miden-crypto/src/hash/rescue/mds/freq.rs
src/hash/rescue/mds/freq.rsRaw
1// FFT-BASED MDS MULTIPLICATION HELPER FUNCTIONS
2// ================================================================================================
3
4/// This module contains helper functions as well as constants used to perform the vector-matrix
5/// multiplication step of the Rescue prime permutation. The special form of our MDS matrix
6/// i.e. being circular, allows us to reduce the vector-matrix multiplication to a Hadamard product
7/// of two vectors in "frequency domain". This follows from the simple fact that every circulant
8/// matrix has the columns of the discrete Fourier transform matrix as orthogonal eigenvectors.
9/// The implementation also avoids the use of 3-point FFTs, and 3-point iFFTs, and substitutes that
10/// with explicit expressions. It also avoids, due to the form of our matrix in the frequency
11/// domain, divisions by 2 and repeated modular reductions. This is because of our explicit choice
12/// of an MDS matrix that has small powers of 2 entries in frequency domain.
13/// The following implementation has benefited greatly from the discussions and insights of
14/// Hamish Ivey-Law and Jacqueline Nabaglo of Polygon Zero and is base on Nabaglo's Plonky2
15/// implementation.
16
17// Rescue MDS matrix in frequency domain.
18// More precisely, this is the output of the three 4-point (real) FFTs of the first column of
19// the MDS matrix i.e. just before the multiplication with the appropriate twiddle factors
20// and application of the final four 3-point FFT in order to get the full 12-point FFT.
21// The entries have been scaled appropriately in order to avoid divisions by 2 in iFFT2 and iFFT4.
22// The code to generate the matrix in frequency domain is based on an adaptation of a code, to
23// generate MDS matrices efficiently in original domain, that was developed by the Polygon Zero
24// team.
25const MDS_FREQ_BLOCK_ONE: [i64; 3] = [16, 8, 16];
26const MDS_FREQ_BLOCK_TWO: [(i64, i64); 3] = [(-1, 2), (-1, 1), (4, 8)];
27const MDS_FREQ_BLOCK_THREE: [i64; 3] = [-8, 1, 1];
28
29// We use split 3 x 4 FFT transform in order to transform our vectors into the frequency domain.
30#[inline(always)]
31pub const fn mds_multiply_freq(state: [u64; 12]) -> [u64; 12] {
32 let [s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11] = state;
33
34 let (u0, u1, u2) = fft4_real([s0, s3, s6, s9]);
35 let (u4, u5, u6) = fft4_real([s1, s4, s7, s10]);
36 let (u8, u9, u10) = fft4_real([s2, s5, s8, s11]);
37
38 // This where the multiplication in frequency domain is done. More precisely, and with
39 // the appropriate permutations in between, the sequence of
40 // 3-point FFTs --> multiplication by twiddle factors --> Hadamard multiplication -->
41 // 3 point iFFTs --> multiplication by (inverse) twiddle factors
42 // is "squashed" into one step composed of the functions "block1", "block2" and "block3".
43 // The expressions in the aforementioned functions are the result of explicit computations
44 // combined with the Karatsuba trick for the multiplication of Complex numbers.
45
46 let [v0, v4, v8] = block1([u0, u4, u8], MDS_FREQ_BLOCK_ONE);
47 let [v1, v5, v9] = block2([u1, u5, u9], MDS_FREQ_BLOCK_TWO);
48 let [v2, v6, v10] = block3([u2, u6, u10], MDS_FREQ_BLOCK_THREE);
49 // The 4th block is not computed as it is similar to the 2nd one, up to complex conjugation,
50 // and is, due to the use of the real FFT and iFFT, redundant.
51
52 let [s0, s3, s6, s9] = ifft4_real((v0, v1, v2));
53 let [s1, s4, s7, s10] = ifft4_real((v4, v5, v6));
54 let [s2, s5, s8, s11] = ifft4_real((v8, v9, v10));
55
56 [s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11]
57}
58
59// We use the real FFT to avoid redundant computations. See https://www.mdpi.com/2076-3417/12/9/4700
60#[inline(always)]
61const fn fft2_real(x: [u64; 2]) -> [i64; 2] {
62 [(x[0] as i64 + x[1] as i64), (x[0] as i64 - x[1] as i64)]
63}
64
65#[inline(always)]
66const fn ifft2_real(y: [i64; 2]) -> [u64; 2] {
67 // We avoid divisions by 2 by appropriately scaling the MDS matrix constants.
68 [(y[0] + y[1]) as u64, (y[0] - y[1]) as u64]
69}
70
71#[inline(always)]
72const fn fft4_real(x: [u64; 4]) -> (i64, (i64, i64), i64) {
73 let [z0, z2] = fft2_real([x[0], x[2]]);
74 let [z1, z3] = fft2_real([x[1], x[3]]);
75 let y0 = z0 + z1;
76 let y1 = (z2, -z3);
77 let y2 = z0 - z1;
78 (y0, y1, y2)
79}
80
81#[inline(always)]
82const fn ifft4_real(y: (i64, (i64, i64), i64)) -> [u64; 4] {
83 // In calculating 'z0' and 'z1', division by 2 is avoided by appropriately scaling
84 // the MDS matrix constants.
85 let z0 = y.0 + y.2;
86 let z1 = y.0 - y.2;
87 let z2 = y.1 .0;
88 let z3 = -y.1 .1;
89
90 let [x0, x2] = ifft2_real([z0, z2]);
91 let [x1, x3] = ifft2_real([z1, z3]);
92
93 [x0, x1, x2, x3]
94}
95
96#[inline(always)]
97const fn block1(x: [i64; 3], y: [i64; 3]) -> [i64; 3] {
98 let [x0, x1, x2] = x;
99 let [y0, y1, y2] = y;
100 let z0 = x0 * y0 + x1 * y2 + x2 * y1;
101 let z1 = x0 * y1 + x1 * y0 + x2 * y2;
102 let z2 = x0 * y2 + x1 * y1 + x2 * y0;
103
104 [z0, z1, z2]
105}
106
107#[inline(always)]
108const fn block2(x: [(i64, i64); 3], y: [(i64, i64); 3]) -> [(i64, i64); 3] {
109 let [(x0r, x0i), (x1r, x1i), (x2r, x2i)] = x;
110 let [(y0r, y0i), (y1r, y1i), (y2r, y2i)] = y;
111 let x0s = x0r + x0i;
112 let x1s = x1r + x1i;
113 let x2s = x2r + x2i;
114 let y0s = y0r + y0i;
115 let y1s = y1r + y1i;
116 let y2s = y2r + y2i;
117
118 // Compute x0y0 − ix1y2 − ix2y1 using Karatsuba for complex numbers multiplication
119 let m0 = (x0r * y0r, x0i * y0i);
120 let m1 = (x1r * y2r, x1i * y2i);
121 let m2 = (x2r * y1r, x2i * y1i);
122 let z0r = (m0.0 - m0.1) + (x1s * y2s - m1.0 - m1.1) + (x2s * y1s - m2.0 - m2.1);
123 let z0i = (x0s * y0s - m0.0 - m0.1) + (-m1.0 + m1.1) + (-m2.0 + m2.1);
124 let z0 = (z0r, z0i);
125
126 // Compute x0y1 + x1y0 − ix2y2 using Karatsuba for complex numbers multiplication
127 let m0 = (x0r * y1r, x0i * y1i);
128 let m1 = (x1r * y0r, x1i * y0i);
129 let m2 = (x2r * y2r, x2i * y2i);
130 let z1r = (m0.0 - m0.1) + (m1.0 - m1.1) + (x2s * y2s - m2.0 - m2.1);
131 let z1i = (x0s * y1s - m0.0 - m0.1) + (x1s * y0s - m1.0 - m1.1) + (-m2.0 + m2.1);
132 let z1 = (z1r, z1i);
133
134 // Compute x0y2 + x1y1 + x2y0 using Karatsuba for complex numbers multiplication
135 let m0 = (x0r * y2r, x0i * y2i);
136 let m1 = (x1r * y1r, x1i * y1i);
137 let m2 = (x2r * y0r, x2i * y0i);
138 let z2r = (m0.0 - m0.1) + (m1.0 - m1.1) + (m2.0 - m2.1);
139 let z2i = (x0s * y2s - m0.0 - m0.1) + (x1s * y1s - m1.0 - m1.1) + (x2s * y0s - m2.0 - m2.1);
140 let z2 = (z2r, z2i);
141
142 [z0, z1, z2]
143}
144
145#[inline(always)]
146const fn block3(x: [i64; 3], y: [i64; 3]) -> [i64; 3] {
147 let [x0, x1, x2] = x;
148 let [y0, y1, y2] = y;
149 let z0 = x0 * y0 - x1 * y2 - x2 * y1;
150 let z1 = x0 * y1 + x1 * y0 - x2 * y2;
151 let z2 = x0 * y2 + x1 * y1 + x2 * y0;
152
153 [z0, z1, z2]
154}
155
156// TESTS
157// ================================================================================================
158
159#[cfg(test)]
160mod tests {
161 use proptest::prelude::*;
162
163 use super::super::{apply_mds, Felt, MDS, ZERO};
164
165 const STATE_WIDTH: usize = 12;
166
167 #[inline(always)]
168 fn apply_mds_naive(state: &mut [Felt; STATE_WIDTH]) {
169 let mut result = [ZERO; STATE_WIDTH];
170 result.iter_mut().zip(MDS).for_each(|(r, mds_row)| {
171 state.iter().zip(mds_row).for_each(|(&s, m)| {
172 *r += m * s;
173 });
174 });
175 *state = result;
176 }
177
178 proptest! {
179 #[test]
180 fn mds_freq_proptest(a in any::<[u64; STATE_WIDTH]>()) {
181
182 let mut v1 = [ZERO; STATE_WIDTH];
183 let mut v2;
184
185 for i in 0..STATE_WIDTH {
186 v1[i] = Felt::new(a[i]);
187 }
188 v2 = v1;
189
190 apply_mds_naive(&mut v1);
191 apply_mds(&mut v2);
192
193 prop_assert_eq!(v1, v2);
194 }
195 }
196}