Update rust.yml

.github/workflows/rust.yml

@@ -327,8 +327,8 @@ jobs:
           cd in-browser-evm-verifier
           pnpm build:commonjs
           cd ..
-      - name: Install solc
-        run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
+      # - name: Install solc
+      #   run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
       - name: Install Anvil
         run: cargo install --git https://github.com/foundry-rs/foundry --rev 62cdea8ff9e6efef011f77e295823b5f2dbeb3a1 --locked anvil --force
       - name: KZG prove and verify tests (EVM + VK rendered seperately)
@@ -536,8 +536,8 @@ jobs:
         with:
           crate: cargo-nextest
           locked: true
-      - name: Install solc
-        run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
+      # - name: Install solc
+      #   run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
       - name: Install Anvil
         run: cargo install --git https://github.com/foundry-rs/foundry --rev 62cdea8ff9e6efef011f77e295823b5f2dbeb3a1 --locked anvil --force
       - name: KZG prove and verify aggr tests
@@ -575,8 +575,8 @@ jobs:
           components: rustfmt, clippy
       - name: Install cmake
         run: sudo apt-get install -y cmake
-      - name: Install solc
-        run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
+      # - name: Install solc
+      #   run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
       - name: Setup Virtual Env and Install python dependencies
         run: python -m venv .env --clear; source .env/bin/activate; pip install -r requirements.txt;
       - name: Install Anvil
@@ -652,8 +652,8 @@ jobs:
         with:
           crate: cargo-nextest
           locked: true
-      - name: Install solc
-        run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
+      # - name: Install solc
+      #   run: (hash svm 2>/dev/null || cargo install svm-rs) && svm install 0.8.20 && solc --version
       - name: Install Anvil
         run: cargo install --git https://github.com/foundry-rs/foundry --rev 62cdea8ff9e6efef011f77e295823b5f2dbeb3a1 --locked anvil --force
       - name: Install pip

Cargo.lock

@@ -1877,6 +1877,7 @@ dependencies = [
  "log",
  "maybe-rayon",
  "metal",
+ "mimalloc",
  "mnist",
  "num",
  "objc",
@@ -2938,6 +2939,16 @@ version = "0.2.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "4ec2a862134d2a7d32d7983ddcdd1c4923530833c9f2ea1a44fc5fa473989058"
 
+[[package]]
+name = "libmimalloc-sys"
+version = "0.1.39"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "23aa6811d3bd4deb8a84dde645f943476d13b248d818edcf8ce0b2f37f036b44"
+dependencies = [
+ "cc",
+ "libc",
+]
+
 [[package]]
 name = "libredox"
 version = "0.0.1"
@@ -3139,6 +3150,15 @@ dependencies = [
  "paste",
 ]
 
+[[package]]
+name = "mimalloc"
+version = "0.1.43"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "68914350ae34959d83f732418d51e2427a794055d0b9529f48259ac07af65633"
+dependencies = [
+ "libmimalloc-sys",
+]
+
 [[package]]
 name = "mime"
 version = "0.3.17"

Cargo.toml

@@ -16,7 +16,7 @@ crate-type = ["cdylib", "rlib"]
 
 [dependencies]
 halo2_gadgets = { git = "https://github.com/zkonduit/halo2", branch = "ac/optional-selector-poly" }
-halo2_proofs = { git = "https://github.com/zkonduit/halo2", branch = "ac/optional-selector-poly" }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2#098ac0ef3b29255e0e2524ecbb4e7e325ae6e7fd", package = "halo2_proofs" }
 halo2curves = { git = "https://github.com/privacy-scaling-explorations/halo2curves", rev = "9fff22c", features = [
     "derive_serde",
 ] }
@@ -89,6 +89,7 @@ colored = { version = "2.0.0", default_features = false, optional = true }
 env_logger = { version = "0.10.0", default_features = false, optional = true }
 chrono = "0.4.31"
 sha256 = "1.4.0"
+mimalloc = "0.1"
 
 [target.'cfg(target_arch = "wasm32")'.dependencies]
 getrandom = { version = "0.2.8", features = ["js"] }
@@ -197,6 +198,7 @@ mv-lookup = [
     "snark-verifier/mv-lookup",
     "halo2_solidity_verifier/mv-lookup",
 ]
+# precompute-coset = ["halo2_proofs/precompute-coset"]
 det-prove = []
 icicle = ["halo2_proofs/icicle_gpu"]
 empty-cmd = []

docs/python/requirements-docs.txt

@@ -1,4 +1,4 @@
-ezkl==12.0.2
+ezkl==0.0.0
 sphinx
 sphinx-rtd-theme
 sphinxcontrib-napoleon

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '12.0.2'
+release = '0.0.0'
 version = release
 
 

src/bin/ezkl.rs

@@ -1,4 +1,7 @@
 // ignore file if compiling for wasm
+#[global_allocator]
+#[cfg(not(target_arch = "wasm32"))]
+static GLOBAL: mimalloc::MiMalloc = mimalloc::MiMalloc;
 
 #[cfg(not(target_arch = "wasm32"))]
 use clap::{CommandFactory, Parser};

src/circuit/ops/errors.rs

@@ -91,4 +91,7 @@ pub enum CircuitError {
     /// Missing layout
     #[error("missing layout for op: {0}")]
     MissingLayout(String),
+    #[error("[io] {0}")]
+    /// IO error
+    IoError(#[from] std::io::Error),
 }

src/circuit/ops/lookup.rs

@@ -135,6 +135,53 @@ impl LookupOp {
         (-range, range)
     }
 
+    /// as path
+    pub fn as_path(&self) -> String {
+        match self {
+            LookupOp::Abs => "abs".into(),
+            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::Sign => "sign".into(),
+            LookupOp::LessThan { a } => format!("less_than_{}", a),
+            LookupOp::LessThanEqual { a } => format!("less_than_equal_{}", a),
+            LookupOp::GreaterThan { a } => format!("greater_than_{}", a),
+            LookupOp::GreaterThanEqual { a } => format!("greater_than_equal_{}", a),
+            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::ReLU => "relu".to_string(),
+            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),
+            LookupOp::ACosh { scale } => format!("acosh_{}", scale),
+            LookupOp::Sin { scale } => format!("sin_{}", scale),
+            LookupOp::ASin { scale } => format!("asin_{}", scale),
+            LookupOp::Sinh { scale } => format!("sinh_{}", scale),
+            LookupOp::ASinh { scale } => format!("asinh_{}", scale),
+            LookupOp::Tan { scale } => format!("tan_{}", scale),
+            LookupOp::ATan { scale } => format!("atan_{}", scale),
+            LookupOp::ATanh { scale } => format!("atanh_{}", scale),
+            LookupOp::Tanh { scale } => format!("tanh_{}", scale),
+            LookupOp::HardSwish { scale } => format!("hardswish_{}", scale),
+        }
+    }
+
     /// Matches a [Op] to an operation in the `tensor::ops` module.
     pub(crate) fn f<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
         &self,

src/circuit/table.rs

@@ -15,6 +15,9 @@ use crate::{
     tensor::{Tensor, TensorType},
 };
 
+#[cfg(not(target_arch = "wasm32"))]
+use crate::execute::EZKL_REPO_PATH;
+
 use crate::circuit::lookup::LookupOp;
 
 /// The range of the lookup table.
@@ -25,6 +28,16 @@ pub const RANGE_MULTIPLIER: IntegerRep = 2;
 /// The safety factor offset for the number of rows in the lookup table.
 pub const RESERVED_BLINDING_ROWS_PAD: usize = 3;
 
+#[cfg(not(target_arch = "wasm32"))]
+lazy_static::lazy_static! {
+    /// an optional directory to read and write the lookup table cache
+    pub static ref LOOKUP_CACHE: String = format!("{}/cache", *EZKL_REPO_PATH);
+}
+
+/// The lookup table cache is disabled on wasm32 target.
+#[cfg(target_arch = "wasm32")]
+pub const LOOKUP_CACHE: &str = "";
+
 #[derive(Debug, Clone)]
 ///
 pub struct SelectorConstructor<F: PrimeField> {
@@ -137,6 +150,14 @@ pub fn num_cols_required(range_len: IntegerRep, col_size: usize) -> usize {
 }
 
 impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
+    fn name(&self) -> String {
+        format!(
+            "{}_{}_{}",
+            self.nonlinearity.as_path(),
+            self.range.0,
+            self.range.1
+        )
+    }
     /// Configures the table.
     pub fn configure(
         cs: &mut ConstraintSystem<F>,
@@ -203,8 +224,51 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
         let smallest = self.range.0;
         let largest = self.range.1;
 
-        let inputs: Tensor<F> = Tensor::from(smallest..=largest).map(|x| integer_rep_to_felt(x));
-        let evals = self.nonlinearity.f(&[inputs.clone()])?;
+        let gen_table = || -> Result<(Tensor<F>, Tensor<F>), crate::tensor::TensorError> {
+            let inputs = Tensor::from(smallest..=largest)
+                .par_enum_map(|_, x| Ok::<_, crate::tensor::TensorError>(integer_rep_to_felt(x)))?;
+            let evals = self.nonlinearity.f(&[inputs.clone()])?;
+            Ok((inputs, evals.output))
+        };
+
+        let (inputs, evals) = if !LOOKUP_CACHE.is_empty() {
+            let cache = std::path::Path::new(&*LOOKUP_CACHE);
+            let cache_path = cache.join(self.name());
+            let input_path = cache_path.join("inputs");
+            let output_path = cache_path.join("outputs");
+            if cache_path.exists() {
+                log::info!("Loading lookup table from cache: {:?}", cache_path);
+                let (input_cache, output_cache) =
+                    (Tensor::load(&input_path)?, Tensor::load(&output_path)?);
+                (input_cache, output_cache)
+            } else {
+                log::info!(
+                    "Generating lookup table and saving to cache: {:?}",
+                    cache_path
+                );
+
+                // mkdir -p cache_path
+                std::fs::create_dir_all(&cache_path).map_err(|e| {
+                    CircuitError::TensorError(crate::tensor::TensorError::FileSaveError(
+                        e.to_string(),
+                    ))
+                })?;
+
+                let (inputs, evals) = gen_table()?;
+                inputs.save(&input_path)?;
+                evals.save(&output_path)?;
+
+                (inputs, evals)
+            }
+        } else {
+            log::info!(
+                "Generating lookup table {} without cache",
+                self.nonlinearity.as_path()
+            );
+
+            gen_table()?
+        };
+
         let chunked_inputs = inputs.chunks(self.col_size);
 
         self.is_assigned = true;
@@ -236,7 +300,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
                                     )?;
                                 }
 
-                                let output = evals.output[row_offset];
+                                let output = evals[row_offset];
 
                                 table.assign_cell(
                                     || format!("nl_o_col row {}", row_offset),
@@ -274,6 +338,11 @@ pub struct RangeCheck<F: PrimeField> {
 }
 
 impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RangeCheck<F> {
+    /// as path
+    pub fn as_path(&self) -> String {
+        format!("rangecheck_{}_{}", self.range.0, self.range.1)
+    }
+
     /// get first_element of column
     pub fn get_first_element(&self, chunk: usize) -> F {
         let chunk = chunk as IntegerRep;
@@ -351,7 +420,32 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RangeCheck<F> {
         let smallest = self.range.0;
         let largest = self.range.1;
 
-        let inputs: Tensor<F> = Tensor::from(smallest..=largest).map(|x| integer_rep_to_felt(x));
+        let inputs: Tensor<F> = if !LOOKUP_CACHE.is_empty() {
+            let cache = std::path::Path::new(&*LOOKUP_CACHE);
+            let cache_path = cache.join(self.as_path());
+            let input_path = cache_path.join("inputs");
+            if cache_path.exists() {
+                log::info!("Loading range check table from cache: {:?}", cache_path);
+                Tensor::load(&input_path)?
+            } else {
+                log::info!(
+                    "Generating range check table and saving to cache: {:?}",
+                    cache_path
+                );
+
+                // mkdir -p cache_path
+                std::fs::create_dir_all(&cache_path)?;
+
+                let inputs = Tensor::from(smallest..=largest).map(|x| integer_rep_to_felt(x));
+                inputs.save(&input_path)?;
+                inputs
+            }
+        } else {
+            log::info!("Generating range check {} without cache", self.as_path());
+
+            Tensor::from(smallest..=largest).map(|x| integer_rep_to_felt(x))
+        };
+
         let chunked_inputs = inputs.chunks(self.col_size);
 
         self.is_assigned = true;

src/lib.rs

@@ -23,6 +23,7 @@
 )]
 // we allow this for our dynamic range based indexing scheme
 #![allow(clippy::single_range_in_vec_init)]
+#![feature(buf_read_has_data_left)]
 #![feature(stmt_expr_attributes)]
 
 //! A library for turning computational graphs, such as neural networks, into ZK-circuits.

src/logger.rs

@@ -76,7 +76,7 @@ pub fn init_logger() {
             prefix_token(&record.level()),
             //    pretty print UTC time
             chrono::Utc::now()
-                .format("%Y-%m-%d %H:%M:%S")
+                .format("%Y-%m-%d %H:%M:%S:%3f")
                 .to_string()
                 .bright_magenta(),
             record.metadata().target(),

src/pfsys/mod.rs

@@ -558,7 +558,8 @@ where
         + PrimeField
         + FromUniformBytes<64>
         + WithSmallOrderMulGroup<3>,
-    Scheme::Curve: Serialize + DeserializeOwned,
+    Scheme::Curve: Serialize + DeserializeOwned + SerdeObject,
+    Scheme::ParamsProver: Send + Sync,
 {
     let strategy = Strategy::new(params.verifier_params());
     let mut transcript = TranscriptWriterBuffer::<_, Scheme::Curve, _>::init(vec![]);

src/tensor/errors.rs

@@ -27,4 +27,10 @@ pub enum TensorError {
     /// Unset visibility
     #[error("unset visibility")]
     UnsetVisibility,
+    /// File save error
+    #[error("save error: {0}")]
+    FileSaveError(String),
+    /// File load error
+    #[error("load error: {0}")]
+    FileLoadError(String),
 }

src/tensor/mod.rs

@@ -18,6 +18,9 @@ use maybe_rayon::{
     slice::ParallelSliceMut,
 };
 use serde::{Deserialize, Serialize};
+use std::io::BufRead;
+use std::io::Write;
+use std::path::PathBuf;
 pub use val::*;
 pub use var::*;
 
@@ -41,6 +44,7 @@ use itertools::Itertools;
 use metal::{Device, MTLResourceOptions, MTLSize};
 use std::error::Error;
 use std::fmt::Debug;
+use std::io::Read;
 use std::iter::Iterator;
 use std::ops::{Add, Deref, DerefMut, Div, Mul, Neg, Range, Sub};
 use std::{cmp::max, ops::Rem};
@@ -409,6 +413,45 @@ impl<'data, T: Clone + TensorType + std::marker::Send + std::marker::Sync>
     }
 }
 
+impl<T: Clone + TensorType + PrimeField> Tensor<T> {
+    /// save to a file
+    pub fn save(&self, path: &PathBuf) -> Result<(), TensorError> {
+        let writer =
+            std::fs::File::create(path).map_err(|e| TensorError::FileSaveError(e.to_string()))?;
+        let mut buf_writer = std::io::BufWriter::new(writer);
+
+        self.inner.iter().map(|x| x.clone()).for_each(|x| {
+            let x = x.to_repr();
+            buf_writer.write_all(x.as_ref()).unwrap();
+        });
+
+        Ok(())
+    }
+
+    /// load from a file
+    pub fn load(path: &PathBuf) -> Result<Self, TensorError> {
+        let reader =
+            std::fs::File::open(path).map_err(|e| TensorError::FileLoadError(e.to_string()))?;
+        let mut buf_reader = std::io::BufReader::new(reader);
+
+        let mut inner = Vec::new();
+        while let Ok(true) = buf_reader.has_data_left() {
+            let mut repr = T::Repr::default();
+            match buf_reader.read_exact(repr.as_mut()) {
+                Ok(_) => {
+                    inner.push(T::from_repr(repr).unwrap());
+                }
+                Err(_) => {
+                    return Err(TensorError::FileLoadError(
+                        "Failed to read tensor".to_string(),
+                    ))
+                }
+            }
+        }
+        Ok(Tensor::new(Some(&inner), &[inner.len()]).unwrap())
+    }
+}
+
 impl<T: Clone + TensorType> Tensor<T> {
     /// Sets (copies) the tensor values to the provided ones.
     pub fn new(values: Option<&[T]>, dims: &[usize]) -> Result<Self, TensorError> {