() == 0.0 {
+                    SupportedOp::Linear(PolyOp::Pow(exponent as u32))
+                } else {
+                    SupportedOp::Nonlinear(LookupOp::Pow {
+                        scale: scale_to_multiplier(input_scales[0]).into(),
+                        a: crate::circuit::utils::F32(exponent),
+                    })
+                }
             } else {
                 unimplemented!("only support constant pow for now")
             }

src/lib.rs

@@ -317,11 +317,18 @@ 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,

src/tensor/ops.rs

@@ -27,7 +27,7 @@ pub fn get_rep(
     n: usize,
 ) -> Result<Vec<IntegerRep>, DecompositionError> {
     // check if x is too large
-    if x.abs() > (base.pow(n as u32) as IntegerRep) {
+    if x.abs() > (base.pow(n as u32) as IntegerRep) - 1 {
         return Err(DecompositionError::TooLarge(*x, base, n));
     }
     let mut rep = vec![0; n + 1];
@@ -1421,85 +1421,6 @@ pub fn slice<T: TensorType + Send + Sync>(
 pub mod nonlinearities {
     use super::*;
 
-    /// Ceiling operator.
-    /// # Arguments
-    /// * `a` - Tensor
-    /// * `scale` - Single value
-    /// # Examples
-    /// ```
-    /// use ezkl::tensor::Tensor;
-    /// use ezkl::fieldutils::IntegerRep;
-    ///
-    /// use ezkl::tensor::ops::nonlinearities::ceil;
-    /// let x = Tensor::<IntegerRep>::new(
-    ///    Some(&[1, 2, 3, 4, 5, 6]),
-    ///  &[3, 2],
-    /// ).unwrap();
-    /// let result = ceil(&x, 2.0);
-    /// let expected = Tensor::<IntegerRep>::new(Some(&[2, 2, 4, 4, 6, 6]), &[3, 2]).unwrap();
-    /// assert_eq!(result, expected);
-    /// ```
-    pub fn ceil(a: &Tensor<IntegerRep>, scale: f64) -> Tensor<IntegerRep> {
-        a.par_enum_map(|_, a_i| {
-            let kix = (a_i as f64) / scale;
-            let rounded = kix.ceil() * scale;
-            Ok::<_, TensorError>(rounded as IntegerRep)
-        })
-        .unwrap()
-    }
-
-    /// Floor operator.
-    /// # Arguments
-    /// * `a` - Tensor
-    /// * `scale` - Single value
-    /// # Examples
-    /// ```
-    /// use ezkl::tensor::Tensor;
-    /// use ezkl::fieldutils::IntegerRep;
-    /// use ezkl::tensor::ops::nonlinearities::floor;
-    /// let x = Tensor::<IntegerRep>::new(
-    ///   Some(&[1, 2, 3, 4, 5, 6]),
-    ///  &[3, 2],
-    /// ).unwrap();
-    /// let result = floor(&x, 2.0);
-    /// let expected = Tensor::<IntegerRep>::new(Some(&[0, 2, 2, 4, 4, 6]), &[3, 2]).unwrap();
-    /// assert_eq!(result, expected);
-    /// ```
-    pub fn floor(a: &Tensor<IntegerRep>, scale: f64) -> Tensor<IntegerRep> {
-        a.par_enum_map(|_, a_i| {
-            let kix = (a_i as f64) / scale;
-            let rounded = kix.floor() * scale;
-            Ok::<_, TensorError>(rounded as IntegerRep)
-        })
-        .unwrap()
-    }
-
-    /// Round operator.
-    /// # Arguments
-    /// * `a` - Tensor
-    /// * `scale` - Single value
-    /// # Examples
-    /// ```
-    /// use ezkl::tensor::Tensor;
-    /// use ezkl::fieldutils::IntegerRep;
-    /// use ezkl::tensor::ops::nonlinearities::round;
-    /// let x = Tensor::<IntegerRep>::new(
-    ///   Some(&[1, 2, 3, 4, 5, 6]),
-    /// &[3, 2],
-    /// ).unwrap();
-    /// let result = round(&x, 2.0);
-    /// let expected = Tensor::<IntegerRep>::new(Some(&[2, 2, 4, 4, 6, 6]), &[3, 2]).unwrap();
-    /// assert_eq!(result, expected);
-    /// ```
-    pub fn round(a: &Tensor<IntegerRep>, scale: f64) -> Tensor<IntegerRep> {
-        a.par_enum_map(|_, a_i| {
-            let kix = (a_i as f64) / scale;
-            let rounded = kix.round() * scale;
-            Ok::<_, TensorError>(rounded as IntegerRep)
-        })
-        .unwrap()
-    }
-
     /// Round half to even operator.
     /// # Arguments
     /// * `a` - Tensor
@@ -1553,31 +1474,81 @@ pub mod nonlinearities {
         .unwrap()
     }
 
-    /// Applies Kronecker delta to a tensor of integers.
+    /// 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::kronecker_delta;
+    /// use ezkl::tensor::ops::nonlinearities::is_odd;
     /// let x = Tensor::<IntegerRep>::new(
-    ///    Some(&[2, 15, 2, 1, 1, 0]),
-    ///  &[2, 3],
+    ///   Some(&[2, 15, 2, 1, 1, 0]),
+    /// &[2, 3],
     /// ).unwrap();
-    /// let result = kronecker_delta(&x);
-    /// let expected = Tensor::<IntegerRep>::new(Some(&[0, 0, 0, 0, 0, 1]), &[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 kronecker_delta<T: TensorType + std::cmp::PartialEq + Send + Sync>(
-        a: &Tensor<T>,
-    ) -> Tensor<T> {
+    pub fn is_odd(a: &Tensor<IntegerRep>) -> Tensor<IntegerRep> {
         a.par_enum_map(|_, a_i| {
-            if a_i == T::zero().unwrap() {
-                Ok::<_, TensorError>(T::one().unwrap())
-            } else {
-                Ok::<_, TensorError>(T::zero().unwrap())
-            }
+            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 kix = (kix).log2();
+            let rounded = kix.round();
+            Ok::<_, TensorError>(rounded as IntegerRep)
         })
         .unwrap()
     }
@@ -1710,12 +1681,11 @@ 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;
@@ -1750,27 +1720,6 @@ pub mod nonlinearities {
         .unwrap()
     }
 
-    /// Elementwise applies sign to a tensor of integers.
-    /// # Arguments
-    /// * `a` - Tensor
-    /// # Examples
-    /// ```
-    /// use ezkl::tensor::Tensor;
-    /// use ezkl::fieldutils::IntegerRep;
-    /// use ezkl::tensor::ops::nonlinearities::sign;
-    /// let x = Tensor::<IntegerRep>::new(
-    ///    Some(&[-2, 15, 2, 1, 1, 0]),
-    ///  &[2, 3],
-    /// ).unwrap();
-    /// let result = sign(&x);
-    /// let expected = Tensor::<IntegerRep>::new(Some(&[-1, 1, 1, 1, 1, 0]), &[2, 3]).unwrap();
-    /// assert_eq!(result, expected);
-    /// ```
-    pub fn sign(a: &Tensor<IntegerRep>) -> Tensor<IntegerRep> {
-        a.par_enum_map(|_, a_i| Ok::<_, TensorError>(a_i.signum()))
-            .unwrap()
-    }
-
     /// Elementwise applies square root to a tensor of integers.
     /// # Arguments
     ///
@@ -2254,101 +2203,6 @@ pub mod nonlinearities {
         .unwrap()
     }
 
-    /// Elementwise applies leaky relu to a tensor of integers.
-    /// # Arguments
-    ///
-    /// * `a` - Tensor
-    /// * `scale` - Single value
-    /// * `slope` - Single value
-    /// # Examples
-    /// ```
-    /// use ezkl::tensor::Tensor;
-    /// use ezkl::fieldutils::IntegerRep;
-    /// use ezkl::tensor::ops::nonlinearities::leakyrelu;
-    /// let x = Tensor::<IntegerRep>::new(
-    ///     Some(&[2, 15, 2, 1, 1, -5]),
-    ///     &[2, 3],
-    /// ).unwrap();
-    /// let result = leakyrelu(&x, 0.1);
-    /// let expected = Tensor::<IntegerRep>::new(Some(&[2, 15, 2, 1, 1, -1]), &[2, 3]).unwrap();
-    /// assert_eq!(result, expected);
-    /// ```
-    pub fn leakyrelu(a: &Tensor<IntegerRep>, slope: f64) -> Tensor<IntegerRep> {
-        a.par_enum_map(|_, a_i| {
-            let rounded = if a_i < 0 {
-                let d_inv_x = (slope) * (a_i as f64);
-                d_inv_x.round() as IntegerRep
-            } else {
-                let d_inv_x = a_i as f64;
-                d_inv_x.round() as IntegerRep
-            };
-            Ok::<_, TensorError>(rounded)
-        })
-        .unwrap()
-    }
-
-    /// Elementwise applies max to a tensor of integers.
-    /// # Arguments
-    /// * `a` - Tensor
-    /// * `b` - scalar
-    /// # Examples
-    /// ```
-    /// use ezkl::tensor::Tensor;
-    /// use ezkl::fieldutils::IntegerRep;
-    /// use ezkl::tensor::ops::nonlinearities::max;
-    /// let x = Tensor::<IntegerRep>::new(
-    ///    Some(&[2, 15, 2, 1, 1, -5]),
-    ///   &[2, 3],
-    /// ).unwrap();
-    /// let result = max(&x, 1.0, 1.0);
-    /// let expected = Tensor::<IntegerRep>::new(Some(&[2, 15, 2, 1, 1, 1]), &[2, 3]).unwrap();
-    /// assert_eq!(result, expected);
-    /// ```
-    pub fn max(a: &Tensor<IntegerRep>, scale_input: f64, threshold: f64) -> Tensor<IntegerRep> {
-        // calculate value of output
-        a.par_enum_map(|_, a_i| {
-            let d_inv_x = (a_i as f64) / scale_input;
-            let rounded = if d_inv_x <= threshold {
-                (threshold * scale_input).round() as IntegerRep
-            } else {
-                (d_inv_x * scale_input).round() as IntegerRep
-            };
-            Ok::<_, TensorError>(rounded)
-        })
-        .unwrap()
-    }
-
-    /// Elementwise applies min to a tensor of integers.
-    /// # Arguments
-    /// * `a` - Tensor
-    /// * `b` - scalar
-    /// # Examples
-    /// ```
-    /// use ezkl::tensor::Tensor;
-    /// use ezkl::fieldutils::IntegerRep;
-    /// use ezkl::tensor::ops::nonlinearities::min;
-    /// let x = Tensor::<IntegerRep>::new(
-    ///    Some(&[2, 15, 2, 1, 1, -5]),
-    ///   &[2, 3],
-    /// ).unwrap();
-    /// let result = min(&x, 1.0, 2.0);
-    /// let expected = Tensor::<IntegerRep>::new(Some(&[2, 2, 2, 1, 1, -5]), &[2, 3]).unwrap();
-    /// assert_eq!(result, expected);
-    /// ```
-    pub fn min(a: &Tensor<IntegerRep>, scale_input: f64, threshold: f64) -> Tensor<IntegerRep> {
-        // calculate value of output
-        a.par_enum_map(|_, a_i| {
-            let d_inv_x = (a_i as f64) / scale_input;
-            let rounded = if d_inv_x >= threshold {
-                (threshold * scale_input).round() as IntegerRep
-            } else {
-                (d_inv_x * scale_input).round() as IntegerRep
-            };
-            Ok::<_, TensorError>(rounded)
-        })
-        .unwrap()
-    }
-
     /// Elementwise divides a tensor with a const integer element.
     /// # Arguments
     ///
@@ -2429,104 +2283,6 @@ pub mod nonlinearities {
         })
         .unwrap()
     }
-
-    /// Elementwise greater than
-    /// # Arguments
-    ///
-    /// * `a` - Tensor
-    /// * `b` - Single value
-    /// # Examples
-    /// ```
-    /// use ezkl::tensor::Tensor;
-    /// use ezkl::fieldutils::IntegerRep;
-    /// use ezkl::tensor::ops::nonlinearities::greater_than;
-    /// let x = Tensor::<IntegerRep>::new(
-    ///     Some(&[2, 1, 2, 7, 1, 1]),
-    ///     &[2, 3],
-    /// ).unwrap();
-    /// let k = 2.0;
-    /// let result = greater_than(&x, k);
-    /// let expected = Tensor::<IntegerRep>::new(Some(&[0, 0, 0, 1, 0, 0]), &[2, 3]).unwrap();
-    /// assert_eq!(result, expected);
-    /// ```
-    pub fn greater_than(a: &Tensor<IntegerRep>, b: f64) -> Tensor<IntegerRep> {
-        a.par_enum_map(|_, a_i| Ok::<_, TensorError>(IntegerRep::from((a_i as f64 - b) > 0_f64)))
-            .unwrap()
-    }
-
-    /// Elementwise greater than
-    /// # Arguments
-    ///
-    /// * `a` - Tensor
-    /// * `b` - Single value
-    /// # Examples
-    /// ```
-    /// use ezkl::tensor::Tensor;
-    /// use ezkl::fieldutils::IntegerRep;
-    /// use ezkl::tensor::ops::nonlinearities::greater_than_equal;
-    /// let x = Tensor::<IntegerRep>::new(
-    ///     Some(&[2, 1, 2, 7, 1, 1]),
-    ///     &[2, 3],
-    /// ).unwrap();
-    /// let k = 2.0;
-    /// let result = greater_than_equal(&x, k);
-    /// let expected = Tensor::<IntegerRep>::new(Some(&[1, 0, 1, 1, 0, 0]), &[2, 3]).unwrap();
-    /// assert_eq!(result, expected);
-    /// ```
-    pub fn greater_than_equal(a: &Tensor<IntegerRep>, b: f64) -> Tensor<IntegerRep> {
-        a.par_enum_map(|_, a_i| Ok::<_, TensorError>(IntegerRep::from((a_i as f64 - b) >= 0_f64)))
-            .unwrap()
-    }
-
-    /// Elementwise less than
-    /// # Arguments
-    /// * `a` - Tensor
-    /// * `b` - Single value
-    /// # Examples
-    /// ```
-    /// use ezkl::tensor::Tensor;
-    /// use ezkl::fieldutils::IntegerRep;
-    /// use ezkl::tensor::ops::nonlinearities::less_than;
-    ///
-    /// let x = Tensor::<IntegerRep>::new(
-    ///    Some(&[2, 1, 2, 7, 1, 1]),
-    ///  &[2, 3],
-    /// ).unwrap();
-    /// let k = 2.0;
-    ///
-    /// let result = less_than(&x, k);
-    /// let expected = Tensor::<IntegerRep>::new(Some(&[0, 1, 0, 0, 1, 1]), &[2, 3]).unwrap();
-    /// assert_eq!(result, expected);
-    /// ```
-    pub fn less_than(a: &Tensor<IntegerRep>, b: f64) -> Tensor<IntegerRep> {
-        a.par_enum_map(|_, a_i| Ok::<_, TensorError>(IntegerRep::from((a_i as f64 - b) < 0_f64)))
-            .unwrap()
-    }
-
-    /// Elementwise less than
-    /// # Arguments
-    /// * `a` - Tensor
-    /// * `b` - Single value
-    /// # Examples
-    /// ```
-    /// use ezkl::tensor::Tensor;
-    /// use ezkl::fieldutils::IntegerRep;
-    /// use ezkl::tensor::ops::nonlinearities::less_than_equal;
-    ///
-    /// let x = Tensor::<IntegerRep>::new(
-    ///    Some(&[2, 1, 2, 7, 1, 1]),
-    ///  &[2, 3],
-    /// ).unwrap();
-    /// let k = 2.0;
-    ///
-    /// let result = less_than_equal(&x, k);
-    /// let expected = Tensor::<IntegerRep>::new(Some(&[1, 1, 1, 0, 1, 1]), &[2, 3]).unwrap();
-    /// assert_eq!(result, expected);
-    /// ```
-    pub fn less_than_equal(a: &Tensor<IntegerRep>, b: f64) -> Tensor<IntegerRep> {
-        a.par_enum_map(|_, a_i| Ok::<_, TensorError>(IntegerRep::from((a_i as f64 - b) <= 0_f64)))
-            .unwrap()
-    }
 }
 
 /// Ops that return the transcript i.e intermediate calcs of an op

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,

tests/integration_tests.rs

@@ -205,7 +205,7 @@ mod native_tests {
         "1l_tiny_div",
     ];
 
-    const TESTS: [&str; 94] = [
+    const TESTS: [&str; 96] = [
         "1l_mlp", //0
         "1l_slice",
         "1l_concat",
@@ -304,6 +304,8 @@ mod native_tests {
         "lstm_large",  // 91
         "lstm_medium", // 92
         "lenet_5",     // 93
+        "rsqrt",       // 94
+        "log",         // 95
     ];
 
     const WASM_TESTS: [&str; 46] = [
@@ -542,7 +544,7 @@ mod native_tests {
             }
         });
 
-            seq!(N in 0..=93 {
+            seq!(N in 0..=95 {
 
             #(#[test_case(TESTS[N])])*
             #[ignore]
@@ -851,9 +853,11 @@ mod native_tests {
             fn kzg_prove_and_verify_tight_lookup_(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);
+                let path = test_dir.into_path();
+                let path = path.to_str().unwrap();
+                crate::native_tests::mv_test_(path, test);
                prove_and_verify(path, test.to_string(), "safe", "private", "private", "public", 1, None, false, "single", Commitments::KZG, 1);
-               test_dir.close().unwrap();
+            //    test_dir.close().unwrap();
             }
 
             #(#[test_case(TESTS[N])])*
@@ -1118,7 +1122,7 @@ mod native_tests {
 
             });
 
-            seq!(N in 0..=93 {
+            seq!(N in 0..4 {
                 #(#[test_case(TESTS[N])])*
                 fn kzg_evm_prove_and_verify_reusable_verifier_(test: &str) {
                     crate::native_tests::init_binary();
@@ -1631,7 +1635,6 @@ mod native_tests {
 
         let status = Command::new(format!("{}/release/ezkl", *CARGO_TARGET_DIR))
             .args(args)
-            .stdout(std::process::Stdio::null())
             .status()
             .expect("failed to execute process");
         assert!(status.success());

tests/py_integration_tests.rs

@@ -124,41 +124,40 @@ mod py_tests {
     }
 
     const TESTS: [&str; 34] = [
-        "ezkl_demo_batch.ipynb",
-        "proof_splitting.ipynb", // 0
-        "variance.ipynb",
-        "mnist_gan.ipynb",
-        // "mnist_vae.ipynb",
-        "keras_simple_demo.ipynb",
-        "mnist_gan_proof_splitting.ipynb", // 4
-        "hashed_vis.ipynb",                // 5
-        "simple_demo_all_public.ipynb",
-        "data_attest.ipynb",
-        "little_transformer.ipynb",
-        "simple_demo_aggregated_proofs.ipynb",
-        "ezkl_demo.ipynb", // 10
-        "lstm.ipynb",
-        "set_membership.ipynb", // 12
-        "decision_tree.ipynb",
-        "random_forest.ipynb",
-        "gradient_boosted_trees.ipynb", // 15
-        "xgboost.ipynb",
-        "lightgbm.ipynb",
-        "svm.ipynb",
-        "simple_demo_public_input_output.ipynb",
-        "simple_demo_public_network_output.ipynb", // 20
-        "gcn.ipynb",
-        "linear_regression.ipynb",
-        "stacked_regression.ipynb",
-        "data_attest_hashed.ipynb",
-        "kzg_vis.ipynb", // 25
-        "kmeans.ipynb",
-        "solvency.ipynb",
-        "sklearn_mlp.ipynb",
-        "generalized_inverse.ipynb",
-        "mnist_classifier.ipynb", // 30
-        "world_rotation.ipynb",
-        "logistic_regression.ipynb",
+        "ezkl_demo_batch.ipynb",                   // 0
+        "proof_splitting.ipynb",                   // 1
+        "variance.ipynb",                          // 2
+        "mnist_gan.ipynb",                         // 3
+        "keras_simple_demo.ipynb",                 // 4
+        "mnist_gan_proof_splitting.ipynb",         // 5
+        "hashed_vis.ipynb",                        // 6
+        "simple_demo_all_public.ipynb",            // 7
+        "data_attest.ipynb",                       // 8
+        "little_transformer.ipynb",                // 9
+        "simple_demo_aggregated_proofs.ipynb",     // 10
+        "ezkl_demo.ipynb",                         // 11
+        "lstm.ipynb",                              // 12
+        "set_membership.ipynb",                    // 13
+        "decision_tree.ipynb",                     // 14
+        "random_forest.ipynb",                     // 15
+        "gradient_boosted_trees.ipynb",            // 16
+        "xgboost.ipynb",                           // 17
+        "lightgbm.ipynb",                          // 18
+        "svm.ipynb",                               // 19
+        "simple_demo_public_input_output.ipynb",   // 20
+        "simple_demo_public_network_output.ipynb", // 21
+        "gcn.ipynb",                               // 22
+        "linear_regression.ipynb",                 // 23
+        "stacked_regression.ipynb",                // 24
+        "data_attest_hashed.ipynb",                // 25
+        "kzg_vis.ipynb",                           // 26
+        "kmeans.ipynb",                            // 27
+        "solvency.ipynb",                          // 28
+        "sklearn_mlp.ipynb",                       // 29
+        "generalized_inverse.ipynb",               // 30
+        "mnist_classifier.ipynb",                  // 31
+        "world_rotation.ipynb",                    // 32
+        "logistic_regression.ipynb",               // 33
     ];
 
     macro_rules! test_func {