Update to arkworks 0.5 + cargo fmt

Cargo.toml

@@ -7,12 +7,12 @@ edition = "2021"
 default-run = "main"
 
 [dependencies]
-ark-std = "0.4"
-ark-ff = { version = "0.4", features = ["asm"] }
-ark-serialize = "0.4"
-ark-crypto-primitives = { version = "0.4", features = ["merkle_tree"] }
-ark-poly = "0.4"
-ark-test-curves = { version = "0.4", features = ["bls12_381_curve"] }
+ark-std = {version = "0.5", features = ["std"]}
+ark-ff = { version = "0.5", features = ["asm", "std"] }
+ark-serialize = "0.5"
+ark-crypto-primitives = { version = "0.5", features = ["merkle_tree"] }
+ark-poly = "0.5"
+ark-test-curves = { version = "0.5", features = ["bls12_381_curve"] }
 derivative = { version = "2", features = ["use_core"] }
 blake3 = "1.5.0"
 blake2 = "0.10"

src/ntt/matrix.rs

@@ -116,7 +116,7 @@ impl<'a, T> MatrixMut<'a, T> {
 
     /// Split the matrix into four quadrants at the indicated `row` and `col` (meaning that in the returned 4-tuple (A,B,C,D), the matrix A is a `row`x`col` matrix)
     ///
-    /// self = [A B] 
+    /// self = [A B]
     ///        [C D]
     pub fn split_quadrants(self, row: usize, col: usize) -> (Self, Self, Self, Self) {
         let (u, l) = self.split_vertical(row); // split into upper and lower parts

src/ntt/ntt.rs

@@ -26,8 +26,8 @@ static ENGINE_CACHE: LazyLock<Mutex<HashMap<TypeId, Arc<dyn Any + Send + Sync>>>
 /// Enginge for computing NTTs over arbitrary fields.
 /// Assumes the field has large two-adicity.
 pub struct NttEngine<F: Field> {
-    order: usize, // order of omega_orger
-    omega_order: F,  // primitive order'th root.
+    order: usize,   // order of omega_orger
+    omega_order: F, // primitive order'th root.
 
     // Roots of small order (zero if unavailable). The naming convention is that omega_foo has order foo.
     half_omega_3_1_plus_2: F, // ½(ω₃ + ω₃²)
@@ -58,7 +58,6 @@ pub fn intt<F: FftField>(values: &mut [F]) {
     NttEngine::<F>::new_from_cache().intt(values);
 }
 
-
 /// Compute the inverse NTT of multiple slice of field elements, each of size `size`, without the 1/n scaling factor and using a cached engine.
 pub fn intt_batch<F: FftField>(values: &mut [F], size: usize) {
     NttEngine::<F>::new_from_cache().intt_batch(values, size);

src/ntt/transpose.rs

@@ -38,7 +38,7 @@ pub fn transpose<F: Sized + Copy + Send>(matrix: &mut [F], rows: usize, cols: us
 }
 
 // The following function have both a parallel and a non-parallel implementation.
-// We fuly split those in a parallel and a non-parallel functions (rather than using #[cfg] within a single function) 
+// We fuly split those in a parallel and a non-parallel functions (rather than using #[cfg] within a single function)
 // and have main entry point fun that just calls the appropriate version (either fun_parallel or fun_not_parallel).
 // The sole reason is that this simplifies unit tests: We otherwise would need to build twice to cover both cases.
 // For effiency, we assume the compiler inlines away the extra "indirection" that we add to the entry point function.
@@ -83,7 +83,6 @@ fn transpose_copy_parallel<'a, 'b, F: Sized + Copy + Send>(
     }
 }
 
-
 /// Sets `dst` to the transpose of `src`. This will panic if the sizes of `src` and `dst` are not compatible.
 /// This is the non-parallel version
 fn transpose_copy_not_parallel<'a, 'b, F: Sized + Copy>(
@@ -308,7 +307,7 @@ mod tests {
         funs.push(&transpose_copy_parallel::<Pair>);
 
         for f in funs {
-            let rows: usize = workload_size::<Pair>() + 1;  // intentionally not a power of two: The function is not described as only working for powers of two.
+            let rows: usize = workload_size::<Pair>() + 1; // intentionally not a power of two: The function is not described as only working for powers of two.
             let columns: usize = 4;
             let mut srcarray = make_example_matrix(rows, columns);
             let mut dstarray: Vec<(usize, usize)> = vec![(0, 0); rows * columns];
@@ -338,7 +337,6 @@ mod tests {
         funs.push(&transpose_square_swap_parallel::<Triple>);
 
         for f in funs {
-
             // Set rows manually. We want to be sure to trigger the actual recursion.
             // (Computing this from workload_size was too much hassle.)
             let rows = 1024; // workload_size::<Triple>();
@@ -370,43 +368,44 @@ mod tests {
     }
 
     #[test]
-    fn test_transpose_square(){
-        let mut funs: Vec<&dyn for <'a> Fn(MatrixMut<'a,_>)> = vec![
-            &transpose_square::<Pair>, &transpose_square_parallel::<Pair>
+    fn test_transpose_square() {
+        let mut funs: Vec<&dyn for<'a> Fn(MatrixMut<'a, _>)> = vec![
+            &transpose_square::<Pair>,
+            &transpose_square_parallel::<Pair>,
         ];
-        #[cfg(feature="parallel")]
+        #[cfg(feature = "parallel")]
         funs.push(&transpose_square::<Pair>);
-        for f in funs{
+        for f in funs {
             // Set rows manually. We want to be sure to trigger the actual recursion.
             // (Computing this from workload_size was too much hassle.)
-            let size = 1024; 
+            let size = 1024;
             assert!(size * size > 2 * workload_size::<Pair>());
 
             let mut example = make_example_matrix(size, size);
             let view = MatrixMut::from_mut_slice(&mut example, size, size);
             f(view);
             let view = MatrixMut::from_mut_slice(&mut example, size, size);
-            for i in 0..size{
-                for j in 0..size{
-                    assert_eq!(view[(i,j)], (j,i));
+            for i in 0..size {
+                for j in 0..size {
+                    assert_eq!(view[(i, j)], (j, i));
                 }
             }
         }
     }
 
     #[test]
-    fn test_transpose(){
+    fn test_transpose() {
         let size = 1024;
-        
+
         // rectangular matrix:
         let rows = size;
         let cols = 16;
         let mut example = make_example_matrix(rows, cols);
         transpose(&mut example, rows, cols);
         let view = MatrixMut::from_mut_slice(&mut example, cols, rows);
-        for i in 0..cols{
-            for j in 0..rows{
-                assert_eq!(view[(i,j)], (j,i));
+        for i in 0..cols {
+            for j in 0..rows {
+                assert_eq!(view[(i, j)], (j, i));
             }
         }
 
@@ -416,9 +415,9 @@ mod tests {
         let mut example = make_example_matrix(rows, cols);
         transpose(&mut example, rows, cols);
         let view = MatrixMut::from_mut_slice(&mut example, cols, rows);
-        for i in 0..cols{
-            for j in 0..rows{
-                assert_eq!(view[(i,j)], (j,i));
+        for i in 0..cols {
+            for j in 0..rows {
+                assert_eq!(view[(i, j)], (j, i));
             }
         }
 
@@ -428,11 +427,15 @@ mod tests {
         let cols = 16;
         let mut example = make_example_matrices(rows, cols, number_of_matrices);
         transpose(&mut example, rows, cols);
-        for index in 0..number_of_matrices{
-            let view = MatrixMut::from_mut_slice(&mut example[index*rows*cols..(index+1)*rows*cols], cols, rows);
-            for i in 0..cols{
-                for j in 0..rows{
-                    assert_eq!(view[(i,j)], (index,j,i));
+        for index in 0..number_of_matrices {
+            let view = MatrixMut::from_mut_slice(
+                &mut example[index * rows * cols..(index + 1) * rows * cols],
+                cols,
+                rows,
+            );
+            for i in 0..cols {
+                for j in 0..rows {
+                    assert_eq!(view[(i, j)], (index, j, i));
                 }
             }
         }
@@ -443,15 +446,17 @@ mod tests {
         let cols = size;
         let mut example = make_example_matrices(rows, cols, number_of_matrices);
         transpose(&mut example, rows, cols);
-        for index in 0..number_of_matrices{
-            let view = MatrixMut::from_mut_slice(&mut example[index*rows*cols..(index+1)*rows*cols], cols, rows);
-            for i in 0..cols{
-                for j in 0..rows{
-                    assert_eq!(view[(i,j)], (index,j,i));
+        for index in 0..number_of_matrices {
+            let view = MatrixMut::from_mut_slice(
+                &mut example[index * rows * cols..(index + 1) * rows * cols],
+                cols,
+                rows,
+            );
+            for i in 0..cols {
+                for j in 0..rows {
+                    assert_eq!(view[(i, j)], (index, j, i));
                 }
             }
         }
-
-
     }
 }

src/poly_utils/coeffs.rs

@@ -8,13 +8,12 @@ use {
     std::mem::size_of,
 };
 
-
 /// A CoefficientList models a (multilinear) polynomial in `num_variable` variables in coefficient form.
-/// 
+///
 /// The order of coefficients follows the following convention: coeffs[j] corresponds to the monomial
 /// determined by the binary decomposition of j with an X_i-variable present if the
 /// i-th highest-significant bit among the `num_variables` least significant bits is set.
-/// 
+///
 /// e.g. is `num_variables` is 3 with variables X_0, X_1, X_2, then
 ///  - coeffs[0] is the coefficient of 1
 ///  - coeffs[1] is the coefficient of X_2
@@ -23,7 +22,7 @@ use {
 #[derive(Debug, Clone)]
 pub struct CoefficientList<F> {
     coeffs: Vec<F>, // list of coefficients. For multilinear polynomials, we have coeffs.len() == 1 << num_variables.
-    num_variables: usize,  // number of variables
+    num_variables: usize, // number of variables
 }
 
 impl<F> CoefficientList<F>
@@ -59,12 +58,16 @@ where
     // NOTE (Gotti): This algorithm uses 2^{n+1}-1 multiplications for a polynomial in n variables.
     // You could do with 2^{n}-1 by just doing a + x * b (and not forwarding scalar through the recursion at all).
     // The difference comes from multiplications by E::ONE at the leaves of the recursion tree.
-    
+
     // recursive helper function for polynomial evaluation:
     // Note that eval(coeffs, [X_0, X1,...]) = eval(coeffs_left, [X_1,...]) + X_0 * eval(coeffs_right, [X_1,...])
-    
+
     /// Recursively compute scalar * poly_eval(coeffs;eval) where poly_eval interprets coeffs as a polynomial and eval are the evaluation points.
-    fn eval_extension_nonparallel<E: Field<BasePrimeField = F>>(coeff: &[F], eval: &[E], scalar: E) -> E {
+    fn eval_extension_nonparallel<E: Field<BasePrimeField = F>>(
+        coeff: &[F],
+        eval: &[E],
+        scalar: E,
+    ) -> E {
         debug_assert_eq!(coeff.len(), 1 << eval.len());
         if let Some((&x, tail)) = eval.split_first() {
             let (low, high) = coeff.split_at(coeff.len() / 2);
@@ -77,7 +80,11 @@ where
     }
 
     #[cfg(feature = "parallel")]
-    fn eval_extension_parallel<E: Field<BasePrimeField = F>>(coeff: &[F], eval: &[E], scalar: E) -> E {
+    fn eval_extension_parallel<E: Field<BasePrimeField = F>>(
+        coeff: &[F],
+        eval: &[E],
+        scalar: E,
+    ) -> E {
         const PARALLEL_THRESHOLD: usize = 10;
         debug_assert_eq!(coeff.len(), 1 << eval.len());
         if let Some((&x, tail)) = eval.split_first() {
@@ -98,7 +105,7 @@ where
     }
 
     /// Evaluate self at `point`, where `point` is from a field extension extending the field over which the polynomial `self` is defined.
-    /// 
+    ///
     /// Note that we only support the case where F is a prime field.
     pub fn evaluate_at_extension<E: Field<BasePrimeField = F>>(
         &self,
@@ -226,7 +233,7 @@ where
 {
     /// fold folds the polynomial at the provided folding_randomness.
     ///
-    /// Namely, when self is interpreted as a multi-linear polynomial f in X_0, ..., X_{n-1}, 
+    /// Namely, when self is interpreted as a multi-linear polynomial f in X_0, ..., X_{n-1},
     /// it partially evaluates f at the provided `folding_randomness`.
     /// Our ordering convention is to evaluate at the higher indices, i.e. we return f(X_0,X_1,..., folding_randomness[0], folding_randomness[1],...)
     pub fn fold(&self, folding_randomness: &MultilinearPoint<F>) -> Self {

src/poly_utils/evals.rs

@@ -6,7 +6,7 @@ use super::{sequential_lag_poly::LagrangePolynomialIterator, MultilinearPoint};
 
 /// An EvaluationsList models a multi-linear polynomial f in `num_variables`
 /// unknowns, stored via their evaluations at {0,1}^{num_variables}
-/// 
+///
 /// `evals` stores the evaluation in lexicographic order.
 #[derive(Debug)]
 pub struct EvaluationsList<F> {
@@ -19,7 +19,7 @@ where
     F: Field,
 {
     /// Constructs a EvaluationList from the given vector `eval` of evaluations.
-    /// 
+    ///
     /// The provided `evals` is supposed to be the list of evaluations, where the ordering of evaluation points in {0,1}^n
     /// is lexicographic.
     pub fn new(evals: Vec<F>) -> Self {

src/poly_utils/hypercube.rs

@@ -1,24 +1,23 @@
 #[derive(Debug, Copy, Clone, PartialEq, Eq, PartialOrd, Ord)]
-
 // TODO (Gotti): Should pos rather be a u64? usize is platform-dependent, giving a platform-dependent limit on the number of variables.
 // num_variables may be smaller as well.
 
 // NOTE: Conversion BinaryHypercube <-> MultilinearPoint is Big Endian, using only the num_variables least significant bits of the number stored inside BinaryHypercube.
 
 /// point on the binary hypercube {0,1}^n for some n.
-/// 
+///
 /// The point is encoded via the n least significant bits of a usize in big endian order and we do not store n.
 pub struct BinaryHypercubePoint(pub usize);
 
 /// BinaryHypercube is an Iterator that is used to range over the points of the hypercube {0,1}^n, where n == `num_variables`
 pub struct BinaryHypercube {
-    pos: usize,  // current position, encoded via the bits of pos
+    pos: usize,           // current position, encoded via the bits of pos
     num_variables: usize, // dimension of the hypercube
 }
 
 impl BinaryHypercube {
     pub fn new(num_variables: usize) -> Self {
-        debug_assert!(num_variables < usize::BITS as usize);  // Note that we need strictly smaller, since some code would overflow otherwise.
+        debug_assert!(num_variables < usize::BITS as usize); // Note that we need strictly smaller, since some code would overflow otherwise.
         BinaryHypercube {
             pos: 0,
             num_variables,

src/poly_utils/mod.rs

@@ -30,7 +30,7 @@ where
         self.0.len()
     }
 
-    // NOTE: Conversion BinaryHypercube <-> MultilinearPoint converts a 
+    // NOTE: Conversion BinaryHypercube <-> MultilinearPoint converts a
     // multilinear point (x1,x2,...,x_n) into the number with bit-pattern 0...0 x_1 x_2 ... x_n, provided all x_i are in {0,1}.
     // That means we pad zero bits in BinaryHypercube from the msb end and use big-endian for the actual conversion.
 
@@ -112,7 +112,7 @@ where
 {
     let mut point = point.0;
     let n_variables = coords.n_variables();
-    assert!(point < (1 << n_variables));  // check that the lengths of coords and point match.
+    assert!(point < (1 << n_variables)); // check that the lengths of coords and point match.
 
     let mut acc = F::ONE;
 

src/poly_utils/sequential_lag_poly.rs

@@ -15,13 +15,13 @@ use super::{hypercube::BinaryHypercubePoint, MultilinearPoint};
 ///
 /// This means that y == eq_poly(point, x)
 pub struct LagrangePolynomialIterator<F: Field> {
-    last_position: Option<usize>,  // the previously output BinaryHypercubePoint (encoded as usize). None before the first output.
-    point: Vec<F>,  // stores a copy of the `point` given when creating the iterator. For easier(?) bit-fiddling, we store in in reverse order.
+    last_position: Option<usize>, // the previously output BinaryHypercubePoint (encoded as usize). None before the first output.
+    point: Vec<F>, // stores a copy of the `point` given when creating the iterator. For easier(?) bit-fiddling, we store in in reverse order.
     point_negated: Vec<F>, // stores the precomputed values 1-point[i] in the same ordering as point.
     /// stack Stores the n+1 values (in order) 1, y_1, y_1*y_2, y_1*y_2*y_3, ..., y_1*...*y_n for the previously output y.
     /// Before the first iteration (if last_position == None), it stores the values for the next (i.e. first) output instead.
     stack: Vec<F>,
-    num_variables: usize,  // dimension
+    num_variables: usize, // dimension
 }
 
 impl<F: Field> LagrangePolynomialIterator<F> {

src/sumcheck/proof.rs

@@ -8,7 +8,7 @@ use crate::{
 // Stored in evaluation form
 #[derive(Debug, Clone)]
 pub struct SumcheckPolynomial<F> {
-    n_variables: usize,  // number of variables;
+    n_variables: usize, // number of variables;
     // evaluations has length 3^{n_variables}
     // The order in which it is stored is such that evaluations[i]
     // corresponds to the evaluation at utils::base_decomposition(i, 3, n_variables),
@@ -29,7 +29,7 @@ where
         }
     }
 
-    /// Returns the vector of evaluations at {0,1,2}^n_variables of the polynomial f 
+    /// Returns the vector of evaluations at {0,1,2}^n_variables of the polynomial f
     /// in the following order: [f(0,0,..,0), f(0,0,..,1), f(0,0,...,2), f(0,0,...,1,0), ...]
     /// (i.e. lexicographic wrt. to the evaluation points.
     pub fn evaluations(&self) -> &[F] {
@@ -40,7 +40,7 @@ where
     // TODO(Gotti): Make more efficient; the base_decomposition and filtering is unneccessary.
 
     /// Returns the sum of evaluations of f, when summed only over {0,1}^n_variables
-    /// 
+    ///
     /// (and not over {0,1,2}^n_variable)
     pub fn sum_over_hypercube(&self) -> F {
         let num_evaluation_points = 3_usize.pow(self.n_variables as u32);
@@ -59,10 +59,10 @@ where
     }
 
     /// evaluates the polynomial at an arbitrary point, not neccessarily in {0,1,2}^n_variables.
-    /// 
-    /// We assert that point.n_variables() == self.n_variables 
+    ///
+    /// We assert that point.n_variables() == self.n_variables
     pub fn evaluate_at_point(&self, point: &MultilinearPoint<F>) -> F {
-        assert!(point.n_variables() == self.n_variables); 
+        assert!(point.n_variables() == self.n_variables);
         let num_evaluation_points = 3_usize.pow(self.n_variables as u32);
 
         let mut evaluation = F::ZERO;