arnaucube
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fhe-study/arith/src/ntt.rs
arith/src/ntt.rsRaw
  1//! Implementation of the NTT & iNTT, following the CT & GS algorighms, more details in
  2//! https://eprint.iacr.org/2017/727.pdf, some notes at
  3//! https://github.com/arnaucube/math/blob/master/notes_ntt.pdf .
  4//!
  5//! NOTE: initially I implemented it with fixed Q & N, given as constant
  6//! generics; but once using real-world parameters, the stack could not handle
  7//! it, so moved to use Vec instead of fixed-sized arrays, and adapted the NTT
  8//! implementation to that too.
  9
 10use std::collections::HashMap;
 11
 12#[derive(Debug)]
 13pub struct NTT {}
 14
 15use std::sync::{Mutex, OnceLock};
 16
 17static CACHE: OnceLock<Mutex<HashMap<(u64, usize), (Vec<u64>, Vec<u64>, u64)>>> = OnceLock::new();
 18
 19fn roots(q: u64, n: usize) -> (Vec<u64>, Vec<u64>, u64) {
 20    let cache_lock = CACHE.get_or_init(|| Mutex::new(HashMap::new()));
 21    let mut cache = cache_lock.lock().unwrap();
 22    if let Some(value) = cache.get(&(q, n)) {
 23        return value.clone();
 24    }
 25
 26    let n_inv: u64 = const_inv_mod(q, n as u64);
 27    let root_of_unity: u64 = primitive_root_of_unity(q, 2 * n);
 28    let roots_of_unity: Vec<u64> = roots_of_unity(q, n, root_of_unity);
 29    let roots_of_unity_inv: Vec<u64> = roots_of_unity_inv(q, n, roots_of_unity.clone());
 30    let value = (roots_of_unity, roots_of_unity_inv, n_inv);
 31
 32    cache.insert((q, n), value.clone());
 33    value
 34}
 35
 36impl NTT {
 37    /// implements the Cooley-Tukey (CT) algorithm. Details at
 38    /// https://eprint.iacr.org/2017/727.pdf, also some notes at section 3.1 of
 39    /// https://github.com/arnaucube/math/blob/master/notes_ntt.pdf
 40    pub fn ntt(q: u64, n: usize, a: &Vec<u64>) -> Vec<u64> {
 41        debug_assert_eq!(n, a.len());
 42
 43        let (roots_of_unity, _, _) = roots(q, n);
 44
 45        let mut t = n / 2;
 46        let mut m = 1;
 47        let mut r: Vec<u64> = a.clone();
 48        while m < n {
 49            let mut k = 0;
 50            for i in 0..m {
 51                let S: u64 = roots_of_unity[m + i];
 52                for j in k..k + t {
 53                    let U: u64 = r[j];
 54                    let V: u64 = (r[j + t] * S) % q;
 55                    // compute r[j] = (U + V) % q:
 56                    r[j] = U + V;
 57                    if r[j] >= q {
 58                        r[j] -= q;
 59                    }
 60                    // compute r[j + t] = (U - V) % q:
 61                    if U >= V {
 62                        r[j + t] = U - V;
 63                    } else {
 64                        r[j + t] = (q + U) - V;
 65                    }
 66                }
 67                k = k + 2 * t;
 68            }
 69            t /= 2;
 70            m *= 2;
 71        }
 72        r
 73    }
 74
 75    /// implements the Cooley-Tukey (CT) algorithm. Details at
 76    /// https://eprint.iacr.org/2017/727.pdf, also some notes at section 3.2 of
 77    /// https://github.com/arnaucube/math/blob/master/notes_ntt.pdf
 78    pub fn intt(q: u64, n: usize, a: &Vec<u64>) -> Vec<u64> {
 79        debug_assert_eq!(n, a.len());
 80
 81        let (_, roots_of_unity_inv, n_inv) = roots(q, n);
 82
 83        let mut t = 1;
 84        let mut m = n / 2;
 85        let mut r: Vec<u64> = a.clone();
 86        while m > 0 {
 87            let mut k = 0;
 88            for i in 0..m {
 89                let S: u64 = roots_of_unity_inv[m + i];
 90                for j in k..k + t {
 91                    let U: u64 = r[j];
 92                    let V: u64 = r[j + t];
 93                    // compute r[j] = (U + V) % q:
 94                    r[j] = U + V;
 95                    if r[j] >= q {
 96                        r[j] -= q;
 97                    }
 98                    // compute r[j + t] = ((U - V) * S) % q;
 99                    if U >= V {
100                        r[j + t] = ((U - V) * S) % q;
101                    } else {
102                        r[j + t] = ((q + U - V) * S) % q;
103                    }
104                }
105                k += 2 * t;
106            }
107            t *= 2;
108            m /= 2;
109        }
110        for i in 0..n {
111            r[i] = (r[i] * n_inv) % q;
112        }
113        r
114    }
115}
116
117/// computes a primitive N-th root of unity using the method described by Thomas
118/// Pornin in https://crypto.stackexchange.com/a/63616
119const fn primitive_root_of_unity(q: u64, n: usize) -> u64 {
120    assert!(n.is_power_of_two());
121    assert!((q - 1) % n as u64 == 0);
122    let n_u64 = n as u64;
123
124    let mut k = 1;
125    while k < q {
126        // alternatively could get a random k at each iteration, if so, add the following if:
127        // `if k == 0 { continue; }`
128        let w = const_exp_mod(q, k, (q - 1) / n_u64);
129        if const_exp_mod(q, w, n_u64 / 2) != 1 {
130            return w; // w is a primitive N-th root of unity
131        }
132        k += 1;
133    }
134    panic!("No primitive root of unity");
135}
136
137fn roots_of_unity(q: u64, n: usize, w: u64) -> Vec<u64> {
138    let mut r: Vec<u64> = vec![0; n];
139    let mut i = 0;
140    let log_n = n.ilog2();
141    while i < n {
142        // (return the roots in bit-reverset order)
143        let j = ((i as u64).reverse_bits() >> (64 - log_n)) as usize;
144        r[i] = const_exp_mod(q, w, j as u64);
145        i += 1;
146    }
147    r
148}
149
150fn roots_of_unity_inv(q: u64, n: usize, v: Vec<u64>) -> Vec<u64> {
151    // assumes that the inputted roots are already in bit-reverset order
152    let mut r: Vec<u64> = vec![0; n];
153    let mut i = 0;
154    while i < n {
155        r[i] = const_inv_mod(q, v[i]);
156        i += 1;
157    }
158    r
159}
160
161/// returns x^k mod Q
162const fn const_exp_mod(q: u64, x: u64, k: u64) -> u64 {
163    // work on u128 to avoid overflow
164    let mut r = 1u128;
165    let mut x = x as u128;
166    let mut k = k as u128;
167    x = x % q as u128;
168    // exponentiation by square strategy
169    while k > 0 {
170        if k % 2 == 1 {
171            r = (r * x) % q as u128;
172        }
173        x = (x * x) % q as u128;
174        k /= 2;
175    }
176    r as u64
177}
178
179/// returns x^-1 mod Q
180const fn const_inv_mod(q: u64, x: u64) -> u64 {
181    // by Fermat's Little Theorem, x^-1 mod q \equiv  x^{q-2} mod q
182    const_exp_mod(q, x, q - 2)
183}
184
185#[cfg(test)]
186mod tests {
187    use super::*;
188    use rand_distr::Distribution;
189
190    use anyhow::Result;
191
192    #[test]
193    fn test_ntt() -> Result<()> {
194        let q: u64 = 2u64.pow(16) + 1;
195        let n: usize = 4;
196
197        let a: Vec<u64> = vec![1u64, 2, 3, 4];
198
199        let a_ntt = NTT::ntt(q, n, &a);
200
201        let a_intt = NTT::intt(q, n, &a_ntt);
202
203        dbg!(&a);
204        dbg!(&a_ntt);
205        dbg!(&a_intt);
206        // dbg!(NTT::ROOT_OF_UNITY);
207        // dbg!(NTT::ROOTS_OF_UNITY);
208
209        assert_eq!(a, a_intt);
210        Ok(())
211    }
212
213    #[test]
214    fn test_ntt_loop() -> Result<()> {
215        let q: u64 = 2u64.pow(16) + 1;
216        let n: usize = 512;
217
218        use rand::distributions::Uniform;
219        let mut rng = rand::thread_rng();
220        let dist = Uniform::new(0_u64, q as u64);
221
222        for _ in 0..1000 {
223            let a: Vec<u64> = std::iter::repeat_with(|| dist.sample(&mut rng))
224                .take(n)
225                .collect();
226            let a_ntt = NTT::ntt(q, n, &a);
227            let a_intt = NTT::intt(q, n, &a_ntt);
228            assert_eq!(a, a_intt);
229        }
230        Ok(())
231    }
232}