feat: lookupless sqrt and rsqrt (#867)

* 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

.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

Cargo.lock

@@ -2543,6 +2543,12 @@ dependencies = [
  "allocator-api2",
 ]
 
+[[package]]
+name = "hashbrown"
+version = "0.15.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1e087f84d4f86bf4b218b927129862374b72199ae7d8657835f1e89000eea4fb"
+
 [[package]]
 name = "heck"
 version = "0.4.1"
@@ -2811,12 +2817,12 @@ dependencies = [
 
 [[package]]
 name = "indexmap"
-version = "2.2.5"
+version = "2.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7b0b929d511467233429c45a44ac1dcaa21ba0f5ba11e4879e6ed28ddb4f9df4"
+checksum = "707907fe3c25f5424cce2cb7e1cbcafee6bdbe735ca90ef77c29e84591e5b9da"
 dependencies = [
  "equivalent",
- "hashbrown 0.14.3",
+ "hashbrown 0.15.0",
 ]
 
 [[package]]
@@ -5628,9 +5634,9 @@ checksum = "121c2a6cda46980bb0fcd1647ffaf6cd3fc79a013de288782836f6df9c48780e"
 
 [[package]]
 name = "tower-service"
-version = "0.3.2"
+version = "0.3.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b6bc1c9ce2b5135ac7f93c72918fc37feb872bdc6a5533a8b85eb4b86bfdae52"
+checksum = "8df9b6e13f2d32c91b9bd719c00d1958837bc7dec474d94952798cc8e69eeec3"
 
 [[package]]
 name = "tracing"

Cargo.toml

@@ -56,6 +56,7 @@ alloy = { git = "https://github.com/alloy-rs/alloy", version = "0.1.0", rev = "5
     "rpc-types-eth",
     "signer-wallet",
     "node-bindings",
+    
 ], optional = true }
 foundry-compilers = { version = "0.4.1", features = ["svm-solc"], optional = true }
 ethabi = { version = "18", optional = true }
@@ -168,7 +169,7 @@ harness = false
 
 
 [[bench]]
-name = "relu"
+name = "sigmoid"
 harness = false
 
 [[bench]]
@@ -176,12 +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": "3.12.7"
         },
         "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/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
-}
+}

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

@@ -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

src/circuit/ops/chip.rs

@@ -177,7 +177,7 @@ impl<'source> FromPyObject<'source> for Tolerance {
 #[derive(Clone, Debug, Default)]
 pub struct DynamicLookups {
     /// [Selector]s generated when configuring the layer. We use a [BTreeMap] as we expect to configure many dynamic lookup ops.
-    pub lookup_selectors: BTreeMap<(usize, usize), Selector>,
+    pub lookup_selectors: BTreeMap<(usize, (usize, usize)), Selector>,
     /// Selectors for the dynamic lookup tables
     pub table_selectors: Vec<Selector>,
     /// Inputs:
@@ -209,7 +209,7 @@ impl DynamicLookups {
 #[derive(Clone, Debug, Default)]
 pub struct Shuffles {
     /// [Selector]s generated when configuring the layer. We use a [BTreeMap] as we expect to configure many dynamic lookup ops.
-    pub input_selectors: BTreeMap<(usize, usize), Selector>,
+    pub input_selectors: BTreeMap<(usize, (usize, usize)), Selector>,
     /// Selectors for the dynamic lookup tables
     pub reference_selectors: Vec<Selector>,
     /// Inputs:
@@ -646,57 +646,73 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
         }
 
         for t in tables.iter() {
-            if !t.is_advice() || t.num_blocks() > 1 || t.num_inner_cols() > 1 {
+            if !t.is_advice() || t.num_inner_cols() > 1 {
                 return Err(CircuitError::WrongDynamicColumnType(t.name().to_string()));
             }
         }
 
+        // assert all tables have the same number of inner columns
+        if tables
+            .iter()
+            .map(|t| t.num_blocks())
+            .collect::<Vec<_>>()
+            .windows(2)
+            .any(|w| w[0] != w[1])
+        {
+            return Err(CircuitError::WrongDynamicColumnType(
+                "tables inner cols".to_string(),
+            ));
+        }
+
         let one = Expression::Constant(F::ONE);
 
-        let s_ltable = cs.complex_selector();
+        for q in 0..tables[0].num_blocks() {
+            let s_ltable = cs.complex_selector();
 
-        for x in 0..lookups[0].num_blocks() {
-            for y in 0..lookups[0].num_inner_cols() {
-                let s_lookup = cs.complex_selector();
+            for x in 0..lookups[0].num_blocks() {
+                for y in 0..lookups[0].num_inner_cols() {
+                    let s_lookup = cs.complex_selector();
 
-                cs.lookup_any("lookup", |cs| {
-                    let s_lookupq = cs.query_selector(s_lookup);
-                    let mut expression = vec![];
-                    let s_ltableq = cs.query_selector(s_ltable);
-                    let mut lookup_queries = vec![one.clone()];
+                    cs.lookup_any("lookup", |cs| {
+                        let s_lookupq = cs.query_selector(s_lookup);
+                        let mut expression = vec![];
+                        let s_ltableq = cs.query_selector(s_ltable);
+                        let mut lookup_queries = vec![one.clone()];
 
-                    for lookup in lookups {
-                        lookup_queries.push(match lookup {
-                            VarTensor::Advice { inner: advices, .. } => {
-                                cs.query_advice(advices[x][y], Rotation(0))
-                            }
-                            _ => unreachable!(),
-                        });
-                    }
+                        for lookup in lookups {
+                            lookup_queries.push(match lookup {
+                                VarTensor::Advice { inner: advices, .. } => {
+                                    cs.query_advice(advices[x][y], Rotation(0))
+                                }
+                                _ => unreachable!(),
+                            });
+                        }
 
-                    let mut table_queries = vec![one.clone()];
-                    for table in tables {
-                        table_queries.push(match table {
-                            VarTensor::Advice { inner: advices, .. } => {
-                                cs.query_advice(advices[0][0], Rotation(0))
-                            }
-                            _ => unreachable!(),
-                        });
-                    }
+                        let mut table_queries = vec![one.clone()];
+                        for table in tables {
+                            table_queries.push(match table {
+                                VarTensor::Advice { inner: advices, .. } => {
+                                    cs.query_advice(advices[q][0], Rotation(0))
+                                }
+                                _ => unreachable!(),
+                            });
+                        }
 
-                    let lhs = lookup_queries.into_iter().map(|c| c * s_lookupq.clone());
-                    let rhs = table_queries.into_iter().map(|c| c * s_ltableq.clone());
-                    expression.extend(lhs.zip(rhs));
+                        let lhs = lookup_queries.into_iter().map(|c| c * s_lookupq.clone());
+                        let rhs = table_queries.into_iter().map(|c| c * s_ltableq.clone());
+                        expression.extend(lhs.zip(rhs));
 
-                    expression
-                });
-                self.dynamic_lookups
-                    .lookup_selectors
-                    .entry((x, y))
-                    .or_insert(s_lookup);
+                        expression
+                    });
+                    self.dynamic_lookups
+                        .lookup_selectors
+                        .entry((q, (x, y)))
+                        .or_insert(s_lookup);
+                }
             }
+
+            self.dynamic_lookups.table_selectors.push(s_ltable);
         }
-        self.dynamic_lookups.table_selectors.push(s_ltable);
 
         // if we haven't previously initialized the input/output, do so now
         if self.dynamic_lookups.tables.is_empty() {
@@ -729,57 +745,72 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> BaseConfig<F> {
         }
 
         for t in references.iter() {
-            if !t.is_advice() || t.num_blocks() > 1 || t.num_inner_cols() > 1 {
+            if !t.is_advice() || t.num_inner_cols() > 1 {
                 return Err(CircuitError::WrongDynamicColumnType(t.name().to_string()));
             }
         }
 
+        // assert all tables have the same number of blocks
+        if references
+            .iter()
+            .map(|t| t.num_blocks())
+            .collect::<Vec<_>>()
+            .windows(2)
+            .any(|w| w[0] != w[1])
+        {
+            return Err(CircuitError::WrongDynamicColumnType(
+                "references inner cols".to_string(),
+            ));
+        }
+
         let one = Expression::Constant(F::ONE);
 
-        let s_reference = cs.complex_selector();
+        for q in 0..references[0].num_blocks() {
+            let s_reference = cs.complex_selector();
 
-        for x in 0..inputs[0].num_blocks() {
-            for y in 0..inputs[0].num_inner_cols() {
-                let s_input = cs.complex_selector();
+            for x in 0..inputs[0].num_blocks() {
+                for y in 0..inputs[0].num_inner_cols() {
+                    let s_input = cs.complex_selector();
 
-                cs.lookup_any("lookup", |cs| {
-                    let s_inputq = cs.query_selector(s_input);
-                    let mut expression = vec![];
-                    let s_referenceq = cs.query_selector(s_reference);
-                    let mut input_queries = vec![one.clone()];
+                    cs.lookup_any("lookup", |cs| {
+                        let s_inputq = cs.query_selector(s_input);
+                        let mut expression = vec![];
+                        let s_referenceq = cs.query_selector(s_reference);
+                        let mut input_queries = vec![one.clone()];
 
-                    for input in inputs {
-                        input_queries.push(match input {
-                            VarTensor::Advice { inner: advices, .. } => {
-                                cs.query_advice(advices[x][y], Rotation(0))
-                            }
-                            _ => unreachable!(),
-                        });
-                    }
+                        for input in inputs {
+                            input_queries.push(match input {
+                                VarTensor::Advice { inner: advices, .. } => {
+                                    cs.query_advice(advices[x][y], Rotation(0))
+                                }
+                                _ => unreachable!(),
+                            });
+                        }
 
-                    let mut ref_queries = vec![one.clone()];
-                    for reference in references {
-                        ref_queries.push(match reference {
-                            VarTensor::Advice { inner: advices, .. } => {
-                                cs.query_advice(advices[0][0], Rotation(0))
-                            }
-                            _ => unreachable!(),
-                        });
-                    }
+                        let mut ref_queries = vec![one.clone()];
+                        for reference in references {
+                            ref_queries.push(match reference {
+                                VarTensor::Advice { inner: advices, .. } => {
+                                    cs.query_advice(advices[q][0], Rotation(0))
+                                }
+                                _ => unreachable!(),
+                            });
+                        }
 
-                    let lhs = input_queries.into_iter().map(|c| c * s_inputq.clone());
-                    let rhs = ref_queries.into_iter().map(|c| c * s_referenceq.clone());
-                    expression.extend(lhs.zip(rhs));
+                        let lhs = input_queries.into_iter().map(|c| c * s_inputq.clone());
+                        let rhs = ref_queries.into_iter().map(|c| c * s_referenceq.clone());
+                        expression.extend(lhs.zip(rhs));
 
-                    expression
-                });
-                self.shuffles
-                    .input_selectors
-                    .entry((x, y))
-                    .or_insert(s_input);
+                        expression
+                    });
+                    self.shuffles
+                        .input_selectors
+                        .entry((q, (x, y)))
+                        .or_insert(s_input);
+                }
             }
+            self.shuffles.reference_selectors.push(s_reference);
         }
-        self.shuffles.reference_selectors.push(s_reference);
 
         // if we haven't previously initialized the input/output, do so now
         if self.shuffles.references.is_empty() {

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

@@ -13,14 +13,38 @@ 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,
+    },
+    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,
-        use_range_check_for_int: bool,
     },
     ReduceMax {
         axes: Vec<usize>,
@@ -45,6 +69,8 @@ pub enum HybridOp {
     ReduceArgMin {
         dim: usize,
     },
+    Max,
+    Min,
     Softmax {
         input_scale: utils::F32,
         output_scale: utils::F32,
@@ -79,6 +105,8 @@ 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]
             }
@@ -93,21 +121,32 @@ 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 => "MAX".to_string(),
+            HybridOp::Min => "MIN".to_string(),
             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
-            ),
-            HybridOp::Div {
-                denom,
-                use_range_check_for_int,
-            } => format!(
-                "DIV (denom={}, use_range_check_for_int={})",
-                denom, use_range_check_for_int
+                "RECIP (input_scale={}, output_scale={})",
+                input_scale, output_scale
             ),
+            HybridOp::Div { denom } => format!("DIV (denom={})", denom),
             HybridOp::SumPool {
                 padding,
                 stride,
@@ -162,6 +201,34 @@ 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)?
+            }
+            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,
@@ -179,38 +246,16 @@ 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,
-                        },
-                    )?
-                }
-            }
-            HybridOp::Div {
-                denom,
-                use_range_check_for_int,
-                ..
-            } => {
-                if denom.0.fract() == 0.0 && *use_range_check_for_int {
-                    layouts::loop_div(
+            } => 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, .. } => {
+                if denom.0.fract() == 0.0 {
+                    layouts::div(
                         config,
                         region,
                         values[..].try_into()?,
@@ -301,9 +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)

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 +14,27 @@ 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 },
+    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 +48,14 @@ 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),
@@ -168,65 +80,28 @@ 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()))
                 }
-                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()))
                 }
@@ -283,29 +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::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),
-            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),
@@ -339,15 +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)
-            }
-            LookupOp::Recip { output_scale, .. } => multiplier_to_scale(output_scale.into()),
-            LookupOp::KroneckerDelta => 0,
-            _ => inputs_scale[0],
-        };
+        let scale = inputs_scale[0];
         Ok(scale)
     }
 

src/circuit/ops/mod.rs

@@ -255,7 +255,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Constant<F> {
         self.raw_values = Tensor::new(None, &[0]).unwrap();
     }
 
-    ///
+    /// Pre-assign a value
     pub fn pre_assign(&mut self, val: ValTensor<F>) {
         self.pre_assigned_val = Some(val)
     }

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

@@ -180,6 +180,7 @@ pub struct RegionCtx<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Ha
     statistics: RegionStatistics,
     settings: RegionSettings,
     assigned_constants: ConstantsMap<F>,
+    max_dynamic_input_len: usize,
 }
 
 impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a, F> {
@@ -193,11 +194,16 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         self.settings.legs
     }
 
+    /// get the max dynamic input len
+    pub fn max_dynamic_input_len(&self) -> usize {
+        self.max_dynamic_input_len
+    }
+
     #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
     ///
     pub fn debug_report(&self) {
         log::debug!(
-            "(rows={}, coord={}, constants={}, max_lookup_inputs={}, min_lookup_inputs={}, max_range_size={}, dynamic_lookup_col_coord={}, shuffle_col_coord={})",
+            "(rows={}, coord={}, constants={}, max_lookup_inputs={}, min_lookup_inputs={}, max_range_size={}, dynamic_lookup_col_coord={}, shuffle_col_coord={}, max_dynamic_input_len={})",
             self.row().to_string().blue(),
             self.linear_coord().to_string().yellow(),
             self.total_constants().to_string().red(),
@@ -205,7 +211,9 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
             self.min_lookup_inputs().to_string().green(),
             self.max_range_size().to_string().green(),
             self.dynamic_lookup_col_coord().to_string().green(),
-            self.shuffle_col_coord().to_string().green());
+            self.shuffle_col_coord().to_string().green(), 
+            self.max_dynamic_input_len().to_string().green()
+        );
     }
 
     ///
@@ -223,6 +231,11 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         self.dynamic_lookup_index.index += n;
     }
 
+    /// increment the max dynamic input len
+    pub fn update_max_dynamic_input_len(&mut self, n: usize) {
+        self.max_dynamic_input_len = self.max_dynamic_input_len.max(n);
+    }
+
     ///
     pub fn increment_dynamic_lookup_col_coord(&mut self, n: usize) {
         self.dynamic_lookup_index.col_coord += n;
@@ -274,6 +287,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
             statistics: RegionStatistics::default(),
             settings: RegionSettings::all_true(decomp_base, decomp_legs),
             assigned_constants: HashMap::new(),
+            max_dynamic_input_len: 0,
         }
     }
 
@@ -310,6 +324,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
             statistics: RegionStatistics::default(),
             settings,
             assigned_constants: HashMap::new(),
+            max_dynamic_input_len: 0,
         }
     }
 
@@ -331,6 +346,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
             statistics: RegionStatistics::default(),
             settings,
             assigned_constants: HashMap::new(),
+            max_dynamic_input_len: 0,
         }
     }
 
@@ -458,6 +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 {
@@ -583,9 +610,12 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         &mut self,
         var: &VarTensor,
         values: &ValTensor<F>,
-    ) -> Result<ValTensor<F>, CircuitError> {
+    ) -> Result<(ValTensor<F>, usize), CircuitError> {
+
+        self.update_max_dynamic_input_len(values.len());
+
         if let Some(region) = &self.region {
-            Ok(var.assign(
+            Ok(var.assign_exact_column(
                 &mut region.borrow_mut(),
                 self.combined_dynamic_shuffle_coord(),
                 values,
@@ -596,7 +626,11 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
                 let values_map = values.create_constants_map_iterator();
                 self.assigned_constants.par_extend(values_map);
             }
-            Ok(values.clone())
+
+            let flush_len = var.get_column_flush(self.combined_dynamic_shuffle_coord(), values)?;
+
+            // get the diff between the current column and the next row
+            Ok((values.clone(), flush_len))
         }
     }
 
@@ -605,7 +639,7 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         &mut self,
         var: &VarTensor,
         values: &ValTensor<F>,
-    ) -> Result<ValTensor<F>, CircuitError> {
+    ) -> Result<(ValTensor<F>, usize), CircuitError> {
         self.assign_dynamic_lookup(var, values)
     }
 

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(())
@@ -1516,7 +1519,7 @@ mod add_w_shape_casting {
         // parameters
         let a = Tensor::from((0..LEN).map(|i| Value::known(F::from(i as u64 + 1))));
 
-        let b = Tensor::from((0..1).map(|i| Value::known(F::from(i as u64 + 1))));
+        let b = Tensor::from((0..1).map(|i| Value::known(F::from(i + 1))));
 
         let circuit = MyCircuit::<F> {
             inputs: [ValTensor::from(a), ValTensor::from(b)],
@@ -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

@@ -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
@@ -508,7 +493,7 @@ 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 +540,7 @@ 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 +567,7 @@ 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 +609,7 @@ 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 +686,7 @@ 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 +718,7 @@ 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 +740,7 @@ 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 +783,7 @@ 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 +816,7 @@ 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 +834,7 @@ 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

@@ -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
@@ -671,17 +669,21 @@ 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 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 {
@@ -727,7 +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)?;
 
@@ -967,7 +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;
@@ -1003,12 +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
@@ -1046,12 +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);
@@ -1060,30 +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()
         };
@@ -1187,7 +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,
@@ -1203,6 +1184,7 @@ pub(crate) async fn calibrate(
                 num_rows: new_settings.num_rows,
                 total_assignments: new_settings.total_assignments,
                 total_const_size: new_settings.total_const_size,
+                total_dynamic_col_size: new_settings.total_dynamic_col_size,
                 ..settings.clone()
             };
 
@@ -1294,7 +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()
@@ -1320,7 +1301,9 @@ pub(crate) async fn calibrate(
         let lookup_log_rows = best_params.lookup_log_rows_with_blinding();
         let module_log_row = best_params.module_constraint_logrows_with_blinding();
         let instance_logrows = best_params.log2_total_instances_with_blinding();
-        let dynamic_lookup_logrows = best_params.dynamic_lookup_and_shuffle_logrows_with_blinding();
+        let dynamic_lookup_logrows =
+            best_params.min_dynamic_lookup_and_shuffle_logrows_with_blinding();
+
         let range_check_logrows = best_params.range_check_log_rows_with_blinding();
 
         let mut reduction = std::cmp::max(lookup_log_rows, module_log_row);
@@ -2018,7 +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
@@ -2029,7 +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())
 }
 
@@ -2123,7 +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
@@ -2272,7 +2255,7 @@ pub(crate) fn aggregate(
     );
     snark.save(&proof_path)?;
 
-        pb.finish_with_message("Done.");
+    pb.finish_with_message("Done.");
 
     Ok(snark)
 }

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,
         }
     }
 
@@ -408,6 +411,8 @@ pub struct GraphSettings {
     pub total_const_size: usize,
     /// total dynamic column size
     pub total_dynamic_col_size: usize,
+    /// max dynamic column input length
+    pub max_dynamic_input_len: usize,
     /// number of dynamic lookups
     pub num_dynamic_lookups: usize,
     /// number of shuffles
@@ -485,6 +490,13 @@ impl GraphSettings {
             .ceil() as u32
     }
 
+    /// calculate the number of rows required for the dynamic lookup and shuffle
+    pub fn min_dynamic_lookup_and_shuffle_logrows_with_blinding(&self) -> u32 {
+        (self.max_dynamic_input_len as f64 + RESERVED_BLINDING_ROWS as f64)
+            .log2()
+            .ceil() as u32
+    }
+
     fn dynamic_lookup_and_shuffle_col_size(&self) -> usize {
         self.total_dynamic_col_size + self.total_shuffle_col_size
     }
@@ -1341,6 +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(

src/graph/model.rs

@@ -103,6 +103,8 @@ pub struct DummyPassRes {
     pub num_rows: usize,
     /// num dynamic lookups
     pub num_dynamic_lookups: usize,
+    /// max dynamic lookup input len
+    pub max_dynamic_input_len: usize,
     /// dynamic lookup col size
     pub dynamic_lookup_col_coord: usize,
     /// num shuffles
@@ -360,6 +362,14 @@ impl NodeType {
             NodeType::SubGraph { .. } => SupportedOp::Unknown(Unknown),
         }
     }
+
+    /// check if it is a softmax
+    pub fn is_softmax(&self) -> bool {
+        match self {
+            NodeType::Node(n) => n.is_softmax(),
+            NodeType::SubGraph { .. } => false,
+        }
+    }
 }
 
 #[derive(Clone, Debug, Default, Serialize, Deserialize, PartialEq)]
@@ -562,6 +572,7 @@ impl Model {
             num_rows: res.num_rows,
             total_assignments: res.linear_coord,
             required_lookups: res.lookup_ops.into_iter().collect(),
+            max_dynamic_input_len: res.max_dynamic_input_len,
             required_range_checks: res.range_checks.into_iter().collect(),
             model_output_scales: self.graph.get_output_scales()?,
             model_input_scales: self.graph.get_input_scales(),
@@ -904,20 +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];
                         }
@@ -1465,6 +1465,7 @@ impl Model {
         let res = DummyPassRes {
             num_rows: region.row(),
             linear_coord: region.linear_coord(),
+            max_dynamic_input_len: region.max_dynamic_input_len(),
             total_const_size: region.total_constants(),
             lookup_ops: region.used_lookups(),
             range_checks: region.used_range_checks(),

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)?;
 
@@ -623,6 +611,15 @@ impl Node {
             num_uses,
         })
     }
+
+    /// check if it is a softmax node
+    pub fn is_softmax(&self) -> bool {
+        if let SupportedOp::Hybrid(HybridOp::Softmax { .. }) = self.opkind {
+            true
+        } else {
+            false
+        }
+    }
 }
 
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]

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.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) {
+                        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 +837,75 @@ 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;
@@ -975,11 +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 {
@@ -1085,7 +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 {
@@ -1123,17 +1095,21 @@ 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 +1122,17 @@ 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/graph/vars.rs

@@ -443,7 +443,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ModelVars<F> {
                 let dynamic_lookup =
                     VarTensor::new_advice(cs, logrows, 1, dynamic_lookup_and_shuffle_size);
                 if dynamic_lookup.num_blocks() > 1 {
-                    panic!("dynamic lookup or shuffle should only have one block");
+                    warn!("dynamic lookup has {} blocks", dynamic_lookup.num_blocks());
                 };
                 advices.push(dynamic_lookup);
             }

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,

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

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
@@ -1553,30 +1474,88 @@ 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())
+            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>(T::zero().unwrap())
+                Ok::<_, TensorError>(ceil as IntegerRep)
             }
         })
         .unwrap()
@@ -1710,12 +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;
@@ -1750,27 +1728,6 @@ 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 +2211,6 @@ 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 +2291,6 @@ 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

src/tensor/val.rs

@@ -541,7 +541,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ValTensor<F> {
                 let mut is_empty = true;
                 x.map(|_| is_empty = false);
                 if is_empty {
-                    return Ok::<_, TensorError>(vec![Value::<F>::unknown(); n + 1]);
+                    Ok::<_, TensorError>(vec![Value::<F>::unknown(); n + 1])
                 } else {
                     let mut res = vec![Value::unknown(); n + 1];
                     let mut int_rep = 0;

src/tensor/var.rs

@@ -396,6 +396,53 @@ impl VarTensor {
         Ok(res)
     }
 
+    /// Helper function to get the remaining size of the column
+    pub fn get_column_flush<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+        &self,
+        offset: usize,
+        values: &ValTensor<F>,
+    ) -> Result<usize, halo2_proofs::plonk::Error> {
+        if values.len() > self.col_size() {
+            error!("Values are too large for the column");
+            return Err(halo2_proofs::plonk::Error::Synthesis);
+        }
+
+        // this can only be called on columns that have a single inner column
+        if self.num_inner_cols() != 1 {
+            error!("This function can only be called on columns with a single inner column");
+            return Err(halo2_proofs::plonk::Error::Synthesis);
+        }
+
+        // check if the values fit in the remaining space of the column
+        let current_cartesian = self.cartesian_coord(offset);
+        let final_cartesian = self.cartesian_coord(offset + values.len());
+
+        let mut flush_len = 0;
+        if current_cartesian.0 != final_cartesian.0 {
+            debug!("Values overflow the column, flushing to next column");
+            // diff is the number of values that overflow the column
+            flush_len += self.col_size() - current_cartesian.2;
+        }
+
+        Ok(flush_len)
+    }
+
+    /// Assigns [ValTensor] to the columns of the inner tensor. Whereby the values are assigned to a single column, without overflowing.
+    /// So for instance if we are assigning 10 values and we are at index 18 of the column, and the columns are of length 20, we skip the last 2 values of current column and start from the beginning of the next column.
+    pub fn assign_exact_column<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+        &self,
+        region: &mut Region<F>,
+        offset: usize,
+        values: &ValTensor<F>,
+        constants: &mut ConstantsMap<F>,
+    ) -> Result<(ValTensor<F>, usize), halo2_proofs::plonk::Error> {
+        let flush_len = self.get_column_flush(offset, values)?;
+
+        let assigned_vals = self.assign(region, offset + flush_len, values, constants)?;
+
+        Ok((assigned_vals, flush_len))
+    }
+
     /// Assigns specific values (`ValTensor`) to the columns of the inner tensor but allows for column wrapping for accumulated operations.
     /// Duplication occurs by copying the last cell of the column to the first cell next column and creating a copy constraint between the two.
     pub fn dummy_assign_with_duplication<

tests/assets/settings.json

@@ -27,12 +27,14 @@
         "check_mode": "UNSAFE",
         "commitment": "KZG",
         "decomp_base": 128,
-        "decomp_legs": 2
+        "decomp_legs": 2,
+        "bounded_log_lookup": false
     },
     "num_rows": 46,
     "total_assignments": 92,
     "total_const_size": 3,
     "total_dynamic_col_size": 0,
+    "max_dynamic_input_len": 0,
     "num_dynamic_lookups": 0,
     "num_shuffles": 0,
     "total_shuffle_col_size": 0,

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"}

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] = [
@@ -537,12 +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..=93 {
+            seq!(N in 0..=95 {
 
             #(#[test_case(TESTS[N])])*
             #[ignore]
@@ -554,15 +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) {
@@ -570,7 +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();
             }
 
@@ -580,7 +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();
             }
 
@@ -590,7 +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();
             }
 
@@ -601,7 +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();
             }
 
@@ -610,7 +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();
             }
 
@@ -621,7 +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();
             }
 
@@ -636,7 +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();
                 }
             }
@@ -646,7 +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();
             }
 
@@ -655,7 +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();
             }
 
@@ -664,7 +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();
             }
 
@@ -673,7 +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();
             }
 
@@ -682,7 +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();
             }
 
@@ -691,7 +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();
             }
 
@@ -700,7 +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();
             }
 
@@ -710,7 +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();
             }
 
@@ -720,7 +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();
             }
 
@@ -729,7 +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();
             }
 
@@ -739,7 +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();
             }
 
@@ -748,7 +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();
             }
 
@@ -758,7 +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();
             }
 
@@ -768,7 +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();
             }
 
@@ -778,7 +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();
             }
 
@@ -788,7 +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();
             }
 
@@ -798,7 +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();
             }
 
@@ -851,9 +855,11 @@ 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])])*
@@ -973,7 +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();
             }
         });
@@ -1118,7 +1124,7 @@ 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();
@@ -1450,6 +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(
@@ -1462,10 +1469,10 @@ mod native_tests {
             cal_target,
             scales_to_use,
             2,
-            false,
             &mut tolerance,
             Commitments::KZG,
             2,
+            bounded_lookup_log,
         );
 
         if tolerance > 0.0 {
@@ -1603,10 +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(),
@@ -1625,13 +1632,12 @@ 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)
-            .stdout(std::process::Stdio::null())
             .status()
             .expect("failed to execute process");
         assert!(status.success());
@@ -1728,7 +1734,6 @@ mod native_tests {
         batch_size: usize,
         cal_target: &str,
         target_perc: f32,
-        div_rebasing: bool,
     ) {
         gen_circuit_settings_and_witness(
             test_dir,
@@ -1740,10 +1745,10 @@ mod native_tests {
             cal_target,
             None,
             2,
-            div_rebasing,
             &mut 0.0,
             Commitments::KZG,
             2,
+            false,
         );
 
         println!(
@@ -2024,10 +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);
@@ -2456,10 +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);

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 {