Add __version__ to python bindings (#739)

examples/onnx/gather_nd/gen.py

@@ -0,0 +1,48 @@
+from torch import nn
+import json
+import numpy as np
+import tf2onnx
+
+
+import tensorflow as tf
+from tensorflow.keras.layers import *
+from tensorflow.keras.models import Model
+
+
+# gather_nd in tf then export to onnx
+
+
+
+
+x = in1 = Input((15, 18,))
+w = in2 = Input((15, 1), dtype=tf.int32)
+x = tf.gather_nd(x, w, batch_dims=1)
+tm = Model((in1, in2), x )
+tm.summary()
+tm.compile(optimizer='adam', loss='mse')
+
+shape = [1, 15, 18]
+index_shape = [1, 15, 1]
+# After training, export to onnx (network.onnx) and create a data file (input.json)
+x = 0.1*np.random.rand(1,*shape)
+# w = random int tensor
+w = np.random.randint(0, 10, index_shape)
+
+spec = tf.TensorSpec(shape, tf.float32, name='input_0')
+index_spec = tf.TensorSpec(index_shape, tf.int32, name='input_1')
+
+model_path = "network.onnx"
+
+tf2onnx.convert.from_keras(tm, input_signature=[spec, index_spec], inputs_as_nchw=['input_0', 'input_1'], opset=12, output_path=model_path)
+
+
+d = x.reshape([-1]).tolist()
+d1 = w.reshape([-1]).tolist()
+
+
+data = dict(
+    input_data=[d, d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))

examples/onnx/scatter_nd/gen.py

@@ -0,0 +1,76 @@
+import torch
+import torch.nn as nn
+import sys
+import json
+
+sys.path.append("..")
+
+class Model(nn.Module):
+    """
+    Just one Linear layer
+    """
+    def __init__(self, configs):
+        super(Model, self).__init__()
+        self.seq_len = configs.seq_len
+        self.pred_len = configs.pred_len
+
+        # Use this line if you want to visualize the weights
+        # self.Linear.weight = nn.Parameter((1/self.seq_len)*torch.ones([self.pred_len,self.seq_len]))
+        self.channels = configs.enc_in
+        self.individual = configs.individual
+        if self.individual:
+            self.Linear = nn.ModuleList()
+            for i in range(self.channels):
+                self.Linear.append(nn.Linear(self.seq_len,self.pred_len))
+        else:
+            self.Linear = nn.Linear(self.seq_len, self.pred_len)
+
+    def forward(self, x):
+        # x: [Batch, Input length, Channel]
+        if self.individual:
+            output = torch.zeros([x.size(0),self.pred_len,x.size(2)],dtype=x.dtype).to(x.device)
+            for i in range(self.channels):
+                output[:,:,i] = self.Linear[i](x[:,:,i])
+            x = output
+        else:
+            x = self.Linear(x.permute(0,2,1)).permute(0,2,1)
+        return x # [Batch, Output length, Channel]
+
+class Configs:
+    def __init__(self, seq_len, pred_len, enc_in=321, individual=True):
+      self.seq_len = seq_len
+      self.pred_len = pred_len
+      self.enc_in = enc_in
+      self.individual = individual
+
+model = 'Linear'
+seq_len = 10
+pred_len = 4
+enc_in = 3
+
+configs = Configs(seq_len, pred_len, enc_in, True)
+circuit = Model(configs)
+
+x = torch.randn(1, seq_len, pred_len)
+
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=15,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  # the model's input names
+                  input_names=['input'],
+                  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/scatter_nd/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.1874287724494934, 1.0498261451721191, 0.22384068369865417, 1.048445224761963, -0.5670360326766968, -0.38653188943862915, 0.12878702580928802, -2.3675858974456787, 0.5800458192825317, -0.43653929233551025, -0.2511898875236511, 0.3324051797389984, 0.27960312366485596, 0.4763695001602173, 0.3796705901622772, 1.1334782838821411, -0.87981778383255, -1.2451434135437012, 0.7672272324562073, -0.24404007196426392, -0.6875824928283691, 0.3619358539581299, -0.10131897777318954, 0.7169521450996399, 1.6585893630981445, -0.5451845526695251, 0.429487019777298, 0.7426952123641968, -0.2543637454509735, 0.06546942889690399, 0.7939824461936951, 0.1579471379518509, -0.043604474514722824, -0.8621711730957031, -0.5344759821891785, -0.05880478024482727, -0.17351101338863373, 0.5095029473304749, -0.7864817976951599, -0.449171245098114]]}

src/circuit/ops/base.rs

@@ -17,7 +17,6 @@ pub enum BaseOp {
     Sub,
     SumInit,
     Sum,
-    IsZero,
     IsBoolean,
 }
 
@@ -35,7 +34,6 @@ impl BaseOp {
             BaseOp::Add => a + b,
             BaseOp::Sub => a - b,
             BaseOp::Mult => a * b,
-            BaseOp::IsZero => b,
             BaseOp::IsBoolean => b,
             _ => panic!("nonaccum_f called on accumulating operation"),
         }
@@ -76,7 +74,6 @@ impl BaseOp {
             BaseOp::Mult => "MULT",
             BaseOp::Sum => "SUM",
             BaseOp::SumInit => "SUMINIT",
-            BaseOp::IsZero => "ISZERO",
             BaseOp::IsBoolean => "ISBOOLEAN",
         }
     }
@@ -93,7 +90,6 @@ impl BaseOp {
             BaseOp::Mult => (0, 1),
             BaseOp::Sum => (-1, 2),
             BaseOp::SumInit => (0, 1),
-            BaseOp::IsZero => (0, 1),
             BaseOp::IsBoolean => (0, 1),
         }
     }
@@ -110,7 +106,6 @@ impl BaseOp {
             BaseOp::Mult => 2,
             BaseOp::Sum => 1,
             BaseOp::SumInit => 1,
-            BaseOp::IsZero => 0,
             BaseOp::IsBoolean => 0,
         }
     }
@@ -127,7 +122,6 @@ impl BaseOp {
             BaseOp::SumInit => 0,
             BaseOp::CumProd => 1,
             BaseOp::CumProdInit => 0,
-            BaseOp::IsZero => 0,
             BaseOp::IsBoolean => 0,
         }
     }

src/circuit/ops/chip.rs

@@ -387,7 +387,6 @@ impl<F: PrimeField + TensorType + PartialOrd> BaseConfig<F> {
                 nonaccum_selectors.insert((BaseOp::Add, i, j), meta.selector());
                 nonaccum_selectors.insert((BaseOp::Sub, i, j), meta.selector());
                 nonaccum_selectors.insert((BaseOp::Mult, i, j), meta.selector());
-                nonaccum_selectors.insert((BaseOp::IsZero, i, j), meta.selector());
                 nonaccum_selectors.insert((BaseOp::IsBoolean, i, j), meta.selector());
             }
         }
@@ -432,12 +431,6 @@ impl<F: PrimeField + TensorType + PartialOrd> BaseConfig<F> {
 
                         vec![(output.clone()) * (output.clone() - Expression::Constant(F::from(1)))]
                     }
-                    BaseOp::IsZero => {
-                        let expected_output: Tensor<Expression<F>> = output
-                            .query_rng(meta, *block_idx, *inner_col_idx, 0, 1)
-                            .expect("non accum: output query failed");
-                        vec![expected_output[base_op.constraint_idx()].clone()]
-                    }
                     _ => {
                         let expected_output: Tensor<Expression<F>> = output
                             .query_rng(meta, *block_idx, *inner_col_idx, rotation_offset, rng)

src/circuit/ops/poly.rs

@@ -14,10 +14,17 @@ pub enum PolyOp {
         dim: usize,
         constant_idx: Option<Tensor<usize>>,
     },
+    GatherND {
+        batch_dims: usize,
+        indices: Option<Tensor<usize>>,
+    },
     ScatterElements {
         dim: usize,
         constant_idx: Option<Tensor<usize>>,
     },
+    ScatterND {
+        constant_idx: Option<Tensor<usize>>,
+    },
     MultiBroadcastTo {
         shape: Vec<usize>,
     },
@@ -60,8 +67,6 @@ pub enum PolyOp {
         len_prod: usize,
     },
     Pow(u32),
-    Pack(u32, u32),
-    GlobalSumPool,
     Concat {
         axis: usize,
     },
@@ -91,7 +96,9 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
     fn as_string(&self) -> String {
         match &self {
             PolyOp::GatherElements { dim, .. } => format!("GATHERELEMENTS (dim={})", dim),
+            PolyOp::GatherND { batch_dims, .. } => format!("GATHERND (batch_dims={})", batch_dims),
             PolyOp::ScatterElements { dim, .. } => format!("SCATTERELEMENTS (dim={})", dim),
+            PolyOp::ScatterND { .. } => "SCATTERND".into(),
             PolyOp::MultiBroadcastTo { shape } => format!("MULTIBROADCASTTO (shape={:?})", shape),
             PolyOp::MoveAxis { .. } => "MOVEAXIS".into(),
             PolyOp::Downsample { .. } => "DOWNSAMPLE".into(),
@@ -110,8 +117,6 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
             PolyOp::Sum { .. } => "SUM".into(),
             PolyOp::Prod { .. } => "PROD".into(),
             PolyOp::Pow(_) => "POW".into(),
-            PolyOp::Pack(_, _) => "PACK".into(),
-            PolyOp::GlobalSumPool => "GLOBALSUMPOOL".into(),
             PolyOp::Conv { .. } => "CONV".into(),
             PolyOp::DeConv { .. } => "DECONV".into(),
             PolyOp::Concat { axis } => format!("CONCAT (axis={})", axis),
@@ -181,13 +186,6 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
                 output_padding,
                 stride,
             } => tensor::ops::deconv(&inputs, *padding, *output_padding, *stride),
-            PolyOp::Pack(base, scale) => {
-                if 1 != inputs.len() {
-                    return Err(TensorError::DimMismatch("pack inputs".to_string()));
-                }
-
-                tensor::ops::pack(&inputs[0], F::from(*base as u64), *scale)
-            }
             PolyOp::Pow(u) => {
                 if 1 != inputs.len() {
                     return Err(TensorError::DimMismatch("pow inputs".to_string()));
@@ -206,7 +204,6 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
                 }
                 tensor::ops::prod_axes(&inputs[0], axes)
             }
-            PolyOp::GlobalSumPool => unreachable!(),
             PolyOp::Concat { axis } => {
                 tensor::ops::concat(&inputs.iter().collect::<Vec<_>>(), *axis)
             }
@@ -225,6 +222,18 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
                 };
                 tensor::ops::gather_elements(&x, &y, *dim)
             }
+            PolyOp::GatherND {
+                indices,
+                batch_dims,
+            } => {
+                let x = inputs[0].clone();
+                let y = if let Some(idx) = indices {
+                    idx.clone()
+                } else {
+                    inputs[1].clone().map(|x| felt_to_i128(x) as usize)
+                };
+                tensor::ops::gather_nd(&x, &y, *batch_dims)
+            }
             PolyOp::ScatterElements { dim, constant_idx } => {
                 let x = inputs[0].clone();
 
@@ -241,6 +250,21 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
                 };
                 tensor::ops::scatter(&x, &idx, &src, *dim)
             }
+
+            PolyOp::ScatterND { constant_idx } => {
+                let x = inputs[0].clone();
+                let idx = if let Some(idx) = constant_idx {
+                    idx.clone()
+                } else {
+                    inputs[1].clone().map(|x| felt_to_i128(x) as usize)
+                };
+                let src = if constant_idx.is_some() {
+                    inputs[1].clone()
+                } else {
+                    inputs[2].clone()
+                };
+                tensor::ops::scatter_nd(&x, &idx, &src)
+            }
         }?;
 
         Ok(ForwardResult { output: res })
@@ -288,7 +312,17 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
                 if let Some(idx) = constant_idx {
                     tensor::ops::gather_elements(values[0].get_inner_tensor()?, idx, *dim)?.into()
                 } else {
-                    layouts::gather_elements(config, region, values[..].try_into()?, *dim)?
+                    layouts::gather_elements(config, region, values[..].try_into()?, *dim)?.0
+                }
+            }
+            PolyOp::GatherND {
+                batch_dims,
+                indices,
+            } => {
+                if let Some(idx) = indices {
+                    tensor::ops::gather_nd(values[0].get_inner_tensor()?, idx, *batch_dims)?.into()
+                } else {
+                    layouts::gather_nd(config, region, values[..].try_into()?, *batch_dims)?.0
                 }
             }
             PolyOp::ScatterElements { dim, constant_idx } => {
@@ -304,6 +338,18 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
                     layouts::scatter_elements(config, region, values[..].try_into()?, *dim)?
                 }
             }
+            PolyOp::ScatterND { constant_idx } => {
+                if let Some(idx) = constant_idx {
+                    tensor::ops::scatter_nd(
+                        values[0].get_inner_tensor()?,
+                        idx,
+                        values[1].get_inner_tensor()?,
+                    )?
+                    .into()
+                } else {
+                    layouts::scatter_nd(config, region, values[..].try_into()?)?
+                }
+            }
             PolyOp::DeConv {
                 padding,
                 output_padding,
@@ -334,10 +380,6 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
                 input
             }
             PolyOp::Pow(exp) => layouts::pow(config, region, values[..].try_into()?, *exp)?,
-            PolyOp::Pack(base, scale) => {
-                layouts::pack(config, region, values[..].try_into()?, *base, *scale)?
-            }
-            PolyOp::GlobalSumPool => unreachable!(),
             PolyOp::Concat { axis } => layouts::concat(values[..].try_into()?, axis)?,
             PolyOp::Slice { axis, start, end } => {
                 layouts::slice(config, region, values[..].try_into()?, axis, start, end)?
@@ -405,7 +447,9 @@ impl<F: PrimeField + TensorType + PartialOrd + Serialize + for<'de> Deserialize<
             vec![1, 2]
         } else if matches!(self, PolyOp::Concat { .. }) {
             (0..100).collect()
-        } else if matches!(self, PolyOp::ScatterElements { .. }) {
+        } else if matches!(self, PolyOp::ScatterElements { .. })
+            | matches!(self, PolyOp::ScatterND { .. })
+        {
             vec![0, 2]
         } else {
             vec![]

src/circuit/tests.rs

@@ -2243,75 +2243,6 @@ mod pow {
     }
 }
 
-#[cfg(test)]
-mod pack {
-    use super::*;
-
-    const K: usize = 8;
-    const LEN: usize = 4;
-
-    #[derive(Clone)]
-    struct MyCircuit<F: PrimeField + TensorType + PartialOrd> {
-        inputs: [ValTensor<F>; 1],
-        _marker: PhantomData<F>,
-    }
-
-    impl Circuit<F> for MyCircuit<F> {
-        type Config = BaseConfig<F>;
-        type FloorPlanner = SimpleFloorPlanner;
-        type Params = TestParams;
-
-        fn without_witnesses(&self) -> Self {
-            self.clone()
-        }
-
-        fn configure(cs: &mut ConstraintSystem<F>) -> Self::Config {
-            let a = VarTensor::new_advice(cs, K, 1, LEN);
-            let b = VarTensor::new_advice(cs, K, 1, LEN);
-            let output = VarTensor::new_advice(cs, K, 1, LEN);
-
-            Self::Config::configure(cs, &[a, b], &output, CheckMode::SAFE)
-        }
-
-        fn synthesize(
-            &self,
-            mut config: Self::Config,
-            mut layouter: impl Layouter<F>,
-        ) -> Result<(), Error> {
-            layouter
-                .assign_region(
-                    || "",
-                    |region| {
-                        let mut region = RegionCtx::new(region, 0, 1);
-                        config
-                            .layout(
-                                &mut region,
-                                &self.inputs.clone(),
-                                Box::new(PolyOp::Pack(2, 1)),
-                            )
-                            .map_err(|_| Error::Synthesis)
-                    },
-                )
-                .unwrap();
-            Ok(())
-        }
-    }
-
-    #[test]
-    fn packcircuit() {
-        // parameters
-        let a = Tensor::from((0..LEN).map(|i| Value::known(F::from(i as u64 + 1))));
-
-        let circuit = MyCircuit::<F> {
-            inputs: [ValTensor::from(a)],
-            _marker: PhantomData,
-        };
-
-        let prover = MockProver::run(K as u32, &circuit, vec![]).unwrap();
-        prover.assert_satisfied();
-    }
-}
-
 #[cfg(test)]
 mod matmul_relu {
     use super::*;

src/commands.rs

@@ -402,9 +402,6 @@ pub enum Commands {
         /// Number of logrows to use for srs. Overrides settings_path if specified.
         #[arg(long, default_value = None)]
         logrows: Option<u32>,
-        /// Check mode for SRS. Verifies downloaded srs is valid. Set to unsafe for speed.
-        #[arg(long, default_value = DEFAULT_CHECKMODE)]
-        check: CheckMode,
     },
     /// Loads model and input and runs mock prover (for testing)
     Mock {

src/execute.rs

@@ -159,8 +159,7 @@ pub async fn run(command: Commands) -> Result<String, Box<dyn Error>> {
             srs_path,
             settings_path,
             logrows,
-            check,
-        } => get_srs_cmd(srs_path, settings_path, logrows, check).await,
+        } => get_srs_cmd(srs_path, settings_path, logrows).await,
         Commands::Table { model, args } => table(model, args),
         #[cfg(feature = "render")]
         Commands::RenderCircuit {
@@ -492,23 +491,28 @@ async fn fetch_srs(uri: &str) -> Result<Vec<u8>, Box<dyn Error>> {
 }
 
 #[cfg(not(target_arch = "wasm32"))]
-fn check_srs_hash(logrows: u32, srs_path: Option<PathBuf>) -> Result<String, Box<dyn Error>> {
+pub(crate) fn get_file_hash(path: &PathBuf) -> Result<String, Box<dyn Error>> {
     use std::io::Read;
-
-    let path = get_srs_path(logrows, srs_path);
-    let file = std::fs::File::open(path.clone())?;
+    let file = std::fs::File::open(path)?;
+    let mut reader = std::io::BufReader::new(file);
     let mut buffer = vec![];
-    let mut reader = std::io::BufReader::with_capacity(*EZKL_BUF_CAPACITY, file);
     let bytes_read = reader.read_to_end(&mut buffer)?;
-
     info!(
-        "read {} bytes from SRS file (vector of len = {})",
+        "read {} bytes from file (vector of len = {})",
         bytes_read,
         buffer.len()
     );
 
     let hash = sha256::digest(buffer);
-    info!("SRS hash: {}", hash);
+    info!("file hash: {}", hash);
+
+    Ok(hash)
+}
+
+#[cfg(not(target_arch = "wasm32"))]
+fn check_srs_hash(logrows: u32, srs_path: Option<PathBuf>) -> Result<String, Box<dyn Error>> {
+    let path = get_srs_path(logrows, srs_path);
+    let hash = get_file_hash(&path)?;
 
     let predefined_hash = match { crate::srs_sha::PUBLIC_SRS_SHA256_HASHES.get(&logrows) } {
         Some(h) => h,
@@ -532,7 +536,6 @@ pub(crate) async fn get_srs_cmd(
     srs_path: Option<PathBuf>,
     settings_path: Option<PathBuf>,
     logrows: Option<u32>,
-    check_mode: CheckMode,
 ) -> Result<String, Box<dyn Error>> {
     // logrows overrides settings
 
@@ -560,21 +563,20 @@ 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
-        if matches!(check_mode, CheckMode::SAFE) {
-            #[cfg(not(target_arch = "wasm32"))]
-            let pb = init_spinner();
-            #[cfg(not(target_arch = "wasm32"))]
-            pb.set_message("Validating SRS (this may take a while) ...");
-            ParamsKZG::<Bn256>::read(&mut reader)?;
-            #[cfg(not(target_arch = "wasm32"))]
-            pb.finish_with_message("SRS validated");
-        }
+        #[cfg(not(target_arch = "wasm32"))]
+        let pb = init_spinner();
+        #[cfg(not(target_arch = "wasm32"))]
+        pb.set_message("Validating SRS (this may take a while) ...");
+        let params = ParamsKZG::<Bn256>::read(&mut reader)?;
+        #[cfg(not(target_arch = "wasm32"))]
+        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()))?;
-
         let mut buffer = BufWriter::with_capacity(*EZKL_BUF_CAPACITY, &mut file);
-        buffer.write_all(reader.get_ref())?;
-        buffer.flush()?;
+        params.write(&mut buffer)?;
+
+        info!("Saved SRS to disk.");
 
         info!("SRS downloaded");
     } else {
@@ -902,7 +904,12 @@ pub(crate) fn calibrate(
             input_scale, param_scale, scale_rebase_multiplier, div_rebasing
         ));
 
-        let key = (input_scale, param_scale, scale_rebase_multiplier);
+        let key = (
+            input_scale,
+            param_scale,
+            scale_rebase_multiplier,
+            div_rebasing,
+        );
         forward_pass_res.insert(key, vec![]);
 
         let local_run_args = RunArgs {
@@ -971,29 +978,21 @@ pub(crate) fn calibrate(
         #[cfg(unix)]
         drop(_g);
 
-        let min_lookup_range = forward_pass_res
-            .get(&key)
-            .unwrap()
+        let result = forward_pass_res.get(&key).ok_or("key not found")?;
+
+        let min_lookup_range = result
             .iter()
             .map(|x| x.min_lookup_inputs)
             .min()
             .unwrap_or(0);
 
-        let max_lookup_range = forward_pass_res
-            .get(&key)
-            .unwrap()
+        let max_lookup_range = result
             .iter()
             .map(|x| x.max_lookup_inputs)
             .max()
             .unwrap_or(0);
 
-        let max_range_size = forward_pass_res
-            .get(&key)
-            .unwrap()
-            .iter()
-            .map(|x| x.max_range_size)
-            .max()
-            .unwrap_or(0);
+        let max_range_size = result.iter().map(|x| x.max_range_size).max().unwrap_or(0);
 
         let res = circuit.calc_min_logrows(
             (min_lookup_range, max_lookup_range),
@@ -1114,6 +1113,7 @@ pub(crate) 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()
@@ -1697,7 +1697,7 @@ pub(crate) fn fuzz(
     let logrows = circuit.settings().run_args.logrows;
 
     info!("setting up tests");
-
+    #[cfg(unix)]
     let _r = Gag::stdout()?;
     let params = gen_srs::<KZGCommitmentScheme<Bn256>>(logrows);
 
@@ -1715,6 +1715,7 @@ pub(crate) fn fuzz(
     let public_inputs = circuit.prepare_public_inputs(&data)?;
 
     let strategy = KZGSingleStrategy::new(&params);
+    #[cfg(unix)]
     std::mem::drop(_r);
 
     info!("starting fuzzing");
@@ -1905,6 +1906,7 @@ pub(crate) fn run_fuzz_fn(
     passed: &AtomicBool,
 ) {
     let num_failures = AtomicI64::new(0);
+    #[cfg(unix)]
     let _r = Gag::stdout().unwrap();
 
     let pb = init_bar(num_runs as u64);
@@ -1918,6 +1920,7 @@ pub(crate) fn run_fuzz_fn(
         pb.inc(1);
     });
     pb.finish_with_message("Done.");
+    #[cfg(unix)]
     std::mem::drop(_r);
     info!(
         "num failures: {} out of {}",

src/graph/utilities.rs

@@ -23,7 +23,10 @@ use std::sync::Arc;
 use tract_onnx::prelude::{DatumType, Node as OnnxNode, TypedFact, TypedOp};
 #[cfg(not(target_arch = "wasm32"))]
 use tract_onnx::tract_core::ops::{
-    array::{Gather, GatherElements, MultiBroadcastTo, OneHot, ScatterElements, Slice, Topk},
+    array::{
+        Gather, GatherElements, GatherNd, MultiBroadcastTo, OneHot, ScatterElements, ScatterNd,
+        Slice, Topk,
+    },
     change_axes::AxisOp,
     cnn::{Conv, Deconv},
     einsum::EinSum,
@@ -467,6 +470,78 @@ pub fn new_op_from_onnx(
 
             // Extract the max value
         }
+        "ScatterNd" => {
+            if inputs.len() != 3 {
+                return Err(Box::new(GraphError::InvalidDims(
+                    idx,
+                    "scatter nd".to_string(),
+                )));
+            };
+            // just verify it deserializes correctly
+            let _op = load_op::<ScatterNd>(node.op(), idx, node.op().name().to_string())?;
+
+            let mut op = SupportedOp::Linear(crate::circuit::ops::poly::PolyOp::ScatterND {
+                constant_idx: None,
+            });
+
+            // if param_visibility.is_public() {
+            if let Some(c) = inputs[1].opkind().get_mutable_constant() {
+                inputs[1].decrement_use();
+                deleted_indices.push(inputs.len() - 1);
+                op = SupportedOp::Linear(crate::circuit::ops::poly::PolyOp::ScatterND {
+                    constant_idx: Some(c.raw_values.map(|x| x as usize)),
+                })
+            }
+            // }
+
+            if inputs[1].opkind().is_input() {
+                inputs[1].replace_opkind(SupportedOp::Input(crate::circuit::ops::Input {
+                    scale: 0,
+                    datum_type: InputType::TDim,
+                }));
+                inputs[1].bump_scale(0);
+            }
+
+            op
+        }
+
+        "GatherNd" => {
+            if inputs.len() != 2 {
+                return Err(Box::new(GraphError::InvalidDims(
+                    idx,
+                    "gather nd".to_string(),
+                )));
+            };
+            let op = load_op::<GatherNd>(node.op(), idx, node.op().name().to_string())?;
+            let batch_dims = op.batch_dims;
+
+            let mut op = SupportedOp::Linear(crate::circuit::ops::poly::PolyOp::GatherND {
+                batch_dims,
+                indices: None,
+            });
+
+            // if param_visibility.is_public() {
+            if let Some(c) = inputs[1].opkind().get_mutable_constant() {
+                inputs[1].decrement_use();
+                deleted_indices.push(inputs.len() - 1);
+                op = SupportedOp::Linear(crate::circuit::ops::poly::PolyOp::GatherND {
+                    batch_dims,
+                    indices: Some(c.raw_values.map(|x| x as usize)),
+                })
+            }
+            // }
+
+            if inputs[1].opkind().is_input() {
+                inputs[1].replace_opkind(SupportedOp::Input(crate::circuit::ops::Input {
+                    scale: 0,
+                    datum_type: InputType::TDim,
+                }));
+                inputs[1].bump_scale(0);
+            }
+
+            op
+        }
+
         "GatherElements" => {
             if inputs.len() != 2 {
                 return Err(Box::new(GraphError::InvalidDims(

src/python.rs

@@ -484,7 +484,6 @@ fn get_srs(
             srs_path,
             settings_path,
             logrows,
-            CheckMode::SAFE,
         ))
         .map_err(|e| {
             let err_str = format!("Failed to get srs: {}", e);
@@ -1104,6 +1103,7 @@ fn ezkl(_py: Python<'_>, m: &PyModule) -> PyResult<()> {
     m.add_class::<PyG1Affine>()?;
     m.add_class::<PyG1>()?;
     m.add_class::<PyTestDataSource>()?;
+    m.add("__version__", env!("CARGO_PKG_VERSION"))?;
     m.add_function(wrap_pyfunction!(felt_to_big_endian, m)?)?;
     m.add_function(wrap_pyfunction!(felt_to_int, m)?)?;
     m.add_function(wrap_pyfunction!(felt_to_float, m)?)?;

src/tensor/mod.rs

@@ -673,6 +673,68 @@ impl<T: Clone + TensorType> Tensor<T> {
         Tensor::new(Some(&res), &dims)
     }
 
+    /// Set a slice of the Tensor.
+    /// ```
+    /// use ezkl::tensor::Tensor;
+    /// let mut a = Tensor::<i32>::new(Some(&[1, 2, 3, 4, 5, 6]), &[2, 3]).unwrap();
+    /// let b = Tensor::<i32>::new(Some(&[1, 2, 3, 1, 2, 3]), &[2, 3]).unwrap();
+    /// a.set_slice(&[1..2], &Tensor::<i32>::new(Some(&[1, 2, 3]), &[1, 3]).unwrap()).unwrap();
+    /// assert_eq!(a, b);
+    /// ```
+    pub fn set_slice(
+        &mut self,
+        indices: &[Range<usize>],
+        value: &Tensor<T>,
+    ) -> Result<(), TensorError>
+    where
+        T: Send + Sync,
+    {
+        if indices.is_empty() {
+            return Ok(());
+        }
+        if self.dims.len() < indices.len() {
+            return Err(TensorError::DimError(format!(
+                "The dimensionality of the slice {:?} is greater than the tensor's {:?}",
+                indices, self.dims
+            )));
+        }
+
+        // if indices weren't specified we fill them in as required
+        let mut full_indices = indices.to_vec();
+
+        let omitted_dims = (indices.len()..self.dims.len())
+            .map(|i| self.dims[i])
+            .collect::<Vec<_>>();
+
+        for dim in &omitted_dims {
+            full_indices.push(0..*dim);
+        }
+
+        let full_dims = full_indices
+            .iter()
+            .map(|x| x.end - x.start)
+            .collect::<Vec<_>>();
+
+        // now broadcast the value to the full dims
+        let value = value.expand(&full_dims)?;
+
+        let cartesian_coord: Vec<Vec<usize>> = full_indices
+            .iter()
+            .cloned()
+            .multi_cartesian_product()
+            .collect();
+
+        let _ = cartesian_coord
+            .iter()
+            .enumerate()
+            .map(|(i, e)| {
+                self.set(e, value[i].clone());
+            })
+            .collect::<Vec<_>>();
+
+        Ok(())
+    }
+
     /// Get the array index from rows / columns indices.
     ///
     /// ```

src/tensor/ops.rs

@@ -2,7 +2,7 @@ use super::TensorError;
 use crate::tensor::{Tensor, TensorType};
 use itertools::Itertools;
 use maybe_rayon::{
-    iter::IndexedParallelIterator, iter::IntoParallelRefMutIterator, iter::ParallelIterator,
+    iter::{IndexedParallelIterator, IntoParallelRefMutIterator, ParallelIterator},
     prelude::IntoParallelRefIterator,
 };
 use std::collections::{HashMap, HashSet};
@@ -1328,6 +1328,316 @@ pub fn gather_elements<T: TensorType + Send + Sync>(
     Ok(output)
 }
 
+/// Gather ND.
+/// # Arguments
+/// * `input` - Tensor
+/// * `index` - Tensor of indices to gather
+/// * `batch_dims` - Number of batch dimensions
+/// # Examples
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::tensor::ops::gather_nd;
+/// let x = Tensor::<i128>::new(
+///   Some(&[0, 1, 2, 3]),
+/// &[2, 2],
+/// ).unwrap();
+/// let index = Tensor::<usize>::new(
+/// Some(&[0, 0, 1, 1]),
+/// &[2, 2],
+/// ).unwrap();
+/// let result = gather_nd(&x, &index, 0).unwrap();
+/// let expected = Tensor::<i128>::new(Some(&[0, 3]), &[2]).unwrap();
+/// assert_eq!(result, expected);
+///
+/// let index = Tensor::<usize>::new(
+/// Some(&[1, 0]),
+/// &[2, 1],
+/// ).unwrap();
+/// let result = gather_nd(&x, &index, 0).unwrap();
+/// let expected = Tensor::<i128>::new(Some(&[2, 3, 0, 1]), &[2, 2]).unwrap();
+/// assert_eq!(result, expected);
+///
+/// let x = Tensor::<i128>::new(
+///  Some(&[0, 1, 2, 3, 4, 5, 6, 7]),
+/// &[2, 2, 2],
+/// ).unwrap();
+/// let index = Tensor::<usize>::new(
+///  Some(&[0, 1, 1, 0]),
+/// &[2, 2],
+/// ).unwrap();
+/// let result = gather_nd(&x, &index, 0).unwrap();
+/// let expected = Tensor::<i128>::new(Some(&[2, 3, 4, 5]), &[2, 2]).unwrap();
+/// assert_eq!(result, expected);
+///
+/// let index = Tensor::<usize>::new(
+///  Some(&[0, 1, 1, 0]),
+/// &[2, 1, 2],
+/// ).unwrap();
+/// let result = gather_nd(&x, &index, 0).unwrap();
+/// let expected = Tensor::<i128>::new(Some(&[2, 3, 4, 5]), &[2, 1, 2]).unwrap();
+/// assert_eq!(result, expected);
+///
+/// let index = Tensor::<usize>::new(
+/// Some(&[1, 0]),
+/// &[2, 1],
+/// ).unwrap();
+/// let result = gather_nd(&x, &index, 1).unwrap();
+/// let expected = Tensor::<i128>::new(Some(&[2, 3, 4, 5]), &[2, 2]).unwrap();
+/// assert_eq!(result, expected);
+///
+/// let index = Tensor::<usize>::new(
+///  Some(&[0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1]),
+/// &[2, 2, 3],
+/// ).unwrap();
+/// let result = gather_nd(&x, &index, 0).unwrap();
+/// let expected = Tensor::<i128>::new(Some(&[2, 3, 4, 5]), &[2, 2]).unwrap();
+/// assert_eq!(result, expected);
+///
+/// let index = Tensor::<usize>::new(
+///  Some(&[0, 1, 0, 0, 1, 1, 1, 0]),
+/// &[2, 2, 2],
+/// ).unwrap();
+/// let result = gather_nd(&x, &index, 0).unwrap();
+/// let expected = Tensor::<i128>::new(Some(&[2, 3, 0, 1, 6, 7, 4, 5]), &[2, 2, 2]).unwrap();
+/// assert_eq!(result, expected);
+///
+/// let index = Tensor::<usize>::new(
+///  Some(&[0, 1, 0, 1, 1, 1]),
+/// &[2, 3],
+/// ).unwrap();
+/// let result = gather_nd(&x, &index, 0).unwrap();
+/// let expected = Tensor::<i128>::new(Some(&[2, 7]), &[2]).unwrap();
+/// assert_eq!(result, expected);
+///
+pub fn gather_nd<T: TensorType + Send + Sync>(
+    input: &Tensor<T>,
+    index: &Tensor<usize>,
+    batch_dims: usize,
+) -> Result<Tensor<T>, TensorError> {
+    // Calculate the output tensor size
+    let index_dims = index.dims().to_vec();
+    let input_dims = input.dims().to_vec();
+    let last_value = index_dims
+        .last()
+        .ok_or(TensorError::DimMismatch("gather_nd".to_string()))?;
+    if last_value > &(input_dims.len() - batch_dims) {
+        return Err(TensorError::DimMismatch("gather_nd".to_string()));
+    }
+
+    let output_size =
+    // If indices_shape[-1] == r-b, since the rank of indices is q,
+    // indices can be thought of as N (q-b-1)-dimensional tensors containing 1-D tensors of dimension r-b,
+    // where N is an integer equals to the product of 1 and all the elements in the batch dimensions of the indices_shape.
+    // Let us think of each such r-b ranked tensor as indices_slice.
+    // Each scalar value corresponding to data[0:b-1,indices_slice] is filled into
+    // the corresponding location of the (q-b-1)-dimensional tensor to form the output tensor
+     // if indices_shape[-1] < r-b, since the rank of indices is q, indices can be thought of as N (q-b-1)-dimensional tensor containing 1-D tensors of dimension < r-b.
+    // Let us think of each such tensors as indices_slice.
+    // Each tensor slice corresponding to data[0:b-1, indices_slice , :] is filled into the corresponding location of the (q-b-1)-dimensional tensor to form the output tensor
+    {
+        let output_rank = input_dims.len() + index_dims.len() - 1 - batch_dims - last_value;
+
+        let mut dims = index_dims[..index_dims.len() - 1].to_vec();
+        let input_offset = batch_dims + last_value;
+        dims.extend(input_dims[input_offset..input_dims.len()].to_vec());
+
+        assert_eq!(output_rank, dims.len());
+        dims
+
+    };
+
+    // cartesian coord over batch dims
+    let mut batch_cartesian_coord = input_dims[0..batch_dims]
+        .iter()
+        .map(|x| 0..*x)
+        .multi_cartesian_product()
+        .collect::<Vec<_>>();
+
+    if batch_cartesian_coord.is_empty() {
+        batch_cartesian_coord.push(vec![]);
+    }
+
+    let outputs = batch_cartesian_coord
+        .par_iter()
+        .map(|batch_coord| {
+            let batch_slice = batch_coord.iter().map(|x| *x..*x + 1).collect::<Vec<_>>();
+            let mut index_slice = index.get_slice(&batch_slice)?;
+            index_slice.reshape(&index.dims()[batch_dims..])?;
+            let mut input_slice = input.get_slice(&batch_slice)?;
+            input_slice.reshape(&input.dims()[batch_dims..])?;
+
+            let mut inner_cartesian_coord = index_slice.dims()[0..index_slice.dims().len() - 1]
+                .iter()
+                .map(|x| 0..*x)
+                .multi_cartesian_product()
+                .collect::<Vec<_>>();
+
+            if inner_cartesian_coord.is_empty() {
+                inner_cartesian_coord.push(vec![]);
+            }
+
+            let output = inner_cartesian_coord
+                .iter()
+                .map(|coord| {
+                    let slice = coord
+                        .iter()
+                        .map(|x| *x..*x + 1)
+                        .chain(batch_coord.iter().map(|x| *x..*x + 1))
+                        .collect::<Vec<_>>();
+
+                    let index_slice = index_slice
+                        .get_slice(&slice)
+                        .unwrap()
+                        .iter()
+                        .map(|x| *x..*x + 1)
+                        .collect::<Vec<_>>();
+
+                    input_slice.get_slice(&index_slice).unwrap()
+                })
+                .collect::<Tensor<_>>();
+
+            output.combine()
+        })
+        .collect::<Result<Vec<_>, _>>()?;
+
+    let mut outputs = outputs.into_iter().flatten().collect::<Tensor<_>>();
+
+    outputs.reshape(&output_size)?;
+
+    Ok(outputs)
+}
+
+/// Scatter ND.
+/// This operator is the inverse of GatherND.
+/// # Arguments
+/// * `input` - Tensor
+/// * `index` - Tensor of indices to scatter
+/// * `src` - Tensor of src
+/// # Examples
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::tensor::ops::scatter_nd;
+/// let x = Tensor::<i128>::new(
+///  Some(&[1, 2, 3, 4, 5, 6, 7, 8]),
+/// &[8],
+/// ).unwrap();
+///
+/// let index = Tensor::<usize>::new(
+/// Some(&[4, 3, 1, 7]),
+/// &[4, 1],
+/// ).unwrap();
+/// let src = Tensor::<i128>::new(
+/// Some(&[9, 10, 11, 12]),
+/// &[4],
+/// ).unwrap();
+/// let result = scatter_nd(&x, &index, &src).unwrap();
+/// let expected = Tensor::<i128>::new(Some(&[1, 11, 3, 10, 9, 6, 7, 12]), &[8]).unwrap();
+/// assert_eq!(result, expected);
+///
+/// let x = Tensor::<i128>::new(
+///  Some(&[1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1,
+///         1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1,
+///         8, 7, 6, 5, 4, 3, 2, 1, 1, 2, 3, 4, 5, 6, 7, 8,
+///         8, 7, 6, 5, 4, 3, 2, 1, 1, 2, 3, 4, 5, 6, 7, 8]),
+/// &[4, 4, 4],
+/// ).unwrap();
+///
+/// let index = Tensor::<usize>::new(
+///   Some(&[0, 2]),
+/// &[2, 1],
+/// ).unwrap();
+///
+/// let src = Tensor::<i128>::new(
+///  Some(&[5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
+///         1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4,
+///   ]),
+/// &[2, 4, 4],
+/// ).unwrap();
+///
+/// let result = scatter_nd(&x, &index, &src).unwrap();
+///
+/// let expected = Tensor::<i128>::new(
+///  Some(&[5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
+///         1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1,
+///         1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4,
+///         8, 7, 6, 5, 4, 3, 2, 1, 1, 2, 3, 4, 5, 6, 7, 8]),
+/// &[4, 4, 4],
+/// ).unwrap();
+/// assert_eq!(result, expected);
+///
+/// let x = Tensor::<i128>::new(
+///  Some(&[1, 2, 3, 4, 5, 6, 7, 8]),
+/// &[2, 4],
+/// ).unwrap();
+///
+/// let index = Tensor::<usize>::new(
+/// Some(&[0, 1]),
+/// &[2, 1],
+/// ).unwrap();
+/// let src = Tensor::<i128>::new(
+/// Some(&[9, 10]),
+/// &[2],
+/// ).unwrap();
+/// let result = scatter_nd(&x, &index, &src).unwrap();
+/// let expected = Tensor::<i128>::new(Some(&[9, 9, 9, 9, 10, 10, 10, 10]), &[2, 4]).unwrap();
+/// assert_eq!(result, expected);
+///
+/// let x = Tensor::<i128>::new(
+///  Some(&[1, 2, 3, 4, 5, 6, 7, 8]),
+/// &[2, 4],
+/// ).unwrap();
+///
+/// let index = Tensor::<usize>::new(
+/// Some(&[0, 1]),
+/// &[1, 1, 2],
+/// ).unwrap();
+/// let src = Tensor::<i128>::new(
+/// Some(&[9]),
+/// &[1, 1],
+/// ).unwrap();
+/// let result = scatter_nd(&x, &index, &src).unwrap();
+/// let expected = Tensor::<i128>::new(Some(&[1, 9, 3, 4, 5, 6, 7, 8]), &[2, 4]).unwrap();
+/// assert_eq!(result, expected);
+/// ````
+///
+pub fn scatter_nd<T: TensorType + Send + Sync>(
+    input: &Tensor<T>,
+    index: &Tensor<usize>,
+    src: &Tensor<T>,
+) -> Result<Tensor<T>, TensorError> {
+    // Calculate the output tensor size
+    let index_dims = index.dims().to_vec();
+    let input_dims = input.dims().to_vec();
+    let last_value = index_dims
+        .last()
+        .ok_or(TensorError::DimMismatch("scatter_nd".to_string()))?;
+    if last_value > &input_dims.len() {
+        return Err(TensorError::DimMismatch("scatter_nd".to_string()));
+    }
+
+    let mut output = input.clone();
+
+    let cartesian_coord = index_dims[0..index_dims.len() - 1]
+        .iter()
+        .map(|x| 0..*x)
+        .multi_cartesian_product()
+        .collect::<Vec<_>>();
+
+    cartesian_coord
+        .iter()
+        .map(|coord| {
+            let slice = coord.iter().map(|x| *x..*x + 1).collect::<Vec<_>>();
+            let index_val = index.get_slice(&slice)?;
+            let index_slice = index_val.iter().map(|x| *x..*x + 1).collect::<Vec<_>>();
+            let src_val = src.get_slice(&slice)?;
+            output.set_slice(&index_slice, &src_val)?;
+            Ok(())
+        })
+        .collect::<Result<Vec<_>, _>>()?;
+
+    Ok(output)
+}
+
 fn axes_op<T: TensorType + Send + Sync>(
     a: &Tensor<T>,
     axes: &[usize],

src/tensor/val.rs

@@ -4,6 +4,37 @@ use super::{
 };
 use halo2_proofs::{arithmetic::Field, plonk::Instance};
 
+pub(crate) fn create_constant_tensor<
+    F: PrimeField + TensorType + std::marker::Send + std::marker::Sync + PartialOrd,
+>(
+    val: F,
+    len: usize,
+) -> ValTensor<F> {
+    let mut constant = Tensor::from(vec![ValType::Constant(val); len].into_iter());
+    constant.set_visibility(&crate::graph::Visibility::Fixed);
+    ValTensor::from(constant)
+}
+
+pub(crate) fn create_unit_tensor<
+    F: PrimeField + TensorType + std::marker::Send + std::marker::Sync + PartialOrd,
+>(
+    len: usize,
+) -> ValTensor<F> {
+    let mut unit = Tensor::from(vec![ValType::Constant(F::ONE); len].into_iter());
+    unit.set_visibility(&crate::graph::Visibility::Fixed);
+    ValTensor::from(unit)
+}
+
+pub(crate) fn create_zero_tensor<
+    F: PrimeField + TensorType + std::marker::Send + std::marker::Sync + PartialOrd,
+>(
+    len: usize,
+) -> ValTensor<F> {
+    let mut zero = Tensor::from(vec![ValType::Constant(F::ZERO); len].into_iter());
+    zero.set_visibility(&crate::graph::Visibility::Fixed);
+    ValTensor::from(zero)
+}
+
 #[derive(Debug, Clone)]
 /// A [ValType] is a wrapper around Halo2 value(s).
 pub enum ValType<F: PrimeField + TensorType + std::marker::Send + std::marker::Sync + PartialOrd> {
@@ -463,7 +494,12 @@ impl<F: PrimeField + TensorType + PartialOrd> ValTensor<F> {
             }
             _ => return Err(Box::new(TensorError::WrongMethod)),
         };
-        Ok(integer_evals.into_iter().into())
+        let mut tensor: Tensor<i128> = integer_evals.into_iter().into();
+        match tensor.reshape(self.dims()) {
+            _ => {}
+        };
+
+        Ok(tensor)
     }
 
     /// Calls `pad_to_zero_rem` on the inner tensor.

tests/integration_tests.rs

@@ -193,7 +193,7 @@ mod native_tests {
         "1l_tiny_div",
     ];
 
-    const TESTS: [&str; 77] = [
+    const TESTS: [&str; 79] = [
         "1l_mlp", //0
         "1l_slice",
         "1l_concat",
@@ -275,6 +275,8 @@ mod native_tests {
         "ltsf",
         "remainder", //75
         "bitshift",
+        "gather_nd",
+        "scatter_nd",
     ];
 
     const WASM_TESTS: [&str; 46] = [
@@ -502,7 +504,7 @@ mod native_tests {
             }
         });
 
-            seq!(N in 0..=76 {
+            seq!(N in 0..=78 {
 
             #(#[test_case(TESTS[N])])*
             #[ignore]
@@ -589,13 +591,16 @@ mod native_tests {
 
             #(#[test_case(TESTS[N])])*
             fn mock_large_batch_public_outputs_(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 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);
-                test_dir.close().unwrap();
+                // currently variable output rank is not supported in ONNX
+                if test != "gather_nd" {
+                    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 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);
+                    test_dir.close().unwrap();
+                }
             }
 
             #(#[test_case(TESTS[N])])*