ci: update version string in docs

BREAKING CHANGE: compiled model serialization is not backwards compatible

benches/poseidon.rs

@@ -23,8 +23,6 @@ use halo2curves::bn256::{Bn256, Fr};
 use rand::rngs::OsRng;
 use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 
-const L: usize = 10;
-
 #[derive(Clone, Debug)]
 struct MyCircuit {
     image: ValTensor<Fr>,
@@ -40,7 +38,7 @@ impl Circuit<Fr> for MyCircuit {
     }
 
     fn configure(cs: &mut ConstraintSystem<Fr>) -> Self::Config {
-        PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, 10>::configure(cs, ())
+        PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::configure(cs, ())
     }
 
     fn synthesize(
@@ -48,7 +46,7 @@ impl Circuit<Fr> for MyCircuit {
         config: Self::Config,
         mut layouter: impl Layouter<Fr>,
     ) -> Result<(), Error> {
-        let chip: PoseidonChip<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, L> =
+        let chip: PoseidonChip<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE> =
             PoseidonChip::new(config);
         chip.layout(&mut layouter, &[self.image.clone()], 0, &mut HashMap::new())?;
         Ok(())
@@ -59,7 +57,7 @@ fn runposeidon(c: &mut Criterion) {
     let mut group = c.benchmark_group("poseidon");
 
     for size in [64, 784, 2352, 12288].iter() {
-        let k = (PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, L>::num_rows(*size)
+        let k = (PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::num_rows(*size)
             as f32)
             .log2()
             .ceil() as u32;
@@ -67,7 +65,7 @@ fn runposeidon(c: &mut Criterion) {
 
         let message = (0..*size).map(|_| Fr::random(OsRng)).collect::<Vec<_>>();
         let _output =
-            PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE, L>::run(message.to_vec())
+            PoseidonChip::<PoseidonSpec, POSEIDON_WIDTH, POSEIDON_RATE>::run(message.to_vec())
                 .unwrap();
 
         let mut image = Tensor::from(message.into_iter().map(Value::known));

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '0.0.0'
+release = '20.0.0'
 version = release
 
 

src/bindings/python.rs

@@ -337,6 +337,8 @@ enum PyInputType {
     Int,
     ///
     TDim,
+    ///
+    Unknown,
 }
 
 impl From<InputType> for PyInputType {
@@ -348,6 +350,7 @@ impl From<InputType> for PyInputType {
             InputType::F64 => PyInputType::F64,
             InputType::Int => PyInputType::Int,
             InputType::TDim => PyInputType::TDim,
+            InputType::Unknown => PyInputType::Unknown,
         }
     }
 }
@@ -361,6 +364,7 @@ impl From<PyInputType> for InputType {
             PyInputType::F64 => InputType::F64,
             PyInputType::Int => InputType::Int,
             PyInputType::TDim => InputType::TDim,
+            PyInputType::Unknown => InputType::Unknown,
         }
     }
 }
@@ -375,6 +379,7 @@ impl FromStr for PyInputType {
             "f64" => Ok(PyInputType::F64),
             "int" => Ok(PyInputType::Int),
             "tdim" => Ok(PyInputType::TDim),
+            "unknown" => Ok(PyInputType::Unknown),
             _ => Err("Invalid value for InputType".to_string()),
         }
     }

src/circuit/ops/mod.rs

@@ -1,6 +1,8 @@
 use std::any::Any;
 
 use serde::{Deserialize, Serialize};
+#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
+use tract_onnx::prelude::DatumType;
 
 use crate::{
     graph::quantize_tensor,
@@ -96,6 +98,8 @@ pub enum InputType {
     Int,
     ///
     TDim,
+    ///
+    Unknown,
 }
 
 impl InputType {
@@ -132,6 +136,7 @@ impl InputType {
                 let int_input = input.clone().to_i64().unwrap();
                 *input = T::from_i64(int_input).unwrap();
             }
+            InputType::Unknown => {}
         }
     }
 }
@@ -152,6 +157,28 @@ impl std::str::FromStr for InputType {
     }
 }
 
+#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
+impl From<DatumType> for InputType {
+    fn from(datum_type: DatumType) -> Self {
+        match datum_type {
+            DatumType::Bool => InputType::Bool,
+            DatumType::F16 => InputType::F16,
+            DatumType::F32 => InputType::F32,
+            DatumType::F64 => InputType::F64,
+            DatumType::I8 => InputType::Int,
+            DatumType::I16 => InputType::Int,
+            DatumType::I32 => InputType::Int,
+            DatumType::I64 => InputType::Int,
+            DatumType::U8 => InputType::Int,
+            DatumType::U16 => InputType::Int,
+            DatumType::U32 => InputType::Int,
+            DatumType::U64 => InputType::Int,
+            DatumType::TDim => InputType::TDim,
+            _ => unimplemented!(),
+        }
+    }
+}
+
 ///
 #[derive(Clone, Debug, PartialEq, Eq, Hash, PartialOrd, Ord, Serialize, Deserialize)]
 pub struct Input {

src/graph/mod.rs

@@ -455,6 +455,10 @@ pub struct GraphSettings {
     pub num_blinding_factors: Option<usize>,
     /// unix time timestamp
     pub timestamp: Option<u128>,
+    /// Model inputs types (if any)
+    pub input_types: Option<Vec<InputType>>,
+    /// Model outputs types (if any)
+    pub output_types: Option<Vec<InputType>>,
 }
 
 impl GraphSettings {

src/graph/model.rs

@@ -379,9 +379,15 @@ pub struct ParsedNodes {
     pub nodes: BTreeMap<usize, NodeType>,
     inputs: Vec<usize>,
     outputs: Vec<Outlet>,
+    output_types: Vec<InputType>,
 }
 
 impl ParsedNodes {
+    /// Returns the output types of the computational graph.
+    pub fn get_output_types(&self) -> Vec<InputType> {
+        self.output_types.clone()
+    }
+
     /// Returns the number of the computational graph's inputs
     pub fn num_inputs(&self) -> usize {
         self.inputs.len()
@@ -491,6 +497,16 @@ impl Model {
         Ok(om)
     }
 
+    /// Gets the input types from the parsed nodes
+    pub fn get_input_types(&self) -> Result<Vec<InputType>, GraphError> {
+        self.graph.get_input_types()
+    }
+
+    /// Gets the output types from the parsed nodes
+    pub fn get_output_types(&self) -> Vec<InputType> {
+        self.graph.get_output_types()
+    }
+
     ///
     pub fn save(&self, path: PathBuf) -> Result<(), GraphError> {
         let f = std::fs::File::create(&path).map_err(|e| {
@@ -574,6 +590,11 @@ impl Model {
             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(),
+            input_types: match self.get_input_types() {
+                Ok(x) => Some(x),
+                Err(_) => None,
+            },
+            output_types: Some(self.get_output_types()),
             num_dynamic_lookups: res.num_dynamic_lookups,
             total_dynamic_col_size: res.dynamic_lookup_col_coord,
             num_shuffles: res.num_shuffles,
@@ -704,6 +725,11 @@ impl Model {
             nodes,
             inputs: model.inputs.iter().map(|o| o.node).collect(),
             outputs: model.outputs.iter().map(|o| (o.node, o.slot)).collect(),
+            output_types: model
+                .outputs
+                .iter()
+                .map(|o| Ok::<InputType, GraphError>(model.outlet_fact(*o)?.datum_type.into()))
+                .collect::<Result<Vec<_>, GraphError>>()?,
         };
 
         let duration = start_time.elapsed();
@@ -862,6 +888,15 @@ impl Model {
                         nodes: subgraph_nodes,
                         inputs: model.inputs.iter().map(|o| o.node).collect(),
                         outputs: model.outputs.iter().map(|o| (o.node, o.slot)).collect(),
+                        output_types: model
+                            .outputs
+                            .iter()
+                            .map(|o| {
+                                Ok::<InputType, GraphError>(
+                                    model.outlet_fact(*o)?.datum_type.into(),
+                                )
+                            })
+                            .collect::<Result<Vec<_>, GraphError>>()?,
                     };
 
                     let om = Model {
@@ -1579,4 +1614,16 @@ impl Model {
         }
         Ok(instance_shapes)
     }
+
+    /// Input types of the computational graph's public inputs (if any)
+    pub fn instance_types(&self) -> Result<Vec<InputType>, GraphError> {
+        let mut instance_types = vec![];
+        if self.visibility.input.is_public() {
+            instance_types.extend(self.graph.get_input_types()?);
+        }
+        if self.visibility.output.is_public() {
+            instance_types.extend(self.graph.get_output_types());
+        }
+        Ok(instance_types)
+    }
 }

src/tensor/ops.rs

@@ -387,7 +387,7 @@ pub fn add<T: TensorType + Add<Output = T> + std::marker::Send + std::marker::Sy
 ) -> Result<Tensor<T>, TensorError> {
     if t.len() == 1 {
         return Ok(t[0].clone());
-    } else if t.len() == 0 {
+    } else if t.is_empty() {
         return Err(TensorError::DimMismatch("add".to_string()));
     }
 
@@ -441,7 +441,7 @@ pub fn sub<T: TensorType + Sub<Output = T> + std::marker::Send + std::marker::Sy
 ) -> Result<Tensor<T>, TensorError> {
     if t.len() == 1 {
         return Ok(t[0].clone());
-    } else if t.len() == 0 {
+    } else if t.is_empty() {
         return Err(TensorError::DimMismatch("sub".to_string()));
     }
     // calculate value of output
@@ -492,7 +492,7 @@ pub fn mult<T: TensorType + Mul<Output = T> + std::marker::Send + std::marker::S
 ) -> Result<Tensor<T>, TensorError> {
     if t.len() == 1 {
         return Ok(t[0].clone());
-    } else if t.len() == 0 {
+    } else if t.is_empty() {
         return Err(TensorError::DimMismatch("mult".to_string()));
     }
     // calculate value of output
@@ -1326,7 +1326,6 @@ pub fn pad<T: TensorType>(
 ///
 /// # Errors
 /// Returns a TensorError if the tensors in `inputs` have incompatible dimensions for concatenation along the specified `axis`.
-
 pub fn concat<T: TensorType + Send + Sync>(
     inputs: &[&Tensor<T>],
     axis: usize,
@@ -2102,7 +2101,6 @@ pub mod nonlinearities {
     /// let expected = Tensor::<IntegerRep>::new(Some(&[4, 25, 8, 1, 1, 0]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     /// ```
-
     pub fn tanh(a: &Tensor<IntegerRep>, scale_input: f64) -> Tensor<IntegerRep> {
         a.par_enum_map(|_, a_i| {
             let kix = (a_i as f64) / scale_input;