ci: update version string in docs

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '0.0.0'
+release = '20.2.4'
 version = release
 
 

src/circuit/ops/mod.rs

@@ -301,8 +301,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Constant<F> {
             Some(v) => v,
             None => return Err(CircuitError::UnsetVisibility),
         };
-        self.quantized_values =
-            quantize_tensor(self.raw_values.clone(), new_scale, &visibility, true)?;
+        self.quantized_values = quantize_tensor(self.raw_values.clone(), new_scale, &visibility)?;
         Ok(())
     }
 
@@ -318,8 +317,13 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Constant<F> {
 }
 
 impl<
-    F: PrimeField + TensorType + PartialOrd + std::hash::Hash + Serialize + for<'de> Deserialize<'de>,
-> Op<F> for Constant<F>
+        F: PrimeField
+            + TensorType
+            + PartialOrd
+            + std::hash::Hash
+            + Serialize
+            + for<'de> Deserialize<'de>,
+    > Op<F> for Constant<F>
 {
     fn as_any(&self) -> &dyn Any {
         self

src/graph/node.rs

@@ -14,14 +14,14 @@ use super::VarScales;
 use super::Visibility;
 
 // Import operation types for different circuit components
+use crate::circuit::hybrid::HybridOp;
+use crate::circuit::lookup::LookupOp;
+use crate::circuit::poly::PolyOp;
 use crate::circuit::CircuitError;
 use crate::circuit::Constant;
 use crate::circuit::Input;
 use crate::circuit::Op;
 use crate::circuit::Unknown;
-use crate::circuit::hybrid::HybridOp;
-use crate::circuit::lookup::LookupOp;
-use crate::circuit::poly::PolyOp;
 
 // Import graph error types for EZKL
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
@@ -740,7 +740,7 @@ fn rescale_const_with_single_use(
         if scale_max > &current_scale {
             let raw_values = constant.raw_values.clone();
             constant.quantized_values =
-                super::quantize_tensor(raw_values, *scale_max, param_visibility, true)?;
+                super::quantize_tensor(raw_values, *scale_max, param_visibility)?;
         }
     }
 

src/graph/utilities.rs

@@ -1,14 +1,14 @@
+use super::errors::GraphError;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use super::VarScales;
-use super::errors::GraphError;
 use super::{Rescaled, SupportedOp, Visibility};
-use crate::circuit::Op;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use crate::circuit::hybrid::HybridOp;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use crate::circuit::lookup::LookupOp;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use crate::circuit::poly::PolyOp;
+use crate::circuit::Op;
 use crate::fieldutils::IntegerRep;
 use crate::tensor::{Tensor, TensorError, TensorType};
 use halo2curves::bn256::Fr as Fp;
@@ -22,7 +22,6 @@ use std::sync::Arc;
 use tract_onnx::prelude::{DatumType, Node as OnnxNode, TypedFact, TypedOp};
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use tract_onnx::tract_core::ops::{
-    Downsample,
     array::{
         Gather, GatherElements, GatherNd, MultiBroadcastTo, OneHot, ScatterElements, ScatterNd,
         Slice, Topk,
@@ -32,6 +31,7 @@ use tract_onnx::tract_core::ops::{
     einsum::EinSum,
     element_wise::ElementWiseOp,
     nn::{LeakyRelu, Reduce, Softmax},
+    Downsample,
 };
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use tract_onnx::tract_hir::{
@@ -68,33 +68,6 @@ pub fn quantize_float(
     Ok(scaled)
 }
 
-/// Quantizes an iterable of f64 to a [Tensor] of IntegerRep using a fixed point representation.
-/// NAN gets mapped to 0. And values that exceed the max are capped.
-/// Arguments
-///
-/// * `elem` - the element to quantize.
-/// * `shift` - offset used in the fixed point representation.
-/// * `scale` - `2^scale` used in the fixed point representation.
-pub fn quantize_float_and_cap(
-    elem: &f64,
-    shift: f64,
-    scale: crate::Scale,
-) -> Result<IntegerRep, TensorError> {
-    let mult = scale_to_multiplier(scale);
-    let max_value = ((IntegerRep::MAX as f64 - shift) / mult).round(); // the maximum value that can be represented w/o sig bit truncation
-
-    if *elem > max_value {
-        return Ok(IntegerRep::MAX);
-    } else if *elem < -max_value {
-        return Ok(IntegerRep::MIN);
-    }
-
-    // we parallelize the quantization process as it seems to be quite slow at times
-    let scaled = (mult * *elem + shift).round() as IntegerRep;
-
-    Ok(scaled)
-}
-
 /// Dequantizes a field element to a f64 using a fixed point representation.
 /// Arguments
 /// * `felt` - the field element to dequantize.
@@ -406,7 +379,7 @@ pub fn new_op_from_onnx(
             let range = (start..end).step_by(delta).collect::<Vec<_>>();
             let raw_value = range.iter().map(|x| *x as f32).collect::<Tensor<_>>();
             // Quantize the raw value (integers)
-            let quantized_value = quantize_tensor(raw_value.clone(), 0, &Visibility::Fixed, false)?;
+            let quantized_value = quantize_tensor(raw_value.clone(), 0, &Visibility::Fixed)?;
 
             let c = crate::circuit::ops::Constant::new(
                 quantized_value,
@@ -738,7 +711,6 @@ pub fn new_op_from_onnx(
                 raw_value.clone(),
                 constant_scale,
                 &run_args.param_visibility,
-                true,
             )?;
             let c = crate::circuit::ops::Constant::new(
                 quantized_value,
@@ -1578,20 +1550,13 @@ pub fn quantize_tensor<F: PrimeField + TensorType + PartialOrd>(
     const_value: Tensor<f32>,
     scale: crate::Scale,
     visibility: &Visibility,
-    cap: bool,
 ) -> Result<Tensor<F>, TensorError> {
     let mut value: Tensor<F> = const_value.par_enum_map(|_, x| {
-        if cap {
-            Ok::<F, TensorError>(crate::fieldutils::integer_rep_to_felt::<F>(
-                quantize_float_and_cap(&(x).into(), 0.0, scale)?,
-            ))
-        } else {
-            Ok(crate::fieldutils::integer_rep_to_felt::<F>(quantize_float(
-                &(x).into(),
-                0.0,
-                scale,
-            )?))
-        }
+        Ok::<_, TensorError>(crate::fieldutils::integer_rep_to_felt::<F>(quantize_float(
+            &(x).into(),
+            0.0,
+            scale,
+        )?))
     })?;
 
     value.set_scale(scale);
@@ -1679,7 +1644,7 @@ pub mod tests {
         let reference: Tensor<Fp> = (0..10).map(|x| x.into()).into();
         let scale = 0;
         let visibility = &Visibility::Public;
-        let quantized: Tensor<Fp> = quantize_tensor(tensor, scale, visibility, false).unwrap();
+        let quantized: Tensor<Fp> = quantize_tensor(tensor, scale, visibility).unwrap();
         assert_eq!(quantized.len(), 10);
         assert_eq!(quantized, reference);
     }

src/tensor/mod.rs

@@ -27,7 +27,7 @@ pub use var::*;
 
 use crate::{
     circuit::utils,
-    fieldutils::{IntegerRep, integer_rep_to_felt},
+    fieldutils::{integer_rep_to_felt, IntegerRep},
     graph::Visibility,
 };
 
@@ -415,7 +415,7 @@ impl<T: Clone + TensorType + PrimeField> Tensor<T> {
                 Err(_) => {
                     return Err(TensorError::FileLoadError(
                         "Failed to read tensor".to_string(),
-                    ));
+                    ))
                 }
             }
         }
@@ -926,6 +926,9 @@ impl<T: Clone + TensorType> Tensor<T> {
                 ));
             }
             self.dims = vec![];
+        }
+        if self.dims() == &[0] && new_dims.iter().product::<usize>() == 1 {
+            self.dims = Vec::from(new_dims);
         } else {
             let product = if new_dims != [0] {
                 new_dims.iter().product::<usize>()
@@ -1104,6 +1107,10 @@ impl<T: Clone + TensorType> Tensor<T> {
             let mut output = self.clone();
             output.reshape(shape)?;
             return Ok(output);
+        } else if self.dims() == &[0] && shape.iter().product::<usize>() == 1 {
+            let mut output = self.clone();
+            output.reshape(shape)?;
+            return Ok(output);
         }
 
         if self.dims().len() > shape.len() {
@@ -1254,7 +1261,7 @@ impl<T: Clone + TensorType> Tensor<T> {
             None => {
                 return Err(TensorError::DimError(
                     "Cannot get last element of empty tensor".to_string(),
-                ));
+                ))
             }
         };
 
@@ -1279,7 +1286,7 @@ impl<T: Clone + TensorType> Tensor<T> {
             None => {
                 return Err(TensorError::DimError(
                     "Cannot get first element of empty tensor".to_string(),
-                ));
+                ))
             }
         };
 
@@ -1692,8 +1699,8 @@ impl<T: TensorType + Rem<Output = T> + std::marker::Send + std::marker::Sync + P
 
         lhs.par_iter_mut()
             .zip(rhs)
-            .map(|(o, r)| match T::zero() {
-                Some(zero) => {
+            .map(|(o, r)| {
+                match T::zero() { Some(zero) => {
                     if r != zero {
                         *o = o.clone() % r;
                         Ok(())
@@ -1702,10 +1709,11 @@ impl<T: TensorType + Rem<Output = T> + std::marker::Send + std::marker::Sync + P
                             "Cannot divide by zero in remainder".to_string(),
                         ))
                     }
-                }
-                _ => Err(TensorError::InvalidArgument(
-                    "Undefined zero value".to_string(),
-                )),
+                } _ => {
+                    Err(TensorError::InvalidArgument(
+                        "Undefined zero value".to_string(),
+                    ))
+                }}
             })
             .collect::<Result<Vec<_>, _>>()?;
 
@@ -1767,6 +1775,7 @@ pub fn get_broadcasted_shape(
     }
 }
 ////////////////////////
+///
 
 /// The shape of data for some operations
 #[derive(Clone, Debug, Serialize, Deserialize, PartialEq, Default, Copy)]

src/tensor/val.rs

@@ -1342,9 +1342,11 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> ValTensor<F> {
     /// Gets the total number of elements in the tensor
     pub fn len(&self) -> usize {
         match self {
-            ValTensor::Value { dims, .. } => {
+            ValTensor::Value { dims, inner, .. } => {
                 if !dims.is_empty() && (dims != &[0]) {
                     dims.iter().product::<usize>()
+                } else if dims.is_empty() {
+                    inner.inner.len()
                 } else {
                     0
                 }