testudo/src/r1csinstance.rs
src/r1csinstance.rsRaw
1use super::dense_mlpoly::DensePolynomial;
2use super::errors::ProofVerifyError;
3use super::math::Math;
4use super::random::RandomTape;
5use super::scalar::Scalar;
6use super::sparse_mlpoly::{
7 MultiSparseMatPolynomialAsDense, SparseMatEntry, SparseMatPolyCommitment,
8 SparseMatPolyCommitmentGens, SparseMatPolyEvalProof, SparseMatPolynomial,
9};
10use super::timer::Timer;
11use merlin::Transcript;
12use rand::rngs::OsRng;
13use serde::{Deserialize, Serialize};
14
15#[derive(Debug)]
16pub struct R1CSInstance {
17 num_cons: usize,
18 num_vars: usize,
19 num_inputs: usize,
20 A: SparseMatPolynomial,
21 B: SparseMatPolynomial,
22 C: SparseMatPolynomial,
23}
24
25pub struct R1CSCommitmentGens {
26 gens: SparseMatPolyCommitmentGens,
27}
28
29impl R1CSCommitmentGens {
30 pub fn new(
31 label: &'static [u8],
32 num_cons: usize,
33 num_vars: usize,
34 num_inputs: usize,
35 num_nz_entries: usize,
36 ) -> R1CSCommitmentGens {
37 assert!(num_inputs < num_vars);
38 let num_poly_vars_x = num_cons.log2() as usize;
39 let num_poly_vars_y = (2 * num_vars).log2() as usize;
40 let gens =
41 SparseMatPolyCommitmentGens::new(label, num_poly_vars_x, num_poly_vars_y, num_nz_entries, 3);
42 R1CSCommitmentGens { gens }
43 }
44}
45
46pub struct R1CSCommitment {
47 num_cons: usize,
48 num_vars: usize,
49 num_inputs: usize,
50 comm: SparseMatPolyCommitment,
51}
52
53pub struct R1CSDecommitment {
54 dense: MultiSparseMatPolynomialAsDense,
55}
56
57impl R1CSCommitment {
58 pub fn get_num_cons(&self) -> usize {
59 self.num_cons
60 }
61
62 pub fn get_num_vars(&self) -> usize {
63 self.num_vars
64 }
65
66 pub fn get_num_inputs(&self) -> usize {
67 self.num_inputs
68 }
69}
70
71impl R1CSInstance {
72 pub fn new(
73 num_cons: usize,
74 num_vars: usize,
75 num_inputs: usize,
76 A: &[(usize, usize, Scalar)],
77 B: &[(usize, usize, Scalar)],
78 C: &[(usize, usize, Scalar)],
79 ) -> R1CSInstance {
80 Timer::print(&format!("number_of_constraints {}", num_cons));
81 Timer::print(&format!("number_of_variables {}", num_vars));
82 Timer::print(&format!("number_of_inputs {}", num_inputs));
83 Timer::print(&format!("number_non-zero_entries_A {}", A.len()));
84 Timer::print(&format!("number_non-zero_entries_B {}", B.len()));
85 Timer::print(&format!("number_non-zero_entries_C {}", C.len()));
86
87 // check that num_cons is a power of 2
88 assert_eq!(num_cons.next_power_of_two(), num_cons);
89
90 // check that num_vars is a power of 2
91 assert_eq!(num_vars.next_power_of_two(), num_vars);
92
93 // check that number_inputs + 1 <= num_vars
94 assert!(num_inputs < num_vars);
95
96 // no errors, so create polynomials
97 let num_poly_vars_x = num_cons.log2() as usize;
98 let num_poly_vars_y = (2 * num_vars).log2() as usize;
99
100 let mat_A = (0..A.len())
101 .map(|i| SparseMatEntry::new(A[i].0, A[i].1, A[i].2))
102 .collect::<Vec<SparseMatEntry>>();
103 let mat_B = (0..B.len())
104 .map(|i| SparseMatEntry::new(B[i].0, B[i].1, B[i].2))
105 .collect::<Vec<SparseMatEntry>>();
106 let mat_C = (0..C.len())
107 .map(|i| SparseMatEntry::new(C[i].0, C[i].1, C[i].2))
108 .collect::<Vec<SparseMatEntry>>();
109
110 let poly_A = SparseMatPolynomial::new(num_poly_vars_x, num_poly_vars_y, mat_A);
111 let poly_B = SparseMatPolynomial::new(num_poly_vars_x, num_poly_vars_y, mat_B);
112 let poly_C = SparseMatPolynomial::new(num_poly_vars_x, num_poly_vars_y, mat_C);
113
114 R1CSInstance {
115 num_cons,
116 num_vars,
117 num_inputs,
118 A: poly_A,
119 B: poly_B,
120 C: poly_C,
121 }
122 }
123
124 pub fn get_num_vars(&self) -> usize {
125 self.num_vars
126 }
127
128 pub fn get_num_cons(&self) -> usize {
129 self.num_cons
130 }
131
132 pub fn get_num_inputs(&self) -> usize {
133 self.num_inputs
134 }
135
136 pub fn produce_synthetic_r1cs(
137 num_cons: usize,
138 num_vars: usize,
139 num_inputs: usize,
140 ) -> (R1CSInstance, Vec<Scalar>, Vec<Scalar>) {
141 Timer::print(&format!("number_of_constraints {}", num_cons));
142 Timer::print(&format!("number_of_variables {}", num_vars));
143 Timer::print(&format!("number_of_inputs {}", num_inputs));
144
145 let mut csprng: OsRng = OsRng;
146
147 // assert num_cons and num_vars are power of 2
148 assert_eq!((num_cons.log2() as usize).pow2(), num_cons);
149 assert_eq!((num_vars.log2() as usize).pow2(), num_vars);
150
151 // num_inputs + 1 <= num_vars
152 assert!(num_inputs < num_vars);
153
154 // z is organized as [vars,1,io]
155 let size_z = num_vars + num_inputs + 1;
156
157 // produce a random satisfying assignment
158 let Z = {
159 let mut Z: Vec<Scalar> = (0..size_z)
160 .map(|_i| Scalar::random(&mut csprng))
161 .collect::<Vec<Scalar>>();
162 Z[num_vars] = Scalar::one(); // set the constant term to 1
163 Z
164 };
165
166 // three sparse matrices
167 let mut A: Vec<SparseMatEntry> = Vec::new();
168 let mut B: Vec<SparseMatEntry> = Vec::new();
169 let mut C: Vec<SparseMatEntry> = Vec::new();
170 let one = Scalar::one();
171 for i in 0..num_cons {
172 let A_idx = i % size_z;
173 let B_idx = (i + 2) % size_z;
174 A.push(SparseMatEntry::new(i, A_idx, one));
175 B.push(SparseMatEntry::new(i, B_idx, one));
176 let AB_val = Z[A_idx] * Z[B_idx];
177
178 let C_idx = (i + 3) % size_z;
179 let C_val = Z[C_idx];
180
181 if C_val == Scalar::zero() {
182 C.push(SparseMatEntry::new(i, num_vars, AB_val));
183 } else {
184 C.push(SparseMatEntry::new(
185 i,
186 C_idx,
187 AB_val * C_val.invert().unwrap(),
188 ));
189 }
190 }
191
192 Timer::print(&format!("number_non-zero_entries_A {}", A.len()));
193 Timer::print(&format!("number_non-zero_entries_B {}", B.len()));
194 Timer::print(&format!("number_non-zero_entries_C {}", C.len()));
195
196 let num_poly_vars_x = num_cons.log2() as usize;
197 let num_poly_vars_y = (2 * num_vars).log2() as usize;
198 let poly_A = SparseMatPolynomial::new(num_poly_vars_x, num_poly_vars_y, A);
199 let poly_B = SparseMatPolynomial::new(num_poly_vars_x, num_poly_vars_y, B);
200 let poly_C = SparseMatPolynomial::new(num_poly_vars_x, num_poly_vars_y, C);
201
202 let inst = R1CSInstance {
203 num_cons,
204 num_vars,
205 num_inputs,
206 A: poly_A,
207 B: poly_B,
208 C: poly_C,
209 };
210
211 assert!(inst.is_sat(&Z[..num_vars].to_vec(), &Z[num_vars + 1..].to_vec()));
212
213 (inst, Z[..num_vars].to_vec(), Z[num_vars + 1..].to_vec())
214 }
215
216 pub fn is_sat(&self, vars: &[Scalar], input: &[Scalar]) -> bool {
217 assert_eq!(vars.len(), self.num_vars);
218 assert_eq!(input.len(), self.num_inputs);
219
220 let z = {
221 let mut z = vars.to_vec();
222 z.extend(&vec![Scalar::one()]);
223 z.extend(input);
224 z
225 };
226
227 // verify if Az * Bz - Cz = [0...]
228 let Az = self
229 .A
230 .multiply_vec(self.num_cons, self.num_vars + self.num_inputs + 1, &z);
231 let Bz = self
232 .B
233 .multiply_vec(self.num_cons, self.num_vars + self.num_inputs + 1, &z);
234 let Cz = self
235 .C
236 .multiply_vec(self.num_cons, self.num_vars + self.num_inputs + 1, &z);
237
238 assert_eq!(Az.len(), self.num_cons);
239 assert_eq!(Bz.len(), self.num_cons);
240 assert_eq!(Cz.len(), self.num_cons);
241 let res: usize = (0..self.num_cons)
242 .map(|i| if Az[i] * Bz[i] == Cz[i] { 0 } else { 1 })
243 .sum();
244
245 res == 0
246 }
247
248 pub fn multiply_vec(
249 &self,
250 num_rows: usize,
251 num_cols: usize,
252 z: &[Scalar],
253 ) -> (DensePolynomial, DensePolynomial, DensePolynomial) {
254 assert_eq!(num_rows, self.num_cons);
255 assert_eq!(z.len(), num_cols);
256 assert!(num_cols > self.num_vars);
257 (
258 DensePolynomial::new(self.A.multiply_vec(num_rows, num_cols, z)),
259 DensePolynomial::new(self.B.multiply_vec(num_rows, num_cols, z)),
260 DensePolynomial::new(self.C.multiply_vec(num_rows, num_cols, z)),
261 )
262 }
263
264 pub fn compute_eval_table_sparse(
265 &self,
266 num_rows: usize,
267 num_cols: usize,
268 evals: &[Scalar],
269 ) -> (Vec<Scalar>, Vec<Scalar>, Vec<Scalar>) {
270 assert_eq!(num_rows, self.num_cons);
271 assert!(num_cols > self.num_vars);
272
273 let evals_A = self.A.compute_eval_table_sparse(evals, num_rows, num_cols);
274 let evals_B = self.B.compute_eval_table_sparse(evals, num_rows, num_cols);
275 let evals_C = self.C.compute_eval_table_sparse(evals, num_rows, num_cols);
276
277 (evals_A, evals_B, evals_C)
278 }
279
280 pub fn evaluate(&self, rx: &[Scalar], ry: &[Scalar]) -> (Scalar, Scalar, Scalar) {
281 let evals = SparseMatPolynomial::multi_evaluate(&[&self.A, &self.B, &self.C], rx, ry);
282 (evals[0], evals[1], evals[2])
283 }
284
285 pub fn commit(&self, gens: &R1CSCommitmentGens) -> (R1CSCommitment, R1CSDecommitment) {
286 let (comm, dense) = SparseMatPolynomial::multi_commit(&[&self.A, &self.B, &self.C], &gens.gens);
287 let r1cs_comm = R1CSCommitment {
288 num_cons: self.num_cons,
289 num_vars: self.num_vars,
290 num_inputs: self.num_inputs,
291 comm,
292 };
293
294 let r1cs_decomm = R1CSDecommitment { dense };
295
296 (r1cs_comm, r1cs_decomm)
297 }
298}
299
300#[derive(Debug, Serialize, Deserialize)]
301pub struct R1CSEvalProof {
302 proof: SparseMatPolyEvalProof,
303}
304
305impl R1CSEvalProof {
306 pub fn prove(
307 decomm: &R1CSDecommitment,
308 rx: &[Scalar], // point at which the polynomial is evaluated
309 ry: &[Scalar],
310 evals: &(Scalar, Scalar, Scalar),
311 gens: &R1CSCommitmentGens,
312 transcript: &mut Transcript,
313 random_tape: &mut RandomTape,
314 ) -> R1CSEvalProof {
315 let timer = Timer::new("R1CSEvalProof::prove");
316 let proof = SparseMatPolyEvalProof::prove(
317 &decomm.dense,
318 rx,
319 ry,
320 &[evals.0, evals.1, evals.2],
321 &gens.gens,
322 transcript,
323 random_tape,
324 );
325 timer.stop();
326
327 R1CSEvalProof { proof }
328 }
329
330 pub fn verify(
331 &self,
332 comm: &R1CSCommitment,
333 rx: &[Scalar], // point at which the R1CS matrix polynomials are evaluated
334 ry: &[Scalar],
335 evals: &(Scalar, Scalar, Scalar),
336 gens: &R1CSCommitmentGens,
337 transcript: &mut Transcript,
338 ) -> Result<(), ProofVerifyError> {
339 self.proof.verify(
340 &comm.comm,
341 rx,
342 ry,
343 &[evals.0, evals.1, evals.2],
344 &gens.gens,
345 transcript,
346 )
347 }
348}