From 6649562f953e50b8a65b1a418c921fdbf33f3a62 Mon Sep 17 00:00:00 2001 From: Michael Zhu Date: Thu, 7 Nov 2024 18:44:22 -0500 Subject: [PATCH] Feat/batched grand products (#481) * temp * Passes prover assertions * temp2 * temp3 * Sparse layers working(?) * temp * temp * Fix timestamp range check opening points * temp * Tests passing * Update profiling command * Optimize init_final_leaves computation * Add SplitEqPolynomial * Integrate SplitEqPolynomial into dense grand product * SparseInterleavedPolynomial * Integrate SparseInterleavedPolynomial into sparse grand product * temp: par_blocks * Replace bind with bind_par_blocks * too slow :( * Revert to paralellizing over batch in SparseInterleavedPoly * temp * temp2 * temp * Use DenseInterlaevedPolynomial in dense grand product * Fix dense tests * Add dense polynomial benchmarks * switch to scratch space approach * Use DenseInterleavedPolynomial for coalesced SparseInterleavedPolynomial * temp * Refactor (move BatchedCubicSumcheck impls into dense_interleaved_poly and sparse_interleaved_poly) * Fix SparseInterleavedPolynomial::layer_output and add debugging * temp * tests passing thank god * cleanup * compute_cubic bench * Switch summation order in DenesInterleavedPolynomial::compute_cubic * Avoid recomputing E1_evals * Switch summation order in SparseInterleavedPolynomial::compute_cubic * Fix SparseInterleavedPolynomial::compute_cubic bench * Drop bound polynomials in background (in primary sumcheck) * Switch summation order in BatchedGrandProductToggleLayer::compute_cubic * Optimize for 1s in SparseInterleavedPolynomial::compute_cubic * cleanup * Use simplified RS fingerprint for timestamp range-checks * Un-comment surge.rs * Update Quarks grand product * clippy * fix benches * Update grand_product_example script and disable test * Add comments --- README.md | 8 +- jolt-core/Cargo.toml | 8 + jolt-core/benches/binding.rs | 175 ++ jolt-core/benches/compute_cubic.rs | 126 ++ jolt-core/benches/grand_product.rs | 26 +- jolt-core/src/jolt/instruction/div.rs | 4 +- jolt-core/src/jolt/vm/bytecode.rs | 70 +- jolt-core/src/jolt/vm/instruction_lookups.rs | 204 +- jolt-core/src/jolt/vm/mod.rs | 7 +- jolt-core/src/jolt/vm/read_write_memory.rs | 123 +- .../src/jolt/vm/timestamp_range_check.rs | 203 +- jolt-core/src/lasso/memory_checking.rs | 148 +- jolt-core/src/lasso/surge.rs | 12 +- jolt-core/src/poly/dense_interleaved_poly.rs | 430 ++++ jolt-core/src/poly/dense_mlpoly.rs | 67 - jolt-core/src/poly/mod.rs | 3 + jolt-core/src/poly/opening_proof.rs | 4 +- jolt-core/src/poly/sparse_interleaved_poly.rs | 849 ++++++++ jolt-core/src/poly/split_eq_poly.rs | 115 ++ jolt-core/src/r1cs/inputs.rs | 1 + jolt-core/src/subprotocols/grand_product.rs | 1775 ++--------------- .../src/subprotocols/grand_product_quarks.rs | 775 ++++--- jolt-core/src/subprotocols/mod.rs | 1 + .../src/subprotocols/sparse_grand_product.rs | 1328 ++++++++++++ jolt-core/src/subprotocols/sumcheck.rs | 47 +- jolt-core/src/utils/sol_types.rs | 12 +- jolt-evm-verifier/script/Cargo.lock | 800 +++++--- .../script/src/bin/grand_product_example.rs | 21 +- jolt-evm-verifier/test/TestGrandProduct.sol | 2 + 29 files changed, 4511 insertions(+), 2833 deletions(-) create mode 100644 jolt-core/benches/binding.rs create mode 100644 jolt-core/benches/compute_cubic.rs create mode 100644 jolt-core/src/poly/dense_interleaved_poly.rs create mode 100644 jolt-core/src/poly/sparse_interleaved_poly.rs create mode 100644 jolt-core/src/poly/split_eq_poly.rs create mode 100644 jolt-core/src/subprotocols/sparse_grand_product.rs diff --git a/README.md b/README.md index 2d5851050..4765c81f9 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ Just One Lookup Table. Jolt is a zkVM (zero-knowledge virtual machine) for RISC-V, built to be the simplest, fastest, and most extensible general-purpose of its kind. This repository currently contains an implementation of Jolt for the RISC-V 32-bit Base Integer instruction set (RV32I). _Contributors are welcome!_ -The Jolt [paper](https://eprint.iacr.org/2023/1217.pdf) was written by Arasu Arun, Srinath Setty, and Justin Thaler. +The Jolt [paper](https://eprint.iacr.org/2023/1217.pdf) was written by Arasu Arun, Srinath Setty, and Justin Thaler. ## Resources @@ -71,15 +71,15 @@ Examples in the [`examples`](./examples/) directory can be run using e.g. ## Performance profiling -Jolt uses [tracing_chrome](https://crates.io/crates/tracing-chrome) for performance profiling. +Jolt uses [tracing_chrome](https://crates.io/crates/tracing-chrome) for performance profiling. To generate a trace, run: -```cargo run --profile build-fast -p jolt-core trace --name sha3 --format chrome``` +```cargo run --release -p jolt-core trace --name sha3 --format chrome --pcs hyper-kzg``` Where `--name` can be `sha2`, `sha3`, `sha2-chain`, or `fibonacci`. The corresponding guest programs can be found in the [`examples`](./examples/) directory. The benchmark inputs are provided in [`bench.rs`](./jolt-core/src/benches/bench.rs). -The above command will output a JSON file, e.g. `trace-1712455107389520.json`, which can be viewed in [Perfetto](https://ui.perfetto.dev/). +The above command will output a JSON file, e.g. `trace-1712455107389520.json`, which can be viewed in [Perfetto](https://ui.perfetto.dev/). ## Acknowledgements diff --git a/jolt-core/Cargo.toml b/jolt-core/Cargo.toml index a2aeecd1a..a08331b22 100644 --- a/jolt-core/Cargo.toml +++ b/jolt-core/Cargo.toml @@ -86,6 +86,14 @@ harness = false name = "commit" harness = false +[[bench]] +name = "binding" +harness = false + +[[bench]] +name = "compute_cubic" +harness = false + [lib] name = "jolt_core" path = "src/lib.rs" diff --git a/jolt-core/benches/binding.rs b/jolt-core/benches/binding.rs new file mode 100644 index 000000000..45eaebd53 --- /dev/null +++ b/jolt-core/benches/binding.rs @@ -0,0 +1,175 @@ +use ark_bn254::Fr; +use ark_std::{rand::Rng, test_rng}; +use criterion::Criterion; +use jolt_core::field::JoltField; +use jolt_core::poly::dense_interleaved_poly::DenseInterleavedPolynomial; +use jolt_core::poly::dense_mlpoly::DensePolynomial; +use jolt_core::poly::sparse_interleaved_poly::{SparseCoefficient, SparseInterleavedPolynomial}; +use jolt_core::subprotocols::sumcheck::Bindable; +use rayon::prelude::*; + +fn random_dense_coeffs(rng: &mut impl Rng, num_vars: usize) -> Vec { + std::iter::repeat_with(|| F::random(rng)) + .take(1 << num_vars) + .collect() +} + +fn random_sparse_coeffs( + rng: &mut impl Rng, + batch_size: usize, + num_vars: usize, + density: f64, +) -> Vec>> { + (0..batch_size) + .map(|batch_index| { + let mut coeffs: Vec> = vec![]; + for i in 0..(1 << num_vars) { + if rng.gen_bool(density) { + coeffs.push((batch_index * (1 << num_vars) + i, F::random(rng)).into()) + } + } + coeffs + }) + .collect() +} + +fn benchmark_dense(c: &mut Criterion, num_vars: usize) { + c.bench_function( + &format!("DensePolynomial::bind {} variables", num_vars), + |b| { + b.iter_with_setup( + || { + let mut rng = test_rng(); + let coeffs = random_dense_coeffs(&mut rng, num_vars); + let poly = DensePolynomial::new(coeffs); + let r: Vec = std::iter::repeat_with(|| F::random(&mut rng)) + .take(num_vars) + .collect(); + (poly, r) + }, + |(mut poly, r)| { + for i in 0..num_vars { + criterion::black_box(poly.bound_poly_var_top_par(&r[i])); + } + }, + ); + }, + ); +} + +fn benchmark_dense_batch(c: &mut Criterion, num_vars: usize, batch_size: usize) { + c.bench_function( + &format!( + "DensePolynomial::bind {} x {} variables", + batch_size, num_vars + ), + |b| { + b.iter_with_setup( + || { + let mut rng = test_rng(); + let mut polys = vec![]; + for _ in 0..batch_size { + let coeffs = random_dense_coeffs(&mut rng, num_vars); + polys.push(DensePolynomial::new(coeffs)); + } + let r: Vec = std::iter::repeat_with(|| F::random(&mut rng)) + .take(num_vars) + .collect(); + (polys, r) + }, + |(mut polys, r)| { + for i in 0..num_vars { + polys + .par_iter_mut() + .for_each(|poly| poly.bound_poly_var_bot(&r[i])) + } + }, + ); + }, + ); +} + +fn benchmark_dense_interleaved(c: &mut Criterion, num_vars: usize) { + c.bench_function( + &format!("DenseInterleavedPolynomial::bind {} variables", num_vars), + |b| { + b.iter_with_setup( + || { + let mut rng = test_rng(); + let coeffs = random_dense_coeffs(&mut rng, num_vars); + let poly = DenseInterleavedPolynomial::new(coeffs); + let r: Vec = std::iter::repeat_with(|| F::random(&mut rng)) + .take(num_vars) + .collect(); + (poly, r) + }, + |(mut poly, r)| { + for i in 0..num_vars { + criterion::black_box(poly.bind(r[i])); + } + }, + ); + }, + ); +} + +fn benchmark_sparse_interleaved( + c: &mut Criterion, + batch_size: usize, + num_vars: usize, + density: f64, +) { + c.bench_function( + &format!( + "SparseInterleavedPolynomial::bind {} x {} variables, {}% ones", + batch_size, + num_vars, + (1.0 - density) * 100.0 + ), + |b| { + b.iter_with_setup( + || { + let mut rng = test_rng(); + let coeffs = random_sparse_coeffs(&mut rng, batch_size, num_vars, density); + let poly = SparseInterleavedPolynomial::new(coeffs, batch_size << num_vars); + let r: Vec = std::iter::repeat_with(|| F::random(&mut rng)) + .take(num_vars) + .collect(); + (poly, r) + }, + |(mut poly, r)| { + for i in 0..num_vars { + criterion::black_box(poly.bind(r[i])); + } + }, + ); + }, + ); +} + +fn main() { + let mut criterion = Criterion::default() + .configure_from_args() + .warm_up_time(std::time::Duration::from_secs(5)); + + benchmark_sparse_interleaved::(&mut criterion, 64, 20, 0.1); + benchmark_sparse_interleaved::(&mut criterion, 128, 20, 0.1); + benchmark_sparse_interleaved::(&mut criterion, 64, 21, 0.1); + benchmark_sparse_interleaved::(&mut criterion, 128, 21, 0.1); + + // benchmark_dense::(&mut criterion, 20); + // benchmark_dense::(&mut criterion, 22); + // benchmark_dense::(&mut criterion, 24); + + // benchmark_dense_interleaved::(&mut criterion, 22); + // benchmark_dense_interleaved::(&mut criterion, 23); + // benchmark_dense_interleaved::(&mut criterion, 24); + // benchmark_dense_interleaved::(&mut criterion, 25); + + // benchmark_dense_batch::(&mut criterion, 20, 4); + // benchmark_dense_batch::(&mut criterion, 20, 8); + // benchmark_dense_batch::(&mut criterion, 20, 16); + // benchmark_dense_batch::(&mut criterion, 20, 32); + + criterion.final_summary(); +} diff --git a/jolt-core/benches/compute_cubic.rs b/jolt-core/benches/compute_cubic.rs new file mode 100644 index 000000000..0262087a0 --- /dev/null +++ b/jolt-core/benches/compute_cubic.rs @@ -0,0 +1,126 @@ +use ark_bn254::Fr; +use ark_std::{rand::Rng, test_rng}; +use criterion::Criterion; +use jolt_core::field::JoltField; +use jolt_core::poly::dense_interleaved_poly::DenseInterleavedPolynomial; +use jolt_core::poly::dense_mlpoly::DensePolynomial; +use jolt_core::poly::sparse_interleaved_poly::{SparseCoefficient, SparseInterleavedPolynomial}; +use jolt_core::poly::split_eq_poly::SplitEqPolynomial; +use jolt_core::subprotocols::sumcheck::{BatchedCubicSumcheck, Bindable}; +use jolt_core::utils::math::Math; +use jolt_core::utils::transcript::KeccakTranscript; +use rayon::prelude::*; + +fn random_dense_coeffs(rng: &mut impl Rng, num_vars: usize) -> Vec { + std::iter::repeat_with(|| F::random(rng)) + .take(1 << num_vars) + .collect() +} + +fn random_sparse_coeffs( + rng: &mut impl Rng, + batch_size: usize, + num_vars: usize, + density: f64, +) -> Vec>> { + (0..batch_size) + .map(|batch_index| { + let mut coeffs: Vec> = vec![]; + for i in 0..(1 << num_vars) { + if rng.gen_bool(density) { + coeffs.push((batch_index * (1 << num_vars) + i, F::random(rng)).into()) + } + } + coeffs + }) + .collect() +} + +fn benchmark_dense_interleaved(c: &mut Criterion, num_vars: usize) { + c.bench_function( + &format!( + "DenseInterleavedPolynomial::compute_cubic {} variables", + num_vars + ), + |b| { + b.iter_with_setup( + || { + let mut rng = test_rng(); + let coeffs = random_dense_coeffs(&mut rng, num_vars); + let poly = DenseInterleavedPolynomial::new(coeffs); + let r_eq: Vec = std::iter::repeat_with(|| F::random(&mut rng)) + .take(num_vars) + .collect(); + let eq_poly = SplitEqPolynomial::new(&r_eq); + let claim = F::random(&mut rng); + (poly, eq_poly, claim) + }, + |(poly, eq_poly, claim)| { + criterion::black_box( + BatchedCubicSumcheck::::compute_cubic( + &poly, &eq_poly, claim, + ), + ); + }, + ); + }, + ); +} + +fn benchmark_sparse_interleaved( + c: &mut Criterion, + batch_size: usize, + num_vars: usize, + density: f64, +) { + c.bench_function( + &format!( + "SparseInterleavedPolynomial::compute_cubic {} x {} variables, {}% ones", + batch_size, + num_vars, + (1.0 - density) * 100.0 + ), + |b| { + b.iter_with_setup( + || { + let mut rng = test_rng(); + let coeffs = random_sparse_coeffs(&mut rng, batch_size, num_vars, density); + let poly = SparseInterleavedPolynomial::new(coeffs, batch_size << num_vars); + let r_eq: Vec = std::iter::repeat_with(|| F::random(&mut rng)) + .take((batch_size << num_vars).next_power_of_two().log_2()) + .collect(); + let eq_poly = SplitEqPolynomial::new(&r_eq); + let claim = F::random(&mut rng); + (poly, eq_poly, claim) + }, + |(poly, eq_poly, claim)| { + criterion::black_box( + BatchedCubicSumcheck::::compute_cubic( + &poly, &eq_poly, claim, + ), + ); + }, + ); + }, + ); +} + +fn main() { + let mut criterion = Criterion::default() + .configure_from_args() + .warm_up_time(std::time::Duration::from_secs(5)); + + // benchmark_dense_interleaved::(&mut criterion, 20); + // benchmark_dense_interleaved::(&mut criterion, 21); + // benchmark_dense_interleaved::(&mut criterion, 22); + // benchmark_dense_interleaved::(&mut criterion, 23); + // benchmark_dense_interleaved::(&mut criterion, 24); + // benchmark_dense_interleaved::(&mut criterion, 25); + + benchmark_sparse_interleaved::(&mut criterion, 64, 20, 0.1); + benchmark_sparse_interleaved::(&mut criterion, 128, 20, 0.1); + benchmark_sparse_interleaved::(&mut criterion, 64, 21, 0.1); + benchmark_sparse_interleaved::(&mut criterion, 128, 21, 0.1); + + criterion.final_summary(); +} diff --git a/jolt-core/benches/grand_product.rs b/jolt-core/benches/grand_product.rs index d6c6a2949..d737bb38f 100644 --- a/jolt-core/benches/grand_product.rs +++ b/jolt-core/benches/grand_product.rs @@ -27,12 +27,12 @@ struct BenchConfig { // Sets up the benchmark by generating leaves and computing known products // and allows configuring the percentage of ones in the leaves fn setup_bench( - num_batches: usize, + batch_size: usize, layer_size: usize, - threshold: u32, + percent_ones: u32, ) -> ( // Leaves - Vec>, + (Vec, usize), PCS::Setup, // Products of leaves Vec, @@ -43,17 +43,17 @@ where ProofTranscript: Transcript, { assert!( - threshold <= 100, + percent_ones <= 100, "Threshold must be between 0 and 100, but got {}", - threshold + percent_ones ); let mut rng = ChaCha20Rng::seed_from_u64(111111u64); - let threshold = ((threshold as u64 * u32::MAX as u64) / 100) as u32; + let threshold = ((percent_ones as u64 * u32::MAX as u64) / 100) as u32; // Generate leaves with percentage of ones - let leaves: Vec> = (0..num_batches) + let leaves: Vec> = (0..batch_size) .map(|_| { (0..layer_size) .map(|_| { @@ -72,7 +72,7 @@ where let setup = PCS::setup(&[CommitShape::new(SRS_SIZE, BatchType::Big)]); - (leaves, setup, known_products) + ((leaves.concat(), batch_size), setup, known_products) } fn benchmark_prove( @@ -82,7 +82,7 @@ fn benchmark_prove( ) where PCS: CommitmentScheme, F: JoltField, - G: BatchedGrandProduct>>, + G: BatchedGrandProduct, usize)>, ProofTranscript: Transcript, { let (leaves, setup, _) = setup_bench::( @@ -123,7 +123,7 @@ fn benchmark_verify( ) where PCS: CommitmentScheme, F: JoltField, - G: BatchedGrandProduct>>, + G: BatchedGrandProduct, usize)>, ProofTranscript: Transcript, { let (leaves, setup, known_products) = setup_bench::( @@ -174,7 +174,7 @@ fn benchmark_prove_and_verify( ) where PCS: CommitmentScheme, F: JoltField, - G: BatchedGrandProduct>>, + G: BatchedGrandProduct, usize)>, ProofTranscript: Transcript, { benchmark_prove::(c, config, grand_product_config); @@ -253,12 +253,12 @@ fn main() { benchmark_prove_and_verify::< HyperKZG, Fr, - BatchedDenseGrandProduct, + BatchedDenseGrandProduct, KeccakTranscript, >( &mut c, config, - as BatchedGrandProduct< + as BatchedGrandProduct< Fr, HyperKZG, KeccakTranscript, diff --git a/jolt-core/src/jolt/instruction/div.rs b/jolt-core/src/jolt/instruction/div.rs index 616f17d85..9b7e7e601 100644 --- a/jolt-core/src/jolt/instruction/div.rs +++ b/jolt-core/src/jolt/instruction/div.rs @@ -76,7 +76,7 @@ impl VirtualInstructionSequence for DIVInstruction(remainder).lookup_entry(); @@ -96,7 +96,7 @@ impl VirtualInstructionSequence for DIVInstruction(r, y).lookup_entry(); diff --git a/jolt-core/src/jolt/vm/bytecode.rs b/jolt-core/src/jolt/vm/bytecode.rs index 48844513c..fbd327551 100644 --- a/jolt-core/src/jolt/vm/bytecode.rs +++ b/jolt-core/src/jolt/vm/bytecode.rs @@ -507,11 +507,11 @@ where _: &JoltPolynomials, gamma: &F, tau: &F, - ) -> (Vec>, Vec>) { + ) -> ((Vec, usize), (Vec, usize)) { let num_ops = polynomials.a_read_write.len(); let bytecode_size = preprocessing.v_init_final[0].len(); - let read_leaves = (0..num_ops) + let read_leaves: Vec = (0..num_ops) .into_par_iter() .map(|i| { Self::fingerprint( @@ -531,7 +531,7 @@ where }) .collect(); - let init_leaves = (0..bytecode_size) + let init_leaves: Vec = (0..bytecode_size) .into_par_iter() .map(|i| { Self::fingerprint( @@ -593,9 +593,10 @@ where }) .collect(); + // TODO(moodlezoup): avoid concat ( - vec![read_leaves, write_leaves], - vec![init_leaves, final_leaves], + ([read_leaves, write_leaves].concat(), 2), + ([init_leaves, final_leaves].concat(), 2), ) } @@ -740,65 +741,6 @@ mod tests { BytecodeOpenings::::test_ordering_consistency(&preprocessing); } - #[test] - fn bytecode_poly_leaf_construction() { - let program = vec![ - BytecodeRow::new(to_ram_address(0), 2u64, 2u64, 2u64, 2u64, 2u64), - BytecodeRow::new(to_ram_address(1), 4u64, 4u64, 4u64, 4u64, 4u64), - BytecodeRow::new(to_ram_address(2), 8u64, 8u64, 8u64, 8u64, 8u64), - BytecodeRow::new(to_ram_address(3), 16u64, 16u64, 16u64, 16u64, 16u64), - ]; - let mut trace = vec![ - trace_step(BytecodeRow::new( - to_ram_address(3), - 16u64, - 16u64, - 16u64, - 16u64, - 16u64, - )), - trace_step(BytecodeRow::new( - to_ram_address(2), - 8u64, - 8u64, - 8u64, - 8u64, - 8u64, - )), - ]; - - let preprocessing = BytecodePreprocessing::preprocess(program.clone()); - let polys: BytecodePolynomials = BytecodeProof::< - Fr, - HyraxScheme, - KeccakTranscript, - >::generate_witness::( - &preprocessing, &mut trace - ); - - let (gamma, tau) = (&Fr::from(100), &Fr::from(35)); - let (read_write_leaves, init_final_leaves) = BytecodeProof::< - Fr, - HyraxScheme, - KeccakTranscript, - >::compute_leaves( - &preprocessing, - &polys, - &JoltPolynomials::default(), - gamma, - tau, - ); - let init_leaves = &init_final_leaves[0]; - let read_leaves = &read_write_leaves[0]; - let write_leaves = &read_write_leaves[1]; - let final_leaves = &init_final_leaves[1]; - - let read_final_leaves = [read_leaves.clone(), final_leaves.clone()].concat(); - let init_write_leaves = [init_leaves.clone(), write_leaves.clone()].concat(); - let difference: Vec = get_difference(&read_final_leaves, &init_write_leaves); - assert_eq!(difference.len(), 0); - } - #[test] #[should_panic] fn bytecode_validation_fake_trace() { diff --git a/jolt-core/src/jolt/vm/instruction_lookups.rs b/jolt-core/src/jolt/vm/instruction_lookups.rs index bd96fe475..a7017c60a 100644 --- a/jolt-core/src/jolt/vm/instruction_lookups.rs +++ b/jolt-core/src/jolt/vm/instruction_lookups.rs @@ -1,5 +1,7 @@ use crate::poly::opening_proof::{ProverOpeningAccumulator, VerifierOpeningAccumulator}; -use crate::subprotocols::grand_product::{BatchedGrandProduct, ToggledBatchedGrandProduct}; +use crate::subprotocols::grand_product::BatchedGrandProduct; +use crate::subprotocols::sparse_grand_product::ToggledBatchedGrandProduct; +use crate::utils::thread::{drop_in_background_thread, unsafe_allocate_zero_vec}; use ark_serialize::{CanonicalDeserialize, CanonicalSerialize}; use itertools::{interleave, Itertools}; use rayon::iter::{IndexedParallelIterator, IntoParallelIterator, ParallelIterator}; @@ -163,7 +165,7 @@ where Subtables: JoltSubtableSet, ProofTranscript: Transcript, { - type ReadWriteGrandProduct = ToggledBatchedGrandProduct; + type ReadWriteGrandProduct = ToggledBatchedGrandProduct; type Polynomials = InstructionLookupPolynomials; type Openings = InstructionLookupOpenings; @@ -174,11 +176,8 @@ where type MemoryTuple = (F, F, F, Option); // (a, v, t, flag) fn fingerprint(inputs: &(F, F, F, Option), gamma: &F, tau: &F) -> F { - let (a, v, t, flag) = *inputs; - match flag { - Some(val) => val * (t * gamma.square() + v * *gamma + a - *tau) + F::one() - val, - None => t * gamma.square() + v * *gamma + a - *tau, - } + let (a, v, t, _flag) = *inputs; + t * gamma.square() + v * *gamma + a - *tau } #[tracing::instrument(skip_all, name = "InstructionLookups::compute_leaves")] @@ -217,38 +216,34 @@ where }) .collect(); - let init_final_leaves: Vec> = preprocessing + let init_final_leaves: Vec = preprocessing .materialized_subtables .par_iter() .enumerate() .flat_map_iter(|(subtable_index, subtable)| { - let init_leaves: Vec = (0..M) - .map(|i| { - let a = &F::from_u64(i as u64).unwrap(); - let v = &subtable[i]; - // let t = F::zero(); - // Compute h(a,v,t) where t == 0 - mul_0_1_optimized(v, gamma) + *a - *tau - }) - .collect(); - - let final_leaves: Vec> = preprocessing.subtable_to_memory_indices - [subtable_index] - .iter() - .map(|memory_index| { - let final_cts = &polynomials.final_cts[*memory_index]; - (0..M) - .map(|i| { - init_leaves[i] + mul_0_1_optimized(&final_cts[i], &gamma_squared) - }) - .collect() - }) - .collect(); + let mut leaves: Vec = unsafe_allocate_zero_vec( + M * (preprocessing.subtable_to_memory_indices[subtable_index].len() + 1), + ); + // Init leaves + (0..M).for_each(|i| { + let a = &F::from_u64(i as u64).unwrap(); + let v = &subtable[i]; + // let t = F::zero(); + // Compute h(a,v,t) where t == 0 + leaves[i] = mul_0_1_optimized(v, gamma) + *a - *tau; + }); + // Final leaves + let mut leaf_index = M; + for memory_index in &preprocessing.subtable_to_memory_indices[subtable_index] { + let final_cts = &polynomials.final_cts[*memory_index]; + (0..M).for_each(|i| { + leaves[leaf_index] = + leaves[i] + mul_0_1_optimized(&final_cts[i], &gamma_squared); + leaf_index += 1; + }); + } - let mut polys = Vec::with_capacity(C + 1); - polys.push(init_leaves); - polys.extend(final_leaves); - polys + leaves }) .collect(); @@ -257,33 +252,37 @@ where polynomials.instruction_flag_bitvectors.as_ref().unwrap(), ); - ((memory_flags, read_write_leaves), init_final_leaves) + ( + (memory_flags, read_write_leaves), + ( + init_final_leaves, + // # init = # subtables; # final = # memories + Self::NUM_SUBTABLES + preprocessing.num_memories, + ), + ) } - fn interleave_hashes( + fn interleave( preprocessing: &InstructionLookupsPreprocessing, - multiset_hashes: &MultisetHashes, - ) -> (Vec, Vec) { + read_values: &Vec, + write_values: &Vec, + init_values: &Vec, + final_values: &Vec, + ) -> (Vec, Vec) { // R W R W R W ... - let read_write_hashes = interleave( - multiset_hashes.read_hashes.clone(), - multiset_hashes.write_hashes.clone(), - ) - .collect(); + let read_write_values = interleave(read_values.clone(), write_values.clone()).collect(); // I F F F F I F F F F ... - let mut init_final_hashes = Vec::with_capacity( - multiset_hashes.init_hashes.len() + multiset_hashes.final_hashes.len(), - ); + let mut init_final_values = Vec::with_capacity(init_values.len() + final_values.len()); for subtable_index in 0..Self::NUM_SUBTABLES { - init_final_hashes.push(multiset_hashes.init_hashes[subtable_index]); + init_final_values.push(init_values[subtable_index]); let memory_indices = &preprocessing.subtable_to_memory_indices[subtable_index]; memory_indices .iter() - .for_each(|i| init_final_hashes.push(multiset_hashes.final_hashes[*i])); + .for_each(|i| init_final_values.push(final_values[*i])); } - (read_write_hashes, init_final_hashes) + (read_write_values, init_final_values) } fn uninterleave_hashes( @@ -362,9 +361,6 @@ where fn protocol_name() -> &'static [u8] { b"Instruction lookups check" } - - type InitFinalGrandProduct = - crate::subprotocols::grand_product::BatchedDenseGrandProduct; } impl @@ -451,6 +447,107 @@ where }) .collect() } + + /// Checks that the claims output by the grand products are consistent with the openings of + /// the polynomials comprising the input layers. + /// + /// Differs from the default `check_fingerprints` implementation because the input layer + /// of the read-write grand product is a `BatchedGrandProductToggleLayer`, so we need to + /// evaluate a multi-*quadratic* extension of the leaves rather than a multilinear extension. + /// This means we handle the openings a bit differently. + fn check_fingerprints( + preprocessing: &Self::Preprocessing, + read_write_claim: F, + init_final_claim: F, + r_read_write_batch_index: &[F], + r_init_final_batch_index: &[F], + openings: &Self::Openings, + exogenous_openings: &NoExogenousOpenings, + gamma: &F, + tau: &F, + ) { + let read_tuples: Vec<_> = Self::read_tuples(preprocessing, openings, exogenous_openings); + let write_tuples: Vec<_> = Self::write_tuples(preprocessing, openings, exogenous_openings); + let init_tuples: Vec<_> = Self::init_tuples(preprocessing, openings, exogenous_openings); + let final_tuples: Vec<_> = Self::final_tuples(preprocessing, openings, exogenous_openings); + + let (read_write_tuples, init_final_tuples) = Self::interleave( + preprocessing, + &read_tuples, + &write_tuples, + &init_tuples, + &final_tuples, + ); + + assert_eq!( + read_write_tuples.len().next_power_of_two(), + r_read_write_batch_index.len().pow2(), + ); + assert_eq!( + init_final_tuples.len().next_power_of_two(), + r_init_final_batch_index.len().pow2() + ); + + let mut read_write_flags: Vec<_> = read_write_tuples + .iter() + .map(|tuple| tuple.3.unwrap()) + .collect(); + // For the toggled grand product, the flags in the input layer are padded with 1s, + // while the fingerprints are padded with 0s, so that all subsequent padding layers + // are all 0s. + // To see why this is the case, observe that the input layer's gates will output + // flag * fingerprint + 1 - flag = 1 * 0 + 1 - 1 = 0. + // Then all subsequent layers will output gate values 0 * 0 = 0. + read_write_flags.resize(read_write_flags.len().next_power_of_two(), F::one()); + + // Let r' := r_read_write_batch_index + // and r'':= r_read_write_opening. + // + // Let k denote the batch size. + // + // The `read_write_flags` vector above contains the evaluations of the k individual + // flag MLEs at r''. + // + // What we want to compute is the evaluation of the MLE of ALL the flags, concatenated together, + // at (r', r''): + // + // flags(r', r'') = \sum_j eq(r', j) * flag_j(r'') + // + // where flag_j(r'') is what we already have in `read_write_flags`. + let combined_flags: F = read_write_flags + .iter() + .zip(EqPolynomial::evals(r_read_write_batch_index).iter()) + .map(|(flag, eq_eval)| *flag * eq_eval) + .sum(); + // Similar thing for the fingerprints: + // + // fingerprints(r', r'') = \sum_j eq(r', j) * (t_j(r'') * \gamma^2 + v_j(r'') * \gamma + a_j(r'') - \tau) + let combined_read_write_fingerprint: F = read_write_tuples + .iter() + .zip(EqPolynomial::evals(r_read_write_batch_index).iter()) + .map(|(tuple, eq_eval)| Self::fingerprint(tuple, gamma, tau) * eq_eval) + .sum(); + + // Now we combine flags(r', r'') and fingerprints(r', r'') to obtain the evaluation of the + // multi-*quadratic* extension W of the input layer at (r', r'') + // + // W(r', r'') = flags(r', r'') * fingerprints(r', r'') + 1 - flags(r', r'') + // + // and this should equal the claim output by the read-write grand product. + assert_eq!( + combined_flags * combined_read_write_fingerprint + F::one() - combined_flags, + read_write_claim + ); + + // The init-final grand product isn't toggled using flags (it's just a "normal" grand product) + // so we combine the openings the normal way. + let combined_init_final_fingerprint: F = init_final_tuples + .iter() + .zip(EqPolynomial::evals(r_init_final_batch_index).iter()) + .map(|(tuple, eq_eval)| Self::fingerprint(tuple, gamma, tau) * eq_eval) + .sum(); + assert_eq!(combined_init_final_fingerprint, init_final_claim); + } } /// Proof of instruction lookups for a single Jolt program execution. @@ -957,6 +1054,9 @@ where let memory_evals = memory_polys_updated.iter().map(|poly| poly[0]).collect(); let outputs_eval = lookup_outputs_poly[0]; + drop_in_background_thread(flag_polys_updated); + drop_in_background_thread(memory_polys_updated); + ( SumcheckInstanceProof::new(compressed_polys), random_vars, diff --git a/jolt-core/src/jolt/vm/mod.rs b/jolt-core/src/jolt/vm/mod.rs index 2a1f6ea8d..b1aac8825 100644 --- a/jolt-core/src/jolt/vm/mod.rs +++ b/jolt-core/src/jolt/vm/mod.rs @@ -245,6 +245,12 @@ impl JoltPolynomials { .zip(trace_comitments.into_iter()) .for_each(|(dest, src)| *dest = src); + println!( + "# commitments: {} + {}", + commitments.read_write_values().len(), + commitments.init_final_values().len(), + ); + commitments.bytecode.t_final = PCS::commit(&self.bytecode.t_final, &preprocessing.generators); ( @@ -606,7 +612,6 @@ where &mut opening_accumulator, &mut transcript, )?; - Self::verify_r1cs( r1cs_proof, &commitments, diff --git a/jolt-core/src/jolt/vm/read_write_memory.rs b/jolt-core/src/jolt/vm/read_write_memory.rs index 423103174..202a92692 100644 --- a/jolt-core/src/jolt/vm/read_write_memory.rs +++ b/jolt-core/src/jolt/vm/read_write_memory.rs @@ -4,9 +4,10 @@ use crate::lasso::memory_checking::{ ExogenousOpenings, Initializable, StructuredPolynomialData, VerifierComputedOpening, }; use crate::poly::opening_proof::{ProverOpeningAccumulator, VerifierOpeningAccumulator}; +use crate::utils::thread::unsafe_allocate_zero_vec; use rand::rngs::StdRng; use rand::RngCore; -use rayon::iter::{IntoParallelIterator, IntoParallelRefIterator, ParallelIterator}; +use rayon::prelude::*; #[cfg(test)] use std::collections::HashSet; use std::marker::PhantomData; @@ -253,8 +254,6 @@ impl ReadWriteMemoryPolynomials { trace: &Vec>, ) -> (Self, [Vec; MEMORY_OPS_PER_INSTRUCTION]) { assert!(program_io.inputs.len() <= program_io.memory_layout.max_input_size as usize); - println!("program_io.outputs.len(): {}", program_io.outputs.len()); - println!("program_io.memory_layout: {:?}", program_io.memory_layout); assert!(program_io.outputs.len() <= program_io.memory_layout.max_output_size as usize); let m = trace.len(); @@ -892,7 +891,7 @@ where jolt_polynomials: &'a JoltPolynomials, gamma: &F, tau: &F, - ) -> (Vec>, Vec>) { + ) -> ((Vec, usize), (Vec, usize)) { let gamma_squared = gamma.square(); let num_ops = polynomials.a_ram.len(); let memory_size = polynomials.v_final.len(); @@ -901,66 +900,64 @@ where let a_rs1 = &jolt_polynomials.bytecode.v_read_write[3]; let a_rs2 = &jolt_polynomials.bytecode.v_read_write[4]; - let read_write_leaves = (0..MEMORY_OPS_PER_INSTRUCTION) - .into_par_iter() - .flat_map(|i| { - let read_fingerprints = (0..num_ops) - .into_par_iter() - .map(|j| { - let a = match i { - RS1 => a_rs1[j], - RS2 => a_rs2[j], - RD => a_rd[j], - _ => polynomials.a_ram[j] + F::from_u64((i - RAM_1) as u64).unwrap(), - }; - polynomials.t_read[i][j] * gamma_squared - + mul_0_optimized(&polynomials.v_read[i][j], gamma) - + a + let mut read_write_leaves: Vec = + unsafe_allocate_zero_vec(2 * MEMORY_OPS_PER_INSTRUCTION * num_ops); + for (i, chunk) in read_write_leaves.chunks_mut(2 * num_ops).enumerate() { + chunk[..num_ops] + .par_iter_mut() + .enumerate() + .for_each(|(j, read_fingerprint)| { + let a = match i { + RS1 => a_rs1[j], + RS2 => a_rs2[j], + RD => a_rd[j], + _ => polynomials.a_ram[j] + F::from_u64((i - RAM_1) as u64).unwrap(), + }; + *read_fingerprint = polynomials.t_read[i][j] * gamma_squared + + mul_0_optimized(&polynomials.v_read[i][j], gamma) + + a + - *tau; + }); + let v_write = match i { + RS1 => &polynomials.v_read[0], // rs1 + RS2 => &polynomials.v_read[1], // rs2 + RD => &polynomials.v_write_rd, // rd + _ => &polynomials.v_write_ram[i - 3], // RAM + }; + + chunk[num_ops..].par_iter_mut().enumerate().for_each( + |(j, write_fingerprint)| match i { + RS1 => { + *write_fingerprint = F::from_u64(j as u64).unwrap() * gamma_squared + + mul_0_optimized(&v_write[j], gamma) + + a_rs1[j] + - *tau; + } + RS2 => { + *write_fingerprint = F::from_u64(j as u64).unwrap() * gamma_squared + + mul_0_optimized(&v_write[j], gamma) + + a_rs2[j] + - *tau; + } + RD => { + *write_fingerprint = F::from_u64(j as u64).unwrap() * gamma_squared + + mul_0_optimized(&v_write[j], gamma) + + a_rd[j] - *tau - }) - .collect(); - let v_write = match i { - RS1 => &polynomials.v_read[0], // rs1 - RS2 => &polynomials.v_read[1], // rs2 - RD => &polynomials.v_write_rd, // rd - _ => &polynomials.v_write_ram[i - 3], // RAM - }; - let write_fingerprints = (0..num_ops) - .into_par_iter() - .map(|j| match i { - RS1 => { - F::from_u64(j as u64).unwrap() * gamma_squared - + mul_0_optimized(&v_write[j], gamma) - + a_rs1[j] - - *tau - } - RS2 => { - F::from_u64(j as u64).unwrap() * gamma_squared - + mul_0_optimized(&v_write[j], gamma) - + a_rs2[j] - - *tau - } - RD => { - F::from_u64(j as u64).unwrap() * gamma_squared - + mul_0_optimized(&v_write[j], gamma) - + a_rd[j] - - *tau - } - _ => { - polynomials.t_write_ram[i - RAM_1][j] * gamma_squared - + mul_0_optimized(&v_write[j], gamma) - + polynomials.a_ram[j] - + F::from_u64((i - RAM_1) as u64).unwrap() - - *tau - } - }) - .collect(); - [read_fingerprints, write_fingerprints] - }) - .collect(); + } + _ => { + *write_fingerprint = polynomials.t_write_ram[i - RAM_1][j] * gamma_squared + + mul_0_optimized(&v_write[j], gamma) + + polynomials.a_ram[j] + + F::from_u64((i - RAM_1) as u64).unwrap() + - *tau; + } + }, + ); + } let v_init = polynomials.v_init.as_ref().unwrap(); - let init_fingerprints = (0..memory_size) + let init_fingerprints: Vec = (0..memory_size) .into_par_iter() .map(|i| /* 0 * gamma^2 + */ mul_0_optimized(&v_init[i], gamma) + F::from_u64(i as u64).unwrap() - *tau) .collect(); @@ -975,8 +972,8 @@ where .collect(); ( - read_write_leaves, - vec![init_fingerprints, final_fingerprints], + (read_write_leaves, 2 * MEMORY_OPS_PER_INSTRUCTION), + ([init_fingerprints, final_fingerprints].concat(), 2), // TODO(moodlezoup): Avoid concat ) } diff --git a/jolt-core/src/jolt/vm/timestamp_range_check.rs b/jolt-core/src/jolt/vm/timestamp_range_check.rs index 037ee536d..da8f2c035 100644 --- a/jolt-core/src/jolt/vm/timestamp_range_check.rs +++ b/jolt-core/src/jolt/vm/timestamp_range_check.rs @@ -1,4 +1,4 @@ -use crate::field::JoltField; +use crate::field::{JoltField, OptimizedMul}; use crate::lasso::memory_checking::{ ExogenousOpenings, Initializable, StructuredPolynomialData, VerifierComputedOpening, }; @@ -7,6 +7,7 @@ use crate::subprotocols::grand_product::{ BatchedDenseGrandProduct, BatchedGrandProduct, BatchedGrandProductLayer, BatchedGrandProductProof, }; +use crate::utils::math::Math; use crate::utils::thread::drop_in_background_thread; use ark_serialize::{CanonicalDeserialize, CanonicalSerialize}; use common::constants::MEMORY_OPS_PER_INSTRUCTION; @@ -14,7 +15,6 @@ use itertools::interleave; use rayon::prelude::*; #[cfg(test)] use std::collections::HashSet; -use std::iter::zip; use crate::poly::commitment::commitment_scheme::{BatchType, CommitShape, CommitmentScheme}; use crate::utils::transcript::Transcript; @@ -26,7 +26,7 @@ use crate::{ poly::{ dense_mlpoly::DensePolynomial, eq_poly::EqPolynomial, identity_poly::IdentityPolynomial, }, - utils::{errors::ProofVerifyError, mul_0_1_optimized}, + utils::errors::ProofVerifyError, }; use super::{JoltCommitments, JoltPolynomials, JoltStuff}; @@ -128,11 +128,11 @@ where let M = read_timestamps[0].len(); #[cfg(test)] - let mut init_tuples: HashSet<(u64, u64, u64)> = HashSet::new(); + let mut init_tuples: HashSet<(u64, u64)> = HashSet::new(); #[cfg(test)] { for i in 0..M { - init_tuples.insert((i as u64, i as u64, 0u64)); + init_tuples.insert((i as u64, 0u64)); } } @@ -174,16 +174,16 @@ where ] .iter() { - let mut read_tuples: HashSet<(u64, u64, u64)> = HashSet::new(); - let mut write_tuples: HashSet<(u64, u64, u64)> = HashSet::new(); + let mut read_tuples: HashSet<(u64, u64)> = HashSet::new(); + let mut write_tuples: HashSet<(u64, u64)> = HashSet::new(); for (v, t) in lookup_indices.iter().zip(read_cts.iter()) { - read_tuples.insert((*v, *v, *t)); - write_tuples.insert((*v, *v, *t + 1)); + read_tuples.insert((*v, *t)); + write_tuples.insert((*v, *t + 1)); } - let mut final_tuples: HashSet<(u64, u64, u64)> = HashSet::new(); + let mut final_tuples: HashSet<(u64, u64)> = HashSet::new(); for (i, t) in final_cts.iter().enumerate() { - final_tuples.insert((i as u64, i as u64, *t)); + final_tuples.insert((i as u64, *t)); } let init_write: HashSet<_> = init_tuples.union(&write_tuples).collect(); @@ -249,6 +249,7 @@ where type Openings = TimestampRangeCheckOpenings; type Commitments = TimestampRangeCheckCommitments; type ExogenousOpenings = ReadTimestampOpenings; + type MemoryTuple = (F, F); // a = v for all range check tuples // Init/final grand products are batched together with read/write grand products type InitFinalGrandProduct = NoopGrandProduct; @@ -264,9 +265,9 @@ where unimplemented!("Use TimestampValidityProof::prove instead"); } - fn fingerprint(inputs: &(F, F, F), gamma: &F, tau: &F) -> F { - let (a, v, t) = *inputs; - t * gamma.square() + v * *gamma + a - *tau + fn fingerprint(inputs: &(F, F), gamma: &F, tau: &F) -> F { + let (a, t) = *inputs; + a * gamma + t - *tau } #[tracing::instrument(skip_all, name = "RangeCheckPolynomials::compute_leaves")] @@ -283,11 +284,10 @@ where jolt_polynomials: &JoltPolynomials, gamma: &F, tau: &F, - ) -> (Vec>, ()) { + ) -> ((Vec, usize), ()) { let read_timestamps = &jolt_polynomials.read_write_memory.t_read; let M = read_timestamps[0].len(); - let gamma_squared = gamma.square(); let read_write_leaves: Vec> = (0..MEMORY_OPS_PER_INSTRUCTION) .into_par_iter() @@ -296,15 +296,14 @@ where .into_par_iter() .map(|j| { let read_timestamp = read_timestamps[i][j]; - polynomials.read_cts_read_timestamp[i][j] * gamma_squared - + read_timestamp * *gamma - + read_timestamp + gamma.mul_01_optimized(read_timestamp) + + polynomials.read_cts_read_timestamp[i][j] - *tau }) .collect(); let write_fingeprints_0 = read_fingerprints_0 .par_iter() - .map(|read_fingerprint| *read_fingerprint + gamma_squared) + .map(|read_fingerprint| *read_fingerprint + F::one()) .collect(); let read_fingerprints_1: Vec = (0..M) @@ -312,15 +311,13 @@ where .map(|j| { let global_minus_read = F::from_u64(j as u64).unwrap() - read_timestamps[i][j]; - polynomials.read_cts_global_minus_read[i][j] * gamma_squared - + global_minus_read * *gamma - + global_minus_read + global_minus_read * gamma + polynomials.read_cts_global_minus_read[i][j] - *tau }) .collect(); let write_fingeprints_1 = read_fingerprints_1 .par_iter() - .map(|read_fingerprint| *read_fingerprint + gamma_squared) + .map(|read_fingerprint| *read_fingerprint + F::one()) .collect(); [ @@ -338,8 +335,8 @@ where .into_par_iter() .map(|i| { let index = F::from_u64(i as u64).unwrap(); - // 0 * gamma^2 + - index * *gamma + index - *tau + // t = 0 + index * gamma - *tau }) .collect(); @@ -349,22 +346,12 @@ where .flat_map(|i| { let final_fingerprints_0 = (0..M) .into_par_iter() - .map(|j| { - mul_0_1_optimized( - &polynomials.final_cts_read_timestamp[i][j], - &gamma_squared, - ) + init_leaves[j] - }) + .map(|j| polynomials.final_cts_read_timestamp[i][j] + init_leaves[j]) .collect(); let final_fingerprints_1 = (0..M) .into_par_iter() - .map(|j| { - mul_0_1_optimized( - &polynomials.final_cts_global_minus_read[i][j], - &gamma_squared, - ) + init_leaves[j] - }) + .map(|j| polynomials.final_cts_global_minus_read[i][j] + init_leaves[j]) .collect(); [final_fingerprints_0, final_fingerprints_1] @@ -372,22 +359,24 @@ where ); leaves.push(init_leaves); - (leaves, ()) + let batch_size = leaves.len(); + + // TODO(moodlezoup): Avoid concat + ((leaves.concat(), batch_size), ()) } - fn interleave_hashes( + fn interleave( _: &NoPreprocessing, - multiset_hashes: &MultisetHashes, - ) -> (Vec, Vec) { - let read_write_hashes = interleave( - multiset_hashes.read_hashes.clone(), - multiset_hashes.write_hashes.clone(), - ) - .collect(); - let mut init_final_hashes = multiset_hashes.final_hashes.clone(); - init_final_hashes.extend(multiset_hashes.init_hashes.clone()); + read_values: &Vec, + write_values: &Vec, + init_values: &Vec, + final_values: &Vec, + ) -> (Vec, Vec) { + let read_write_values = interleave(read_values.clone(), write_values.clone()).collect(); + let mut init_final_values = final_values.clone(); + init_final_values.extend(init_values.clone()); - (read_write_hashes, init_final_hashes) + (read_write_values, init_final_values) } fn uninterleave_hashes( @@ -480,12 +469,10 @@ where .flat_map(|i| { [ ( - read_timestamp_openings[i], read_timestamp_openings[i], openings.read_cts_read_timestamp[i], ), ( - openings.identity.unwrap() - read_timestamp_openings[i], openings.identity.unwrap() - read_timestamp_openings[i], openings.read_cts_global_minus_read[i], ), @@ -503,12 +490,10 @@ where .flat_map(|i| { [ ( - read_timestamp_openings[i], read_timestamp_openings[i], openings.read_cts_read_timestamp[i] + F::one(), ), ( - openings.identity.unwrap() - read_timestamp_openings[i], openings.identity.unwrap() - read_timestamp_openings[i], openings.read_cts_global_minus_read[i] + F::one(), ), @@ -522,11 +507,7 @@ where openings: &Self::Openings, _: &[F; MEMORY_OPS_PER_INSTRUCTION], ) -> Vec { - vec![( - openings.identity.unwrap(), - openings.identity.unwrap(), - F::zero(), - )] + vec![(openings.identity.unwrap(), F::zero())] } fn final_tuples( @@ -538,12 +519,10 @@ where .flat_map(|i| { [ ( - openings.identity.unwrap(), openings.identity.unwrap(), openings.final_cts_read_timestamp[i], ), ( - openings.identity.unwrap(), openings.identity.unwrap(), openings.final_cts_global_minus_read[i], ), @@ -572,7 +551,7 @@ where fn num_layers(&self) -> usize { unimplemented!("init/final grand products are batched with read/write grand products"); } - fn claims(&self) -> Vec { + fn claimed_outputs(&self) -> Vec { unimplemented!("init/final grand products are batched with read/write grand products"); } @@ -592,11 +571,11 @@ where } fn verify_grand_product( _proof: &BatchedGrandProductProof, - _claims: &Vec, + _claims: &[F], _opening_accumulator: Option<&mut VerifierOpeningAccumulator>, _transcript: &mut ProofTranscript, _setup: Option<&PCS::Setup>, - ) -> (Vec, Vec) { + ) -> (F, Vec) { unimplemented!("init/final grand products are batched with read/write grand products") } } @@ -640,7 +619,12 @@ where let mut openings = TimestampRangeCheckOpenings::default(); let mut timestamp_openings = ReadTimestampOpenings::::default(); - let chis = EqPolynomial::evals(&r_grand_product); + let batch_size = multiset_hashes.read_hashes.len() + + multiset_hashes.write_hashes.len() + + multiset_hashes.init_hashes.len() + + multiset_hashes.final_hashes.len(); + let (_, r_opening) = r_grand_product.split_at(batch_size.next_power_of_two().log_2()); + let chis = EqPolynomial::evals(r_opening); polynomials .read_write_values() @@ -663,7 +647,7 @@ where .chain(ReadTimestampOpenings::::exogenous_data(jolt_polynomials).into_iter()) .collect::>(), DensePolynomial::new(chis), - r_grand_product.clone(), + r_opening.to_vec(), &openings .read_write_values() .into_iter() @@ -707,19 +691,17 @@ where &tau, ); - let mut batched_circuit = - as BatchedGrandProduct< - F, - PCS, - ProofTranscript, - >>::construct(leaves); - - let hashes: Vec = - as BatchedGrandProduct< - F, - PCS, - ProofTranscript, - >>::claims(&batched_circuit); + let mut batched_circuit = as BatchedGrandProduct< + F, + PCS, + ProofTranscript, + >>::construct(leaves); + + let hashes: Vec = as BatchedGrandProduct< + F, + PCS, + ProofTranscript, + >>::claimed_outputs(&batched_circuit); let (read_write_hashes, init_final_hashes) = hashes.split_at(4 * MEMORY_OPS_PER_INSTRUCTION); let multiset_hashes = @@ -764,19 +746,24 @@ where self.multiset_hashes.append_to_transcript(transcript); let (read_write_hashes, init_final_hashes) = - TimestampValidityProof::::interleave_hashes( + TimestampValidityProof::::interleave( &NoPreprocessing, - &self.multiset_hashes, + &self.multiset_hashes.read_hashes, + &self.multiset_hashes.write_hashes, + &self.multiset_hashes.init_hashes, + &self.multiset_hashes.final_hashes, ); let concatenated_hashes = [read_write_hashes, init_final_hashes].concat(); - let (grand_product_claims, r_grand_product) = - BatchedDenseGrandProduct::verify_grand_product( - &self.batched_grand_product, - &concatenated_hashes, - Some(opening_accumulator), - transcript, - Some(generators), - ); + let batch_size = concatenated_hashes.len(); + let (grand_product_claim, r_grand_product) = BatchedDenseGrandProduct::verify_grand_product( + &self.batched_grand_product, + &concatenated_hashes, + Some(opening_accumulator), + transcript, + Some(generators), + ); + let (r_batch_index, r_opening) = + r_grand_product.split_at(batch_size.next_power_of_two().log_2()); opening_accumulator.append( &commitments @@ -785,7 +772,7 @@ where .into_iter() .chain(commitments.read_write_memory.t_read.iter()) .collect::>(), - r_grand_product.clone(), + r_opening.to_vec(), &self .openings .read_write_values() @@ -795,8 +782,7 @@ where transcript, ); - self.openings.identity = - Some(IdentityPolynomial::new(r_grand_product.len()).evaluate(&r_grand_product)); + self.openings.identity = Some(IdentityPolynomial::new(r_opening.len()).evaluate(r_opening)); let read_hashes: Vec<_> = TimestampValidityProof::::read_tuples( &NoPreprocessing, @@ -839,31 +825,22 @@ where }) .collect(); - assert_eq!( - grand_product_claims.len(), - 6 * MEMORY_OPS_PER_INSTRUCTION + 1 - ); - let (read_write_claims, init_final_claims) = - grand_product_claims.split_at(4 * MEMORY_OPS_PER_INSTRUCTION); - - let multiset_hashes = MultisetHashes { - read_hashes, - write_hashes, - init_hashes, - final_hashes, - }; let (read_write_hashes, init_final_hashes) = - TimestampValidityProof::::interleave_hashes( + TimestampValidityProof::::interleave( &NoPreprocessing, - &multiset_hashes, + &read_hashes, + &write_hashes, + &init_hashes, + &final_hashes, ); - for (claim, fingerprint) in zip(read_write_claims, read_write_hashes) { - assert_eq!(*claim, fingerprint); - } - for (claim, fingerprint) in zip(init_final_claims, init_final_hashes) { - assert_eq!(*claim, fingerprint); - } + let combined_hash: F = read_write_hashes + .iter() + .chain(init_final_hashes.iter()) + .zip(EqPolynomial::evals(r_batch_index).iter()) + .map(|(hash, eq_eval)| *hash * eq_eval) + .sum(); + assert_eq!(combined_hash, grand_product_claim); Ok(()) } diff --git a/jolt-core/src/lasso/memory_checking.rs b/jolt-core/src/lasso/memory_checking.rs index cfc7e8ab8..9e3fc121e 100644 --- a/jolt-core/src/lasso/memory_checking.rs +++ b/jolt-core/src/lasso/memory_checking.rs @@ -6,6 +6,7 @@ use crate::poly::dense_mlpoly::DensePolynomial; use crate::poly::eq_poly::EqPolynomial; use crate::poly::opening_proof::{ProverOpeningAccumulator, VerifierOpeningAccumulator}; use crate::utils::errors::ProofVerifyError; +use crate::utils::math::Math; use crate::utils::thread::drop_in_background_thread; use crate::utils::transcript::Transcript; use crate::{ @@ -19,7 +20,6 @@ use crate::field::JoltField; use ark_serialize::{CanonicalDeserialize, CanonicalSerialize}; use itertools::interleave; use rayon::prelude::*; -use std::iter::zip; #[derive(CanonicalSerialize, CanonicalDeserialize)] pub struct MultisetHashes { @@ -199,7 +199,7 @@ pub trait Initializable: StructuredPolynomialData + Default } } -// Empty struct to represent that no preprocessing data is used. +/// Empty struct to represent that no preprocessing data is used. pub struct NoPreprocessing; pub trait MemoryCheckingProver @@ -210,9 +210,9 @@ where Self: Sync, { type ReadWriteGrandProduct: BatchedGrandProduct + Send + 'static = - BatchedDenseGrandProduct; + BatchedDenseGrandProduct; type InitFinalGrandProduct: BatchedGrandProduct + Send + 'static = - BatchedDenseGrandProduct; + BatchedDenseGrandProduct; type Polynomials: StructuredPolynomialData>; type Openings: StructuredPolynomialData + Sync + Initializable; @@ -222,7 +222,7 @@ where type Preprocessing = NoPreprocessing; /// The data associated with each memory slot. A triple (a, v, t) by default. - type MemoryTuple = (F, F, F); + type MemoryTuple: Copy + Clone = (F, F, F); #[tracing::instrument(skip_all, name = "MemoryCheckingProver::prove_memory_checking")] /// Generates a memory checking proof for the given committed polynomials. @@ -249,13 +249,26 @@ where pcs_setup, ); + let read_write_batch_size = + multiset_hashes.read_hashes.len() + multiset_hashes.write_hashes.len(); + let init_final_batch_size = + multiset_hashes.init_hashes.len() + multiset_hashes.final_hashes.len(); + + // For a batch size of k, the first log2(k) elements of `r_read_write`/`r_init_final` + // form the point at which the output layer's MLE is evaluated. The remaining elements + // then form the point at which the leaf layer's polynomials are evaluated. + let (_, r_read_write_opening) = + r_read_write.split_at(read_write_batch_size.next_power_of_two().log_2()); + let (_, r_init_final_opening) = + r_init_final.split_at(init_final_batch_size.next_power_of_two().log_2()); + let (openings, exogenous_openings) = Self::compute_openings( preprocessing, opening_accumulator, polynomials, jolt_polynomials, - &r_read_write, - &r_init_final, + r_read_write_opening, + r_init_final_opening, transcript, ); @@ -402,7 +415,7 @@ where >>::Leaves, ) -> (Self::ReadWriteGrandProduct, Vec) { let batched_circuit = Self::ReadWriteGrandProduct::construct(read_write_leaves); - let claims = batched_circuit.claims(); + let claims = batched_circuit.claimed_outputs(); (batched_circuit, claims) } @@ -419,26 +432,21 @@ where >>::Leaves, ) -> (Self::InitFinalGrandProduct, Vec) { let batched_circuit = Self::InitFinalGrandProduct::construct(init_final_leaves); - let claims = batched_circuit.claims(); + let claims = batched_circuit.claimed_outputs(); (batched_circuit, claims) } - fn interleave_hashes( + fn interleave( _preprocessing: &Self::Preprocessing, - multiset_hashes: &MultisetHashes, - ) -> (Vec, Vec) { - let read_write_hashes = interleave( - multiset_hashes.read_hashes.clone(), - multiset_hashes.write_hashes.clone(), - ) - .collect(); - let init_final_hashes = interleave( - multiset_hashes.init_hashes.clone(), - multiset_hashes.final_hashes.clone(), - ) - .collect(); - - (read_write_hashes, init_final_hashes) + read_values: &Vec, + write_values: &Vec, + init_values: &Vec, + final_values: &Vec, + ) -> (Vec, Vec) { + let read_write_values = interleave(read_values, write_values).cloned().collect(); + let init_final_values = interleave(init_values, final_values).cloned().collect(); + + (read_write_values, init_final_values) } fn uninterleave_hashes( @@ -548,23 +556,38 @@ where Self::check_multiset_equality(preprocessing, &proof.multiset_hashes); proof.multiset_hashes.append_to_transcript(transcript); - let (read_write_hashes, init_final_hashes) = - Self::interleave_hashes(preprocessing, &proof.multiset_hashes); + let (read_write_hashes, init_final_hashes) = Self::interleave( + preprocessing, + &proof.multiset_hashes.read_hashes, + &proof.multiset_hashes.write_hashes, + &proof.multiset_hashes.init_hashes, + &proof.multiset_hashes.final_hashes, + ); - let (claims_read_write, r_read_write) = Self::ReadWriteGrandProduct::verify_grand_product( + let read_write_batch_size = read_write_hashes.len(); + let (read_write_claim, r_read_write) = Self::ReadWriteGrandProduct::verify_grand_product( &proof.read_write_grand_product, &read_write_hashes, Some(opening_accumulator), transcript, Some(pcs_setup), ); - let (claims_init_final, r_init_final) = Self::InitFinalGrandProduct::verify_grand_product( + // For a batch size of k, the first log2(k) elements of `r_read_write`/`r_init_final` + // form the point at which the output layer's MLE is evaluated. The remaining elements + // then form the point at which the leaf layer's polynomials are evaluated. + let (r_read_write_batch_index, r_read_write_opening) = + r_read_write.split_at(read_write_batch_size.next_power_of_two().log_2()); + + let init_final_batch_size = init_final_hashes.len(); + let (init_final_claim, r_init_final) = Self::InitFinalGrandProduct::verify_grand_product( &proof.init_final_grand_product, &init_final_hashes, Some(opening_accumulator), transcript, Some(pcs_setup), ); + let (r_init_final_batch_index, r_init_final_opening) = + r_init_final.split_at(init_final_batch_size.next_power_of_two().log_2()); let read_write_commits: Vec<_> = [ commitments.read_write_values(), @@ -578,14 +601,14 @@ where .concat(); opening_accumulator.append( &read_write_commits, - r_read_write.to_vec(), + r_read_write_opening.to_vec(), &read_write_claims, transcript, ); opening_accumulator.append( &commitments.init_final_values(), - r_init_final.to_vec(), + r_init_final_opening.to_vec(), &proof.openings.init_final_values(), transcript, ); @@ -593,14 +616,16 @@ where Self::compute_verifier_openings( &mut proof.openings, preprocessing, - &r_read_write, - &r_init_final, + r_read_write_opening, + r_init_final_opening, ); Self::check_fingerprints( preprocessing, - claims_read_write, - claims_init_final, + read_write_claim, + init_final_claim, + r_read_write_batch_index, + r_init_final_batch_index, &proof.openings, &proof.exogenous_openings, &gamma, @@ -646,12 +671,14 @@ where exogenous_openings: &Self::ExogenousOpenings, ) -> Vec; - /// Checks that the claimed multiset hashes (output by grand product) are consistent with the - /// openings given by `read_write_openings` and `init_final_openings`. + /// Checks that the claims output by the grand products are consistent with the openings of + /// the polynomials comprising the input layers. fn check_fingerprints( preprocessing: &Self::Preprocessing, - claims_read_write: Vec, - claims_init_final: Vec, + read_write_claim: F, + init_final_claim: F, + r_read_write_batch_index: &[F], + r_init_final_batch_index: &[F], openings: &Self::Openings, exogenous_openings: &Self::ExogenousOpenings, gamma: &F, @@ -673,29 +700,38 @@ where .iter() .map(|tuple| Self::fingerprint(tuple, gamma, tau)) .collect(); + + let (read_write_hashes, init_final_hashes) = Self::interleave( + preprocessing, + &read_hashes, + &write_hashes, + &init_hashes, + &final_hashes, + ); + assert_eq!( - read_hashes.len() + write_hashes.len(), - claims_read_write.len() + read_write_hashes.len().next_power_of_two(), + r_read_write_batch_index.len().pow2(), ); assert_eq!( - init_hashes.len() + final_hashes.len(), - claims_init_final.len() + init_final_hashes.len().next_power_of_two(), + r_init_final_batch_index.len().pow2() ); - let multiset_hashes = MultisetHashes { - read_hashes, - write_hashes, - init_hashes, - final_hashes, - }; - let (read_write_hashes, init_final_hashes) = - Self::interleave_hashes(preprocessing, &multiset_hashes); + // `r_read_write_batch_index`/`r_init_final_batch_index` are used to + // combine the k claims in the batch into a single claim. + let combined_read_write_hash: F = read_write_hashes + .iter() + .zip(EqPolynomial::evals(r_read_write_batch_index).iter()) + .map(|(hash, eq_eval)| *hash * eq_eval) + .sum(); + assert_eq!(combined_read_write_hash, read_write_claim); - for (claim, fingerprint) in zip(claims_read_write, read_write_hashes) { - assert_eq!(claim, fingerprint); - } - for (claim, fingerprint) in zip(claims_init_final, init_final_hashes) { - assert_eq!(claim, fingerprint); - } + let combined_init_final_hash: F = init_final_hashes + .iter() + .zip(EqPolynomial::evals(r_init_final_batch_index).iter()) + .map(|(hash, eq_eval)| *hash * eq_eval) + .sum(); + assert_eq!(combined_init_final_hash, init_final_claim); } } diff --git a/jolt-core/src/lasso/surge.rs b/jolt-core/src/lasso/surge.rs index 2e3fba262..10b560b88 100644 --- a/jolt-core/src/lasso/surge.rs +++ b/jolt-core/src/lasso/surge.rs @@ -119,11 +119,11 @@ where _: &JoltPolynomials, gamma: &F, tau: &F, - ) -> (Vec>, Vec>) { + ) -> ((Vec, usize), (Vec, usize)) { let gamma_squared = gamma.square(); let num_lookups = polynomials.dim[0].len(); - let read_write_leaves = (0..Self::num_memories()) + let read_write_leaves: Vec<_> = (0..Self::num_memories()) .into_par_iter() .flat_map_iter(|memory_index| { let dim_index = Self::memory_to_dimension_index(memory_index); @@ -144,7 +144,7 @@ where }) .collect(); - let init_final_leaves = (0..Self::num_memories()) + let init_final_leaves: Vec<_> = (0..Self::num_memories()) .into_par_iter() .flat_map_iter(|memory_index| { let dim_index = Self::memory_to_dimension_index(memory_index); @@ -176,7 +176,11 @@ where }) .collect(); - (read_write_leaves, init_final_leaves) + // TODO(moodlezoup): avoid concat + ( + (read_write_leaves.concat(), 2 * Self::num_memories()), + (init_final_leaves.concat(), 2 * Self::num_memories()), + ) } fn protocol_name() -> &'static [u8] { diff --git a/jolt-core/src/poly/dense_interleaved_poly.rs b/jolt-core/src/poly/dense_interleaved_poly.rs new file mode 100644 index 000000000..361fc5cfe --- /dev/null +++ b/jolt-core/src/poly/dense_interleaved_poly.rs @@ -0,0 +1,430 @@ +use crate::{ + field::JoltField, + subprotocols::{ + grand_product::BatchedGrandProductLayer, + sumcheck::{BatchedCubicSumcheck, Bindable}, + }, + utils::{thread::unsafe_allocate_zero_vec, transcript::Transcript}, +}; +use rayon::{prelude::*, slice::Chunks}; + +#[cfg(test)] +use super::dense_mlpoly::DensePolynomial; +use super::{split_eq_poly::SplitEqPolynomial, unipoly::UniPoly}; + +/// Represents a single layer of a grand product circuit. +/// A layer is assumed to be arranged in "interleaved" order, i.e. the natural +/// order in the visual representation of the circuit: +/// Λ Λ Λ Λ +/// / \ / \ / \ / \ +/// L0 R0 L1 R1 L2 R2 L3 R3 <- This is layer would be represented as [L0, R0, L1, R1, L2, R2, L3, R3] +/// (as opposed to e.g. [L0, L1, L2, L3, R0, R1, R2, R3]) +#[derive(Default, Debug, Clone)] +pub struct DenseInterleavedPolynomial { + /// The coefficients for the "left" and "right" polynomials comprising a + /// dense grand product layer. + /// The coefficients are in interleaved order: + /// [L0, R0, L1, R1, L2, R2, L3, R3, ...] + pub(crate) coeffs: Vec, + /// The effective length of `coeffs`. When binding, we update this length + /// instead of truncating `coeffs`, which incurs the cost of dropping the + /// truncated values. + len: usize, + /// A reused buffer where bound values are written to during `bind`. + /// With every bind, `coeffs` and `binding_scratch_space` are swapped. + binding_scratch_space: Vec, +} + +impl PartialEq for DenseInterleavedPolynomial { + fn eq(&self, other: &Self) -> bool { + if self.len != other.len { + false + } else { + self.coeffs[..self.len] == other.coeffs[..other.len] + } + } +} + +impl DenseInterleavedPolynomial { + pub fn new(coeffs: Vec) -> Self { + assert!(coeffs.len() % 2 == 0); + let len = coeffs.len(); + Self { + coeffs, + len, + binding_scratch_space: unsafe_allocate_zero_vec(len.next_multiple_of(4) / 2), + } + } + + pub fn len(&self) -> usize { + self.len + } + + pub fn iter(&self) -> impl Iterator { + self.coeffs[..self.len].iter() + } + + pub fn par_chunks(&self, chunk_size: usize) -> Chunks<'_, F> { + self.coeffs[..self.len].par_chunks(chunk_size) + } + + #[cfg(test)] + pub fn interleave(left: &Vec, right: &Vec) -> Self { + assert_eq!(left.len(), right.len()); + let mut interleaved = vec![]; + for i in 0..left.len() { + interleaved.push(left[i]); + interleaved.push(right[i]); + } + Self::new(interleaved) + } + + pub fn uninterleave(&self) -> (Vec, Vec) { + let left: Vec = self.coeffs[..self.len].iter().copied().step_by(2).collect(); + let mut right: Vec = self.coeffs[..self.len] + .iter() + .copied() + .skip(1) + .step_by(2) + .collect(); + if right.len() < left.len() { + right.resize(left.len(), F::zero()); + } + (left, right) + } + + pub fn layer_output(&self) -> Self { + let output = self + .par_chunks(2) + .map(|chunk| chunk[0] * chunk[1]) + .collect(); + Self::new(output) + } +} + +impl Bindable for DenseInterleavedPolynomial { + /// Incrementally binds a variable of the interleaved left and right polynomials. + /// To preserve the interleaved order of coefficients, we bind values like this: + /// 0' 1' 2' 3' + /// |\ |\ |\ |\ + /// | \| \ | \| \ + /// | \ \ | \ \ + /// | |\ \ | |\ \ + /// 0 1 2 3 4 5 6 7 + /// Left nodes have even indices, right nodes have odd indices. + #[tracing::instrument(skip_all, name = "DenseInterleavedPolynomial::bind")] + fn bind(&mut self, r: F) { + #[cfg(test)] + let (mut left_before_binding, mut right_before_binding) = self.uninterleave(); + + let padded_len = self.len.next_multiple_of(4); + // In order to parallelize binding while obeying Rust ownership rules, we + // must write to a different vector than we are reading from. `binding_scratch_space` + // serves this purpose. + self.binding_scratch_space + .par_chunks_mut(2) + .zip(self.coeffs[..self.len].par_chunks(4)) + .for_each(|(bound_chunk, unbound_chunk)| { + let unbound_chunk = [ + *unbound_chunk.first().unwrap_or(&F::zero()), + *unbound_chunk.get(1).unwrap_or(&F::zero()), + *unbound_chunk.get(2).unwrap_or(&F::zero()), + *unbound_chunk.get(3).unwrap_or(&F::zero()), + ]; + + bound_chunk[0] = unbound_chunk[0] + r * (unbound_chunk[2] - unbound_chunk[0]); + bound_chunk[1] = unbound_chunk[1] + r * (unbound_chunk[3] - unbound_chunk[1]); + }); + + self.len = padded_len / 2; + // Point `self.coeffs` to the bound coefficients, and `self.coeffs` will serve as the + // binding scratch space in the next invocation of `bind`. + std::mem::swap(&mut self.coeffs, &mut self.binding_scratch_space); + + #[cfg(test)] + { + let (left_after_binding, right_after_binding) = self.uninterleave(); + bind_left_and_right(&mut left_before_binding, &mut right_before_binding, r); + + assert_eq!( + *self, + Self::interleave(&left_before_binding, &right_before_binding) + ); + assert_eq!(left_after_binding, left_before_binding); + assert_eq!(right_after_binding, right_before_binding); + } + } +} + +#[cfg(test)] +pub fn bind_left_and_right(left: &mut Vec, right: &mut Vec, r: F) { + if left.len() % 2 != 0 { + left.push(F::zero()) + } + if right.len() % 2 != 0 { + right.push(F::zero()) + } + let mut left_poly = DensePolynomial::new_padded(left.clone()); + let mut right_poly = DensePolynomial::new_padded(right.clone()); + left_poly.bound_poly_var_bot(&r); + right_poly.bound_poly_var_bot(&r); + + *left = left_poly.Z[..left.len() / 2].to_vec(); + *right = right_poly.Z[..right.len() / 2].to_vec(); +} + +impl BatchedGrandProductLayer + for DenseInterleavedPolynomial +{ +} +impl BatchedCubicSumcheck + for DenseInterleavedPolynomial +{ + #[cfg(test)] + fn sumcheck_sanity_check(&self, eq_poly: &SplitEqPolynomial, round_claim: F) { + let (left, right) = self.uninterleave(); + let merged_eq = eq_poly.merge(); + let expected: F = left + .iter() + .zip(right.iter()) + .zip(merged_eq.evals_ref().iter()) + .map(|((l, r), eq)| *eq * l * r) + .sum(); + assert_eq!(expected, round_claim); + } + + /// We want to compute the evaluations of the following univariate cubic polynomial at + /// points {0, 1, 2, 3}: + /// Σ eq(r, x) * left(x) * right(x) + /// where the inner summation is over all but the "least significant bit" of the multilinear + /// polynomials `eq`, `left`, and `right`. We denote this "least significant" variable x_b. + /// + /// Computing these evaluations requires processing pairs of adjacent coefficients of + /// `eq`, `left`, and `right`. + /// Recall that the `left` and `right` polynomials are interleaved in `self.coeffs`, + /// so we process 4 values at a time: + /// coeffs = [L, R, L, R, L, R, ...] + /// | | | | + /// left(0, 0, 0, ..., x_b=0) | | right(0, 0, 0, ..., x_b=1) + /// right(0, 0, 0, ..., x_b=0) left(0, 0, 0, ..., x_b=1) + #[tracing::instrument(skip_all, name = "DenseInterleavedPolynomial::compute_cubic")] + fn compute_cubic(&self, eq_poly: &SplitEqPolynomial, previous_round_claim: F) -> UniPoly { + // We use the Dao-Thaler optimization for the EQ polynomial, so there are two cases we + // must handle. For details, refer to Section 2.2 of https://eprint.iacr.org/2024/1210.pdf + let cubic_evals = if eq_poly.E1_len == 1 { + // If `eq_poly.E1` has been fully bound, we compute the cubic polynomial as we + // would without the Dao-Thaler optimization, using the standard linear-time + // sumcheck algorithm. + self.par_chunks(4) + .zip(eq_poly.E2.par_chunks(2)) + .map(|(layer_chunk, eq_chunk)| { + let eq_evals = { + let eval_point_0 = eq_chunk[0]; + let m_eq = eq_chunk[1] - eq_chunk[0]; + let eval_point_2 = eq_chunk[1] + m_eq; + let eval_point_3 = eval_point_2 + m_eq; + (eval_point_0, eval_point_2, eval_point_3) + }; + let left = ( + *layer_chunk.first().unwrap_or(&F::zero()), + *layer_chunk.get(2).unwrap_or(&F::zero()), + ); + let right = ( + *layer_chunk.get(1).unwrap_or(&F::zero()), + *layer_chunk.get(3).unwrap_or(&F::zero()), + ); + + let m_left = left.1 - left.0; + let m_right = right.1 - right.0; + + let left_eval_2 = left.1 + m_left; + let left_eval_3 = left_eval_2 + m_left; + + let right_eval_2 = right.1 + m_right; + let right_eval_3 = right_eval_2 + m_right; + + ( + eq_evals.0 * left.0 * right.0, + eq_evals.1 * left_eval_2 * right_eval_2, + eq_evals.2 * left_eval_3 * right_eval_3, + ) + }) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ) + } else { + // If `eq_poly.E1` has NOT been fully bound, we compute the cubic polynomial + // using the nested summation approach described in Section 2.2 of https://eprint.iacr.org/2024/1210.pdf + // + // Note, however, that we reverse the inner/outer summation compared to the + // description in the paper. I.e. instead of: + // + // \sum_x1 ((1 - j) * E1[0, x1] + j * E1[1, x1]) * (\sum_x2 E2[x2] * \prod_k ((1 - j) * P_k(0 || x1 || x2) + j * P_k(1 || x1 || x2))) + // + // we do: + // + // \sum_x2 E2[x2] * (\sum_x1 ((1 - j) * E1[0, x1] + j * E1[1, x1]) * \prod_k ((1 - j) * P_k(0 || x1 || x2) + j * P_k(1 || x1 || x2))) + // + // because it has better memory locality. + + // We start by computing the E1 evals: + // (1 - j) * E1[0, x1] + j * E1[1, x1] + let E1_evals: Vec<_> = eq_poly.E1[..eq_poly.E1_len] + .par_chunks(2) + .map(|E1_chunk| { + let eval_point_0 = E1_chunk[0]; + let m_eq = E1_chunk[1] - E1_chunk[0]; + let eval_point_2 = E1_chunk[1] + m_eq; + let eval_point_3 = eval_point_2 + m_eq; + (eval_point_0, eval_point_2, eval_point_3) + }) + .collect(); + + let chunk_size = self.len.next_power_of_two() / eq_poly.E2_len; + eq_poly.E2[..eq_poly.E2_len] + .par_iter() + .zip(self.par_chunks(chunk_size)) + .map(|(E2_eval, P_x2)| { + // The for-loop below corresponds to the inner sum: + // \sum_x1 ((1 - j) * E1[0, x1] + j * E1[1, x1]) * \prod_k ((1 - j) * P_k(0 || x1 || x2) + j * P_k(1 || x1 || x2)) + let mut inner_sum = (F::zero(), F::zero(), F::zero()); + for (E1_evals, P_chunk) in E1_evals.iter().zip(P_x2.chunks(4)) { + let left = ( + *P_chunk.first().unwrap_or(&F::zero()), + *P_chunk.get(2).unwrap_or(&F::zero()), + ); + let right = ( + *P_chunk.get(1).unwrap_or(&F::zero()), + *P_chunk.get(3).unwrap_or(&F::zero()), + ); + let m_left = left.1 - left.0; + let m_right = right.1 - right.0; + + let left_eval_2 = left.1 + m_left; + let left_eval_3 = left_eval_2 + m_left; + + let right_eval_2 = right.1 + m_right; + let right_eval_3 = right_eval_2 + m_right; + + inner_sum.0 += E1_evals.0 * left.0 * right.0; + inner_sum.1 += E1_evals.1 * left_eval_2 * right_eval_2; + inner_sum.2 += E1_evals.2 * left_eval_3 * right_eval_3; + } + + // Multiply the inner sum by E2[x2] + ( + *E2_eval * inner_sum.0, + *E2_eval * inner_sum.1, + *E2_eval * inner_sum.2, + ) + }) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ) + }; + + let cubic_evals = [ + cubic_evals.0, + previous_round_claim - cubic_evals.0, + cubic_evals.1, + cubic_evals.2, + ]; + UniPoly::from_evals(&cubic_evals) + } + + fn final_claims(&self) -> (F, F) { + assert_eq!(self.len(), 2); + let left_claim = self.coeffs[0]; + let right_claim = self.coeffs[1]; + (left_claim, right_claim) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use ark_bn254::Fr; + use ark_std::test_rng; + use itertools::Itertools; + + #[test] + fn interleave_uninterleave() { + let mut rng = test_rng(); + const NUM_VARS: [usize; 8] = [0, 1, 2, 3, 4, 5, 6, 7]; + const BATCH_SIZE: [usize; 5] = [2, 3, 4, 5, 6]; + + for (num_vars, batch_size) in NUM_VARS + .into_iter() + .cartesian_product(BATCH_SIZE.into_iter()) + { + let left: Vec<_> = std::iter::repeat_with(|| Fr::random(&mut rng)) + .take(batch_size << num_vars) + .collect(); + let right: Vec<_> = std::iter::repeat_with(|| Fr::random(&mut rng)) + .take(batch_size << num_vars) + .collect(); + + let interleaved = DenseInterleavedPolynomial::interleave(&left, &right); + assert_eq!(interleaved.uninterleave(), (left, right)); + } + } + + #[test] + fn uninterleave_interleave() { + let mut rng = test_rng(); + const NUM_VARS: [usize; 8] = [0, 1, 2, 3, 4, 5, 6, 7]; + const BATCH_SIZE: [usize; 5] = [2, 3, 4, 5, 6]; + + for (num_vars, batch_size) in NUM_VARS + .into_iter() + .cartesian_product(BATCH_SIZE.into_iter()) + { + let coeffs: Vec<_> = std::iter::repeat_with(|| Fr::random(&mut rng)) + .take(2 * (batch_size << num_vars)) + .collect(); + let interleaved = DenseInterleavedPolynomial::new(coeffs); + let (left, right) = interleaved.uninterleave(); + + assert_eq!( + interleaved.iter().collect::>(), + DenseInterleavedPolynomial::interleave(&left, &right) + .iter() + .collect::>() + ); + } + } + + #[test] + fn bind() { + let mut rng = test_rng(); + const NUM_VARS: [usize; 8] = [0, 1, 2, 3, 4, 5, 6, 7]; + const BATCH_SIZE: [usize; 5] = [2, 3, 4, 5, 6]; + + for (num_vars, batch_size) in NUM_VARS + .into_iter() + .cartesian_product(BATCH_SIZE.into_iter()) + { + let mut left: Vec<_> = std::iter::repeat_with(|| Fr::random(&mut rng)) + .take(batch_size << num_vars) + .collect(); + let mut right: Vec<_> = std::iter::repeat_with(|| Fr::random(&mut rng)) + .take(batch_size << num_vars) + .collect(); + + let mut interleaved = DenseInterleavedPolynomial::interleave(&left, &right); + + let r = Fr::random(&mut rng); + interleaved.bind(r); + bind_left_and_right(&mut left, &mut right, r); + + assert_eq!( + interleaved.iter().collect::>(), + DenseInterleavedPolynomial::interleave(&left, &right) + .iter() + .collect::>() + ); + } + } +} diff --git a/jolt-core/src/poly/dense_mlpoly.rs b/jolt-core/src/poly/dense_mlpoly.rs index f9351b950..550840005 100644 --- a/jolt-core/src/poly/dense_mlpoly.rs +++ b/jolt-core/src/poly/dense_mlpoly.rs @@ -9,7 +9,6 @@ use ark_serialize::{CanonicalDeserialize, CanonicalSerialize}; use core::ops::Index; use rand_core::{CryptoRng, RngCore}; use rayon::prelude::*; -use std::ops::{AddAssign, Mul}; #[derive(Default, Debug, PartialEq, CanonicalSerialize, CanonicalDeserialize)] pub struct DensePolynomial { @@ -59,18 +58,6 @@ impl DensePolynomial { self.len == 0 } - pub fn split(&self, idx: usize) -> (Self, Self) { - assert!(idx < self.len()); - ( - Self::new(self.Z[..idx].to_vec()), - Self::new(self.Z[idx..2 * idx].to_vec()), - ) - } - - pub fn split_evals(&self, idx: usize) -> (&[F], &[F]) { - (&self.Z[..idx], &self.Z[idx..]) - } - pub fn bound_poly_var_top(&mut self, r: &F) { let n = self.len() / 2; let (left, right) = self.Z.split_at_mut(n); @@ -260,27 +247,6 @@ impl DensePolynomial { self.Z.as_ref() } - #[tracing::instrument(skip_all, name = "DensePoly::flatten")] - pub fn flatten(polys: &[DensePolynomial]) -> Vec { - let poly_len = polys[0].len(); - polys - .iter() - .for_each(|poly| assert_eq!(poly_len, poly.len())); - - let num_polys = polys.len(); - let flat_len = num_polys * poly_len; - let mut flat: Vec = unsafe_allocate_zero_vec(flat_len); - flat.par_chunks_mut(poly_len) - .enumerate() - .for_each(|(poly_index, result)| { - let evals = polys[poly_index].evals_ref(); - for (eval_index, eval) in evals.iter().enumerate() { - result[eval_index] = *eval; - } - }); - flat - } - #[tracing::instrument(skip_all, name = "DensePolynomial::from")] pub fn from_usize(Z: &[usize]) -> Self { DensePolynomial::new( @@ -323,39 +289,6 @@ impl Index for DensePolynomial { } } -impl AsRef> for DensePolynomial { - fn as_ref(&self) -> &DensePolynomial { - self - } -} - -impl AddAssign<&DensePolynomial> for DensePolynomial { - fn add_assign(&mut self, rhs: &DensePolynomial) { - assert_eq!(self.num_vars, rhs.num_vars); - assert_eq!(self.len, rhs.len); - let summed_evaluations: Vec = self.Z.iter().zip(&rhs.Z).map(|(a, b)| *a + *b).collect(); - - *self = Self { - num_vars: self.num_vars, - len: self.len, - Z: summed_evaluations, - } - } -} - -impl Mul for DensePolynomial { - type Output = Self; - - fn mul(self, rhs: F) -> Self::Output { - let evals: Vec = self.Z.iter().map(|a| *a * rhs).collect(); - Self { - num_vars: self.num_vars, - len: self.len, - Z: evals, - } - } -} - #[cfg(test)] mod tests { use crate::poly::commitment::hyrax::matrix_dimensions; diff --git a/jolt-core/src/poly/mod.rs b/jolt-core/src/poly/mod.rs index 0bfaecca4..2da03db67 100644 --- a/jolt-core/src/poly/mod.rs +++ b/jolt-core/src/poly/mod.rs @@ -1,6 +1,9 @@ pub mod commitment; +pub mod dense_interleaved_poly; pub mod dense_mlpoly; pub mod eq_poly; pub mod identity_poly; pub mod opening_proof; +pub mod sparse_interleaved_poly; +pub mod split_eq_poly; pub mod unipoly; diff --git a/jolt-core/src/poly/opening_proof.rs b/jolt-core/src/poly/opening_proof.rs index 92b32ae06..05e508fc1 100644 --- a/jolt-core/src/poly/opening_proof.rs +++ b/jolt-core/src/poly/opening_proof.rs @@ -177,8 +177,8 @@ impl ProverOpeningAccumulator { + pub(crate) index: usize, + pub(crate) value: F, +} + +impl From<(usize, F)> for SparseCoefficient { + fn from(x: (usize, F)) -> Self { + Self { + index: x.0, + value: x.1, + } + } +} + +/// Represents a single layer of a sparse grand product circuit. +/// A layer is assumed to be arranged in "interleaved" order, i.e. the natural +/// order in the visual representation of the circuit: +/// Λ Λ Λ Λ +/// / \ / \ / \ / \ +/// L0 R0 L1 R1 L2 R2 L3 R3 <- This is layer would be represented as [L0, R0, L1, R1, L2, R2, L3, R3] +/// (as opposed to e.g. [L0, L1, L2, L3, R0, R1, R2, R3]) +/// +/// Where SparseInterleavedPolynomial differs from DenseInterleavedPolynomial +/// is that many of the coefficients are expected to be 1s, so the circuit may +/// look something like this: +/// Λ Λ Λ Λ +/// / \ / \ / \ / \ +/// 1 R0 1 1 L2 1 1 1 +/// +/// Instead of materializing all the 1s, we use a sparse vector to represent the layer, +/// where each element of the vector contains the index and value of a non-one coefficient. +/// So the above layer would be represented by: +/// vec![(1, R0), (4, L2)] (except with `SparseCoefficient` structs, not tuples) +/// +/// In the context of a batched grand product (see sparse_grand_product.rs), there +/// are k of these sparse vectors, where k is the batch size. +/// For the first log2(n) rounds of binding, these k vectors can be processed in parallel. +/// After that, they are "coalesced" into a single DenseInterleavedPolynomial for the +/// remaining rounds of binding. +#[derive(Default, Debug, Clone)] +pub struct SparseInterleavedPolynomial { + /// A vector of sparse vectors representing the coefficients in a batched grand product + /// layer, where batch size = coeffs.len(). + pub(crate) coeffs: Vec>>, + /// Once `coeffs` cannot be bound further (i.e. binding would require processing values + /// in different vectors), we switch to using `coalesced` to represent the grand product + /// layer. See `SparseInterleavedPolynomial::coalesce()`. + pub(crate) coalesced: Option>, + /// The length of the layer if it were represented by a single dense vector. + pub(crate) dense_len: usize, +} + +impl PartialEq for SparseInterleavedPolynomial { + fn eq(&self, other: &Self) -> bool { + if self.dense_len != other.dense_len { + return false; + } + if self.coalesced.is_some() != other.coalesced.is_some() { + return false; + } + + if self.coalesced.is_some() { + self.coalesced == other.coalesced + } else { + self.coeffs == other.coeffs + } + } +} + +impl SparseInterleavedPolynomial { + pub fn new(coeffs: Vec>>, dense_len: usize) -> Self { + let batch_size = coeffs.len(); + assert!((dense_len / batch_size).is_power_of_two()); + if (dense_len / batch_size) <= 2 { + // Coalesce + let mut coalesced = vec![F::one(); dense_len]; + coeffs + .iter() + .flatten() + .for_each(|sparse_coeff| coalesced[sparse_coeff.index] = sparse_coeff.value); + Self { + dense_len, + // The batch size is implied by coeffs.len(), so we must initialize this + // vector: + coeffs: vec![vec![]; batch_size], + coalesced: Some(DenseInterleavedPolynomial::new(coalesced)), + } + } else { + Self { + dense_len, + coeffs, + coalesced: None, + } + } + } + + pub fn batch_size(&self) -> usize { + self.coeffs.len() + } + + /// Converts a `SparseInterleavedPolynomial` into the equivalent `DensePolynomial`. + pub fn to_dense(&self) -> DensePolynomial { + if let Some(coalesced) = &self.coalesced { + DensePolynomial::new_padded(coalesced.coeffs[..coalesced.len()].to_vec()) + } else { + DensePolynomial::new_padded(self.coalesce()) + } + } + + #[tracing::instrument(skip_all, name = "SparseInterleavedPolynomial::coalesce")] + /// Coalesces a `SparseInterleavedPolynomial` into a `DenseInterleavedPolynomial`. + pub fn coalesce(&self) -> Vec { + if let Some(coalesced) = &self.coalesced { + coalesced.coeffs.clone() + } else { + let mut coalesced = vec![F::one(); self.dense_len]; + self.coeffs + .iter() + .flatten() + .for_each(|sparse_coeff| coalesced[sparse_coeff.index] = sparse_coeff.value); + coalesced + } + } + + #[cfg(test)] + pub fn interleave(left: &Vec, right: &Vec, batch_size: usize) -> Self { + use itertools::Itertools; + assert_eq!(left.len(), right.len()); + + if left.len() <= batch_size { + // Coalesced + let coalesced: Vec = left + .into_iter() + .interleave(right.into_iter()) + .cloned() + .collect(); + let dense_len = coalesced.len(); + return Self { + coeffs: vec![vec![]; batch_size], + coalesced: Some(DenseInterleavedPolynomial::new(coalesced)), + dense_len, + }; + } + + let mut coeffs = vec![]; + let mut index_offset = 0usize; + for (left_chunk, right_chunk) in left + .chunks(left.len() / batch_size) + .zip(right.chunks(right.len() / batch_size)) + { + coeffs.push( + left_chunk + .iter() + .interleave(right_chunk) + .enumerate() + .filter_map(|(index, coeff)| { + if coeff.is_one() { + None + } else { + Some((index_offset + index, *coeff).into()) + } + }) + .collect(), + ); + index_offset += left_chunk.len() + right_chunk.len(); + } + + Self::new(coeffs, left.len() + right.len()) + } + + /// Uninterleaves a `SparseInterleavedPolynomial` into two vectors + /// containing the left and right coefficients. + pub fn uninterleave(&self) -> (Vec, Vec) { + if let Some(coalesced) = &self.coalesced { + coalesced.uninterleave() + } else { + let mut left = vec![F::one(); self.dense_len / 2]; + let mut right = vec![F::one(); self.dense_len / 2]; + + self.coeffs.iter().flatten().for_each(|coeff| { + if coeff.index % 2 == 0 { + left[coeff.index / 2] = coeff.value; + } else { + right[coeff.index / 2] = coeff.value; + } + }); + (left, right) + } + } + + /// Computes the grand product layer output by this one. + /// L0' R0' L1' R1' <- Output layer + /// Λ Λ Λ Λ + /// / \ / \ / \ / \ + /// L0 R0 L1 R1 L2 R2 L3 R3 <- This layer + #[tracing::instrument(skip_all, name = "SparseInterleavedPolynomial::layer_output")] + pub fn layer_output(&self) -> Self { + if let Some(coalesced) = &self.coalesced { + Self { + dense_len: self.dense_len / 2, + coeffs: vec![vec![]; self.batch_size()], + coalesced: Some(coalesced.layer_output()), + } + } else { + let coeffs: Vec> = self + .coeffs + .par_iter() + .map(|segment| { + segment + .chunk_by(|x, y| x.index / 2 == y.index / 2) + .map(|sparse_block| { + let mut dense_block = [F::one(); 2]; + for coeff in sparse_block { + dense_block[coeff.index % 2] = coeff.value; + } + + let output_index = sparse_block[0].index / 2; + let output_value = dense_block[0].mul_1_optimized(dense_block[1]); + (output_index, output_value).into() + }) + .collect() + }) + .collect(); + + Self::new(coeffs, self.dense_len / 2) + } + } +} + +impl Bindable for SparseInterleavedPolynomial { + /// Incrementally binds a variable of the interleaved left and right polynomials. + /// If `self` is coalesced, we invoke `DenseInterleavedPolynomial::bind`, + /// processing nodes 4 at a time to preserve the interleaved order: + /// 0' 1' 2' 3' + /// |\ |\ |\ |\ + /// | \| \ | \| \ + /// | \ \ | \ \ + /// | |\ \ | |\ \ + /// 0 1 2 3 4 5 6 7 + /// Left nodes have even indices, right nodes have odd indices. + /// + /// If `self` is not coalesced, we basically do the same thing but with the + /// sparse vectors in `self.coeffs`, and many more cases to check 😬 + #[tracing::instrument(skip_all, name = "SparseInterleavedPolynomial::bind")] + fn bind(&mut self, r: F) { + #[cfg(test)] + let (mut left_before_binding, mut right_before_binding) = self.uninterleave(); + + if let Some(coalesced) = &mut self.coalesced { + let padded_len = self.dense_len.next_multiple_of(4); + coalesced.bind(r); + self.dense_len = padded_len / 2; + } else { + self.coeffs + .par_iter_mut() + .for_each(|segment: &mut Vec>| { + let mut next_left_node_to_process = 0; + let mut next_right_node_to_process = 0; + let mut bound_index = 0; + + for j in 0..segment.len() { + let current = segment[j]; + if current.index % 2 == 0 && current.index < next_left_node_to_process { + // This left node was already bound with its sibling in a previous iteration + continue; + } + if current.index % 2 == 1 && current.index < next_right_node_to_process { + // This right node was already bound with its sibling in a previous iteration + continue; + } + + let neighbors = [ + segment + .get(j + 1) + .cloned() + .unwrap_or((current.index + 1, F::one()).into()), + segment + .get(j + 2) + .cloned() + .unwrap_or((current.index + 2, F::one()).into()), + ]; + let find_neighbor = |query_index: usize| { + neighbors + .iter() + .find_map(|neighbor| { + if neighbor.index == query_index { + Some(neighbor.value) + } else { + None + } + }) + .unwrap_or(F::one()) + }; + + match current.index % 4 { + 0 => { + // Find sibling left node + let sibling_value: F = find_neighbor(current.index + 2); + segment[bound_index] = ( + current.index / 2, + current.value + r * (sibling_value - current.value), + ) + .into(); + next_left_node_to_process = current.index + 4; + } + 1 => { + // Edge case: If this right node's neighbor is not 1 and has _not_ + // been bound yet, we need to bind the neighbor first to preserve + // the monotonic ordering of the bound layer. + if next_left_node_to_process <= current.index + 1 { + let left_neighbor: F = find_neighbor(current.index + 1); + if !left_neighbor.is_one() { + segment[bound_index] = ( + current.index / 2, + F::one() + r * (left_neighbor - F::one()), + ) + .into(); + bound_index += 1; + } + next_left_node_to_process = current.index + 3; + } + + // Find sibling right node + let sibling_value: F = find_neighbor(current.index + 2); + segment[bound_index] = ( + current.index / 2 + 1, + current.value + r * (sibling_value - current.value), + ) + .into(); + next_right_node_to_process = current.index + 4; + } + 2 => { + // Sibling left node wasn't encountered in previous iteration, + // so sibling must have value 1. + segment[bound_index] = ( + current.index / 2 - 1, + F::one() + r * (current.value - F::one()), + ) + .into(); + next_left_node_to_process = current.index + 2; + } + 3 => { + // Sibling right node wasn't encountered in previous iteration, + // so sibling must have value 1. + segment[bound_index] = + (current.index / 2, F::one() + r * (current.value - F::one())) + .into(); + next_right_node_to_process = current.index + 2; + } + _ => unreachable!("?_?"), + } + bound_index += 1; + } + segment.truncate(bound_index); + }); + + self.dense_len /= 2; + if (self.dense_len / self.batch_size()) == 2 { + // Coalesce + self.coalesced = Some(DenseInterleavedPolynomial::new(self.coalesce())); + } + } + + #[cfg(test)] + { + let (left_after_binding, right_after_binding) = self.uninterleave(); + bind_left_and_right(&mut left_before_binding, &mut right_before_binding, r); + + assert_eq!( + *self, + Self::interleave( + &left_before_binding, + &right_before_binding, + self.batch_size() + ) + ); + assert_eq!(left_after_binding, left_before_binding); + assert_eq!(right_after_binding, right_before_binding); + } + } +} + +impl BatchedGrandProductLayer + for SparseInterleavedPolynomial +{ +} +impl BatchedCubicSumcheck + for SparseInterleavedPolynomial +{ + #[cfg(test)] + fn sumcheck_sanity_check(&self, eq_poly: &SplitEqPolynomial, round_claim: F) { + let merged_eq = eq_poly.merge(); + let (left, right) = self.uninterleave(); + let expected: F = left + .iter() + .zip(right.iter()) + .zip(merged_eq.evals_ref().iter()) + .map(|((l, r), eq)| *eq * l * r) + .sum(); + assert_eq!(expected, round_claim); + } + + /// We want to compute the evaluations of the following univariate cubic polynomial at + /// points {0, 1, 2, 3}: + /// Σ eq(r, x) * left(x) * right(x) + /// where the inner summation is over all but the "least significant bit" of the multilinear + /// polynomials `eq`, `left`, and `right`. We denote this "least significant" variable x_b. + /// + /// Computing these evaluations requires processing pairs of adjacent coefficients of + /// `eq`, `left`, and `right`. + /// If `self` is coalesced, we invoke `DenseInterleavedPolynomial::compute_cubic`, processing + /// 4 values at a time: + /// coeffs = [L, R, L, R, L, R, ...] + /// | | | | + /// left(0, 0, 0, ..., x_b=0) | | right(0, 0, 0, ..., x_b=1) + /// right(0, 0, 0, ..., x_b=0) left(0, 0, 0, ..., x_b=1) + /// + /// If `self` is not coalesced, we basically do the same thing but with with the + /// sparse vectors in `self.coeffs`, some fancy optimizations, and many more cases to check 😬 + #[tracing::instrument(skip_all, name = "SparseInterleavedPolynomial::compute_cubic")] + fn compute_cubic(&self, eq_poly: &SplitEqPolynomial, previous_round_claim: F) -> UniPoly { + if let Some(coalesced) = &self.coalesced { + return BatchedCubicSumcheck::::compute_cubic( + coalesced, + eq_poly, + previous_round_claim, + ); + } + + // We use the Dao-Thaler optimization for the EQ polynomial, so there are two cases we + // must handle. For details, refer to Section 2.2 of https://eprint.iacr.org/2024/1210.pdf + let cubic_evals = if eq_poly.E1_len == 1 { + // If `eq_poly.E1` has been fully bound, we compute the cubic polynomial as we + // would without the Dao-Thaler optimization, using the standard linear-time + // sumcheck algorithm with optimizations for sparsity. + + let eq_evals: Vec<(F, F, F)> = eq_poly + .E2 + .par_chunks(2) + .take(self.dense_len / 4) + .map(|eq_chunk| { + let eval_point_0 = eq_chunk[0]; + let m_eq = eq_chunk[1] - eq_chunk[0]; + let eval_point_2 = eq_chunk[1] + m_eq; + let eval_point_3 = eval_point_2 + m_eq; + (eval_point_0, eval_point_2, eval_point_3) + }) + .collect(); + // This is what Σ eq(r, x) * left(x) * right(x) would be if + // `left` and `right` were both all ones. + let eq_eval_sums: (F, F, F) = eq_evals + .par_iter() + .fold( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ); + // Now we compute the deltas, correcting `eq_eval_sums` for the + // elements of `left` and `right` that aren't ones. + let deltas: (F, F, F) = self + .coeffs + .par_iter() + .flat_map(|segment| { + segment + .par_chunk_by(|x, y| x.index / 4 == y.index / 4) + .map(|sparse_block| { + let block_index = sparse_block[0].index / 4; + let mut block = [F::one(); 4]; + for coeff in sparse_block { + block[coeff.index % 4] = coeff.value; + } + + let left = (block[0], block[2]); + let right = (block[1], block[3]); + + let m_left = left.1 - left.0; + let m_right = right.1 - right.0; + + let left_eval_2 = left.1 + m_left; + let left_eval_3 = left_eval_2 + m_left; + + let right_eval_2 = right.1 + m_right; + let right_eval_3 = right_eval_2 + m_right; + + let eq_evals = eq_evals[block_index]; + ( + eq_evals + .0 + .mul_0_optimized(left.0.mul_1_optimized(right.0) - F::one()), + eq_evals.1 * (left_eval_2 * right_eval_2 - F::one()), + eq_evals.2 * (left_eval_3 * right_eval_3 - F::one()), + ) + }) + }) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ); + + ( + eq_eval_sums.0 + deltas.0, + eq_eval_sums.1 + deltas.1, + eq_eval_sums.2 + deltas.2, + ) + } else { + // This is a more complicated version of the `else` case in + // `DenseInterleavedPolynomial::compute_cubic`. Read that one first. + + // We start by computing the E1 evals: + // (1 - j) * E1[0, x1] + j * E1[1, x1] + let E1_evals: Vec<_> = eq_poly.E1[..eq_poly.E1_len] + .par_chunks(2) + .map(|E1_chunk| { + let eval_point_0 = E1_chunk[0]; + let m_eq = E1_chunk[1] - E1_chunk[0]; + let eval_point_2 = E1_chunk[1] + m_eq; + let eval_point_3 = eval_point_2 + m_eq; + (eval_point_0, eval_point_2, eval_point_3) + }) + .collect(); + // Now compute \sum_x1 ((1 - j) * E1[0, x1] + j * E1[1, x1]) + let E1_eval_sums: (F, F, F) = E1_evals + .par_iter() + .fold( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ); + + let num_x1_bits = eq_poly.E1_len.log_2() - 1; + let x1_bitmask = (1 << num_x1_bits) - 1; + + // Iterate over the non-one coefficients and compute the deltas (relative to + // what the cubic would be if all the coefficients were ones). + let deltas = self + .coeffs + .par_iter() + .flat_map(|segment| { + segment + .par_chunk_by(|a, b| { + // Group by x2 + let a_x2 = (a.index / 4) >> num_x1_bits; + let b_x2 = (b.index / 4) >> num_x1_bits; + a_x2 == b_x2 + }) + .map(|chunk| { + let mut inner_sum = (F::zero(), F::zero(), F::zero()); + for sparse_block in chunk.chunk_by(|x, y| x.index / 4 == y.index / 4) { + let block_index = sparse_block[0].index / 4; + let mut block = [F::one(); 4]; + for coeff in sparse_block { + block[coeff.index % 4] = coeff.value; + } + + let left = (block[0], block[2]); + let right = (block[1], block[3]); + + let m_left = left.1 - left.0; + let m_right = right.1 - right.0; + + let left_eval_2 = left.1 + m_left; + let left_eval_3 = left_eval_2 + m_left; + + let right_eval_2 = right.1 + m_right; + let right_eval_3 = right_eval_2 + m_right; + + let x1 = block_index & x1_bitmask; + let delta = ( + E1_evals[x1].0.mul_0_optimized( + left.0.mul_1_optimized(right.0) - F::one(), + ), + E1_evals[x1].1 * (left_eval_2 * right_eval_2 - F::one()), + E1_evals[x1].2 * (left_eval_3 * right_eval_3 - F::one()), + ); + inner_sum.0 += delta.0; + inner_sum.1 += delta.1; + inner_sum.2 += delta.2; + } + + let x2 = (chunk[0].index / 4) >> num_x1_bits; + ( + eq_poly.E2[x2] * inner_sum.0, + eq_poly.E2[x2] * inner_sum.1, + eq_poly.E2[x2] * inner_sum.2, + ) + }) + }) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ); + + // The cubic evals assuming all the coefficients are ones is affected by the + // `dense_len`, since we implicitly 0-pad the `dense_len` to a power of 2. + // + // As a refresher, the cubic evals we're computing are: + // + // \sum_x2 E2[x2] * (\sum_x1 ((1 - j) * E1[0, x1] + j * E1[1, x1]) * \prod_k ((1 - j) * P_k(0 || x1 || x2) + j * P_k(1 || x1 || x2))) + let evals_assuming_all_ones = if self.dense_len.is_power_of_two() { + // If `dense_len` is a power of 2, there is no 0-padding. + // + // So we have: + // \sum_x2 (E2[x2] * (\sum_x1 ((1 - j) * E1[0, x1] + j * E1[1, x1]) * 1)) + // = \sum_x2 (E2[x2] * \sum_x1 E1_evals[x1]) + // = (\sum_x2 E2[x2]) * (\sum_x1 E1_evals[x1]) + // = 1 * E1_eval_sums + E1_eval_sums + } else { + let chunk_size = self.dense_len.next_power_of_two() / eq_poly.E2_len; + let num_all_one_chunks = self.dense_len / chunk_size; + let E2_sum: F = eq_poly.E2[..num_all_one_chunks].iter().sum(); + if self.dense_len % chunk_size == 0 { + // If `dense_len` isn't a power of 2 but evenly divides `chunk_size`, + // that means that for the last values of x2, we have: + // (1 - j) * P_k(0 || x1 || x2) + j * P_k(1 || x1 || x2)) = 0 + // due to the 0-padding. + // + // This makes the entire inner sum 0 for those values of x2. + // So we can simply sum over E2 for the _other_ values of x2, and + // multiply by `E1_eval_sums`. + ( + E2_sum * E1_eval_sums.0, + E2_sum * E1_eval_sums.1, + E2_sum * E1_eval_sums.2, + ) + } else { + // If `dense_len` isn't a power of 2 and doesn't divide `chunk_size`, + // the last nonzero "chunk" will have (self.dense_len % chunk_size) ones, + // followed by (chunk_size - self.dense_len % chunk_size) zeros, + // e.g. 1 1 1 1 1 1 1 1 0 0 0 0 + // + // This handles this last chunk: + let last_chunk_evals = E1_evals[..(self.dense_len % chunk_size) / 4] + .par_iter() + .fold( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ); + ( + E2_sum * E1_eval_sums.0 + + eq_poly.E2[num_all_one_chunks] * last_chunk_evals.0, + E2_sum * E1_eval_sums.1 + + eq_poly.E2[num_all_one_chunks] * last_chunk_evals.1, + E2_sum * E1_eval_sums.2 + + eq_poly.E2[num_all_one_chunks] * last_chunk_evals.2, + ) + } + }; + + ( + evals_assuming_all_ones.0 + deltas.0, + evals_assuming_all_ones.1 + deltas.1, + evals_assuming_all_ones.2 + deltas.2, + ) + }; + + let cubic_evals = [ + cubic_evals.0, + previous_round_claim - cubic_evals.0, + cubic_evals.1, + cubic_evals.2, + ]; + + let cubic = UniPoly::from_evals(&cubic_evals); + + #[cfg(test)] + { + let dense = DenseInterleavedPolynomial::new(self.coalesce()); + let dense_cubic = BatchedCubicSumcheck::::compute_cubic( + &dense, + eq_poly, + previous_round_claim, + ); + assert_eq!(cubic, dense_cubic); + } + + cubic + } + + fn final_claims(&self) -> (F, F) { + assert_eq!(self.dense_len, 2); + let dense = self.to_dense(); + (dense[0], dense[1]) + } +} + +#[cfg(test)] +pub fn bind_left_and_right(left: &mut Vec, right: &mut Vec, r: F) { + if left.len() % 2 != 0 { + left.push(F::zero()) + } + if right.len() % 2 != 0 { + right.push(F::zero()) + } + let mut left_poly = DensePolynomial::new_padded(left.clone()); + let mut right_poly = DensePolynomial::new_padded(right.clone()); + left_poly.bound_poly_var_bot(&r); + right_poly.bound_poly_var_bot(&r); + + *left = left_poly.Z[..left.len() / 2].to_vec(); + *right = right_poly.Z[..right.len() / 2].to_vec(); +} + +#[cfg(test)] +mod tests { + use crate::utils::math::Math; + + use super::*; + use ark_bn254::Fr; + use ark_std::{rand::Rng, test_rng, One}; + use itertools::Itertools; + + fn random_sparse_vector(rng: &mut impl Rng, len: usize, density: f64) -> Vec { + std::iter::repeat_with(|| { + if rng.gen_bool(density) { + Fr::random(rng) + } else { + Fr::one() + } + }) + .take(len) + .collect() + } + + #[test] + fn interleave_uninterleave() { + const NUM_VARS: [usize; 8] = [0, 1, 2, 3, 4, 5, 6, 7]; + const DENSITY: [f64; 6] = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]; + const BATCH_SIZE: [usize; 5] = [2, 4, 6, 8, 10]; + + let mut rng = test_rng(); + for ((num_vars, density), batch_size) in NUM_VARS + .into_iter() + .cartesian_product(DENSITY.into_iter()) + .cartesian_product(BATCH_SIZE.into_iter()) + { + let left = random_sparse_vector(&mut rng, batch_size * (1 << num_vars), density); + let right = random_sparse_vector(&mut rng, batch_size * (1 << num_vars), density); + + let interleaved = SparseInterleavedPolynomial::interleave(&left, &right, batch_size); + + assert_eq!(interleaved.uninterleave(), (left, right)); + } + } + + #[test] + fn uninterleave_interleave() { + const NUM_VARS: [usize; 8] = [0, 1, 2, 3, 4, 5, 6, 7]; + const DENSITY: [f64; 6] = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]; + const BATCH_SIZE: [usize; 5] = [2, 4, 6, 8, 10]; + + let mut rng = test_rng(); + for ((num_vars, density), batch_size) in NUM_VARS + .into_iter() + .cartesian_product(DENSITY.into_iter()) + .cartesian_product(BATCH_SIZE.into_iter()) + { + let coeffs = (0..batch_size) + .map(|batch_index| { + let mut coeffs: Vec> = vec![]; + for i in 0..(1 << num_vars) { + if rng.gen_bool(density) { + coeffs.push( + (batch_index * (1 << num_vars) + i, Fr::random(&mut rng)).into(), + ) + } + } + coeffs + }) + .collect(); + let interleaved = SparseInterleavedPolynomial::new(coeffs, batch_size << num_vars); + let (left, right) = interleaved.uninterleave(); + + assert_eq!( + interleaved, + SparseInterleavedPolynomial::interleave(&left, &right, batch_size) + ); + } + } + + #[test] + fn bind() { + let mut rng = test_rng(); + const NUM_VARS: [usize; 8] = [0, 1, 2, 3, 4, 5, 6, 7]; + const DENSITY: [f64; 6] = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]; + const BATCH_SIZE: [usize; 5] = [2, 4, 6, 8, 10]; + for ((num_vars, density), batch_size) in NUM_VARS + .into_iter() + .cartesian_product(DENSITY.into_iter()) + .cartesian_product(BATCH_SIZE.into_iter()) + { + let coeffs = (0..batch_size) + .map(|batch_index| { + let mut coeffs: Vec> = vec![]; + for i in 0..(1 << num_vars) { + if rng.gen_bool(density) { + coeffs.push( + (batch_index * (1 << num_vars) + i, Fr::random(&mut rng)).into(), + ) + } + } + coeffs + }) + .collect(); + let mut interleaved = SparseInterleavedPolynomial::new(coeffs, batch_size << num_vars); + let (mut left, mut right) = interleaved.uninterleave(); + assert_eq!( + interleaved, + SparseInterleavedPolynomial::interleave(&left, &right, batch_size) + ); + + for _ in 0..num_vars + batch_size.log_2() - 1 { + let r = Fr::random(&mut rng); + interleaved.bind(r); + bind_left_and_right(&mut left, &mut right, r); + + assert_eq!( + interleaved, + SparseInterleavedPolynomial::interleave(&left, &right, batch_size) + ); + } + } + } +} diff --git a/jolt-core/src/poly/split_eq_poly.rs b/jolt-core/src/poly/split_eq_poly.rs new file mode 100644 index 000000000..0fe169103 --- /dev/null +++ b/jolt-core/src/poly/split_eq_poly.rs @@ -0,0 +1,115 @@ +//! Implements the Dao-Thaler optimization for EQ polynomial evaluations +//! https://eprint.iacr.org/2024/1210.pdf +#[cfg(test)] +use super::dense_mlpoly::DensePolynomial; +use crate::{field::JoltField, poly::eq_poly::EqPolynomial}; + +#[derive(Debug, Clone, PartialEq)] +pub struct SplitEqPolynomial { + num_vars: usize, + pub(crate) E1: Vec, + pub(crate) E1_len: usize, + pub(crate) E2: Vec, + pub(crate) E2_len: usize, +} + +impl SplitEqPolynomial { + #[tracing::instrument(skip_all, name = "SplitEqPolynomial::new")] + pub fn new(w: &[F]) -> Self { + let m = w.len() / 2; + let (w2, w1) = w.split_at(m); + let (E2, E1) = rayon::join(|| EqPolynomial::evals(w2), || EqPolynomial::evals(w1)); + let E1_len = E1.len(); + let E2_len = E2.len(); + Self { + num_vars: w.len(), + E1, + E1_len, + E2, + E2_len, + } + } + + pub fn get_num_vars(&self) -> usize { + self.num_vars + } + + pub fn len(&self) -> usize { + if self.E1_len == 1 { + self.E2_len + } else { + self.E1_len * self.E2_len + } + } + + #[tracing::instrument(skip_all, name = "SplitEqPolynomial::bind")] + pub fn bind(&mut self, r: F) { + if self.E1_len == 1 { + // E_1 is already completely bound, so we bind E_2 + let n = self.E2_len / 2; + for i in 0..n { + self.E2[i] = self.E2[2 * i] + r * (self.E2[2 * i + 1] - self.E2[2 * i]); + } + self.E2_len = n; + } else { + // Bind E_1 + let n = self.E1_len / 2; + for i in 0..n { + self.E1[i] = self.E1[2 * i] + r * (self.E1[2 * i + 1] - self.E1[2 * i]); + } + self.E1_len = n; + + // If E_1 is now completely bound, we will be switching over to the + // linear-time sumcheck prover, using E_1 * E_2: + if self.E1_len == 1 { + self.E2[..self.E2_len] + .iter_mut() + .for_each(|eval| *eval *= self.E1[0]); + } + } + } + + #[cfg(test)] + pub fn merge(&self) -> DensePolynomial { + if self.E1_len == 1 { + DensePolynomial::new(self.E2[..self.E2_len].to_vec()) + } else { + let mut merged = vec![]; + for i in 0..self.E2_len { + for j in 0..self.E1_len { + merged.push(self.E2[i] * self.E1[j]) + } + } + DensePolynomial::new(merged) + } + } +} + +#[cfg(test)] +mod tests { + use super::*; + use ark_bn254::Fr; + use ark_std::test_rng; + + #[test] + fn bind() { + const NUM_VARS: usize = 9; + let mut rng = test_rng(); + let w: Vec = std::iter::repeat_with(|| Fr::random(&mut rng)) + .take(NUM_VARS) + .collect(); + + let mut regular_eq = DensePolynomial::new(EqPolynomial::evals(&w)); + let mut split_eq = SplitEqPolynomial::new(&w); + assert_eq!(regular_eq, split_eq.merge()); + + for _ in 0..NUM_VARS { + let r = Fr::random(&mut rng); + regular_eq.bound_poly_var_bot(&r); + split_eq.bind(r); + + let merged = split_eq.merge(); + assert_eq!(regular_eq.Z[..regular_eq.len()], merged.Z[..merged.len()]); + } + } +} diff --git a/jolt-core/src/r1cs/inputs.rs b/jolt-core/src/r1cs/inputs.rs index 2755aeba6..c06aa9b78 100644 --- a/jolt-core/src/r1cs/inputs.rs +++ b/jolt-core/src/r1cs/inputs.rs @@ -162,6 +162,7 @@ pub type R1CSCommitments, ProofTranscript R1CSStuff; impl R1CSPolynomials { + #[tracing::instrument(skip_all, name = "R1CSPolynomials::new")] pub fn new< const C: usize, const M: usize, diff --git a/jolt-core/src/subprotocols/grand_product.rs b/jolt-core/src/subprotocols/grand_product.rs index 5f69bb772..87b4028ac 100644 --- a/jolt-core/src/subprotocols/grand_product.rs +++ b/jolt-core/src/subprotocols/grand_product.rs @@ -1,25 +1,23 @@ use super::grand_product_quarks::QuarkGrandProductProof; use super::sumcheck::{BatchedCubicSumcheck, SumcheckInstanceProof}; -use crate::field::{JoltField, OptimizedMul}; +use crate::field::JoltField; use crate::poly::commitment::commitment_scheme::CommitmentScheme; -use crate::poly::eq_poly::EqPolynomial; +use crate::poly::dense_interleaved_poly::DenseInterleavedPolynomial; +use crate::poly::dense_mlpoly::DensePolynomial; use crate::poly::opening_proof::{ProverOpeningAccumulator, VerifierOpeningAccumulator}; -use crate::poly::{dense_mlpoly::DensePolynomial, unipoly::UniPoly}; +use crate::poly::split_eq_poly::SplitEqPolynomial; use crate::utils::math::Math; use crate::utils::thread::drop_in_background_thread; use crate::utils::transcript::Transcript; -use ark_ff::Zero; use ark_serialize::*; use itertools::Itertools; use rayon::prelude::*; -use std::marker::PhantomData; #[derive(CanonicalSerialize, CanonicalDeserialize)] pub struct BatchedGrandProductLayerProof { pub proof: SumcheckInstanceProof, - pub left_claims: Vec, - pub right_claims: Vec, - _marker: PhantomData, + pub left_claim: F, + pub right_claim: F, } impl BatchedGrandProductLayerProof { @@ -42,7 +40,7 @@ where PCS: CommitmentScheme, ProofTranscript: Transcript, { - pub layers: Vec>, + pub gkr_layers: Vec>, pub quark_proof: Option>, } @@ -65,7 +63,7 @@ where /// The number of layers in the grand product. fn num_layers(&self) -> usize; /// The claimed outputs of the grand products. - fn claims(&self) -> Vec; + fn claimed_outputs(&self) -> Vec; /// Returns an iterator over the layers of this batched grand product circuit. /// Each layer is mutable so that its polynomials can be bound over the course /// of proving. @@ -82,56 +80,49 @@ where _setup: Option<&PCS::Setup>, ) -> (BatchedGrandProductProof, Vec) { let mut proof_layers = Vec::with_capacity(self.num_layers()); - let mut claims_to_verify = self.claims(); - let mut r_grand_product = Vec::new(); + + // Evaluate the MLE of the output layer at a random point to reduce the outputs to + // a single claim. + let outputs = self.claimed_outputs(); + transcript.append_scalars(&outputs); + let output_mle = DensePolynomial::new_padded(outputs); + let mut r: Vec = transcript.challenge_vector(output_mle.get_num_vars()); + let mut claim = output_mle.evaluate(&r); for layer in self.layers() { - proof_layers.push(layer.prove_layer( - &mut claims_to_verify, - &mut r_grand_product, - transcript, - )); + proof_layers.push(layer.prove_layer(&mut claim, &mut r, transcript)); } ( BatchedGrandProductProof { - layers: proof_layers, + gkr_layers: proof_layers, quark_proof: None, }, - r_grand_product, + r, ) } /// Verifies that the `sumcheck_claim` output by sumcheck verification is consistent - /// with the `left_claims` and `right_claims` of corresponding `BatchedGrandProductLayerProof`. + /// with the `left_claim` and `right_claim` of corresponding `BatchedGrandProductLayerProof`. /// This function may be overridden if the layer isn't just multiplication gates, e.g. in the /// case of `ToggledBatchedGrandProduct`. fn verify_sumcheck_claim( layer_proofs: &[BatchedGrandProductLayerProof], layer_index: usize, - coeffs: &[F], sumcheck_claim: F, eq_eval: F, - grand_product_claims: &mut Vec, + grand_product_claim: &mut F, r_grand_product: &mut Vec, transcript: &mut ProofTranscript, ) { let layer_proof = &layer_proofs[layer_index]; - - let expected_sumcheck_claim: F = (0..grand_product_claims.len()) - .map(|i| coeffs[i] * layer_proof.left_claims[i] * layer_proof.right_claims[i] * eq_eval) - .sum(); - + let expected_sumcheck_claim: F = layer_proof.left_claim * layer_proof.right_claim * eq_eval; assert_eq!(expected_sumcheck_claim, sumcheck_claim); + // produce a random challenge to condense two claims into a single claim let r_layer = transcript.challenge_scalar(); - - *grand_product_claims = layer_proof - .left_claims - .iter() - .zip(layer_proof.right_claims.iter()) - .map(|(&left_claim, &right_claim)| left_claim + r_layer * (right_claim - left_claim)) - .collect(); + *grand_product_claim = + layer_proof.left_claim + r_layer * (layer_proof.right_claim - layer_proof.left_claim); r_grand_product.push(r_layer); } @@ -139,42 +130,22 @@ where /// Function used for layer sumchecks in the generic batch verifier as well as the quark layered sumcheck hybrid fn verify_layers( proof_layers: &[BatchedGrandProductLayerProof], - claims: &Vec, + mut claim: F, transcript: &mut ProofTranscript, r_start: Vec, - ) -> (Vec, Vec) { - let mut claims_to_verify = claims.to_owned(); - // We allow a non empty start in this function call because the quark hybrid form provides prespecified random for - // most of the positions and then we proceed with GKR on the remaining layers using the preset random values. - // For default thaler '13 layered grand products this should be empty. + ) -> (F, Vec) { + // `r_start` is the random point at which the MLE of the first layer of the grand product is evaluated. + // In the case of the Quarks hybrid grand product, this is obtained from the Quarks grand product sumcheck. + // In the case of Thaler'13 GKR-based grand products, this is from Fiat-Shamir. let mut r_grand_product = r_start.clone(); let fixed_at_start = r_start.len(); for (layer_index, layer_proof) in proof_layers.iter().enumerate() { - // produce a fresh set of coeffs - let coeffs: Vec = transcript.challenge_vector(claims_to_verify.len()); - // produce a joint claim - let claim: F = claims_to_verify - .iter() - .zip(coeffs.iter()) - .map(|(&claim, &coeff)| claim * coeff) - .sum(); - let (sumcheck_claim, r_sumcheck) = layer_proof.verify(claim, layer_index + fixed_at_start, 3, transcript); - assert_eq!(claims.len(), layer_proof.left_claims.len()); - assert_eq!(claims.len(), layer_proof.right_claims.len()); - for (left, right) in layer_proof - .left_claims - .iter() - .zip(layer_proof.right_claims.iter()) - { - transcript.append_scalar(left); - transcript.append_scalar(right); - } - - assert_eq!(r_grand_product.len(), r_sumcheck.len()); + transcript.append_scalar(&layer_proof.left_claim); + transcript.append_scalar(&layer_proof.right_claim); let eq_eval: F = r_grand_product .iter() @@ -187,35 +158,38 @@ where Self::verify_sumcheck_claim( proof_layers, layer_index, - &coeffs, sumcheck_claim, eq_eval, - &mut claims_to_verify, + &mut claim, &mut r_grand_product, transcript, ); } - (claims_to_verify, r_grand_product) + (claim, r_grand_product) } /// Verifies the given grand product proof. fn verify_grand_product( proof: &BatchedGrandProductProof, - claims: &Vec, + claimed_outputs: &[F], _opening_accumulator: Option<&mut VerifierOpeningAccumulator>, transcript: &mut ProofTranscript, _setup: Option<&PCS::Setup>, - ) -> (Vec, Vec) { - // Pass the inputs to the layer verification function, by default we have no quarks and so we do not - // use the quark proof fields. - let r_start = Vec::::new(); - Self::verify_layers(&proof.layers, claims, transcript, r_start) + ) -> (F, Vec) { + // Evaluate the MLE of the output layer at a random point to reduce the outputs to + // a single claim. + transcript.append_scalars(claimed_outputs); + let r: Vec = + transcript.challenge_vector(claimed_outputs.len().next_power_of_two().log_2()); + let claim = DensePolynomial::new_padded(claimed_outputs.to_vec()).evaluate(&r); + + Self::verify_layers(&proof.gkr_layers, claim, transcript, r) } } pub trait BatchedGrandProductLayer: - BatchedCubicSumcheck + BatchedCubicSumcheck + std::fmt::Debug where F: JoltField, ProofTranscript: Transcript, @@ -223,31 +197,20 @@ where /// Proves a single layer of a batched grand product circuit fn prove_layer( &mut self, - claims: &mut Vec, + claim: &mut F, r_grand_product: &mut Vec, transcript: &mut ProofTranscript, ) -> BatchedGrandProductLayerProof { - // produce a fresh set of coeffs - let coeffs: Vec = transcript.challenge_vector(claims.len()); - // produce a joint claim - let claim = claims - .iter() - .zip(coeffs.iter()) - .map(|(&claim, &coeff)| claim * coeff) - .sum(); - - let mut eq_poly = DensePolynomial::new(EqPolynomial::::evals(r_grand_product)); + let mut eq_poly = SplitEqPolynomial::new(r_grand_product); let (sumcheck_proof, r_sumcheck, sumcheck_claims) = - self.prove_sumcheck(&claim, &coeffs, &mut eq_poly, transcript); + self.prove_sumcheck(claim, &mut eq_poly, transcript); drop_in_background_thread(eq_poly); - let (left_claims, right_claims) = sumcheck_claims; - for (left, right) in left_claims.iter().zip(right_claims.iter()) { - transcript.append_scalar(left); - transcript.append_scalar(right); - } + let (left_claim, right_claim) = sumcheck_claims; + transcript.append_scalar(&left_claim); + transcript.append_scalar(&right_claim); r_sumcheck .into_par_iter() @@ -256,234 +219,58 @@ where // produce a random challenge to condense two claims into a single claim let r_layer = transcript.challenge_scalar(); - - *claims = left_claims - .iter() - .zip(right_claims.iter()) - .map(|(&left_claim, &right_claim)| left_claim + r_layer * (right_claim - left_claim)) - .collect::>(); + *claim = left_claim + r_layer * (right_claim - left_claim); r_grand_product.push(r_layer); BatchedGrandProductLayerProof { proof: sumcheck_proof, - left_claims, - right_claims, - _marker: PhantomData, + left_claim, + right_claim, } } } -/// Represents a single layer of a single grand product circuit. -/// A layer is assumed to be arranged in "interleaved" order, i.e. the natural -/// order in the visual representation of the circuit: -/// Λ Λ Λ Λ -/// / \ / \ / \ / \ -/// L0 R0 L1 R1 L2 R2 L3 R3 <- This is layer would be represented as [L0, R0, L1, R1, L2, R2, L3, R3] -/// (as opposed to e.g. [L0, L1, L2, L3, R0, R1, R2, R3]) -pub type DenseGrandProductLayer = Vec; - -/// Represents a batch of `DenseGrandProductLayer`, all of the same length `layer_len`. -#[derive(Debug, Clone)] -pub struct BatchedDenseGrandProductLayer { - pub layers: Vec>, - pub layer_len: usize, - _marker: PhantomData, -} - -impl BatchedDenseGrandProductLayer { - pub fn new(values: Vec>) -> Self { - let layer_len = values[0].len(); - Self { - layers: values, - layer_len, - _marker: PhantomData, - } - } -} - -impl BatchedGrandProductLayer - for BatchedDenseGrandProductLayer -{ -} -impl BatchedCubicSumcheck - for BatchedDenseGrandProductLayer -{ - fn num_rounds(&self) -> usize { - self.layer_len.log_2() - 1 - } - - /// Incrementally binds a variable of this batched layer's polynomials. - /// Even though each layer is backed by a single Vec, it represents two polynomials - /// one for the left nodes in the circuit, one for the right nodes in the circuit. - /// These two polynomials' coefficients are interleaved into one Vec. To preserve - /// this interleaved order, we bind values like this: - /// 0' 1' 2' 3' - /// |\ |\ |\ |\ - /// | \| \ | \| \ - /// | \ \ | \ \ - /// | |\ \ | |\ \ - /// 0 1 2 3 4 5 6 7 - /// Left nodes have even indices, right nodes have odd indices. - #[tracing::instrument(skip_all, name = "BatchedDenseGrandProductLayer::bind")] - fn bind(&mut self, eq_poly: &mut DensePolynomial, r: &F) { - debug_assert!(self.layer_len % 4 == 0); - let n = self.layer_len / 4; - // TODO(moodlezoup): parallelize over chunks instead of over batch - rayon::join( - || { - self.layers - .par_iter_mut() - .for_each(|layer: &mut DenseGrandProductLayer| { - for i in 0..n { - // left - layer[2 * i] = layer[4 * i] + *r * (layer[4 * i + 2] - layer[4 * i]); - // right - layer[2 * i + 1] = - layer[4 * i + 1] + *r * (layer[4 * i + 3] - layer[4 * i + 1]); - } - }) - }, - || eq_poly.bound_poly_var_bot(r), - ); - self.layer_len /= 2; - } - - /// We want to compute the evaluations of the following univariate cubic polynomial at - /// points {0, 1, 2, 3}: - /// Σ coeff[batch_index] * (Σ eq(r, x) * left(x) * right(x)) - /// where the inner summation is over all but the "least significant bit" of the multilinear - /// polynomials `eq`, `left`, and `right`. We denote this "least significant" variable x_b. - /// - /// Computing these evaluations requires processing pairs of adjacent coefficients of - /// `eq`, `left`, and `right`. - /// Recall that the `left` and `right` polynomials are interleaved in each layer of `self.layers`, - /// so we process each layer 4 values at a time: - /// layer = [L, R, L, R, L, R, ...] - /// | | | | - /// left(0, 0, 0, ..., x_b=0) | | right(0, 0, 0, ..., x_b=1) - /// right(0, 0, 0, ..., x_b=0) left(0, 0, 0, ..., x_b=1) - #[tracing::instrument(skip_all, name = "BatchedDenseGrandProductLayer::compute_cubic")] - fn compute_cubic( - &self, - coeffs: &[F], - eq_poly: &DensePolynomial, - previous_round_claim: F, - ) -> UniPoly { - let evals = (0..eq_poly.len() / 2) - .into_par_iter() - .map(|i| { - let eq_evals = { - let eval_point_0 = eq_poly[2 * i]; - let m_eq = eq_poly[2 * i + 1] - eq_poly[2 * i]; - let eval_point_2 = eq_poly[2 * i + 1] + m_eq; - let eval_point_3 = eval_point_2 + m_eq; - (eval_point_0, eval_point_2, eval_point_3) - }; - let mut evals = (F::zero(), F::zero(), F::zero()); - - self.layers - .iter() - .enumerate() - .for_each(|(batch_index, layer)| { - // We want to compute: - // evals.0 += coeff * left.0 * right.0 - // evals.1 += coeff * (2 * left.1 - left.0) * (2 * right.1 - right.0) - // evals.2 += coeff * (3 * left.1 - 2 * left.0) * (3 * right.1 - 2 * right.0) - // which naively requires 3 multiplications by `coeff`. - // By multiplying by the coefficient early, we only use 2 multiplications by `coeff`. - let left = ( - coeffs[batch_index] * layer[4 * i], - coeffs[batch_index] * layer[4 * i + 2], - ); - let right = (layer[4 * i + 1], layer[4 * i + 3]); - - let m_left = left.1 - left.0; - let m_right = right.1 - right.0; - - let left_eval_2 = left.1 + m_left; - let left_eval_3 = left_eval_2 + m_left; - - let right_eval_2 = right.1 + m_right; - let right_eval_3 = right_eval_2 + m_right; - - evals.0 += left.0 * right.0; - evals.1 += left_eval_2 * right_eval_2; - evals.2 += left_eval_3 * right_eval_3; - }); - - evals.0 *= eq_evals.0; - evals.1 *= eq_evals.1; - evals.2 *= eq_evals.2; - evals - }) - .reduce( - || (F::zero(), F::zero(), F::zero()), - |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), - ); - - let evals = [evals.0, previous_round_claim - evals.0, evals.1, evals.2]; - UniPoly::from_evals(&evals) - } - - fn final_claims(&self) -> (Vec, Vec) { - assert_eq!(self.layer_len, 2); - let (left_claims, right_claims) = - self.layers.iter().map(|layer| (layer[0], layer[1])).unzip(); - (left_claims, right_claims) - } -} - /// A batched grand product circuit. /// Note that the circuit roots are not included in `self.layers` -/// o -/// / \ -/// o o <- layers[layers.len() - 1] -/// / \ / \ -/// o o o o <- layers[layers.len() - 2] -/// ... -pub struct BatchedDenseGrandProduct { - layers: Vec>, - _marker: PhantomData, +/// o o +/// / \ / \ +/// o o o o <- layers[layers.len() - 1] +/// / \ / \ / \ / \ +/// o o o o o o o o <- layers[layers.len() - 2] +/// ... ... +pub struct BatchedDenseGrandProduct { + layers: Vec>, } impl BatchedGrandProduct - for BatchedDenseGrandProduct + for BatchedDenseGrandProduct where F: JoltField, PCS: CommitmentScheme, ProofTranscript: Transcript, { - type Leaves = Vec>; + // (leaf values, batch size) + type Leaves = (Vec, usize); type Config = (); #[tracing::instrument(skip_all, name = "BatchedDenseGrandProduct::construct")] fn construct(leaves: Self::Leaves) -> Self { - let num_layers = leaves[0].len().log_2(); - let mut layers: Vec> = - Vec::with_capacity(num_layers); - layers.push(BatchedDenseGrandProductLayer::new(leaves)); + let (leaves, batch_size) = leaves; + assert!(leaves.len() % batch_size == 0); + assert!((leaves.len() / batch_size).is_power_of_two()); + + let num_layers = (leaves.len() / batch_size).log_2(); + let mut layers: Vec> = Vec::with_capacity(num_layers); + layers.push(DenseInterleavedPolynomial::new(leaves)); for i in 0..num_layers - 1 { - let previous_layers = &layers[i]; - let len = previous_layers.layer_len / 2; - // TODO(moodlezoup): parallelize over chunks instead of over batch - let new_layers = previous_layers - .layers - .par_iter() - .map(|previous_layer| { - (0..len) - .map(|i| previous_layer[2 * i] * previous_layer[2 * i + 1]) - .collect::>() - }) - .collect(); - layers.push(BatchedDenseGrandProductLayer::new(new_layers)); + let previous_layer = &layers[i]; + let new_layer = previous_layer.layer_output(); + layers.push(new_layer); } - Self { - layers, - _marker: PhantomData, - } + Self { layers } } #[tracing::instrument(skip_all, name = "BatchedDenseGrandProduct::construct_with_config")] fn construct_with_config(leaves: Self::Leaves, _config: Self::Config) -> Self { @@ -494,18 +281,11 @@ where self.layers.len() } - fn claims(&self) -> Vec { - let num_layers = as BatchedGrandProduct< - F, - PCS, - ProofTranscript, - >>::num_layers(self); - let last_layers = &self.layers[num_layers - 1]; - assert_eq!(last_layers.layer_len, 2); - last_layers - .layers - .iter() - .map(|layer| layer[0] * layer[1]) + fn claimed_outputs(&self) -> Vec { + let last_layer = &self.layers[self.layers.len() - 1]; + last_layer + .par_chunks(2) + .map(|chunk| chunk[0] * chunk[1]) .collect() } @@ -519,1327 +299,148 @@ where } } -/// Represents a single layer of a single grand product circuit using a sparse vector, -/// i.e. a vector containing (index, value) pairs. -/// Nodes with value 1 are omitted from the sparse vector. -/// Like `DenseGrandProductLayer`, a `SparseGrandProductLayer` is assumed to be -/// arranged in "interleaved" order: -/// Λ Λ Λ Λ -/// / \ / \ / \ / \ -/// L0 1 L1 R1 1 1 L3 1 <- This is layer would be represented as [(0, L0), (2, L1), (3, R1), (6, L3)] -pub type SparseGrandProductLayer = Vec<(usize, F)>; - -/// A "dynamic density" grand product layer can switch from sparse representation -/// to dense representation once it's no longer sparse (after binding). -#[derive(Debug, Clone, PartialEq)] -pub enum DynamicDensityGrandProductLayer { - Sparse(SparseGrandProductLayer), - Dense(DenseGrandProductLayer), -} - -/// This constant determines: -/// - whether the `layer_output` of a `DynamicDensityGrandProductLayer` is dense -/// or sparse -/// - when to switch from sparse to dense representation during the binding of a -/// `DynamicDensityGrandProductLayer` -/// If the layer has >DENSIFICATION_THRESHOLD fraction of non-1 values, it'll switch -/// to the dense representation. Value tuned experimentally. -const DENSIFICATION_THRESHOLD: f64 = 0.8; - -impl DynamicDensityGrandProductLayer { - /// Computes the grand product layer that is output by this layer. - /// L0' R0' L1' R1' <- output layer - /// Λ Λ Λ Λ - /// / \ / \ / \ / \ - /// L0 R0 L1 R1 L2 R2 L3 R3 <- this layer - /// - /// If the current layer is dense, the output layer will be dense. - /// If the current layer is sparse, but already not very sparse (as parametrized by - /// `DENSIFICATION_THRESHOLD`), the output layer will be dense. - /// Otherwise, the output layer will be sparse. - pub fn layer_output(&self, output_len: usize) -> Self { - match self { - DynamicDensityGrandProductLayer::Sparse(sparse_layer) => { - #[cfg(test)] - let product: F = sparse_layer.iter().map(|(_, value)| value).product(); - - if (sparse_layer.len() as f64 / (output_len * 2) as f64) > DENSIFICATION_THRESHOLD { - // Current layer is already not very sparse, so make the next layer dense - let mut output_layer: DenseGrandProductLayer = vec![F::one(); output_len]; - let mut next_index_to_process = 0usize; - for (j, (index, value)) in sparse_layer.iter().enumerate() { - if *index < next_index_to_process { - // Node was already multiplied with its sibling in a previous iteration - continue; - } - if index % 2 == 0 { - // Left node; try to find correspoding right node - let right = sparse_layer - .get(j + 1) - .cloned() - .unwrap_or((index + 1, F::one())); - if right.0 == index + 1 { - // Corresponding right node was found; multiply them together - output_layer[index / 2] = right.1 * *value; - } else { - // Corresponding right node not found, so it must be 1 - output_layer[index / 2] = *value; - } - next_index_to_process = index + 2; - } else { - // Right node; corresponding left node was not encountered in - // previous iteration, so it must have value 1 - output_layer[index / 2] = *value; - next_index_to_process = index + 1; - } - } - #[cfg(test)] - { - let output_product: F = output_layer.iter().product(); - assert_eq!(product, output_product); - } - DynamicDensityGrandProductLayer::Dense(output_layer) - } else { - // Current layer is still pretty sparse, so make the next layer sparse - let mut output_layer: SparseGrandProductLayer = - Vec::with_capacity(output_len); - let mut next_index_to_process = 0usize; - for (j, (index, value)) in sparse_layer.iter().enumerate() { - if *index < next_index_to_process { - // Node was already multiplied with its sibling in a previous iteration - continue; - } - if index % 2 == 0 { - // Left node; try to find correspoding right node - let right = sparse_layer - .get(j + 1) - .cloned() - .unwrap_or((index + 1, F::one())); - if right.0 == index + 1 { - // Corresponding right node was found; multiply them together - output_layer.push((index / 2, right.1 * *value)); - } else { - // Corresponding right node not found, so it must be 1 - output_layer.push((index / 2, *value)); - } - next_index_to_process = index + 2; - } else { - // Right node; corresponding left node was not encountered in - // previous iteration, so it must have value 1 - output_layer.push((index / 2, *value)); - next_index_to_process = index + 1; - } - } - #[cfg(test)] - { - let output_product: F = - output_layer.iter().map(|(_, value)| value).product(); - assert_eq!(product, output_product); - } - DynamicDensityGrandProductLayer::Sparse(output_layer) - } - } - DynamicDensityGrandProductLayer::Dense(dense_layer) => { - #[cfg(test)] - let product: F = dense_layer.iter().product(); - - // If current layer is dense, next layer should also be dense. - let output_layer: DenseGrandProductLayer = (0..output_len) - .map(|i| { - let (left, right) = (dense_layer[2 * i], dense_layer[2 * i + 1]); - left * right - }) - .collect(); - #[cfg(test)] - { - let output_product: F = output_layer.iter().product(); - assert_eq!(product, output_product); - } - DynamicDensityGrandProductLayer::Dense(output_layer) - } - } - } -} - -/// Represents a batch of `DynamicDensityGrandProductLayer`, all of which have the same -/// size `layer_len`. Note that within a single batch, some layers may be represented by -/// sparse vectors and others by dense vectors. -#[derive(Debug, Clone)] -pub struct BatchedSparseGrandProductLayer { - pub layer_len: usize, - pub layers: Vec>, - _marker: PhantomData, -} - -impl BatchedGrandProductLayer - for BatchedSparseGrandProductLayer -{ -} -impl BatchedCubicSumcheck - for BatchedSparseGrandProductLayer -{ - fn num_rounds(&self) -> usize { - self.layer_len.log_2() - 1 - } - - /// Incrementally binds a variable of this batched layer's polynomials. - /// If `self` is dense, we bind as in `BatchedDenseGrandProductLayer`, - /// processing nodes 4 at a time to preserve the interleaved order: - /// 0' 1' 2' 3' - /// |\ |\ |\ |\ - /// | \| \ | \| \ - /// | \ \ | \ \ - /// | |\ \ | |\ \ - /// 0 1 2 3 4 5 6 7 - /// Left nodes have even indices, right nodes have odd indices. - /// If `self` is sparse, we basically do the same thing but with more - /// cases to check 😬 - #[tracing::instrument(skip_all, name = "BatchedSparseGrandProductLayer::bind")] - fn bind(&mut self, eq_poly: &mut DensePolynomial, r: &F) { - debug_assert!(self.layer_len % 4 == 0); - rayon::join( - || { - self.layers.par_iter_mut().for_each(|layer| match layer { - DynamicDensityGrandProductLayer::Sparse(sparse_layer) => { - let mut dense_bound_layer = if (sparse_layer.len() as f64 - / self.layer_len as f64) - > DENSIFICATION_THRESHOLD - { - // Current layer is already not very sparse, so make the next layer dense - Some(vec![F::one(); self.layer_len / 2]) - } else { - None - }; - - let mut num_bound = 0usize; - let mut push_to_bound_layer = - |sparse_layer: &mut Vec<(usize, F)>, dense_index: usize, value: F| { - match &mut dense_bound_layer { - Some(ref mut dense_vec) => { - debug_assert_eq!(dense_vec[dense_index], F::one()); - dense_vec[dense_index] = value; - } - None => { - sparse_layer[num_bound] = (dense_index, value); - } - }; - num_bound += 1; - }; - - let mut next_left_node_to_process = 0usize; - let mut next_right_node_to_process = 0usize; - - for j in 0..sparse_layer.len() { - let (index, value) = sparse_layer[j]; - if index % 2 == 0 && index < next_left_node_to_process { - // This left node was already bound with its sibling in a previous iteration - continue; - } - if index % 2 == 1 && index < next_right_node_to_process { - // This right node was already bound with its sibling in a previous iteration - continue; - } - - let neighbors = [ - sparse_layer - .get(j + 1) - .cloned() - .unwrap_or((index + 1, F::one())), - sparse_layer - .get(j + 2) - .cloned() - .unwrap_or((index + 2, F::one())), - ]; - let find_neighbor = |query_index: usize| { - neighbors - .iter() - .find_map(|(neighbor_index, neighbor_value)| { - if *neighbor_index == query_index { - Some(neighbor_value) - } else { - None - } - }) - .cloned() - .unwrap_or(F::one()) - }; - - match index % 4 { - 0 => { - // Find sibling left node - let sibling_value: F = find_neighbor(index + 2); - push_to_bound_layer( - sparse_layer, - index / 2, - value + *r * (sibling_value - value), - ); - next_left_node_to_process = index + 4; - } - 1 => { - // Edge case: If this right node's neighbor is not 1 and has _not_ - // been bound yet, we need to bind the neighbor first to preserve - // the monotonic ordering of the bound layer. - if next_left_node_to_process <= index + 1 { - let left_neighbor: F = find_neighbor(index + 1); - if !left_neighbor.is_one() { - push_to_bound_layer( - sparse_layer, - index / 2, - F::one() + *r * (left_neighbor - F::one()), - ); - } - next_left_node_to_process = index + 3; - } - - // Find sibling right node - let sibling_value: F = find_neighbor(index + 2); - push_to_bound_layer( - sparse_layer, - index / 2 + 1, - value + *r * (sibling_value - value), - ); - next_right_node_to_process = index + 4; - } - 2 => { - // Sibling left node wasn't encountered in previous iteration, - // so sibling must have value 1. - push_to_bound_layer( - sparse_layer, - index / 2 - 1, - F::one() + *r * (value - F::one()), - ); - next_left_node_to_process = index + 2; - } - 3 => { - // Sibling right node wasn't encountered in previous iteration, - // so sibling must have value 1. - push_to_bound_layer( - sparse_layer, - index / 2, - F::one() + *r * (value - F::one()), - ); - next_right_node_to_process = index + 2; - } - _ => unreachable!("?_?"), - } - } - if let Some(dense_vec) = dense_bound_layer { - *layer = DynamicDensityGrandProductLayer::Dense(dense_vec); - } else { - sparse_layer.truncate(num_bound); - } - } - DynamicDensityGrandProductLayer::Dense(dense_layer) => { - // If current layer is dense, next layer should also be dense. - let n = self.layer_len / 4; - for i in 0..n { - // left - dense_layer[2 * i] = dense_layer[4 * i] - + *r * (dense_layer[4 * i + 2] - dense_layer[4 * i]); - // right - dense_layer[2 * i + 1] = dense_layer[4 * i + 1] - + *r * (dense_layer[4 * i + 3] - dense_layer[4 * i + 1]); - } - } - }) - }, - || eq_poly.bound_poly_var_bot(r), - ); - self.layer_len /= 2; - } - - /// We want to compute the evaluations of the following univariate cubic polynomial at - /// points {0, 1, 2, 3}: - /// Σ coeff[batch_index] * (Σ eq(r, x) * left(x) * right(x)) - /// where the inner summation is over all but the "least significant bit" of the multilinear - /// polynomials `eq`, `left`, and `right`. We denote this "least significant" variable x_b. - /// - /// Computing these evaluations requires processing pairs of adjacent coefficients of - /// `eq`, `left`, and `right`. - /// If `self` is dense, we process each layer 4 values at a time: - /// layer = [L, R, L, R, L, R, ...] - /// | | | | - /// left(0, 0, 0, ..., x_b=0) | | right(0, 0, 0, ..., x_b=1) - /// right(0, 0, 0, ..., x_b=0) left(0, 0, 0, ..., x_b=1) - /// If `self` is sparse, we basically do the same thing but with some fancy optimizations and - /// more cases to check 😬 - #[tracing::instrument(skip_all, name = "BatchedSparseGrandProductLayer::compute_cubic")] - fn compute_cubic( - &self, - coeffs: &[F], - eq_poly: &DensePolynomial, - previous_round_claim: F, - ) -> UniPoly { - let eq_evals: Vec<(F, F, F)> = (0..eq_poly.len() / 2) - .into_par_iter() - .map(|i| { - let eval_point_0 = eq_poly[2 * i]; - let m_eq = eq_poly[2 * i + 1] - eq_poly[2 * i]; - let eval_point_2 = eq_poly[2 * i + 1] + m_eq; - let eval_point_3 = eval_point_2 + m_eq; - (eval_point_0, eval_point_2, eval_point_3) - }) - .collect(); - - // This is what the cubic evals would be if a layer were *all 1s* - // We pre-emptively compute these sums to speed up sparse layers; see below. - let eq_eval_sums: (F, F, F) = eq_evals - .par_iter() - .fold( - || (F::zero(), F::zero(), F::zero()), - |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), - ) - .reduce( - || (F::zero(), F::zero(), F::zero()), - |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), - ); - - let evals: Vec<(F, F, F)> = coeffs - .par_iter() - .enumerate() - .map(|(batch_index, coeff)| match &self.layers[batch_index] { - // If sparse, we use the pre-emptively computed `eq_eval_sums` as a starting point: - // eq_eval_sum := Σ eq_evals[i] - // What we ultimately want to compute: - // Σ coeff[batch_index] * (Σ eq_evals[i] * left[i] * right[i]) - // Note that if left[i] and right[i] are all 1s, the inner sum is: - // Σ eq_evals[i] = eq_eval_sum - // To recover the actual inner sum, we find all the non-1 - // left[i] and right[i] terms and compute the delta: - // ∆ := Σ eq_evals[j] * (left[j] * right[j] - 1) ∀j where left[j] ≠ 1 or right[j] ≠ 1 - // Then we can compute: - // coeff[batch_index] * (eq_eval_sum + ∆) = coeff[batch_index] * (Σ eq_evals[i] + Σ eq_evals[j] * (left[j] * right[j] - 1)) - // = coeff[batch_index] * (Σ eq_evals[j] * left[j] * right[j]) - // ...which is exactly the summand we want. - DynamicDensityGrandProductLayer::Sparse(sparse_layer) => { - // Computes: - // ∆ := Σ eq_evals[j] * (left[j] * right[j] - 1) ∀j where left[j] ≠ 1 or right[j] ≠ 1 - // for the evaluation points {0, 2, 3} - let mut delta = (F::zero(), F::zero(), F::zero()); - - let mut next_index_to_process = 0usize; - for (j, (index, value)) in sparse_layer.iter().enumerate() { - if *index < next_index_to_process { - // This node was already processed in a previous iteration - continue; - } - let neighbors = [ - sparse_layer - .get(j + 1) - .cloned() - .unwrap_or((index + 1, F::one())), - sparse_layer - .get(j + 2) - .cloned() - .unwrap_or((index + 2, F::one())), - sparse_layer - .get(j + 3) - .cloned() - .unwrap_or((index + 3, F::one())), - ]; - - let find_neighbor = |query_index: usize| { - neighbors - .iter() - .find_map(|(neighbor_index, neighbor_value)| { - if *neighbor_index == query_index { - Some(neighbor_value) - } else { - None - } - }) - .cloned() - .unwrap_or(F::one()) - }; - - // Recall that in the dense case, we process four values at a time: - // layer = [L, R, L, R, L, R, ...] - // | | | | - // left(0, 0, 0, ..., x_b=0) | | right(0, 0, 0, ..., x_b=1) - // right(0, 0, 0, ..., x_b=0) left(0, 0, 0, ..., x_b=1) - // - // In the sparse case, we do something similar, but some of the four - // values may be omitted from the sparse vector. - // We match on `index % 4` to determine which of the four values are - // present in the sparse vector, and infer the rest are 1. - let (left, right) = match index % 4 { - 0 => { - let left = (*value, find_neighbor(index + 2)); - let right = (find_neighbor(index + 1), find_neighbor(index + 3)); - next_index_to_process = index + 4; - (left, right) - } - 1 => { - let left = (F::one(), find_neighbor(index + 1)); - let right = (*value, find_neighbor(index + 2)); - next_index_to_process = index + 3; - (left, right) - } - 2 => { - let left = (F::one(), *value); - let right = (F::one(), find_neighbor(index + 1)); - next_index_to_process = index + 2; - (left, right) - } - 3 => { - let left = (F::one(), F::one()); - let right = (F::one(), *value); - next_index_to_process = index + 1; - (left, right) - } - _ => unreachable!("?_?"), - }; - - let m_left = left.1 - left.0; - let m_right = right.1 - right.0; - - let left_eval_2 = left.1 + m_left; - let left_eval_3 = left_eval_2 + m_left; - - let right_eval_2 = right.1 + m_right; - let right_eval_3 = right_eval_2 + m_right; - - let (eq_eval_0, eq_eval_2, eq_eval_3) = eq_evals[index / 4]; - delta.0 += - eq_eval_0.mul_0_optimized(left.0.mul_1_optimized(right.0) - F::one()); - delta.1 += eq_eval_2 - .mul_0_optimized(left_eval_2.mul_1_optimized(right_eval_2) - F::one()); - delta.2 += eq_eval_3 - .mul_0_optimized(left_eval_3.mul_1_optimized(right_eval_3) - F::one()); - } - - // coeff[batch_index] * (eq_eval_sum + ∆) = coeff[batch_index] * (Σ eq_evals[i] + Σ eq_evals[j] * (left[j] * right[j] - 1)) - // = coeff[batch_index] * (Σ eq_evals[j] * left[j] * right[j]) - ( - *coeff * (eq_eval_sums.0 + delta.0), - *coeff * (eq_eval_sums.1 + delta.1), - *coeff * (eq_eval_sums.2 + delta.2), - ) - } - // If dense, we just compute - // Σ coeff[batch_index] * (Σ eq_evals[i] * left[i] * right[i]) - // directly in `self.compute_cubic`, without using `eq_eval_sums`. - DynamicDensityGrandProductLayer::Dense(dense_layer) => { - // Computes: - // coeff[batch_index] * (Σ eq_evals[i] * left[i] * right[i]) - // for the evaluation points {0, 2, 3} - let evals = eq_evals - .iter() - .zip(dense_layer.chunks_exact(4)) - .map(|(eq_evals, chunk)| { - let left = (chunk[0], chunk[2]); - let right = (chunk[1], chunk[3]); - - let m_left = left.1 - left.0; - let m_right = right.1 - right.0; - - let left_eval_2 = left.1 + m_left; - let left_eval_3 = left_eval_2 + m_left; - - let right_eval_2 = right.1 + m_right; - let right_eval_3 = right_eval_2 + m_right; - - ( - eq_evals.0 * left.0 * right.0, - eq_evals.1 * left_eval_2 * right_eval_2, - eq_evals.2 * left_eval_3 * right_eval_3, - ) - }) - .fold( - (F::zero(), F::zero(), F::zero()), - |(sum_0, sum_2, sum_3), (a, b, c)| (sum_0 + a, sum_2 + b, sum_3 + c), - ); - (*coeff * evals.0, *coeff * evals.1, *coeff * evals.2) - } - }) - .collect(); - - let evals_combined_0 = evals.iter().map(|eval| eval.0).sum(); - let evals_combined_2 = evals.iter().map(|eval| eval.1).sum(); - let evals_combined_3 = evals.iter().map(|eval| eval.2).sum(); - - let cubic_evals = [ - evals_combined_0, - previous_round_claim - evals_combined_0, - evals_combined_2, - evals_combined_3, - ]; - UniPoly::from_evals(&cubic_evals) - } - - fn final_claims(&self) -> (Vec, Vec) { - assert_eq!(self.layer_len, 2); - self.layers - .iter() - .map(|layer| match layer { - DynamicDensityGrandProductLayer::Sparse(layer) => match layer.len() { - 0 => (F::one(), F::one()), // Neither left nor right claim is present, so they must both be 1 - 1 => { - if layer[0].0.is_zero() { - // Only left claim is present, so right claim must be 1 - (layer[0].1, F::one()) - } else { - // Only right claim is present, so left claim must be 1 - (F::one(), layer[0].1) - } - } - 2 => (layer[0].1, layer[1].1), // Both left and right claim are present - _ => panic!("Sparse layer length > 2"), - }, - DynamicDensityGrandProductLayer::Dense(layer) => (layer[0], layer[1]), - }) - .unzip() - } -} - -/// A special bottom layer of a grand product, where boolean flags are used to -/// toggle the other inputs (fingerprints) going into the rest of the tree. -/// Note that the gates for this layer are *not* simple multiplication gates. -/// ```ignore -/// -/// … … -/// / \ / \ the rest of the tree, which is now sparse (lots of 1s) -/// o o o o ↑ -/// / \ / \ / \ / \ ––––––––––––––––––––––––––––––––––––––––––– -/// 🏴 o 🏳️ o 🏳️ o 🏴 o toggle layer ↓ -struct BatchedGrandProductToggleLayer { - /// The list of non-zero flag indices for each layer in the batch. - flag_indices: Vec>, - /// The list of non-zero flag values for each layer in the batch. - /// Before the first binding iteration of sumcheck, this will be empty - /// (we know that all non-zero, unbound flag values are 1). - flag_values: Vec>, - fingerprints: Vec>, - layer_len: usize, - _marker: PhantomData, -} - -impl BatchedGrandProductToggleLayer { - fn new(flag_indices: Vec>, fingerprints: Vec>) -> Self { - let layer_len = fingerprints[0].len(); - Self { - flag_indices, - // While flags remain unbound, all values are boolean, so we can assume any flag that appears in `flag_indices` has value 1. - flag_values: vec![], - fingerprints, - layer_len, - _marker: PhantomData, - } - } - - fn layer_output(&self) -> BatchedSparseGrandProductLayer { - let output_layers = self - .fingerprints - .par_iter() - .enumerate() - .map(|(batch_index, fingerprints)| { - let flag_indices = &self.flag_indices[batch_index / 2]; - let mut sparse_layer = Vec::with_capacity(self.layer_len); - for i in flag_indices { - sparse_layer.push((*i, fingerprints[*i])); - } - DynamicDensityGrandProductLayer::Sparse(sparse_layer) - }) - .collect(); - BatchedSparseGrandProductLayer { - layer_len: self.layer_len, - layers: output_layers, - _marker: PhantomData, - } - } -} - -impl BatchedCubicSumcheck - for BatchedGrandProductToggleLayer -{ - fn num_rounds(&self) -> usize { - self.layer_len.log_2() - } - - /// Incrementally binds a variable of this batched layer's polynomials. - /// Similar to `BatchedSparseGrandProductLayer::bind`, in that fingerprints use - /// a sparse representation, but different in a couple of key ways: - /// - flags use two separate vectors (for indices and values) rather than - /// a single vector of (index, value) pairs - /// - The left and right nodes in this layer are flags and fingerprints, respectively. - /// They are represented by *separate* vectors, so they are *not* interleaved. This - /// means we process 2 flag values at a time, rather than 4. - /// - In `BatchedSparseGrandProductLayer`, the absence of a node implies that it has - /// value 1. For our sparse representation of flags, the absence of a node implies - /// that it has value 0. In other words, a flag with value 1 will be present in both - /// `self.flag_indices` and `self.flag_values`. - #[tracing::instrument(skip_all, name = "BatchedGrandProductToggleLayer::bind")] - fn bind(&mut self, eq_poly: &mut DensePolynomial, r: &F) { - self.fingerprints - .par_iter_mut() - .for_each(|layer: &mut Vec| { - debug_assert!(self.layer_len % 2 == 0); - let n = self.layer_len / 2; - for i in 0..n { - // TODO(moodlezoup): Try mul_0_optimized here - layer[i] = layer[2 * i] + *r * (layer[2 * i + 1] - layer[2 * i]); - } - }); - - rayon::join( - || { - let is_first_bind = self.flag_values.is_empty(); - if is_first_bind { - self.flag_values = vec![vec![]; self.flag_indices.len()]; - } - - self.flag_indices - .par_iter_mut() - .zip(self.flag_values.par_iter_mut()) - .for_each(|(flag_indices, flag_values)| { - let mut next_index_to_process = 0usize; - - let mut bound_index = 0usize; - for j in 0..flag_indices.len() { - let index = flag_indices[j]; - if index < next_index_to_process { - // This flag was already bound with its sibling in the previous iteration. - continue; - } - - // Bind indices in place - flag_indices[bound_index] = index / 2; - - if index % 2 == 0 { - let neighbor = flag_indices.get(j + 1).cloned().unwrap_or(0); - if neighbor == index + 1 { - // Neighbor is flag's sibling - - if is_first_bind { - // For first bind, all non-zero flag values are 1. - // bound_flags[i] = flags[2 * i] + r * (flags[2 * i + 1] - flags[2 * i]) - // = 1 - r * (1 - 1) - // = 1 - flag_values.push(F::one()); - } else { - // bound_flags[i] = flags[2 * i] + r * (flags[2 * i + 1] - flags[2 * i]) - flag_values[bound_index] = flag_values[j] - + *r * (flag_values[j + 1] - flag_values[j]); - }; - } else { - // This flag's sibling wasn't found, so it must have value 0. - - if is_first_bind { - // For first bind, all non-zero flag values are 1. - // bound_flags[i] = flags[2 * i] + r * (flags[2 * i + 1] - flags[2 * i]) - // = flags[2 * i] - r * flags[2 * i] - // = 1 - r - flag_values.push(F::one() - *r); - } else { - // bound_flags[i] = flags[2 * i] + r * (flags[2 * i + 1] - flags[2 * i]) - // = flags[2 * i] - r * flags[2 * i] - flag_values[bound_index] = - flag_values[j] - *r * flag_values[j]; - }; - } - next_index_to_process = index + 2; - } else { - // This flag's sibling wasn't encountered in a previous iteration, - // so it must have had value 0. - - if is_first_bind { - // For first bind, all non-zero flag values are 1. - // bound_flags[i] = flags[2 * i] + r * (flags[2 * i + 1] - flags[2 * i]) - // = r * flags[2 * i + 1] - // = r - flag_values.push(*r); - } else { - // bound_flags[i] = flags[2 * i] + r * (flags[2 * i + 1] - flags[2 * i]) - // = r * flags[2 * i + 1] - flag_values[bound_index] = *r * flag_values[j]; - }; - next_index_to_process = index + 1; - } - - bound_index += 1; - } - - flag_indices.truncate(bound_index); - // We only ever use `flag_indices.len()`, so no need to truncate `flag_values` - // flag_values.truncate(bound_index); - }); - }, - || eq_poly.bound_poly_var_bot(r), - ); - self.layer_len /= 2; - } - - /// Similar to `BatchedSparseGrandProductLayer::compute_cubic`, but with changes to - /// accomodate the differences between `BatchedSparseGrandProductLayer` and - /// `BatchedGrandProductToggleLayer`. These differences are described in the doc comments - /// for `BatchedGrandProductToggleLayer::bind`. - #[tracing::instrument(skip_all, name = "BatchedGrandProductToggleLayer::compute_cubic")] - fn compute_cubic( - &self, - coeffs: &[F], - eq_poly: &DensePolynomial, - previous_round_claim: F, - ) -> UniPoly { - let eq_evals: Vec<(F, F, F)> = (0..eq_poly.len() / 2) - .into_par_iter() - .map(|i| { - let eval_point_0 = eq_poly[2 * i]; - let m_eq = eq_poly[2 * i + 1] - eq_poly[2 * i]; - let eval_point_2 = eq_poly[2 * i + 1] + m_eq; - let eval_point_3 = eval_point_2 + m_eq; - (eval_point_0, eval_point_2, eval_point_3) - }) - .collect(); - - // This is what the cubic evals would be if a layer's flags were *all 0* - // We pre-emptively compute these sums as a starting point: - // eq_eval_sum := Σ eq_evals[i] - // What we ultimately want to compute: - // Σ coeff[batch_index] * (Σ eq_evals[i] * (flag[i] * fingerprint[i] + 1 - flag[i])) - // Note that if flag[i] is all 1s, the inner sum is: - // Σ eq_evals[i] = eq_eval_sum - // To recover the actual inner sum, we find all the non-zero flag[i] terms - // computes the delta: - // ∆ := Σ eq_evals[j] * (flag[j] * fingerprint[j] - flag[j])) ∀j where flag[j] ≠ 0 - // Then we can compute: - // coeff[batch_index] * (eq_eval_sum + ∆) = coeff[batch_index] * (Σ eq_evals[i] + Σ eq_evals[i] * (flag[i] * fingerprint[i] - flag[i]))) - // = coeff[batch_index] * (Σ eq_evals[j] * (flag[i] * fingerprint[i] + 1 - flag[i])) - // ...which is exactly the summand we want. - let eq_eval_sums: (F, F, F) = eq_evals - .par_iter() - .fold( - || (F::zero(), F::zero(), F::zero()), - |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), - ) - .reduce( - || (F::zero(), F::zero(), F::zero()), - |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), - ); - - let evals: Vec<(F, F, F)> = coeffs - .par_iter() - .enumerate() - .map(|(batch_index, coeff)| { - // Computes: - // ∆ := Σ eq_evals[j] * (flag[j] * fingerprint[j] - flag[j]) ∀j where flag[j] ≠ 0 - // for the evaluation points {0, 2, 3} - - let fingerprints = &self.fingerprints[batch_index]; - let flag_indices = &self.flag_indices[batch_index / 2]; - - let unbound = self.flag_values.is_empty(); - let mut delta = (F::zero(), F::zero(), F::zero()); - - let mut next_index_to_process = 0usize; - for (j, index) in flag_indices.iter().enumerate() { - if *index < next_index_to_process { - // This node was already processed in a previous iteration - continue; - } - - let (flags, fingerprints) = if index % 2 == 0 { - let neighbor = flag_indices.get(j + 1).cloned().unwrap_or(0); - let flags = if neighbor == index + 1 { - // Neighbor is flag's sibling - if unbound { - (F::one(), F::one()) - } else { - ( - self.flag_values[batch_index / 2][j], - self.flag_values[batch_index / 2][j + 1], - ) - } - } else { - // This flag's sibling wasn't found, so it must have value 0. - if unbound { - (F::one(), F::zero()) - } else { - (self.flag_values[batch_index / 2][j], F::zero()) - } - }; - let fingerprints = (fingerprints[*index], fingerprints[index + 1]); - - next_index_to_process = index + 2; - (flags, fingerprints) - } else { - // This flag's sibling wasn't encountered in a previous iteration, - // so it must have had value 0. - let flags = if unbound { - (F::zero(), F::one()) - } else { - (F::zero(), self.flag_values[batch_index / 2][j]) - }; - let fingerprints = (fingerprints[index - 1], fingerprints[*index]); - - next_index_to_process = index + 1; - (flags, fingerprints) - }; - - let m_flag = flags.1 - flags.0; - let m_fingerprint = fingerprints.1 - fingerprints.0; - - // If flags are still unbound, flag evals will mostly be 0s and 1s - // Bound flags are still mostly 0s, so flag evals will mostly be 0s. - let flag_eval_2 = flags.1 + m_flag; - let flag_eval_3 = flag_eval_2 + m_flag; - - let fingerprint_eval_2 = fingerprints.1 + m_fingerprint; - let fingerprint_eval_3 = fingerprint_eval_2 + m_fingerprint; - - let (eq_eval_0, eq_eval_2, eq_eval_3) = eq_evals[index / 2]; - delta.0 += eq_eval_0 - .mul_0_optimized(flags.0.mul_01_optimized(fingerprints.0) - flags.0); - delta.1 += eq_eval_2.mul_0_optimized( - flag_eval_2.mul_01_optimized(fingerprint_eval_2) - flag_eval_2, - ); - delta.2 += eq_eval_3.mul_0_optimized( - flag_eval_3.mul_01_optimized(fingerprint_eval_3) - flag_eval_3, - ); - } - - // coeff[batch_index] * (eq_eval_sum + ∆) = coeff[batch_index] * (Σ eq_evals[i] + Σ eq_evals[i] * (flag[i] * fingerprint[i] - flag[i]))) - // = coeff[batch_index] * (Σ eq_evals[j] * (flag[i] * fingerprint[i] + 1 - flag[i])) - ( - *coeff * (eq_eval_sums.0 + delta.0), - *coeff * (eq_eval_sums.1 + delta.1), - *coeff * (eq_eval_sums.2 + delta.2), - ) - }) - .collect(); - - let evals_combined_0 = evals.iter().map(|eval| eval.0).sum(); - let evals_combined_2 = evals.iter().map(|eval| eval.1).sum(); - let evals_combined_3 = evals.iter().map(|eval| eval.2).sum(); - - let cubic_evals = [ - evals_combined_0, - previous_round_claim - evals_combined_0, - evals_combined_2, - evals_combined_3, - ]; - UniPoly::from_evals(&cubic_evals) - } - - fn final_claims(&self) -> (Vec, Vec) { - assert_eq!(self.layer_len, 1); - let flag_claims = self - .flag_values - .iter() - .flat_map(|layer| { - if layer.is_empty() { - [F::zero(), F::zero()] - } else { - [layer[0], layer[0]] - } - }) - .collect(); - let fingerprint_claims = self.fingerprints.iter().map(|layer| layer[0]).collect(); - (flag_claims, fingerprint_claims) - } -} - -impl BatchedGrandProductLayer - for BatchedGrandProductToggleLayer -{ - fn prove_layer( - &mut self, - claims_to_verify: &mut Vec, - r_grand_product: &mut Vec, - transcript: &mut ProofTranscript, - ) -> BatchedGrandProductLayerProof { - // produce a fresh set of coeffs - let coeffs: Vec = transcript.challenge_vector(claims_to_verify.len()); - // produce a joint claim - let claim = claims_to_verify - .iter() - .zip(coeffs.iter()) - .map(|(&claim, &coeff)| claim * coeff) - .sum(); - - let mut eq_poly = DensePolynomial::new(EqPolynomial::::evals(r_grand_product)); - - let (sumcheck_proof, r_sumcheck, sumcheck_claims) = - self.prove_sumcheck(&claim, &coeffs, &mut eq_poly, transcript); - - drop_in_background_thread(eq_poly); - - let (left_claims, right_claims) = sumcheck_claims; - for (left, right) in left_claims.iter().zip(right_claims.iter()) { - transcript.append_scalar(left); - transcript.append_scalar(right); - } - - r_sumcheck - .into_par_iter() - .rev() - .collect_into_vec(r_grand_product); - - BatchedGrandProductLayerProof { - proof: sumcheck_proof, - left_claims, - right_claims, - _marker: PhantomData, - } - } -} - -pub struct ToggledBatchedGrandProduct { - toggle_layer: BatchedGrandProductToggleLayer, - sparse_layers: Vec>, - _marker: PhantomData, -} - -impl< - F: JoltField, - PCS: CommitmentScheme, - ProofTranscript: Transcript, - > BatchedGrandProduct - for ToggledBatchedGrandProduct -{ - type Leaves = (Vec>, Vec>); // (flags, fingerprints) - type Config = (); - - #[tracing::instrument(skip_all, name = "ToggledBatchedGrandProduct::construct")] - fn construct(leaves: Self::Leaves) -> Self { - let (flags, fingerprints) = leaves; - let num_layers = fingerprints[0].len().log_2(); - - let toggle_layer = BatchedGrandProductToggleLayer::new(flags, fingerprints); - let mut layers: Vec> = - Vec::with_capacity(num_layers); - layers.push(toggle_layer.layer_output()); - - for i in 0..num_layers - 1 { - let previous_layers = &layers[i]; - let len = previous_layers.layer_len / 2; - let new_layers = previous_layers - .layers - .par_iter() - .map(|previous_layer| previous_layer.layer_output(len)) - .collect(); - layers.push(BatchedSparseGrandProductLayer { - layer_len: len, - layers: new_layers, - _marker: PhantomData, - }); - } - - Self { - toggle_layer, - sparse_layers: layers, - _marker: PhantomData, - } - } - - #[tracing::instrument(skip_all, name = "ToggledBatchedGrandProduct::construct_with_config")] - fn construct_with_config(leaves: Self::Leaves, _config: Self::Config) -> Self { - >::construct(leaves) - } - - fn num_layers(&self) -> usize { - self.sparse_layers.len() + 1 - } - - fn claims(&self) -> Vec { - let last_layers: &BatchedSparseGrandProductLayer = - self.sparse_layers.last().unwrap(); - let (left_claims, right_claims) = last_layers.final_claims(); - left_claims - .iter() - .zip(right_claims.iter()) - .map(|(left_claim, right_claim)| *left_claim * *right_claim) - .collect() - } - - fn layers( - &'_ mut self, - ) -> impl Iterator> { - [&mut self.toggle_layer as &mut dyn BatchedGrandProductLayer] - .into_iter() - .chain( - self.sparse_layers - .iter_mut() - .map(|layer| layer as &mut dyn BatchedGrandProductLayer), - ) - .rev() - } - - fn verify_sumcheck_claim( - layer_proofs: &[BatchedGrandProductLayerProof], - layer_index: usize, - coeffs: &[F], - sumcheck_claim: F, - eq_eval: F, - grand_product_claims: &mut Vec, - r_grand_product: &mut Vec, - transcript: &mut ProofTranscript, - ) { - let layer_proof = &layer_proofs[layer_index]; - if layer_index != layer_proofs.len() - 1 { - // Normal grand product layer (multiplication gates) - let expected_sumcheck_claim: F = (0..grand_product_claims.len()) - .map(|i| { - coeffs[i] * layer_proof.left_claims[i] * layer_proof.right_claims[i] * eq_eval - }) - .sum(); - - assert_eq!(expected_sumcheck_claim, sumcheck_claim); - - // produce a random challenge to condense two claims into a single claim - let r_layer = transcript.challenge_scalar(); - - *grand_product_claims = layer_proof - .left_claims - .iter() - .zip(layer_proof.right_claims.iter()) - .map(|(&left_claim, &right_claim)| { - left_claim + r_layer * (right_claim - left_claim) - }) - .collect(); - - r_grand_product.push(r_layer); - } else { - // Grand product toggle layer: layer_proof.left_claims are flags, - // layer_proof.right_claims are fingerprints - let expected_sumcheck_claim: F = (0..grand_product_claims.len()) - .map(|i| { - coeffs[i] - * eq_eval - * (layer_proof.left_claims[i] * layer_proof.right_claims[i] + F::one() - - layer_proof.left_claims[i]) - }) - .sum(); - - assert_eq!(expected_sumcheck_claim, sumcheck_claim); - - *grand_product_claims = layer_proof - .left_claims - .iter() - .zip(layer_proof.right_claims.iter()) - .map(|(&flag_claim, &fingerprint_claim)| { - flag_claim * fingerprint_claim + F::one() - flag_claim - }) - .collect(); - } - } -} - #[cfg(test)] -mod grand_product_tests { +mod tests { use super::*; - use crate::poly::commitment::zeromorph::Zeromorph; use crate::utils::transcript::{KeccakTranscript, Transcript}; + use crate::{ + poly::{commitment::zeromorph::Zeromorph, dense_interleaved_poly::bind_left_and_right}, + subprotocols::sumcheck::Bindable, + }; use ark_bn254::{Bn254, Fr}; - use ark_std::{test_rng, One}; - use rand_core::RngCore; + use ark_std::test_rng; #[test] - fn dense_prove_verify() { - const LAYER_SIZE: usize = 1 << 8; - const BATCH_SIZE: usize = 4; + fn dense_construct() { let mut rng = test_rng(); - let leaves: Vec> = std::iter::repeat_with(|| { - std::iter::repeat_with(|| Fr::random(&mut rng)) - .take(LAYER_SIZE) - .collect() - }) - .take(BATCH_SIZE) - .collect(); + const LAYER_SIZE: [usize; 8] = [ + 1 << 1, + 1 << 2, + 1 << 3, + 1 << 4, + 1 << 5, + 1 << 6, + 1 << 7, + 1 << 8, + ]; + const BATCH_SIZE: [usize; 5] = [2, 3, 4, 5, 6]; - let mut batched_circuit = - as BatchedGrandProduct< + for (layer_size, batch_size) in LAYER_SIZE + .into_iter() + .cartesian_product(BATCH_SIZE.into_iter()) + { + let leaves: Vec> = std::iter::repeat_with(|| { + std::iter::repeat_with(|| Fr::random(&mut rng)) + .take(layer_size) + .collect::>() + }) + .take(batch_size) + .collect(); + + let expected_product: Fr = leaves.par_iter().flatten().product(); + + let batched_circuit = as BatchedGrandProduct< Fr, Zeromorph, KeccakTranscript, - >>::construct(leaves); - let mut transcript: KeccakTranscript = KeccakTranscript::new(b"test_transcript"); + >>::construct((leaves.concat(), batch_size)); + + for layer in &batched_circuit.layers { + assert_eq!(layer.coeffs.par_iter().product::(), expected_product); + } - // I love the rust type system - let claims = as BatchedGrandProduct< - Fr, - Zeromorph, - KeccakTranscript, - >>::claims(&batched_circuit); - let (proof, r_prover) = - as BatchedGrandProduct< + let claimed_outputs: Vec = as BatchedGrandProduct< Fr, Zeromorph, KeccakTranscript, - >>::prove_grand_product(&mut batched_circuit, None, &mut transcript, None); - - let mut transcript: KeccakTranscript = KeccakTranscript::new(b"test_transcript"); - let (_, r_verifier) = BatchedDenseGrandProduct::verify_grand_product( - &proof, - &claims, - None, - &mut transcript, - None, - ); - assert_eq!(r_prover, r_verifier); + >>::claimed_outputs(&batched_circuit); + let expected_outputs: Vec = + leaves.iter().map(|x| x.iter().product::()).collect(); + assert!(claimed_outputs == expected_outputs); + } } #[test] - fn dense_sparse_bind_parity() { - const LAYER_SIZE: usize = 1 << 4; - const BATCH_SIZE: usize = 1; + fn dense_bind() { let mut rng = test_rng(); + const LAYER_SIZE: [usize; 7] = [1 << 2, 1 << 3, 1 << 4, 1 << 5, 1 << 6, 1 << 7, 1 << 8]; + const BATCH_SIZE: [usize; 5] = [2, 3, 4, 5, 6]; - let dense_layers: Vec> = std::iter::repeat_with(|| { - std::iter::repeat_with(|| { - if rng.next_u32() % 4 == 0 { - Fr::random(&mut rng) - } else { - Fr::one() - } - }) - .take(LAYER_SIZE) - .collect() - }) - .take(BATCH_SIZE) - .collect(); - let mut batched_dense_layer = - BatchedDenseGrandProductLayer::::new(dense_layers.clone()); - - let sparse_layers: Vec> = dense_layers - .iter() - .map(|dense_layer| { - let mut sparse_layer = vec![]; - for (i, val) in dense_layer.iter().enumerate() { - if !val.is_one() { - sparse_layer.push((i, *val)); - } - } - DynamicDensityGrandProductLayer::Sparse(sparse_layer) + for (layer_size, batch_size) in LAYER_SIZE + .into_iter() + .cartesian_product(BATCH_SIZE.into_iter()) + { + let values: Vec> = std::iter::repeat_with(|| { + std::iter::repeat_with(|| Fr::random(&mut rng)) + .take(layer_size) + .collect::>() }) + .take(batch_size) .collect(); - let mut batched_sparse_layer: BatchedSparseGrandProductLayer = - BatchedSparseGrandProductLayer { - layer_len: LAYER_SIZE, - layers: sparse_layers, - _marker: PhantomData, - }; - - let condense = |sparse_layers: BatchedSparseGrandProductLayer| { - sparse_layers - .layers - .iter() - .map(|layer| match layer { - DynamicDensityGrandProductLayer::Sparse(sparse_layer) => { - let mut densified = - DenseGrandProductLayer::from(vec![Fr::one(); sparse_layers.layer_len]); - for (index, value) in sparse_layer { - densified[*index] = *value; - } - densified - } - DynamicDensityGrandProductLayer::Dense(dense_layer) => dense_layer.clone(), - }) - .collect::>() - }; - assert_eq!( - batched_dense_layer.layer_len, - batched_sparse_layer.layer_len - ); - let len = batched_dense_layer.layer_len; - for (dense, sparse) in batched_dense_layer - .layers - .iter() - .zip(condense(batched_sparse_layer.clone()).iter()) - { - assert_eq!(dense[..len], sparse[..len]); - } - - for _ in 0..LAYER_SIZE.log_2() - 1 { - let r_eq = std::iter::repeat_with(|| Fr::random(&mut rng)) - .take(4) - .collect::>(); - let mut eq_poly_dense = DensePolynomial::new(EqPolynomial::::evals(&r_eq)); - let mut eq_poly_sparse = eq_poly_dense.clone(); + let mut layer = DenseInterleavedPolynomial::::new(values.concat()); + let (mut expected_left_poly, mut expected_right_poly) = layer.uninterleave(); let r = Fr::random(&mut rng); - batched_dense_layer.bind(&mut eq_poly_dense, &r); - batched_sparse_layer.bind(&mut eq_poly_sparse, &r); + layer.bind(r); + bind_left_and_right(&mut expected_left_poly, &mut expected_right_poly, r); - assert_eq!(eq_poly_dense, eq_poly_sparse); - assert_eq!( - batched_dense_layer.layer_len, - batched_sparse_layer.layer_len - ); - let len = batched_dense_layer.layer_len; - for (dense, sparse) in batched_dense_layer - .layers - .iter() - .zip(condense(batched_sparse_layer.clone()).iter()) - { - assert_eq!(dense[..len], sparse[..len]); - } + let (actual_left_poly, actual_right_poly) = layer.uninterleave(); + assert_eq!(expected_left_poly, actual_left_poly); + assert_eq!(expected_right_poly, actual_right_poly); } } #[test] - fn dense_sparse_compute_cubic_parity() { - const LAYER_SIZE: usize = 1 << 10; - const BATCH_SIZE: usize = 4; + fn dense_prove_verify() { let mut rng = test_rng(); + const LAYER_SIZE: [usize; 7] = [1 << 2, 1 << 3, 1 << 4, 1 << 5, 1 << 6, 1 << 7, 1 << 8]; + const BATCH_SIZE: [usize; 5] = [2, 3, 4, 5, 6]; - let coeffs: Vec = std::iter::repeat_with(|| Fr::random(&mut rng)) - .take(BATCH_SIZE) - .collect(); - - let dense_layers: Vec> = std::iter::repeat_with(|| { - let layer: DenseGrandProductLayer = std::iter::repeat_with(|| { - if rng.next_u32() % 4 == 0 { - Fr::random(&mut rng) - } else { - Fr::one() - } - }) - .take(LAYER_SIZE) - .collect::>(); - DynamicDensityGrandProductLayer::Dense(layer) - }) - .take(BATCH_SIZE) - .collect(); - let dense_layers: BatchedSparseGrandProductLayer = - BatchedSparseGrandProductLayer { - layer_len: LAYER_SIZE, - layers: dense_layers, - _marker: PhantomData, - }; - - let sparse_layers: Vec> = dense_layers - .layers - .iter() - .map(|dense_layer| { - let mut sparse_layer = vec![]; - if let DynamicDensityGrandProductLayer::Dense(layer) = dense_layer { - for (i, val) in layer.iter().enumerate() { - if !val.is_one() { - sparse_layer.push((i, *val)); - } - } - } else { - panic!("Unexpected sparse layer"); - } - DynamicDensityGrandProductLayer::Sparse(sparse_layer) + for (layer_size, batch_size) in LAYER_SIZE + .into_iter() + .cartesian_product(BATCH_SIZE.into_iter()) + { + let leaves: Vec> = std::iter::repeat_with(|| { + std::iter::repeat_with(|| Fr::random(&mut rng)) + .take(layer_size) + .collect::>() }) + .take(batch_size) .collect(); - let sparse_layers: BatchedSparseGrandProductLayer = - BatchedSparseGrandProductLayer { - layer_len: LAYER_SIZE, - layers: sparse_layers, - _marker: PhantomData, - }; - let r_eq = std::iter::repeat_with(|| Fr::random(&mut rng)) - .take(LAYER_SIZE.log_2() - 1) - .collect::>(); - let eq_poly = DensePolynomial::new(EqPolynomial::::evals(&r_eq)); - let claim = Fr::random(&mut rng); + let mut batched_circuit = as BatchedGrandProduct< + Fr, + Zeromorph, + KeccakTranscript, + >>::construct((leaves.concat(), batch_size)); + let mut prover_transcript: KeccakTranscript = KeccakTranscript::new(b"test_transcript"); + + // I love the rust type system + let claims = as BatchedGrandProduct< + Fr, + Zeromorph, + KeccakTranscript, + >>::claimed_outputs(&batched_circuit); + let (proof, r_prover) = as BatchedGrandProduct< + Fr, + Zeromorph, + KeccakTranscript, + >>::prove_grand_product( + &mut batched_circuit, None, &mut prover_transcript, None + ); - let dense_evals = dense_layers.compute_cubic(&coeffs, &eq_poly, claim); - let sparse_evals = sparse_layers.compute_cubic(&coeffs, &eq_poly, claim); - assert_eq!(dense_evals, sparse_evals); + let mut verifier_transcript: KeccakTranscript = + KeccakTranscript::new(b"test_transcript"); + verifier_transcript.compare_to(prover_transcript); + let (_, r_verifier) = BatchedDenseGrandProduct::verify_grand_product( + &proof, + &claims, + None, + &mut verifier_transcript, + None, + ); + assert_eq!(r_prover, r_verifier); + } } } diff --git a/jolt-core/src/subprotocols/grand_product_quarks.rs b/jolt-core/src/subprotocols/grand_product_quarks.rs index c67ecc568..6096b0734 100644 --- a/jolt-core/src/subprotocols/grand_product_quarks.rs +++ b/jolt-core/src/subprotocols/grand_product_quarks.rs @@ -1,10 +1,10 @@ use super::grand_product::{ - BatchedDenseGrandProductLayer, BatchedGrandProduct, BatchedGrandProductLayer, - BatchedGrandProductProof, + BatchedGrandProduct, BatchedGrandProductLayer, BatchedGrandProductProof, }; use super::sumcheck::SumcheckInstanceProof; use crate::field::JoltField; -use crate::poly::commitment::commitment_scheme::{BatchType, CommitmentScheme}; +use crate::poly::commitment::commitment_scheme::CommitmentScheme; +use crate::poly::dense_interleaved_poly::DenseInterleavedPolynomial; use crate::poly::dense_mlpoly::DensePolynomial; use crate::poly::eq_poly::EqPolynomial; use crate::poly::opening_proof::{ProverOpeningAccumulator, VerifierOpeningAccumulator}; @@ -23,15 +23,17 @@ pub struct QuarkGrandProductProof< ProofTranscript: Transcript, > { sumcheck_proof: SumcheckInstanceProof, - g_commitment: Vec, - claimed_eval_g_r: Vec, - claimed_eval_g_r_x: (Vec, Vec), - helper_values: (Vec, Vec), + g_commitment: PCS::Commitment, + g_r_sumcheck: PCS::Field, + g_r_prime: (PCS::Field, PCS::Field), + v_r_prime: (PCS::Field, PCS::Field), num_vars: usize, } + pub struct QuarkGrandProduct { - polynomials: Vec>, - base_layers: Vec>, + batch_size: usize, + quark_poly: Vec, + base_layers: Vec>, _marker: PhantomData, } @@ -45,7 +47,8 @@ pub enum QuarkHybridLayerDepth { } impl QuarkHybridLayerDepth { - // The depth in the product tree of the GKR grand product at which the hybrid scheme will switch to using quarks grand product proofs + /// The depth in the binary tree of the GKR grand product at which the hybrid scheme + /// will switch to using Quarks Section 5 grand product argument. pub fn get_crossover_depth(&self) -> usize { match self { QuarkHybridLayerDepth::Min => 0, @@ -69,7 +72,8 @@ where ProofTranscript: Transcript, { /// The bottom/input layer of the grand products - type Leaves = Vec>; + // (leaf values, batch size) + type Leaves = (Vec, usize); type Config = QuarkGrandProductConfig; /// Constructs the grand product circuit(s) from `leaves` @@ -84,40 +88,33 @@ where /// Constructs the grand product circuit(s) from `leaves` with the given `config`. #[tracing::instrument(skip_all, name = "BatchedGrandProduct::construct_with_config")] fn construct_with_config(leaves: Self::Leaves, config: Self::Config) -> Self { - let leave_depth = leaves[0].len().log_2(); + let (leaves, batch_size) = leaves; + assert!(leaves.len() % batch_size == 0); + assert!((leaves.len() / batch_size).is_power_of_two()); + + let tree_depth = (leaves.len() / batch_size).log_2(); let crossover = config.hybrid_layer_depth.get_crossover_depth(); - let num_layers = if leave_depth <= crossover { - leave_depth - 1 + let num_layers = if tree_depth <= crossover { + tree_depth - 1 } else { crossover }; - // Taken 1 to 1 from the code in the BatchedDenseGrandProductLayer implementation - let mut layers = Vec::>::new(); - layers.push(BatchedDenseGrandProductLayer::::new( - leaves, - )); + // Taken 1 to 1 from the code in the BatchedDenseGrandProduct implementation + let mut layers = Vec::>::new(); + layers.push(DenseInterleavedPolynomial::new(leaves)); for i in 0..num_layers { - let previous_layers = &layers[i]; - let len = previous_layers.layer_len / 2; - // TODO(moodlezoup): parallelize over chunks instead of over batch - let new_layers = previous_layers - .layers - .par_iter() - .map(|previous_layer| { - (0..len) - .map(|i| previous_layer[2 * i] * previous_layer[2 * i + 1]) - .collect::>() - }) - .collect(); - layers.push(BatchedDenseGrandProductLayer::new(new_layers)); + let previous_layer = &layers[i]; + let new_layer = previous_layer.layer_output(); + layers.push(new_layer); } - // If the leaf depth is too small we return no polynomials and all base layers - if leave_depth <= num_layers { + // If the tree depth is too small we just do the GKR grand product + if tree_depth <= num_layers { return Self { - polynomials: Vec::>::new(), + batch_size, + quark_poly: Vec::new(), base_layers: layers, _marker: PhantomData, }; @@ -125,24 +122,28 @@ where // Take the top layer and then turn it into a quark poly // Note - We always push the base layer so the unwrap will work even with depth = 0 - let quark_polys = layers.pop().unwrap().layers; + let quark_poly = layers.pop().unwrap().coeffs; Self { - polynomials: quark_polys, + batch_size, + quark_poly, base_layers: layers, _marker: PhantomData, } } - /// The number of layers in the grand product, in this case it is the log of the quark layer size plus the gkr layer depth. + fn num_layers(&self) -> usize { - self.polynomials[0].len().log_2() + unimplemented!("Unused"); } + /// The claimed outputs of the grand products. - fn claims(&self) -> Vec { - self.polynomials - .par_iter() - .map(|f| f.iter().product()) + fn claimed_outputs(&self) -> Vec { + let chunk_size = self.quark_poly.len() / self.batch_size; + self.quark_poly + .par_chunks(chunk_size) + .map(|chunk| chunk.iter().product()) .collect() } + /// Returns an iterator over the layers of this batched grand product circuit. /// Each layer is mutable so that its polynomials can be bound over the course /// of proving. @@ -150,7 +151,7 @@ where fn layers( &'_ mut self, ) -> impl Iterator> { - panic!("We don't use the default prover and so we don't need the generic iterator"); + unimplemented!("We don't use the default prover and so we don't need the generic iterator"); std::iter::empty() } @@ -164,38 +165,40 @@ where ) -> (BatchedGrandProductProof, Vec) { let mut proof_layers = Vec::with_capacity(self.base_layers.len()); - // For proofs of polynomials of size less than 16 we support these with no quark proof - let (quark_option, mut random, mut claims_to_verify) = if !self.polynomials.is_empty() { + let outputs: Vec = + >::claimed_outputs(self); + transcript.append_scalars(&outputs); + let output_mle = DensePolynomial::new_padded(outputs); + let r_outputs: Vec = transcript.challenge_vector(output_mle.get_num_vars()); + let claim = output_mle.evaluate(&r_outputs); + + // For polynomials of size less than 16 we just use the GKR grand product + let (quark_proof, mut random, mut claim) = if !self.quark_poly.is_empty() { // When doing the quark hybrid proof, we first prove the grand product of a layer of a polynomial which is 4 layers deep in the tree - // of a standard layered sumcheck grand product, then we use the sumcheck layers to prove via gkr layers that the random point opened + // of a standard layered sumcheck grand product, then we use the sumcheck layers to prove via GKR layers that the random point opened // by the quark proof is in fact the folded result of the base layer. - let (quark, random, claims) = QuarkGrandProductProof::::prove( - &self.polynomials, - opening_accumulator.unwrap(), - transcript, - setup.unwrap(), - ); - (Some(quark), random, claims) + let (quark, random, quark_claim) = + QuarkGrandProductProof::::prove( + &self.quark_poly, + r_outputs, + claim, + opening_accumulator.unwrap(), + transcript, + setup.unwrap(), + ); + (Some(quark), random, quark_claim) } else { - ( - None, - Vec::::new(), - as BatchedGrandProduct< - F, - PCS, - ProofTranscript, - >>::claims(self), - ) + (None, r_outputs, claim) }; for layer in self.base_layers.iter_mut().rev() { - proof_layers.push(layer.prove_layer(&mut claims_to_verify, &mut random, transcript)); + proof_layers.push(layer.prove_layer(&mut claim, &mut random, transcript)); } ( BatchedGrandProductProof { - layers: proof_layers, - quark_proof: quark_option, + gkr_layers: proof_layers, + quark_proof, }, random, ) @@ -205,36 +208,49 @@ where #[tracing::instrument(skip_all, name = "BatchedGrandProduct::verify_grand_product")] fn verify_grand_product( proof: &BatchedGrandProductProof, - claims: &Vec, + claimed_outputs: &[F], opening_accumulator: Option<&mut VerifierOpeningAccumulator>, transcript: &mut ProofTranscript, _setup: Option<&PCS::Setup>, - ) -> (Vec, Vec) { + ) -> (F, Vec) { + // Evaluate the MLE of the output layer at a random point to reduce the outputs to + // a single claim. + transcript.append_scalars(claimed_outputs); + let r_outputs: Vec = + transcript.challenge_vector(claimed_outputs.len().next_power_of_two().log_2()); + let claim = DensePolynomial::new_padded(claimed_outputs.to_vec()).evaluate(&r_outputs); + // Here we must also support the case where the number of layers is very small - let (v_points, rand) = match proof.quark_proof.as_ref() { + let (claim, rand) = match proof.quark_proof.as_ref() { Some(quark) => { // In this case we verify the quark which fixes the first log(n)-4 vars in the random eval point. let v_len = quark.num_vars; // Todo (aleph_v) - bubble up errors quark - .verify(claims, opening_accumulator.unwrap(), transcript, v_len) + .verify( + r_outputs, + claim, + opening_accumulator.unwrap(), + transcript, + v_len, + ) .unwrap() } None => { // Otherwise we must check the actual claims and the preset random will be empty. - (claims.clone(), Vec::::new()) + (claim, r_outputs) } }; - let (sumcheck_claims, sumcheck_r) = >::verify_layers( - &proof.layers, &v_points, transcript, rand + &proof.gkr_layers, claim, transcript, rand ); - (sumcheck_claims, sumcheck_r) + (grand_product_claim, grand_product_r) } } @@ -262,83 +278,100 @@ where /// Finally - computes opening proofs for a random sampled during sumcheck proof and returns /// Returns a random point and evaluation to be verified by the caller (which our hybrid prover does with GKR) fn prove( - leaves: &[Vec], + v: &[PCS::Field], + r_outputs: Vec, + claim: PCS::Field, opening_accumulator: &mut ProverOpeningAccumulator, transcript: &mut ProofTranscript, setup: &PCS::Setup, - ) -> (Self, Vec, Vec) { - let v_length = leaves[0].len(); + ) -> (Self, Vec, PCS::Field) { + let v_length = v.len(); let v_variables = v_length.log_2(); - let mut g_polys = Vec::>::new(); - let mut v_polys = Vec::>::new(); - let mut sumcheck_polys = Vec::>::new(); - let mut products = Vec::::new(); - - for v in leaves.iter() { - let v_polynomial = DensePolynomial::::new(v.to_vec()); - let (f_1_r, f_r_0, f_r_1, p) = v_into_f::(&v_polynomial); - v_polys.push(v_polynomial); - g_polys.push(f_1_r.clone()); - sumcheck_polys.push(f_1_r); - sumcheck_polys.push(f_r_0); - sumcheck_polys.push(f_r_1); - products.push(p); - } + let v_polynomial = DensePolynomial::::new(v.to_vec()); + // Compute f(1, x), f(x, 0), and f(x, 1) from v(x) + let (f_1x, f_x0, f_x1) = v_into_f::(&v_polynomial); - // We bind to these polynomials - transcript.append_scalars(&products); - let g_commitment = PCS::batch_commit_polys(&g_polys, setup, BatchType::GrandProduct); - for g in g_commitment.iter() { - g.append_to_transcript(transcript); - } + let g_polynomial = f_1x.clone(); + let mut sumcheck_polys = vec![f_1x, f_x0, f_x1]; + + // We commit to g(x) = f(1, x) + let g_commitment = PCS::commit(&g_polynomial, setup); + g_commitment.append_to_transcript(transcript); - // Now we do the sumcheck using the prove arbitrary - // First instantiate our polynomials let tau: Vec = transcript.challenge_vector(v_variables); - let evals: DensePolynomial<>::Field> = + let eq_tau: DensePolynomial<>::Field> = DensePolynomial::new(EqPolynomial::evals(&tau)); - //We add evals as the second to last polynomial in the sumcheck - sumcheck_polys.push(evals); - - // Next we calculate the polynomial equal to 1 at all points but 1,1,1...,0 - let challenge_sum = vec![PCS::Field::one(); v_variables]; - let eq_sum: DensePolynomial<>::Field> = - DensePolynomial::new(EqPolynomial::evals(&challenge_sum)); - //We add evals as the last polynomial in the sumcheck - sumcheck_polys.push(eq_sum); + // We add eq_tau as the second to last polynomial in the sumcheck + sumcheck_polys.push(eq_tau); + + // This is where things start to deviate from the protocol described in + // Quarks Section 5. + // + // We batch our grand products by laying out the circuits side-by-side, and + // proving them together as one big circuit with k outputs, where k is the batch size. + // In `prove_grand_product`, we evaluate the MLE of these outputs at a random point, + // claim := \tilde{outputs}(r_outputs) + // + // Quarks Section 5 assumes there's only one output, P = f(1, ..., 1, 0). + // But claim != f(1, ..., 1, 0), so we have to use a different sumcheck expression. + // + // If you closely examine `v_into_f` and work it out, you'll find that our k grand product + // outputs are contained in f(1, x) at x = (1, ..., 1, 0, b), where b \in {0, 1}^{log2(k)}. + // So we have: + // claim = \tilde{outputs}(r_outputs) + // = \sum_b EQ(r_outputs, b) * outputs(b) + // = \sum_x EQ(1, ..., 1, 0, r_outputs, x) * f(1, x) where r_outputs ∈ 𝔽^{log2(k)}, x ∈ {0, 1}^{log2(kn)} + // + // Modifying the sumcheck instance described in Section 5 of the Quarks paper, we will + // be proving: + // claim = \sum_x (EQ(\tau, x) * (f(1, x) - f(x, 0) * f(x, 1)) + EQ(1, ..., 1, 0, r_outputs, x) * f(1, x)) + // + // Note that the first half of the summand EQ(\tau, x) * (f(1, x) - f(x, 0) * f(x, 1)) + // should equal 0 for all x ∈ {0, 1}^{log2(kn)}, ensuring that every output value f(1, x) is equal to the + // product of its input values f(x, 0) and f(x, 1). + + // First we compute EQ(1, ..., 1, 0, r_outputs, x) + let mut one_padded_r_outputs = vec![PCS::Field::one(); v_variables]; + let slice_index = one_padded_r_outputs.len() - r_outputs.len(); + one_padded_r_outputs[slice_index..].copy_from_slice(r_outputs.as_slice()); + one_padded_r_outputs[slice_index - 1] = PCS::Field::zero(); + let eq_output = DensePolynomial::new(EqPolynomial::evals(&one_padded_r_outputs)); + + #[cfg(test)] + { + let expected_claim: PCS::Field = eq_output + .evals() + .iter() + .zip(sumcheck_polys[0].evals().iter()) + .map(|(eq, f)| *eq * f) + .sum(); + + assert_eq!(expected_claim, claim); + } - // Sample a constant to do a random linear combination to combine the sumchecks - let r_combination: Vec = transcript.challenge_vector(g_polys.len()); + // We add eq_output as the last polynomial in the sumcheck + sumcheck_polys.push(eq_output); - // We define a closure using vals[i] = f(1, x), vals[i+1] = f(x, 0), vals[i+2] = f(x, 1) + // This is the sumcheck polynomial + // EQ(\tau, x) * (f(1, x) - f(x, 0) * f(x, 1)) + EQ(1, ..., 1, 0, r_outputs, x) * f(1, x) let output_check_fn = |vals: &[PCS::Field]| -> PCS::Field { - let eval = vals[vals.len() - 2]; - let eq_sum = vals[vals.len() - 1]; - let mut sum_1 = PCS::Field::zero(); - let mut sum_2 = PCS::Field::zero(); - - for i in 0..(vals.len() / 3) { - sum_1 += r_combination[i] * (vals[i * 3] - vals[3 * i + 1] * vals[3 * i + 2]); - sum_2 += r_combination[i] * vals[i * 3 + 1]; - } - sum_1 * eval + sum_2 * eq_sum + assert_eq!(vals.len(), 5); + let f_1x = vals[0]; + let f_x0 = vals[1]; + let f_x1 = vals[2]; + let eq_tau = vals[3]; + let eq_output = vals[4]; + + eq_tau * (f_1x - f_x0 * f_x1) + eq_output * f_1x }; - // The sumcheck should have the claims times the random coefficents as the sum as all terms are zero except - // 1,1,..,0 which is r*f(1,1,..0) - let rlc_claims = products - .iter() - .zip(r_combination.iter()) - .map(|(x, r)| *x * r) - .sum(); - // Now run the sumcheck in arbitrary mode // Note - We use the final randomness from binding all variables (x) as the source random for the openings so the verifier can // check that the base layer is the same as is committed too. - let (sumcheck_proof, x, _) = + let (sumcheck_proof, r_sumcheck, _) = SumcheckInstanceProof::::prove_arbitrary::<_>( - &rlc_claims, + &claim, v_variables, &mut sumcheck_polys, output_check_fn, @@ -346,182 +379,161 @@ where transcript, ); - let borrowed: Vec<&DensePolynomial> = g_polys.iter().collect(); - let chis_r = EqPolynomial::evals(&x); - let openings_r: Vec = g_polys - .iter() - .map(|g| g.evaluate_at_chi_low_optimized(&chis_r)) - .collect(); - // For the version of quarks which only commits to g(1, x) we first do a direct batch proof on x + // To finish up the sumcheck above, we need the following openings: + // 1. f(1, r_sumcheck) + // 2. f(r_sumcheck, 0) + // 3. f(r_sumcheck, 1) + + // We have a commitment to g(x) = f(1, x), so we can prove opening 1 directly: + let chis_r = EqPolynomial::evals(&r_sumcheck); + let g_r_sumcheck = g_polynomial.evaluate_at_chi_low_optimized(&chis_r); opening_accumulator.append( - &borrowed, + &[&g_polynomial], DensePolynomial::new(chis_r), - x.clone(), - &openings_r.iter().collect::>(), + r_sumcheck.clone(), + &[&g_r_sumcheck], transcript, ); - let claimed_eval_g_r = openings_r; - // We are using f(a, x) = a*g(x) + (1-a)*h(x) where f is the polynomial with the cached internal products - // Let r = (r_1, r') - // f(r, 0) = r_1 * g(r', 0) + (1-r_1)*h(r', 0) - // f(r, 1) = r_1 * g(r', 1) + (1-r_1)*h(r', 1) - // Therefore we do a line reduced opening on g(r', 0) and g(r', 1)e(); - let mut r_prime = vec![PCS::Field::zero(); x.len() - 1]; - r_prime.clone_from_slice(&x[1..x.len()]); - let claimed_eval_g_r_x = open_and_prove::( - &r_prime, - &g_polys, - opening_accumulator, + // To prove openings 2 and 3, we use the following relation: + // + // f(a, x) = a * f(1, x) + (1 - a) * f(0, x) + // = a * g(x) + (1 - a) * v(x) + // + // where v(x) = f(0, x) is the MLE of the inputs to the Quarks grand product + + // Let (r_1, r') := r_sumcheck. + let r_prime = r_sumcheck[1..r_sumcheck.len()].to_vec(); + + // Then openings 2 and 3 can be written as: + // + // f(r_sumcheck, 0) = r_1 * g(r', 0) + (1 - r_1) * v(r', 0) + // f(r_sumcheck, 1) = r_1 * g(r', 1) + (1 - r_1) * v(r', 1) + // + // So we have reduced our two openings to four different ones: + // g(r', 0), g(r', 1), v(r', 0), v(r', 1) + // + // We can reduce g(r', 0) and g(r', 1) to a single opening of g: + let ((reduced_opening_point_g, reduced_opening_g), g_r_prime) = + line_reduce::(&r_prime, &g_polynomial, transcript); + opening_accumulator.append( + &[&g_polynomial], + DensePolynomial::new(EqPolynomial::evals(&reduced_opening_point_g)), + reduced_opening_point_g, + &[&reduced_opening_g], transcript, ); - // next we need to make a claim about h(r', 0) and h(r', 1) so we use our line reduction to make one claim - let ((r_t, h_r_t), helper_values) = - line_reduce::(&r_prime, &v_polys, transcript); - let num_vars = v_variables; + // Similarly, we can reduce v(r', 0) and v(r', 1) to a single claim about v: + let ((reduced_opening_point_v, reduced_opening_v), v_r_prime) = + line_reduce::(&r_prime, &v_polynomial, transcript); + // This is the claim that will be recursively proven using the GKR grand product layers. + + let quark_proof = Self { + sumcheck_proof, + g_commitment, + g_r_sumcheck, + g_r_prime, + v_r_prime, + num_vars: v_variables, + }; - ( - Self { - sumcheck_proof, - g_commitment, - claimed_eval_g_r, - claimed_eval_g_r_x, - helper_values, - num_vars, - }, - r_t, - h_r_t, - ) + (quark_proof, reduced_opening_point_v, reduced_opening_v) } /// Verifies the given grand product proof. #[allow(clippy::type_complexity)] fn verify( &self, - claims: &[PCS::Field], + r_outputs: Vec, + claim: PCS::Field, opening_accumulator: &mut VerifierOpeningAccumulator, transcript: &mut ProofTranscript, n_rounds: usize, - ) -> Result<(Vec, Vec), QuarkError> { - // First we append the claimed values for the commitment and the product - transcript.append_scalars(claims); - for g in self.g_commitment.iter() { - g.append_to_transcript(transcript); - } + ) -> Result<(PCS::Field, Vec), QuarkError> { + self.g_commitment.append_to_transcript(transcript); - //Next sample the tau and construct the evals poly + // Next sample the tau and construct the evals poly let tau: Vec = transcript.challenge_vector(n_rounds); - let r_combination: Vec = transcript.challenge_vector(self.g_commitment.len()); - - // Our sumcheck is expected to equal the RLC of the claims - let claim_rlc: PCS::Field = claims - .iter() - .zip(r_combination.iter()) - .map(|(x, r)| *x * r) - .sum(); // To complete the sumcheck proof we have to validate that our polynomial openings match and are right. - let (expected, r) = self + let (expected, r_sumcheck) = self .sumcheck_proof - .verify(claim_rlc, n_rounds, 3, transcript) + .verify(claim, n_rounds, 3, transcript) .map_err(|_| QuarkError::InvalidQuarkSumcheck)?; - // Again the batch verify expects we have a slice of borrows but we have a slice of Commitments - let borrowed_g: Vec<&PCS::Commitment> = self.g_commitment.iter().collect(); - - // Get the r_1 and r_prime values - let r_1 = r[0]; - let mut r_prime = vec![PCS::Field::zero(); r.len() - 1]; - r_prime.clone_from_slice(&r[1..r.len()]); - // Firstly we verify that the openings of g(r) are correct + // Firstly we append g(r_sumcheck) opening_accumulator.append( - &borrowed_g, - r.clone(), - &self.claimed_eval_g_r.iter().collect::>(), + &[&self.g_commitment], + r_sumcheck.clone(), + &[&self.g_r_sumcheck], transcript, ); + + // (r1, r') := r_sumcheck + let r_1 = r_sumcheck[0]; + let r_prime = r_sumcheck[1..r_sumcheck.len()].to_vec(); + // Next do the line reduction verification of g(r', 0) and g(r', 1) - line_reduce_opening_verify::( - &self.claimed_eval_g_r_x, - &r_prime, - &borrowed_g, - opening_accumulator, - transcript, - ); - // Load the h(r,t) values using a line reduction without opening because the opening is done in calling function - let (r_t, h_r_t) = line_reduce_verify(&self.helper_values, &r_prime, transcript); + let (r_g, claim_g) = + line_reduce_verify(self.g_r_prime.0, self.g_r_prime.1, &r_prime, transcript); + opening_accumulator.append(&[&self.g_commitment], r_g, &[&claim_g], transcript); - // We enforce that f opened at (1,1,...,1, 0) is in fact the product - let challenge_sum = vec![PCS::Field::one(); n_rounds]; + // Similarly, we can reduce v(r', 0) and v(r', 1) to a single claim about v: + let (r_v, claim_v) = + line_reduce_verify(self.v_r_prime.0, self.v_r_prime.1, &r_prime, transcript); - // Use the log(n) form to calculate eq(tau, r) - let eq_eval: PCS::Field = r + // Calculate eq(tau, r_sumcheck) in O(log(n)) + let eq_tau_eval: PCS::Field = r_sumcheck .iter() .zip_eq(tau.iter()) - .map(|(&r_gp, &r_sc)| { - r_gp * r_sc + (PCS::Field::one() - r_gp) * (PCS::Field::one() - r_sc) + .map(|(&r_i, &tau_i)| { + r_i * tau_i + (PCS::Field::one() - r_i) * (PCS::Field::one() - tau_i) }) .product(); - // Use the log(n) form to calculate eq(1...1, r) - let eq_1_eval: PCS::Field = r + // Calculate eq(11...10 || r_outputs, r_sumcheck) in O(log(n)) + let mut one_padded_r_outputs = vec![PCS::Field::one(); n_rounds]; + let slice_index = one_padded_r_outputs.len() - r_outputs.len(); + one_padded_r_outputs[slice_index..].copy_from_slice(r_outputs.as_slice()); + one_padded_r_outputs[slice_index - 1] = PCS::Field::zero(); + let eq_output_eval: PCS::Field = r_sumcheck .iter() - .zip_eq(challenge_sum.iter()) - .map(|(&r_gp, &r_sc)| { - r_gp * r_sc + (PCS::Field::one() - r_gp) * (PCS::Field::one() - r_sc) + .zip_eq(one_padded_r_outputs.iter()) + .map(|(&r_i, &r_output)| { + r_i * r_output + (PCS::Field::one() - r_i) * (PCS::Field::one() - r_output) }) .product(); - // We calculate f(1, r) = g(r), f(r, 0) = r_1 * g(r', 0) + (1-r_1)*h(r', 0), and f(r, 1) = r_1 * g(r', 1) + (1-r_1)*h(r', 1) - let one_r = &self.claimed_eval_g_r; - let r_0: Vec = self - .claimed_eval_g_r_x - .0 - .iter() - .zip(self.helper_values.0.iter()) - .map(|(r, h)| *h + r_1 * (*r - *h)) - .collect(); - let r_1: Vec = self - .claimed_eval_g_r_x - .1 - .iter() - .zip(self.helper_values.1.iter()) - .map(|(r, h)| *h + r_1 * (*r - *h)) - .collect(); + // We calculate: + // - f(1, r_sumcheck) = g(r_sumcheck) + // - f(r_sumcheck, 0) = r_1 * g(r', 0) + (1 - r_1) * v(r', 0) + // - f(r_sumcheck, 1) = r_1 * g(r', 1) + (1 - r_1) * v(r', 1) + let f_1r = self.g_r_sumcheck; + let f_r0 = self.v_r_prime.0 + r_1 * (self.g_r_prime.0 - self.v_r_prime.0); + let f_r1 = self.v_r_prime.1 + r_1 * (self.g_r_prime.1 - self.v_r_prime.1); - // Finally we check that in fact the polynomial bound by the sumcheck is equal to eq(tau, r)*(f(1, r) - f(r, 0)*f(r,1)) + eq((1,1,.0),r)*f(r,0) - let mut result_from_openings = PCS::Field::zero(); - for i in 0..r_0.len() { - result_from_openings += - r_combination[i] * (eq_eval * (one_r[i] - r_0[i] * r_1[i]) + eq_1_eval * r_0[i]); - } + // Finally we check that in fact the polynomial bound by the sumcheck is equal to + // eq(tau, r) * (f(1, r) - f(r, 0) * f(r, 1)) + eq(11...10|| r_outputs, r) * f(1, r) + let result_from_openings = eq_tau_eval * (f_1r - f_r0 * f_r1) + eq_output_eval * f_1r; if result_from_openings != expected { return Err(QuarkError::InvalidBinding); } - Ok((h_r_t, r_t)) + Ok((claim_v, r_v)) } } -// Computes slices of f for the sumcheck +/// Computes the polynomials f(1, x), f(x, 0), and f(x, 1) from the v polynomial, +/// as described in Lemma 5.1 of the Quarks paper. #[allow(clippy::type_complexity)] -fn v_into_f( - v: &DensePolynomial, -) -> ( - DensePolynomial, - DensePolynomial, - DensePolynomial, - PCS::Field, -) -where - PCS: CommitmentScheme, - ProofTranscript: Transcript, -{ +fn v_into_f( + v: &DensePolynomial, +) -> (DensePolynomial, DensePolynomial, DensePolynomial) { let v_length = v.len(); - let mut f_evals = vec![PCS::Field::zero(); 2 * v_length]; - let (evals, _) = v.split_evals(v.len()); + let mut f_evals = vec![F::zero(); 2 * v_length]; + let (evals, _) = v.Z.split_at(v.len()); f_evals[..v_length].clone_from_slice(evals); for i in v_length..2 * v_length { @@ -531,7 +543,7 @@ where f_evals[i] = f_evals[i_shift_mod] * f_evals[i_shift_mod + 1] } - // We pull out the co-efficient which instantiate the lower d polys for the sumcheck + // We pull out the coefficient which instantiate the lower d polys for the sumcheck let f_1_x = f_evals[v_length..].to_vec(); let mut f_x_0 = Vec::new(); @@ -544,144 +556,69 @@ where } } - let f_r_0 = DensePolynomial::new(f_x_0); - let f_r_1 = DensePolynomial::new(f_x_1); - let f_1_r = DensePolynomial::new(f_1_x); + let f_x_0 = DensePolynomial::new(f_x_0); + let f_x_1 = DensePolynomial::new(f_x_1); + let f_1_x = DensePolynomial::new(f_1_x); - // f(1, ..., 1, 0) = P - let product = f_evals[2 * v_length - 2]; - - (f_1_r, f_r_0, f_r_1, product) -} - -// Open a set of polynomials at a point and return the openings and proof -// Note - This uses a special case of the line reduction protocol for the case where we are opening -// a random which is either 0 or 1 in a position (either the first or last position). -// In this case the interpolated lined function is constant in all other points except the last one -// the by picking 0 and 1 as the points we interpolate at we can treat the evals of f(0r) and f(1r) -// (or vice versa) as implicitly defining the line t*f(0r) + (t-1)f(1r) and so the evals data alone -// is sufficient to calculate the claimed line, then we sample a random value r_star and do an opening proof -// on (r_star - 1) * f(0r) + r_star * f(1r) in the commitment to f. -fn open_and_prove, ProofTranscript: Transcript>( - r: &[PCS::Field], - f_polys: &[DensePolynomial], - opening_accumulator: &mut ProverOpeningAccumulator, - transcript: &mut ProofTranscript, -) -> (Vec, Vec) { - // Do the line reduction protocol - let ((r_star, openings_star), (openings_0, openings_1)) = - line_reduce::(r, f_polys, transcript); - opening_accumulator.append( - &f_polys.iter().collect::>(), - DensePolynomial::new(EqPolynomial::evals(&r_star)), - r_star, - &openings_star.iter().collect::>(), - transcript, - ); - - (openings_0, openings_1) + (f_1_x, f_x_0, f_x_1) } #[allow(clippy::type_complexity)] -/// Calculates the r0 r1 values and writes their evaluation to the transcript before calculating r star and -/// the opening of this, but does not prove the opening as that is left to the calling function -fn line_reduce, ProofTranscript: Transcript>( - r: &[PCS::Field], - f_polys: &[DensePolynomial], +// This is a special case of the line reduction protocol for the case where we are opening +// a random which is either 0 or 1 in the last position. +// In this case the interpolated line function is constant in all other points except the last one +// By picking 0 and 1 as the points we interpolate at we can treat the evals of f(r, 0) and f(r, 1) +// as implicitly defining the line t * f(r, 0) + (t-1) * f(r, 1) and so the evals data alone +// is sufficient to calculate the claimed line, then we sample a random value r_star and do an opening proof +// on (r_star - 1) * f(r, 0) + r_star * f(r, 1) in the commitment to f. +fn line_reduce( + r: &[F], + polynomial: &DensePolynomial, transcript: &mut ProofTranscript, -) -> ( - (Vec, Vec), - (Vec, Vec), -) { - // Calculates r0 and r1 +) -> ((Vec, F), (F, F)) { + // Calculates r || 0 and r || 1 let mut r_0 = r.to_vec(); let mut r_1 = r.to_vec(); - r_0.push(PCS::Field::zero()); - r_1.push(PCS::Field::one()); - - let chis_1 = EqPolynomial::evals(&r_0); - let openings_0: Vec = f_polys - .iter() - .map(|f| f.evaluate_at_chi_low_optimized(&chis_1)) - .collect(); - let chis_2 = EqPolynomial::evals(&r_1); - let openings_1: Vec = f_polys - .iter() - .map(|f| f.evaluate_at_chi_low_optimized(&chis_2)) - .collect(); - - // We add these to the transcript then sample an r which depends on them all - transcript.append_scalars(&openings_0); - transcript.append_scalars(&openings_1); - let rand: PCS::Field = transcript.challenge_scalar(); - - // Now calculate l(rand) = r.rand if is before or rand.r if not is before + r_0.push(F::zero()); + r_1.push(F::one()); + + let opening_0 = polynomial.evaluate(&r_0); + let opening_1 = polynomial.evaluate(&r_1); + + // We add these to the transcript then sample an r which depends on both + transcript.append_scalar(&opening_0); + transcript.append_scalar(&opening_1); + let rand: F = transcript.challenge_scalar(); + + // Now calculate r* := r || rand let mut r_star = r.to_vec(); r_star.push(rand); - // Now calculate the evals of f at r_star - let chis_3 = EqPolynomial::evals(&r_star); - let openings_star: Vec = f_polys - .iter() - .map(|f| f.evaluate_at_chi_low_optimized(&chis_3)) - .collect(); - - // For debug purposes we will check that (rand - 1) * f(0r) + rand * f(1r) = openings_star - for (star, (e_0, e_1)) in openings_star - .iter() - .zip(openings_0.iter().zip(openings_1.iter())) - { - assert_eq!(*e_0 + rand * (*e_1 - *e_0), *star); - } - - ((r_star, openings_star), (openings_0, openings_1)) -} + // Now evaluate the polynomial at r_star + let opening_star: F = polynomial.evaluate(&r_star); + debug_assert_eq!(opening_star, opening_0 + rand * (opening_1 - opening_0)); -/// Does the counterpart of the open_and_prove by computing an r_star vector point and then validating this opening -fn line_reduce_opening_verify< - PCS: CommitmentScheme, - ProofTranscript: Transcript, ->( - data: &(Vec, Vec), - r: &[PCS::Field], - commitments: &[&PCS::Commitment], - opening_accumulator: &mut VerifierOpeningAccumulator, - transcript: &mut ProofTranscript, -) { - // First compute the line reduction and points - let (r_star, claimed) = line_reduce_verify(&(data.0.clone(), data.1.clone()), r, transcript); - - // append to the verifier opening accumulator - opening_accumulator.append( - commitments, - r_star, - &claimed.iter().collect::>(), - transcript, - ); + ((r_star, opening_star), (opening_0, opening_1)) } +// The verifier's dual of `line_reduce` fn line_reduce_verify( - data: &(Vec, Vec), + claim_0: F, + claim_1: F, r: &[F], transcript: &mut ProofTranscript, -) -> (Vec, Vec) { - // To get our random we first append the openings data - transcript.append_scalars(&data.0); - transcript.append_scalars(&data.1); +) -> (Vec, F) { + // We add these to the transcript then sample an r which depends on both + transcript.append_scalar(&claim_0); + transcript.append_scalar(&claim_1); let rand: F = transcript.challenge_scalar(); - // Compute l(rand) = (r, rand) or (rand,r) + // Now calculate r* := r || rand let mut r_star = r.to_vec(); r_star.push(rand); - // Compute our claimed openings - let claimed: Vec = data - .0 - .iter() - .zip(data.1.iter()) - .map(|(e0, e1)| *e0 + rand * (*e1 - *e0)) - .collect(); - (r_star, claimed) + let reduced_claim = claim_0 + rand * (claim_1 - claim_0); + (r_star, reduced_claim) } #[cfg(test)] @@ -692,54 +629,6 @@ mod quark_grand_product_tests { use ark_bn254::{Bn254, Fr}; use rand_core::SeedableRng; - #[test] - fn quark_e2e() { - const LAYER_SIZE: usize = 1 << 8; - - let mut rng = rand_chacha::ChaCha20Rng::seed_from_u64(9_u64); - - let leaves_1: Vec = std::iter::repeat_with(|| Fr::random(&mut rng)) - .take(LAYER_SIZE) - .collect(); - let leaves_2: Vec = std::iter::repeat_with(|| Fr::random(&mut rng)) - .take(LAYER_SIZE) - .collect(); - let known_products = vec![leaves_1.iter().product(), leaves_2.iter().product()]; - let v = vec![leaves_1, leaves_2]; - let mut transcript: KeccakTranscript = KeccakTranscript::new(b"test_transcript"); - - let srs = ZeromorphSRS::::setup(&mut rng, 1 << 9); - let setup = srs.trim(1 << 9); - - let mut prover_accumulator: ProverOpeningAccumulator = - ProverOpeningAccumulator::new(); - let mut verifier_accumulator: VerifierOpeningAccumulator< - Fr, - Zeromorph, - KeccakTranscript, - > = VerifierOpeningAccumulator::new(); - - let (proof, _, _) = QuarkGrandProductProof::< - Zeromorph, - KeccakTranscript, - >::prove(&v, &mut prover_accumulator, &mut transcript, &setup); - let batched_proof = prover_accumulator.reduce_and_prove(&setup, &mut transcript); - - // Note resetting the transcript is important - transcript = KeccakTranscript::new(b"test_transcript"); - let result = proof.verify( - &known_products, - &mut verifier_accumulator, - &mut transcript, - 8, - ); - assert!(result.is_ok(), "Proof did not verify"); - - assert!(verifier_accumulator - .reduce_and_verify(&setup, &batched_proof, &mut transcript) - .is_ok()); - } - fn quark_hybrid_test_with_config(config: QuarkGrandProductConfig) { const LAYER_SIZE: usize = 1 << 8; @@ -753,43 +642,49 @@ mod quark_grand_product_tests { .collect(); let known_products: Vec = vec![leaves_1.iter().product(), leaves_2.iter().product()]; - let v = vec![leaves_1, leaves_2]; - let mut transcript: KeccakTranscript = KeccakTranscript::new(b"test_transcript"); + let v = [leaves_1, leaves_2].concat(); + let mut prover_transcript: KeccakTranscript = KeccakTranscript::new(b"test_transcript"); let srs = ZeromorphSRS::::setup(&mut rng, 1 << 9); let setup = srs.trim(1 << 9); - let mut prover_accumulator: ProverOpeningAccumulator = - ProverOpeningAccumulator::new(); - let mut verifier_accumulator: VerifierOpeningAccumulator< - Fr, - Zeromorph, - KeccakTranscript, - > = VerifierOpeningAccumulator::new(); - let mut hybrid_grand_product = as BatchedGrandProduct< Fr, Zeromorph, KeccakTranscript, - >>::construct_with_config(v, config); + >>::construct_with_config((v, 2), config); + let mut prover_accumulator: ProverOpeningAccumulator = + ProverOpeningAccumulator::new(); let proof: BatchedGrandProductProof, KeccakTranscript> = hybrid_grand_product - .prove_grand_product(Some(&mut prover_accumulator), &mut transcript, Some(&setup)) + .prove_grand_product( + Some(&mut prover_accumulator), + &mut prover_transcript, + Some(&setup), + ) .0; - let batched_proof = prover_accumulator.reduce_and_prove(&setup, &mut transcript); + let batched_proof = prover_accumulator.reduce_and_prove(&setup, &mut prover_transcript); // Note resetting the transcript is important - transcript = KeccakTranscript::new(b"test_transcript"); + let mut verifier_transcript = KeccakTranscript::new(b"test_transcript"); + verifier_transcript.compare_to(prover_transcript); + let mut verifier_accumulator: VerifierOpeningAccumulator< + Fr, + Zeromorph, + KeccakTranscript, + > = VerifierOpeningAccumulator::new(); + verifier_accumulator.compare_to(prover_accumulator, &setup); + let _ = QuarkGrandProduct::verify_grand_product( &proof, &known_products, Some(&mut verifier_accumulator), - &mut transcript, + &mut verifier_transcript, Some(&setup), ); assert!(verifier_accumulator - .reduce_and_verify(&setup, &batched_proof, &mut transcript) + .reduce_and_verify(&setup, &batched_proof, &mut verifier_transcript) .is_ok()); } diff --git a/jolt-core/src/subprotocols/mod.rs b/jolt-core/src/subprotocols/mod.rs index 1955f5981..66d0b30ab 100644 --- a/jolt-core/src/subprotocols/mod.rs +++ b/jolt-core/src/subprotocols/mod.rs @@ -2,4 +2,5 @@ pub mod grand_product; pub mod grand_product_quarks; +pub mod sparse_grand_product; pub mod sumcheck; diff --git a/jolt-core/src/subprotocols/sparse_grand_product.rs b/jolt-core/src/subprotocols/sparse_grand_product.rs new file mode 100644 index 000000000..9122e9e43 --- /dev/null +++ b/jolt-core/src/subprotocols/sparse_grand_product.rs @@ -0,0 +1,1328 @@ +use super::grand_product::{ + BatchedGrandProduct, BatchedGrandProductLayer, BatchedGrandProductLayerProof, +}; +use super::sumcheck::{BatchedCubicSumcheck, Bindable}; +use crate::field::{JoltField, OptimizedMul}; +use crate::poly::commitment::commitment_scheme::CommitmentScheme; +#[cfg(test)] +use crate::poly::dense_mlpoly::DensePolynomial; +use crate::poly::sparse_interleaved_poly::SparseInterleavedPolynomial; +use crate::poly::split_eq_poly::SplitEqPolynomial; +use crate::poly::unipoly::UniPoly; +use crate::utils::math::Math; +use crate::utils::thread::drop_in_background_thread; +use crate::utils::transcript::Transcript; +use rayon::prelude::*; + +/// A special bottom layer of a grand product, where boolean flags are used to +/// toggle the other inputs (fingerprints) going into the rest of the tree. +/// Note that the gates for this layer are *not* simple multiplication gates. +/// ```ignore +/// +/// … … +/// / \ / \ the rest of the tree, which is now sparse (lots of 1s) +/// o o o o ↑ +/// / \ / \ / \ / \ ––––––––––––––––––––––––––––––––––––––––––– +/// 🏴 o 🏳️ o 🏳️ o 🏴 o toggle layer ↓ +#[derive(Debug)] +struct BatchedGrandProductToggleLayer { + /// The list of non-zero flag indices for each circuit in the batch. + flag_indices: Vec>, + /// The list of non-zero flag values for each circuit in the batch. + /// Before the first binding iteration of sumcheck, this will be empty + /// (we know that all non-zero, unbound flag values are 1). + flag_values: Vec>, + /// The Reed-Solomon fingerprints for each circuit in the batch. + fingerprints: Vec>, + /// Once the sparse flag/fingerprint vectors cannnot be bound further + /// (i.e. binding would require processing values in different vectors), + /// we switch to using `coalesced_flags` to represent the flag values. + coalesced_flags: Option>, + /// Once the sparse flag/fingerprint vectors cannnot be bound further + /// (i.e. binding would require processing values in different vectors), + /// we switch to using `coalesced_fingerprints` to represent the fingerprint values. + coalesced_fingerprints: Option>, + /// The length of a layer in one of the circuits in the batch. + layer_len: usize, + + batched_layer_len: usize, +} + +impl BatchedGrandProductToggleLayer { + #[cfg(test)] + fn to_dense(&self) -> (DensePolynomial, DensePolynomial) { + if let Some(coalesced_flags) = &self.coalesced_flags { + let coalesced_fingerprints = self.coalesced_fingerprints.as_ref().unwrap(); + ( + DensePolynomial::new(coalesced_flags.clone()), + DensePolynomial::new(coalesced_fingerprints.clone()), + ) + } else if self.flag_values.is_empty() { + let fingerprints: Vec<_> = self.fingerprints.concat(); + let mut flags = vec![F::zero(); fingerprints.len()]; + for (batch_index, flag_indices) in self.flag_indices.iter().enumerate() { + for flag_index in flag_indices { + flags[batch_index * self.layer_len + flag_index] = F::one(); + flags[batch_index * self.layer_len + self.layer_len / 2 + flag_index] = + F::one(); + } + } + // Fingerprints are padded with 0s, flags are padded with 1s + flags.resize(flags.len().next_power_of_two(), F::one()); + + ( + DensePolynomial::new(flags), + DensePolynomial::new_padded(fingerprints), + ) + } else { + let fingerprints: Vec<_> = self + .fingerprints + .iter() + .flat_map(|f| f[..self.layer_len / 2].iter()) + .cloned() + .collect(); + let mut flags = vec![F::zero(); fingerprints.len()]; + for (batch_index, (flag_indices, flag_values)) in self + .flag_indices + .iter() + .zip(self.flag_values.iter()) + .enumerate() + { + for (flag_index, flag_value) in flag_indices.iter().zip(flag_values) { + flags[batch_index * self.layer_len + flag_index] = *flag_value; + flags[batch_index * self.layer_len + self.layer_len / 2 + flag_index] = + *flag_value; + } + } + // Fingerprints are padded with 0s, flags are padded with 1s + flags.resize(flags.len().next_power_of_two(), F::one()); + + ( + DensePolynomial::new(flags), + DensePolynomial::new_padded(fingerprints), + ) + } + } +} + +impl BatchedGrandProductToggleLayer { + fn new(flag_indices: Vec>, fingerprints: Vec>) -> Self { + let layer_len = 2 * fingerprints[0].len(); + let batched_layer_len = fingerprints.len() * layer_len; + Self { + flag_indices, + // While flags remain unbound, all values are boolean, so we can assume any flag that appears in `flag_indices` has value 1. + flag_values: vec![], + fingerprints, + layer_len, + batched_layer_len, + coalesced_flags: None, + coalesced_fingerprints: None, + } + } + + /// Computes the grand product layer output by this one. + /// Since this is a toggle layer, most of the output values are 1s, so + /// the return type is a SparseInterleavedPolyomial + /// o o o o <- output layer + /// / \ / \ / \ / \ + /// 🏴 o 🏳️ o 🏳️ o 🏴 o <- toggle layer + #[tracing::instrument(skip_all, name = "BatchedGrandProductToggleLayer::layer_output")] + fn layer_output(&self) -> SparseInterleavedPolynomial { + let values: Vec<_> = self + .fingerprints + .par_iter() + .enumerate() + .map(|(batch_index, fingerprints)| { + let flag_indices = &self.flag_indices[batch_index / 2]; + let mut sparse_coeffs = vec![]; + for i in flag_indices { + sparse_coeffs + .push((batch_index * self.layer_len / 2 + i, fingerprints[*i]).into()); + } + sparse_coeffs + }) + .collect(); + + SparseInterleavedPolynomial::new(values, self.batched_layer_len / 2) + } + + /// Coalesces flags and fingerprints into one (dense) vector each. + /// After a certain number of bindings, we can no longer process the k + /// circuits in the batch in independently, at which point we coalesce. + #[tracing::instrument(skip_all, name = "BatchedGrandProductToggleLayer::coalesce")] + fn coalesce(&mut self) { + let mut coalesced_fingerprints: Vec = + self.fingerprints.iter().map(|f| f[0]).collect::>(); + coalesced_fingerprints.resize(coalesced_fingerprints.len().next_power_of_two(), F::zero()); + + let mut coalesced_flags: Vec<_> = self + .flag_indices + .iter() + .zip(self.flag_values.iter()) + .flat_map(|(indices, values)| { + debug_assert!(indices.len() <= 1); + let mut coalesced = [F::zero(), F::zero()]; + for (index, value) in indices.iter().zip(values.iter()) { + assert_eq!(*index, 0); + coalesced[0] = *value; + coalesced[1] = *value; + } + coalesced + }) + .collect(); + // Fingerprints are padded with 0s, flags are padded with 1s + coalesced_flags.resize(coalesced_flags.len().next_power_of_two(), F::one()); + + self.coalesced_fingerprints = Some(coalesced_fingerprints); + self.coalesced_flags = Some(coalesced_flags); + } +} + +impl Bindable for BatchedGrandProductToggleLayer { + /// Incrementally binds a variable of the flag and fingerprint polynomials. + /// Similar to `SparseInterleavedPolynomial::bind`, in that flags use + /// a sparse representation, but different in a couple of key ways: + /// - flags use two separate vectors (for indices and values) rather than + /// a single vector of (index, value) pairs + /// - The left and right nodes in this layer are flags and fingerprints, respectively. + /// They are represented by *separate* vectors, so they are *not* interleaved. This + /// means we process 2 flag values at a time, rather than 4. + /// - In `BatchedSparseGrandProductLayer`, the absence of a node implies that it has + /// value 1. For our sparse representation of flags, the absence of a node implies + /// that it has value 0. In other words, a flag with value 1 will be present in both + /// `self.flag_indices` and `self.flag_values`. + #[tracing::instrument(skip_all, name = "BatchedGrandProductToggleLayer::bind")] + fn bind(&mut self, r: F) { + #[cfg(test)] + let (mut flags_before_binding, mut fingerprints_before_binding) = self.to_dense(); + + if let Some(coalesced_flags) = &mut self.coalesced_flags { + // Polynomials have already been coalesced, so bind the coalesced vectors. + let mut bound_flags = vec![F::one(); coalesced_flags.len() / 2]; + for i in 0..bound_flags.len() { + bound_flags[i] = coalesced_flags[2 * i] + + r * (coalesced_flags[2 * i + 1] - coalesced_flags[2 * i]); + } + self.coalesced_flags = Some(bound_flags); + + let coalesced_fingerpints = self.coalesced_fingerprints.as_mut().unwrap(); + let mut bound_fingerprints = vec![F::zero(); coalesced_fingerpints.len() / 2]; + for i in 0..bound_fingerprints.len() { + bound_fingerprints[i] = coalesced_fingerpints[2 * i] + + r * (coalesced_fingerpints[2 * i + 1] - coalesced_fingerpints[2 * i]); + } + self.coalesced_fingerprints = Some(bound_fingerprints); + self.batched_layer_len /= 2; + + #[cfg(test)] + { + let (bound_flags, bound_fingerprints) = self.to_dense(); + flags_before_binding.bound_poly_var_bot(&r); + fingerprints_before_binding.bound_poly_var_bot(&r); + assert_eq!( + bound_flags.Z[..bound_flags.len()], + flags_before_binding.Z[..flags_before_binding.len()] + ); + assert_eq!( + bound_fingerprints.Z[..bound_fingerprints.len()], + fingerprints_before_binding.Z[..fingerprints_before_binding.len()] + ); + } + + return; + } + + debug_assert!(self.layer_len % 4 == 0); + + // Bind the fingerprints + self.fingerprints + .par_iter_mut() + .for_each(|layer: &mut Vec| { + let n = self.layer_len / 4; + for i in 0..n { + layer[i] = layer[2 * i] + r.mul_0_optimized(layer[2 * i + 1] - layer[2 * i]); + } + }); + + let is_first_bind = self.flag_values.is_empty(); + if is_first_bind { + self.flag_values = vec![vec![]; self.flag_indices.len()]; + } + + // Bind the flags + self.flag_indices + .par_iter_mut() + .zip(self.flag_values.par_iter_mut()) + .for_each(|(flag_indices, flag_values)| { + let mut next_index_to_process = 0usize; + + let mut bound_index = 0usize; + for j in 0..flag_indices.len() { + let index = flag_indices[j]; + if index < next_index_to_process { + // This flag was already bound with its sibling in the previous iteration. + continue; + } + + // Bind indices in place + flag_indices[bound_index] = index / 2; + + if index % 2 == 0 { + let neighbor = flag_indices.get(j + 1).cloned().unwrap_or(0); + if neighbor == index + 1 { + // Neighbor is flag's sibling + + if is_first_bind { + // For first bind, all non-zero flag values are 1. + // bound_flags[i] = flags[2 * i] + r * (flags[2 * i + 1] - flags[2 * i]) + // = 1 - r * (1 - 1) + // = 1 + flag_values.push(F::one()); + } else { + // bound_flags[i] = flags[2 * i] + r * (flags[2 * i + 1] - flags[2 * i]) + flag_values[bound_index] = + flag_values[j] + r * (flag_values[j + 1] - flag_values[j]); + }; + } else { + // This flag's sibling wasn't found, so it must have value 0. + + if is_first_bind { + // For first bind, all non-zero flag values are 1. + // bound_flags[i] = flags[2 * i] + r * (flags[2 * i + 1] - flags[2 * i]) + // = flags[2 * i] - r * flags[2 * i] + // = 1 - r + flag_values.push(F::one() - r); + } else { + // bound_flags[i] = flags[2 * i] + r * (flags[2 * i + 1] - flags[2 * i]) + // = flags[2 * i] - r * flags[2 * i] + flag_values[bound_index] = flag_values[j] - r * flag_values[j]; + }; + } + next_index_to_process = index + 2; + } else { + // This flag's sibling wasn't encountered in a previous iteration, + // so it must have had value 0. + + if is_first_bind { + // For first bind, all non-zero flag values are 1. + // bound_flags[i] = flags[2 * i] + r * (flags[2 * i + 1] - flags[2 * i]) + // = r * flags[2 * i + 1] + // = r + flag_values.push(r); + } else { + // bound_flags[i] = flags[2 * i] + r * (flags[2 * i + 1] - flags[2 * i]) + // = r * flags[2 * i + 1] + flag_values[bound_index] = r * flag_values[j]; + }; + next_index_to_process = index + 1; + } + + bound_index += 1; + } + + flag_indices.truncate(bound_index); + // We only ever use `flag_indices.len()`, so no need to truncate `flag_values` + // flag_values.truncate(bound_index); + }); + self.layer_len /= 2; + self.batched_layer_len /= 2; + + #[cfg(test)] + { + let (bound_flags, bound_fingerprints) = self.to_dense(); + flags_before_binding.bound_poly_var_bot(&r); + fingerprints_before_binding.bound_poly_var_bot(&r); + assert_eq!( + bound_flags.Z[..bound_flags.len()], + flags_before_binding.Z[..flags_before_binding.len()] + ); + assert_eq!( + bound_fingerprints.Z[..bound_fingerprints.len()], + fingerprints_before_binding.Z[..fingerprints_before_binding.len()] + ); + } + + if self.layer_len == 2 { + // Time to coalesce + assert!(self.coalesced_fingerprints.is_none()); + assert!(self.coalesced_flags.is_none()); + self.coalesce(); + + #[cfg(test)] + { + let (bound_flags, bound_fingerprints) = self.to_dense(); + assert_eq!( + bound_flags.Z[..bound_flags.len()], + flags_before_binding.Z[..flags_before_binding.len()] + ); + assert_eq!( + bound_fingerprints.Z[..bound_fingerprints.len()], + fingerprints_before_binding.Z[..fingerprints_before_binding.len()] + ); + } + } + } +} + +impl BatchedCubicSumcheck + for BatchedGrandProductToggleLayer +{ + #[cfg(test)] + fn sumcheck_sanity_check(&self, eq_poly: &SplitEqPolynomial, round_claim: F) { + let (flags, fingerprints) = self.to_dense(); + let merged_eq = eq_poly.merge(); + let expected: F = flags + .evals_ref() + .iter() + .zip(fingerprints.evals_ref().iter()) + .zip(merged_eq.evals_ref().iter()) + .map(|((flag, fingerprint), eq)| *eq * (*flag * fingerprint + F::one() - flag)) + .sum(); + assert_eq!(expected, round_claim); + } + + /// Similar to `SparseInterleavedPolynomial::compute_cubic`, but with changes to + /// accomodate the differences between `SparseInterleavedPolynomial` and + /// `BatchedGrandProductToggleLayer`. These differences are described in the doc comments + /// for `BatchedGrandProductToggleLayer::bind`. + /// + /// Since we are using the Dao-Thaler EQ optimization, there are four cases to handle: + /// 1. Flags/fingerprints are coalesced, and E1 is fully bound + /// 2. Flags/fingerprints are coalesced, and E1 isn't fully bound + /// 3. Flags/fingerprints aren't coalesced, and E1 is fully bound + /// 4. Flags/fingerprints aren't coalesced, and E1 isn't fully bound + #[tracing::instrument(skip_all, name = "BatchedGrandProductToggleLayer::compute_cubic")] + fn compute_cubic(&self, eq_poly: &SplitEqPolynomial, previous_round_claim: F) -> UniPoly { + if let Some(coalesced_flags) = &self.coalesced_flags { + let coalesced_fingerpints = self.coalesced_fingerprints.as_ref().unwrap(); + + let cubic_evals = if eq_poly.E1_len == 1 { + // 1. Flags/fingerprints are coalesced, and E1 is fully bound + // This is similar to the if case of `DenseInterleavedPolynomial::compute_cubic` + coalesced_flags + .par_chunks(2) + .zip(coalesced_fingerpints.par_chunks(2)) + .zip(eq_poly.E2.par_chunks(2)) + .map(|((flags, fingerprints), eq_chunk)| { + let eq_evals = { + let eval_point_0 = eq_chunk[0]; + let m_eq = eq_chunk[1] - eq_chunk[0]; + let eval_point_2 = eq_chunk[1] + m_eq; + let eval_point_3 = eval_point_2 + m_eq; + (eval_point_0, eval_point_2, eval_point_3) + }; + let m_flag = flags[1] - flags[0]; + let m_fingerprint = fingerprints[1] - fingerprints[0]; + + let flag_eval_2 = flags[1] + m_flag; + let flag_eval_3 = flag_eval_2 + m_flag; + + let fingerprint_eval_2 = fingerprints[1] + m_fingerprint; + let fingerprint_eval_3 = fingerprint_eval_2 + m_fingerprint; + + ( + eq_evals.0 * (flags[0] * fingerprints[0] + F::one() - flags[0]), + eq_evals.1 + * (flag_eval_2 * fingerprint_eval_2 + F::one() - flag_eval_2), + eq_evals.2 + * (flag_eval_3 * fingerprint_eval_3 + F::one() - flag_eval_3), + ) + }) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ) + } else { + // 2. Flags/fingerprints are coalesced, and E1 isn't fully bound + // This is similar to the else case of `DenseInterleavedPolynomial::compute_cubic` + let E1_evals: Vec<_> = eq_poly.E1[..eq_poly.E1_len] + .par_chunks(2) + .map(|E1_chunk| { + let eval_point_0 = E1_chunk[0]; + let m_eq = E1_chunk[1] - E1_chunk[0]; + let eval_point_2 = E1_chunk[1] + m_eq; + let eval_point_3 = eval_point_2 + m_eq; + (eval_point_0, eval_point_2, eval_point_3) + }) + .collect(); + + let flag_chunk_size = coalesced_flags.len().next_power_of_two() / eq_poly.E2_len; + let fingerprint_chunk_size = + coalesced_fingerpints.len().next_power_of_two() / eq_poly.E2_len; + + eq_poly.E2[..eq_poly.E2_len] + .par_iter() + .zip(coalesced_flags.par_chunks(flag_chunk_size)) + .zip(coalesced_fingerpints.par_chunks(fingerprint_chunk_size)) + .map(|((E2_eval, flag_x2), fingerprint_x2)| { + let mut inner_sum = (F::zero(), F::zero(), F::zero()); + for ((E1_evals, flag_chunk), fingerprint_chunk) in E1_evals + .iter() + .zip(flag_x2.chunks(2)) + .zip(fingerprint_x2.chunks(2)) + { + let m_flag = flag_chunk[1] - flag_chunk[0]; + let m_fingerprint = fingerprint_chunk[1] - fingerprint_chunk[0]; + + let flag_eval_2 = flag_chunk[1] + m_flag; + let flag_eval_3 = flag_eval_2 + m_flag; + + let fingerprint_eval_2 = fingerprint_chunk[1] + m_fingerprint; + let fingerprint_eval_3 = fingerprint_eval_2 + m_fingerprint; + + inner_sum.0 += E1_evals.0 + * (flag_chunk[0] * fingerprint_chunk[0] + F::one() - flag_chunk[0]); + inner_sum.1 += E1_evals.1 + * (flag_eval_2 * fingerprint_eval_2 + F::one() - flag_eval_2); + inner_sum.2 += E1_evals.2 + * (flag_eval_3 * fingerprint_eval_3 + F::one() - flag_eval_3); + } + + ( + *E2_eval * inner_sum.0, + *E2_eval * inner_sum.1, + *E2_eval * inner_sum.2, + ) + }) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ) + }; + + let cubic_evals = [ + cubic_evals.0, + previous_round_claim - cubic_evals.0, + cubic_evals.1, + cubic_evals.2, + ]; + return UniPoly::from_evals(&cubic_evals); + } + + let cubic_evals = if eq_poly.E1_len == 1 { + // 3. Flags/fingerprints aren't coalesced, and E1 is fully bound + // This is similar to the if case of `SparseInterleavedPolynomial::compute_cubic` + let eq_evals: Vec<(F, F, F)> = eq_poly.E2[..eq_poly.E2_len] + .par_chunks(2) + .take(self.batched_layer_len / 4) + .map(|eq_chunk| { + let eval_point_0 = eq_chunk[0]; + let m_eq = eq_chunk[1] - eq_chunk[0]; + let eval_point_2 = eq_chunk[1] + m_eq; + let eval_point_3 = eval_point_2 + m_eq; + (eval_point_0, eval_point_2, eval_point_3) + }) + .collect(); + let eq_eval_sums: (F, F, F) = eq_evals + .par_iter() + .fold( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ); + + let deltas: (F, F, F) = (0..self.fingerprints.len()) + .into_par_iter() + .map(|batch_index| { + // Computes: + // ∆ := Σ eq_evals[j] * (flag[j] * fingerprint[j] - flag[j]) ∀j where flag[j] ≠ 0 + // for the evaluation points {0, 2, 3} + + let fingerprints = &self.fingerprints[batch_index]; + let flag_indices = &self.flag_indices[batch_index / 2]; + + let unbound = self.flag_values.is_empty(); + let mut delta = (F::zero(), F::zero(), F::zero()); + + let mut next_index_to_process = 0usize; + for (j, index) in flag_indices.iter().enumerate() { + if *index < next_index_to_process { + // This node was already processed in a previous iteration + continue; + } + + let (flags, fingerprints) = if index % 2 == 0 { + let neighbor = flag_indices.get(j + 1).cloned().unwrap_or(0); + let flags = if neighbor == index + 1 { + // Neighbor is flag's sibling + if unbound { + (F::one(), F::one()) + } else { + ( + self.flag_values[batch_index / 2][j], + self.flag_values[batch_index / 2][j + 1], + ) + } + } else { + // This flag's sibling wasn't found, so it must have value 0. + if unbound { + (F::one(), F::zero()) + } else { + (self.flag_values[batch_index / 2][j], F::zero()) + } + }; + let fingerprints = (fingerprints[*index], fingerprints[index + 1]); + + next_index_to_process = index + 2; + (flags, fingerprints) + } else { + // This flag's sibling wasn't encountered in a previous iteration, + // so it must have had value 0. + let flags = if unbound { + (F::zero(), F::one()) + } else { + (F::zero(), self.flag_values[batch_index / 2][j]) + }; + let fingerprints = (fingerprints[index - 1], fingerprints[*index]); + + next_index_to_process = index + 1; + (flags, fingerprints) + }; + + let m_flag = flags.1 - flags.0; + let m_fingerprint = fingerprints.1 - fingerprints.0; + + // If flags are still unbound, flag evals will mostly be 0s and 1s + // Bound flags are still mostly 0s, so flag evals will mostly be 0s. + let flag_eval_2 = flags.1 + m_flag; + let flag_eval_3 = flag_eval_2 + m_flag; + + let fingerprint_eval_2 = fingerprints.1 + m_fingerprint; + let fingerprint_eval_3 = fingerprint_eval_2 + m_fingerprint; + + let block_index = (self.layer_len * batch_index) / 4 + index / 2; + let eq_evals = eq_evals[block_index]; + + delta.0 += eq_evals + .0 + .mul_0_optimized(flags.0.mul_01_optimized(fingerprints.0) - flags.0); + delta.1 += eq_evals.1.mul_0_optimized( + flag_eval_2.mul_01_optimized(fingerprint_eval_2) - flag_eval_2, + ); + delta.2 += eq_evals.2.mul_0_optimized( + flag_eval_3.mul_01_optimized(fingerprint_eval_3) - flag_eval_3, + ); + } + + (delta.0, delta.1, delta.2) + }) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ); + // eq_eval_sum + ∆ = Σ eq_evals[i] + Σ eq_evals[i] * (flag[i] * fingerprint[i] - flag[i])) + // = Σ eq_evals[j] * (flag[i] * fingerprint[i] + 1 - flag[i]) + ( + eq_eval_sums.0 + deltas.0, + eq_eval_sums.1 + deltas.1, + eq_eval_sums.2 + deltas.2, + ) + } else { + // 4. Flags/fingerprints aren't coalesced, and E1 isn't fully bound + // This is similar to the else case of `SparseInterleavedPolynomial::compute_cubic` + let E1_evals: Vec<_> = eq_poly.E1[..eq_poly.E1_len] + .par_chunks(2) + .map(|E1_chunk| { + let eval_point_0 = E1_chunk[0]; + let m_eq = E1_chunk[1] - E1_chunk[0]; + let eval_point_2 = E1_chunk[1] + m_eq; + let eval_point_3 = eval_point_2 + m_eq; + (eval_point_0, eval_point_2, eval_point_3) + }) + .collect(); + let E1_eval_sums: (F, F, F) = E1_evals + .par_iter() + .fold( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ); + + let num_x1_bits = eq_poly.E1_len.log_2() - 1; + let x1_bitmask = (1 << num_x1_bits) - 1; + + let deltas = (0..self.fingerprints.len()) + .into_par_iter() + .map(|batch_index| { + // Computes: + // ∆ := Σ eq_evals[j] * (flag[j] * fingerprint[j] - flag[j]) ∀j where flag[j] ≠ 0 + // for the evaluation points {0, 2, 3} + + let fingerprints = &self.fingerprints[batch_index]; + let flag_indices = &self.flag_indices[batch_index / 2]; + + let unbound = self.flag_values.is_empty(); + let mut delta = (F::zero(), F::zero(), F::zero()); + let mut inner_sum = (F::zero(), F::zero(), F::zero()); + let mut prev_x2 = 0; + + let mut next_index_to_process = 0usize; + for (j, index) in flag_indices.iter().enumerate() { + if *index < next_index_to_process { + // This node was already processed in a previous iteration + continue; + } + + let (flags, fingerprints) = if index % 2 == 0 { + let neighbor = flag_indices.get(j + 1).cloned().unwrap_or(0); + let flags = if neighbor == index + 1 { + // Neighbor is flag's sibling + if unbound { + (F::one(), F::one()) + } else { + ( + self.flag_values[batch_index / 2][j], + self.flag_values[batch_index / 2][j + 1], + ) + } + } else { + // This flag's sibling wasn't found, so it must have value 0. + if unbound { + (F::one(), F::zero()) + } else { + (self.flag_values[batch_index / 2][j], F::zero()) + } + }; + let fingerprints = (fingerprints[*index], fingerprints[index + 1]); + + next_index_to_process = index + 2; + (flags, fingerprints) + } else { + // This flag's sibling wasn't encountered in a previous iteration, + // so it must have had value 0. + let flags = if unbound { + (F::zero(), F::one()) + } else { + (F::zero(), self.flag_values[batch_index / 2][j]) + }; + let fingerprints = (fingerprints[index - 1], fingerprints[*index]); + + next_index_to_process = index + 1; + (flags, fingerprints) + }; + + let m_flag = flags.1 - flags.0; + let m_fingerprint = fingerprints.1 - fingerprints.0; + + // If flags are still unbound, flag evals will mostly be 0s and 1s + // Bound flags are still mostly 0s, so flag evals will mostly be 0s. + let flag_eval_2 = flags.1 + m_flag; + let flag_eval_3 = flag_eval_2 + m_flag; + + let fingerprint_eval_2 = fingerprints.1 + m_fingerprint; + let fingerprint_eval_3 = fingerprint_eval_2 + m_fingerprint; + + let block_index = (self.layer_len * batch_index) / 4 + index / 2; + let x2 = block_index >> num_x1_bits; + if x2 != prev_x2 { + delta.0 += eq_poly.E2[prev_x2] * inner_sum.0; + delta.1 += eq_poly.E2[prev_x2] * inner_sum.1; + delta.2 += eq_poly.E2[prev_x2] * inner_sum.2; + inner_sum = (F::zero(), F::zero(), F::zero()); + prev_x2 = x2; + } + + let x1 = block_index & x1_bitmask; + inner_sum.0 += E1_evals[x1] + .0 + .mul_0_optimized(flags.0.mul_01_optimized(fingerprints.0) - flags.0); + inner_sum.1 += E1_evals[x1].1.mul_0_optimized( + flag_eval_2.mul_01_optimized(fingerprint_eval_2) - flag_eval_2, + ); + inner_sum.2 += E1_evals[x1].2.mul_0_optimized( + flag_eval_3.mul_01_optimized(fingerprint_eval_3) - flag_eval_3, + ); + } + + delta.0 += eq_poly.E2[prev_x2] * inner_sum.0; + delta.1 += eq_poly.E2[prev_x2] * inner_sum.1; + delta.2 += eq_poly.E2[prev_x2] * inner_sum.2; + + delta + }) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ); + + // The cubic evals assuming all the coefficients are ones is affected by the + // `batched_layer_len`, since we implicitly pad the `batched_layer_len` to a power of 2. + // By pad here we mean that flags are padded with 1s, and fingerprints are + // padded with 0s. + // + // As a refresher, the cubic evals we're computing are: + // + // \sum_x2 E2[x2] * (\sum_x1 ((1 - j) * E1[0, x1] + j * E1[1, x1]) * \prod_k ((1 - j) * P_k(0 || x1 || x2) + j * P_k(1 || x1 || x2))) + let evals_assuming_all_ones = if self.batched_layer_len.is_power_of_two() { + // If `batched_layer_len` is a power of 2, there is no 0-padding. + // + // So we have: + // \sum_x2 (E2[x2] * (\sum_x1 ((1 - j) * E1[0, x1] + j * E1[1, x1]) * 1)) + // = \sum_x2 (E2[x2] * \sum_x1 E1_evals[x1]) + // = (\sum_x2 E2[x2]) * (\sum_x1 E1_evals[x1]) + // = 1 * E1_eval_sums + E1_eval_sums + } else { + let chunk_size = self.batched_layer_len.next_power_of_two() / eq_poly.E2_len; + let num_all_one_chunks = self.batched_layer_len / chunk_size; + let E2_sum: F = eq_poly.E2[..num_all_one_chunks].iter().sum(); + if self.batched_layer_len % chunk_size == 0 { + // If `batched_layer_len` isn't a power of 2 but evenly divides `chunk_size`, + // that means that for the last values of x2, we have: + // (1 - j) * P_k(0 || x1 || x2) + j * P_k(1 || x1 || x2)) = 0 + // due to the 0-padding. + // + // This makes the entire inner sum 0 for those values of x2. + // So we can simply sum over E2 for the _other_ values of x2, and + // multiply by `E1_eval_sums`. + ( + E2_sum * E1_eval_sums.0, + E2_sum * E1_eval_sums.1, + E2_sum * E1_eval_sums.2, + ) + } else { + // If `batched_layer_len` isn't a power of 2 and doesn't divide `chunk_size`, + // the last nonzero "chunk" will have (self.dense_len % chunk_size) ones, + // followed by (chunk_size - self.dense_len % chunk_size) zeros, + // e.g. 1 1 1 1 1 1 1 1 0 0 0 0 + // + // This handles this last chunk: + let last_chunk_evals = E1_evals[..(self.batched_layer_len % chunk_size) / 4] + .par_iter() + .fold( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ); + ( + E2_sum * E1_eval_sums.0 + + eq_poly.E2[num_all_one_chunks] * last_chunk_evals.0, + E2_sum * E1_eval_sums.1 + + eq_poly.E2[num_all_one_chunks] * last_chunk_evals.1, + E2_sum * E1_eval_sums.2 + + eq_poly.E2[num_all_one_chunks] * last_chunk_evals.2, + ) + } + }; + + ( + evals_assuming_all_ones.0 + deltas.0, + evals_assuming_all_ones.1 + deltas.1, + evals_assuming_all_ones.2 + deltas.2, + ) + }; + + let cubic_evals = [ + cubic_evals.0, + previous_round_claim - cubic_evals.0, + cubic_evals.1, + cubic_evals.2, + ]; + + #[cfg(test)] + { + let (dense_flags, dense_fingerprints) = self.to_dense(); + let eq_merged = eq_poly.merge(); + let dense_cubic_evals = dense_flags + .evals() + .par_chunks(2) + .zip(dense_fingerprints.evals().par_chunks(2)) + .zip(eq_merged.evals().par_chunks(2)) + .map(|((flag_chunk, fingerprint_chunk), eq_chunk)| { + let eq_evals = { + let eval_point_0 = eq_chunk[0]; + let m_eq = eq_chunk[1] - eq_chunk[0]; + let eval_point_2 = eq_chunk[1] + m_eq; + let eval_point_3 = eval_point_2 + m_eq; + (eval_point_0, eval_point_2, eval_point_3) + }; + let flags = ( + *flag_chunk.get(0).unwrap_or(&F::one()), + *flag_chunk.get(1).unwrap_or(&F::one()), + ); + let fingerprints = ( + *fingerprint_chunk.get(0).unwrap_or(&F::zero()), + *fingerprint_chunk.get(1).unwrap_or(&F::zero()), + ); + + let m_flag = flags.1 - flags.0; + let m_fingerprint = fingerprints.1 - fingerprints.0; + + let flag_eval_2 = flags.1 + m_flag; + let flag_eval_3 = flag_eval_2 + m_flag; + + let fingerprint_eval_2 = fingerprints.1 + m_fingerprint; + let fingerprint_eval_3 = fingerprint_eval_2 + m_fingerprint; + + ( + eq_evals.0 * (flags.0 * fingerprints.0 + F::one() - flags.0), + eq_evals.1 * (flag_eval_2 * fingerprint_eval_2 + F::one() - flag_eval_2), + eq_evals.2 * (flag_eval_3 * fingerprint_eval_3 + F::one() - flag_eval_3), + ) + }) + .reduce( + || (F::zero(), F::zero(), F::zero()), + |sum, evals| (sum.0 + evals.0, sum.1 + evals.1, sum.2 + evals.2), + ); + let dense_cubic_evals = [ + dense_cubic_evals.0, + previous_round_claim - dense_cubic_evals.0, + dense_cubic_evals.1, + dense_cubic_evals.2, + ]; + assert_eq!(dense_cubic_evals, cubic_evals); + } + + UniPoly::from_evals(&cubic_evals) + } + + fn final_claims(&self) -> (F, F) { + assert_eq!(self.layer_len, 2); + let flags = self.coalesced_flags.as_ref().unwrap(); + let fingerprints = self.coalesced_fingerprints.as_ref().unwrap(); + + (flags[0], fingerprints[0]) + } +} + +impl BatchedGrandProductLayer + for BatchedGrandProductToggleLayer +{ + fn prove_layer( + &mut self, + claim: &mut F, + r_grand_product: &mut Vec, + transcript: &mut ProofTranscript, + ) -> BatchedGrandProductLayerProof { + let mut eq_poly = SplitEqPolynomial::new(r_grand_product); + + let (sumcheck_proof, r_sumcheck, sumcheck_claims) = + self.prove_sumcheck(claim, &mut eq_poly, transcript); + + drop_in_background_thread(eq_poly); + + let (left_claim, right_claim) = sumcheck_claims; + transcript.append_scalar(&left_claim); + transcript.append_scalar(&right_claim); + + r_sumcheck + .into_par_iter() + .rev() + .collect_into_vec(r_grand_product); + + BatchedGrandProductLayerProof { + proof: sumcheck_proof, + left_claim, + right_claim, + } + } +} + +pub struct ToggledBatchedGrandProduct { + toggle_layer: BatchedGrandProductToggleLayer, + sparse_layers: Vec>, +} + +impl BatchedGrandProduct + for ToggledBatchedGrandProduct +where + F: JoltField, + PCS: CommitmentScheme, + ProofTranscript: Transcript, +{ + type Leaves = (Vec>, Vec>); // (flags, fingerprints) + type Config = (); + + #[tracing::instrument(skip_all, name = "ToggledBatchedGrandProduct::construct")] + fn construct(leaves: Self::Leaves) -> Self { + let (flags, fingerprints) = leaves; + let num_layers = fingerprints[0].len().log_2(); + + let toggle_layer = BatchedGrandProductToggleLayer::new(flags, fingerprints); + let mut layers: Vec> = Vec::with_capacity(num_layers); + layers.push(toggle_layer.layer_output()); + + for i in 0..num_layers - 1 { + let previous_layer = &layers[i]; + layers.push(previous_layer.layer_output()); + } + + Self { + toggle_layer, + sparse_layers: layers, + } + } + + fn num_layers(&self) -> usize { + self.sparse_layers.len() + 1 + } + + fn claimed_outputs(&self) -> Vec { + let last_layer = self.sparse_layers.last().unwrap(); + let (left, right) = last_layer.uninterleave(); + left.iter().zip(right.iter()).map(|(l, r)| *l * r).collect() + } + + fn layers( + &'_ mut self, + ) -> impl Iterator> { + [&mut self.toggle_layer as &mut dyn BatchedGrandProductLayer] + .into_iter() + .chain( + self.sparse_layers + .iter_mut() + .map(|layer| layer as &mut dyn BatchedGrandProductLayer), + ) + .rev() + } + + fn verify_sumcheck_claim( + layer_proofs: &[BatchedGrandProductLayerProof], + layer_index: usize, + sumcheck_claim: F, + eq_eval: F, + grand_product_claim: &mut F, + r_grand_product: &mut Vec, + transcript: &mut ProofTranscript, + ) { + let layer_proof = &layer_proofs[layer_index]; + if layer_index != layer_proofs.len() - 1 { + // Normal grand product layer (multiplication gates) + let expected_sumcheck_claim: F = + layer_proof.left_claim * layer_proof.right_claim * eq_eval; + + assert_eq!(expected_sumcheck_claim, sumcheck_claim); + + // produce a random challenge to condense two claims into a single claim + let r_layer = transcript.challenge_scalar(); + + *grand_product_claim = layer_proof.left_claim + + r_layer * (layer_proof.right_claim - layer_proof.left_claim); + + r_grand_product.push(r_layer); + } else { + // Grand product toggle layer: layer_proof.left_claim is flag, + // layer_proof.right_claim is fingerprint + let expected_sumcheck_claim: F = eq_eval + * (layer_proof.left_claim * layer_proof.right_claim + F::one() + - layer_proof.left_claim); + + assert_eq!(expected_sumcheck_claim, sumcheck_claim); + + // flag * fingerprint + 1 - flag + *grand_product_claim = layer_proof.left_claim * layer_proof.right_claim + F::one() + - layer_proof.left_claim; + } + } + + fn construct_with_config(leaves: Self::Leaves, _config: Self::Config) -> Self { + >::construct(leaves) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::{ + poly::{ + commitment::zeromorph::Zeromorph, dense_interleaved_poly::DenseInterleavedPolynomial, + }, + utils::transcript::KeccakTranscript, + }; + use ark_bn254::{Bn254, Fr}; + use ark_std::{rand::Rng, test_rng, One}; + use itertools::Itertools; + + fn condense(sparse_layer: SparseInterleavedPolynomial) -> Vec { + sparse_layer.to_dense().Z + } + + #[test] + fn dense_sparse_bind_parity() { + let mut rng = test_rng(); + const NUM_VARS: [usize; 7] = [1, 2, 3, 4, 5, 6, 7]; + const DENSITY: [f64; 6] = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]; + const BATCH_SIZE: [usize; 5] = [2, 4, 6, 8, 10]; + + for ((num_vars, density), batch_size) in NUM_VARS + .into_iter() + .cartesian_product(DENSITY.into_iter()) + .cartesian_product(BATCH_SIZE.into_iter()) + { + let layer_size = 1 << num_vars; + let dense_layers: Vec> = std::iter::repeat_with(|| { + std::iter::repeat_with(|| { + if rng.gen_bool(density) { + Fr::random(&mut rng) + } else { + Fr::one() + } + }) + .take(layer_size) + .collect() + }) + .take(batch_size) + .collect(); + let mut dense_poly = DenseInterleavedPolynomial::new(dense_layers.concat()); + + let sparse_coeffs: Vec<_> = dense_layers + .iter() + .enumerate() + .map(|(i, dense_layer)| { + let mut sparse_layer = vec![]; + for (j, val) in dense_layer.iter().enumerate() { + if !val.is_one() { + sparse_layer.push((i * layer_size + j, *val).into()); + } + } + sparse_layer + }) + .collect(); + let mut sparse_poly = + SparseInterleavedPolynomial::new(sparse_coeffs, batch_size * layer_size); + + for (dense, sparse) in dense_poly.iter().zip(condense(sparse_poly.clone()).iter()) { + assert_eq!(dense, sparse); + } + + for _ in 0..(batch_size * layer_size).log_2() - 1 { + let r = Fr::random(&mut rng); + dense_poly.bind(r); + sparse_poly.bind(r); + + for (dense, sparse) in dense_poly.iter().zip(condense(sparse_poly.clone()).iter()) { + assert_eq!(dense, sparse); + } + } + } + } + + #[test] + fn dense_sparse_compute_cubic_parity() { + let mut rng = test_rng(); + const NUM_VARS: [usize; 7] = [1, 2, 3, 4, 5, 6, 7]; + const DENSITY: [f64; 6] = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]; + const BATCH_SIZE: [usize; 5] = [2, 4, 6, 8, 10]; + + for ((num_vars, density), batch_size) in NUM_VARS + .into_iter() + .cartesian_product(DENSITY.into_iter()) + .cartesian_product(BATCH_SIZE.into_iter()) + { + let layer_size = 1 << num_vars; + let dense_layers: Vec> = std::iter::repeat_with(|| { + let layer: Vec = std::iter::repeat_with(|| { + if rng.gen_bool(density) { + Fr::random(&mut rng) + } else { + Fr::one() + } + }) + .take(layer_size) + .collect::>(); + layer + }) + .take(batch_size) + .collect(); + let dense_poly = DenseInterleavedPolynomial::new(dense_layers.concat()); + + let sparse_coeffs: Vec<_> = dense_layers + .iter() + .enumerate() + .map(|(i, dense_layer)| { + let mut sparse_layer = vec![]; + for (j, val) in dense_layer.iter().enumerate() { + if !val.is_one() { + sparse_layer.push((i * layer_size + j, *val).into()); + } + } + sparse_layer + }) + .collect(); + let sparse_poly = + SparseInterleavedPolynomial::new(sparse_coeffs, batch_size * layer_size); + + for (dense, sparse) in dense_poly.iter().zip(condense(sparse_poly.clone()).iter()) { + assert_eq!(dense, sparse); + } + + let r_eq = std::iter::repeat_with(|| Fr::random(&mut rng)) + .take((batch_size * layer_size).next_power_of_two().log_2() - 1) + .collect::>(); + let eq_poly = SplitEqPolynomial::new(&r_eq); + let r = Fr::random(&mut rng); + + let dense_evals = BatchedCubicSumcheck::::compute_cubic( + &dense_poly, + &eq_poly, + r, + ); + let sparse_evals = BatchedCubicSumcheck::::compute_cubic( + &sparse_poly, + &eq_poly, + r, + ); + assert_eq!(dense_evals, sparse_evals); + } + } + + #[test] + fn sparse_prove_verify() { + let mut rng = test_rng(); + const NUM_VARS: [usize; 7] = [1, 2, 3, 4, 5, 6, 7]; + const DENSITY: [f64; 6] = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]; + const BATCH_SIZE: [usize; 5] = [2, 4, 6, 8, 10]; + + for ((num_vars, density), batch_size) in NUM_VARS + .into_iter() + .cartesian_product(DENSITY.into_iter()) + .cartesian_product(BATCH_SIZE.into_iter()) + { + let layer_size = 1 << num_vars; + let fingerprints: Vec> = std::iter::repeat_with(|| { + let layer: Vec = std::iter::repeat_with(|| Fr::random(&mut rng)) + .take(layer_size) + .collect::>(); + layer + }) + .take(batch_size) + .collect(); + + let flags: Vec> = std::iter::repeat_with(|| { + let mut layer = vec![]; + for i in 0..layer_size { + if rng.gen_bool(density) { + layer.push(i); + } + } + layer + }) + .take(batch_size / 2) + .collect(); + + let mut circuit = as BatchedGrandProduct< + Fr, + Zeromorph, + KeccakTranscript, + >>::construct((flags, fingerprints)); + + let claims = as BatchedGrandProduct< + Fr, + Zeromorph, + KeccakTranscript, + >>::claimed_outputs(&circuit); + + let mut prover_transcript: KeccakTranscript = KeccakTranscript::new(b"test_transcript"); + let (proof, r_prover) = as BatchedGrandProduct< + Fr, + Zeromorph, + KeccakTranscript, + >>::prove_grand_product( + &mut circuit, None, &mut prover_transcript, None + ); + + let mut verifier_transcript: KeccakTranscript = + KeccakTranscript::new(b"test_transcript"); + verifier_transcript.compare_to(prover_transcript); + let (_, r_verifier) = ToggledBatchedGrandProduct::verify_grand_product( + &proof, + &claims, + None, + &mut verifier_transcript, + None, + ); + assert_eq!(r_prover, r_verifier); + } + } + + #[test] + fn sparse_construct() { + let mut rng = test_rng(); + const NUM_VARS: [usize; 7] = [1, 2, 3, 4, 5, 6, 7]; + const DENSITY: [f64; 6] = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]; + const BATCH_SIZE: [usize; 5] = [2, 4, 6, 8, 10]; + + for ((num_vars, density), batch_size) in NUM_VARS + .into_iter() + .cartesian_product(DENSITY.into_iter()) + .cartesian_product(BATCH_SIZE.into_iter()) + { + let layer_size = 1 << num_vars; + let fingerprints: Vec> = std::iter::repeat_with(|| { + let layer: Vec = std::iter::repeat_with(|| Fr::random(&mut rng)) + .take(layer_size) + .collect::>(); + layer + }) + .take(batch_size) + .collect(); + + let flag_indices: Vec> = std::iter::repeat_with(|| { + let mut layer = vec![]; + for i in 0..layer_size { + if rng.gen_bool(density) { + layer.push(i); + } + } + layer + }) + .take(batch_size / 2) + .collect(); + + let mut expected_outputs: Vec = vec![]; + for (indices, fingerprints) in flag_indices.iter().zip(fingerprints.chunks(2)) { + let read_fingerprints = &fingerprints[0]; + let write_fingerprints = &fingerprints[1]; + + expected_outputs.push( + indices + .iter() + .map(|index| read_fingerprints[*index]) + .product(), + ); + expected_outputs.push( + indices + .iter() + .map(|index| write_fingerprints[*index]) + .product(), + ); + } + + let circuit = as BatchedGrandProduct< + Fr, + Zeromorph, + KeccakTranscript, + >>::construct((flag_indices, fingerprints)); + + for layers in &circuit.sparse_layers { + let dense = layers.to_dense(); + let chunk_size = layers.dense_len / batch_size; + for (chunk, expected_product) in + dense.Z.chunks(chunk_size).zip(expected_outputs.iter()) + { + let actual_product: Fr = chunk.iter().product(); + assert_eq!(*expected_product, actual_product); + } + } + + let claimed_outputs: Vec = + as BatchedGrandProduct< + Fr, + Zeromorph, + KeccakTranscript, + >>::claimed_outputs(&circuit); + + assert!(claimed_outputs == expected_outputs); + } + } +} diff --git a/jolt-core/src/subprotocols/sumcheck.rs b/jolt-core/src/subprotocols/sumcheck.rs index bb2371d8f..a62f1a403 100644 --- a/jolt-core/src/subprotocols/sumcheck.rs +++ b/jolt-core/src/subprotocols/sumcheck.rs @@ -3,6 +3,7 @@ use crate::field::JoltField; use crate::poly::dense_mlpoly::DensePolynomial; +use crate::poly::split_eq_poly::SplitEqPolynomial; use crate::poly::unipoly::{CompressedUniPoly, UniPoly}; use crate::r1cs::special_polys::{SparsePolynomial, SparseTripleIterator}; use crate::utils::errors::ProofVerifyError; @@ -13,56 +14,58 @@ use ark_serialize::*; use rayon::prelude::*; use std::marker::PhantomData; +pub trait Bindable: Sync { + fn bind(&mut self, r: F); +} + /// Batched cubic sumcheck used in grand products -pub trait BatchedCubicSumcheck: Sync +pub trait BatchedCubicSumcheck: Bindable where F: JoltField, ProofTranscript: Transcript, { - fn num_rounds(&self) -> usize; - fn bind(&mut self, eq_poly: &mut DensePolynomial, r: &F); - fn compute_cubic( - &self, - coeffs: &[F], - eq_poly: &DensePolynomial, - previous_round_claim: F, - ) -> UniPoly; - fn final_claims(&self) -> (Vec, Vec); + fn compute_cubic(&self, eq_poly: &SplitEqPolynomial, previous_round_claim: F) -> UniPoly; + fn final_claims(&self) -> (F, F); + + #[cfg(test)] + fn sumcheck_sanity_check(&self, eq_poly: &SplitEqPolynomial, round_claim: F); #[tracing::instrument(skip_all, name = "BatchedCubicSumcheck::prove_sumcheck")] fn prove_sumcheck( &mut self, claim: &F, - coeffs: &[F], - eq_poly: &mut DensePolynomial, + eq_poly: &mut SplitEqPolynomial, transcript: &mut ProofTranscript, - ) -> ( - SumcheckInstanceProof, - Vec, - (Vec, Vec), - ) { - debug_assert_eq!(eq_poly.get_num_vars(), self.num_rounds()); + ) -> (SumcheckInstanceProof, Vec, (F, F)) { + let num_rounds = eq_poly.get_num_vars(); let mut previous_claim = *claim; let mut r: Vec = Vec::new(); let mut cubic_polys: Vec> = Vec::new(); - for _round in 0..self.num_rounds() { - let cubic_poly = self.compute_cubic(coeffs, eq_poly, previous_claim); + for _ in 0..num_rounds { + #[cfg(test)] + self.sumcheck_sanity_check(eq_poly, previous_claim); + + let cubic_poly = self.compute_cubic(eq_poly, previous_claim); let compressed_poly = cubic_poly.compress(); // append the prover's message to the transcript compressed_poly.append_to_transcript(transcript); - //derive the verifier's challenge for the next round + // derive the verifier's challenge for the next round let r_j = transcript.challenge_scalar(); r.push(r_j); // bind polynomials to verifier's challenge - self.bind(eq_poly, &r_j); + self.bind(r_j); + eq_poly.bind(r_j); previous_claim = cubic_poly.evaluate(&r_j); cubic_polys.push(compressed_poly); } + #[cfg(test)] + self.sumcheck_sanity_check(eq_poly, previous_claim); + debug_assert_eq!(eq_poly.len(), 1); ( diff --git a/jolt-core/src/utils/sol_types.rs b/jolt-core/src/utils/sol_types.rs index f4cc8f292..eb05a7f77 100644 --- a/jolt-core/src/utils/sol_types.rs +++ b/jolt-core/src/utils/sol_types.rs @@ -51,8 +51,8 @@ sol!( sol!( struct GKRLayer { SumcheckProof sumcheck; - uint256[] leftClaims; - uint256[] rightClaims; + uint256 leftClaim; + uint256 rightClaim; } ); @@ -184,12 +184,10 @@ impl Into for BatchedGrandProductLayerProof { fn into(self) -> GKRLayer { - let left = self.left_claims.into_iter().map(into_uint256).collect(); - let right = self.right_claims.into_iter().map(into_uint256).collect(); GKRLayer { sumcheck: (&self.proof).into(), - leftClaims: left, - rightClaims: right, + leftClaim: into_uint256(self.left_claim), + rightClaim: into_uint256(self.left_claim), } } } @@ -198,7 +196,7 @@ impl Into for BatchedGrandProductProof, ProofTranscript> { fn into(self) -> GrandProductProof { - let layers: Vec = self.layers.into_iter().map(|i| i.into()).collect(); + let layers: Vec = self.gkr_layers.into_iter().map(|i| i.into()).collect(); assert!(self.quark_proof.is_none(), "Quarks are unsupported"); GrandProductProof { layers } } diff --git a/jolt-evm-verifier/script/Cargo.lock b/jolt-evm-verifier/script/Cargo.lock index 1296fe247..a0e5ce218 100644 --- a/jolt-evm-verifier/script/Cargo.lock +++ b/jolt-evm-verifier/script/Cargo.lock @@ -4,19 +4,13 @@ version = 3 [[package]] name = "addr2line" -version = "0.22.0" +version = "0.24.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6e4503c46a5c0c7844e948c9a4d6acd9f50cccb4de1c48eb9e291ea17470c678" +checksum = "dfbe277e56a376000877090da837660b4427aad530e3028d44e0bffe4f89a1c1" dependencies = [ "gimli", ] -[[package]] -name = "adler" -version = "1.0.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f26201604c87b1e01bd3d98f8d5d9a8fcbb815e8cedb41ffccbeb4bf593a35fe" - [[package]] name = "adler2" version = "2.0.0" @@ -68,9 +62,9 @@ dependencies = [ [[package]] name = "alloy-rlp" -version = "0.3.8" +version = "0.3.9" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "26154390b1d205a4a7ac7352aa2eb4f81f391399d4e2f546fb81a2f8bb383f62" +checksum = "da0822426598f95e45dd1ea32a738dac057529a709ee645fcc516ffa4cbde08f" dependencies = [ "arrayvec", "bytes", @@ -87,7 +81,7 @@ dependencies = [ "proc-macro-error", "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", ] [[package]] @@ -103,7 +97,7 @@ dependencies = [ "proc-macro-error", "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", "syn-solidity", "tiny-keccak", ] @@ -119,7 +113,7 @@ dependencies = [ "heck", "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", "syn-solidity", ] @@ -137,9 +131,9 @@ dependencies = [ [[package]] name = "anstream" -version = "0.6.15" +version = "0.6.18" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "64e15c1ab1f89faffbf04a634d5e1962e9074f2741eef6d97f3c4e322426d526" +checksum = "8acc5369981196006228e28809f761875c0327210a891e941f4c683b3a99529b" dependencies = [ "anstyle", "anstyle-parse", @@ -152,36 +146,36 @@ dependencies = [ [[package]] name = "anstyle" -version = "1.0.8" +version = "1.0.10" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1bec1de6f59aedf83baf9ff929c98f2ad654b97c9510f4e70cf6f661d49fd5b1" +checksum = "55cc3b69f167a1ef2e161439aa98aed94e6028e5f9a59be9a6ffb47aef1651f9" [[package]] name = "anstyle-parse" -version = "0.2.5" +version = "0.2.6" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "eb47de1e80c2b463c735db5b217a0ddc39d612e7ac9e2e96a5aed1f57616c1cb" +checksum = "3b2d16507662817a6a20a9ea92df6652ee4f94f914589377d69f3b21bc5798a9" dependencies = [ "utf8parse", ] [[package]] name = "anstyle-query" -version = "1.1.1" +version = "1.1.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6d36fc52c7f6c869915e99412912f22093507da8d9e942ceaf66fe4b7c14422a" +checksum = "79947af37f4177cfead1110013d678905c37501914fba0efea834c3fe9a8d60c" dependencies = [ - "windows-sys 0.52.0", + "windows-sys 0.59.0", ] [[package]] name = "anstyle-wincon" -version = "3.0.4" +version = "3.0.6" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5bf74e1b6e971609db8ca7a9ce79fd5768ab6ae46441c572e46cf596f59e57f8" +checksum = "2109dbce0e72be3ec00bed26e6a7479ca384ad226efdd66db8fa2e3a38c83125" dependencies = [ "anstyle", - "windows-sys 0.52.0", + "windows-sys 0.59.0", ] [[package]] @@ -246,7 +240,7 @@ dependencies = [ "num-traits", "paste", "rayon", - "rustc_version 0.4.0", + "rustc_version 0.4.1", "zeroize", ] @@ -377,28 +371,28 @@ checksum = "3c87f3f15e7794432337fc718554eaa4dc8f04c9677a950ffe366f20a162ae42" dependencies = [ "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", ] [[package]] name = "autocfg" -version = "1.3.0" +version = "1.4.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0c4b4d0bd25bd0b74681c0ad21497610ce1b7c91b1022cd21c80c6fbdd9476b0" +checksum = "ace50bade8e6234aa140d9a2f552bbee1db4d353f69b8217bc503490fc1a9f26" [[package]] name = "backtrace" -version = "0.3.73" +version = "0.3.74" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5cc23269a4f8976d0a4d2e7109211a419fe30e8d88d677cd60b6bc79c5732e0a" +checksum = "8d82cb332cdfaed17ae235a638438ac4d4839913cc2af585c3c6746e8f8bee1a" dependencies = [ "addr2line", - "cc", "cfg-if", "libc", - "miniz_oxide 0.7.4", - "object 0.36.3", + "miniz_oxide", + "object 0.36.5", "rustc-demangle", + "windows-targets 0.52.6", ] [[package]] @@ -431,10 +425,11 @@ dependencies = [ [[package]] name = "binius_field" version = "0.1.0" -source = "git+https://gitlab.com/UlvetannaOSS/binius#f53d0e7aeb6dc3d7315636df3aeca9e981dda10c" +source = "git+https://gitlab.com/UlvetannaOSS/binius#7001a65317ae91a64ac03984a3bf425d716e1883" dependencies = [ "binius_utils", "bytemuck", + "bytes", "cfg-if", "derive_more", "p3-util", @@ -450,15 +445,16 @@ dependencies = [ [[package]] name = "binius_utils" version = "0.1.0" -source = "git+https://gitlab.com/UlvetannaOSS/binius#f53d0e7aeb6dc3d7315636df3aeca9e981dda10c" +source = "git+https://gitlab.com/UlvetannaOSS/binius#7001a65317ae91a64ac03984a3bf425d716e1883" dependencies = [ "bytemuck", + "bytes", "cfg-if", "itertools 0.13.0", "rayon", + "thiserror", "thread_local", "tracing", - "tracing-profile", "tracing-subscriber", ] @@ -527,13 +523,13 @@ dependencies = [ [[package]] name = "bytemuck_derive" -version = "1.7.1" +version = "1.8.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0cc8b54b395f2fcfbb3d90c47b01c7f444d94d05bdeb775811dec868ac3bbc26" +checksum = "bcfcc3cd946cb52f0bbfdbbcfa2f4e24f75ebb6c0e1002f7c25904fada18b9ec" dependencies = [ "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", ] [[package]] @@ -544,15 +540,15 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b" [[package]] name = "bytes" -version = "1.7.1" +version = "1.8.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8318a53db07bb3f8dca91a600466bdb3f2eaadeedfdbcf02e1accbad9271ba50" +checksum = "9ac0150caa2ae65ca5bd83f25c7de183dea78d4d366469f148435e2acfbad0da" [[package]] name = "cc" -version = "1.1.14" +version = "1.1.34" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "50d2eb3cd3d1bf4529e31c215ee6f93ec5a3d536d9f578f93d9d33ee19562932" +checksum = "67b9470d453346108f93a59222a9a1a5724db32d0a4727b7ab7ace4b4d822dc9" dependencies = [ "shlex", ] @@ -563,17 +559,11 @@ version = "1.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd" -[[package]] -name = "cfg_aliases" -version = "0.2.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "613afe47fcd5fac7ccf1db93babcb082c5994d996f20b8b159f2ad1658eb5724" - [[package]] name = "clap" -version = "4.5.16" +version = "4.5.20" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ed6719fffa43d0d87e5fd8caeab59be1554fb028cd30edc88fc4369b17971019" +checksum = "b97f376d85a664d5837dbae44bf546e6477a679ff6610010f17276f686d867e8" dependencies = [ "clap_builder", "clap_derive", @@ -581,9 +571,9 @@ dependencies = [ [[package]] name = "clap_builder" -version = "4.5.15" +version = "4.5.20" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "216aec2b177652e3846684cbfe25c9964d18ec45234f0f5da5157b207ed1aab6" +checksum = "19bc80abd44e4bed93ca373a0704ccbd1b710dc5749406201bb018272808dc54" dependencies = [ "anstream", "anstyle", @@ -593,14 +583,14 @@ dependencies = [ [[package]] name = "clap_derive" -version = "4.5.13" +version = "4.5.18" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "501d359d5f3dcaf6ecdeee48833ae73ec6e42723a1e52419c79abf9507eec0a0" +checksum = "4ac6a0c7b1a9e9a5186361f67dfa1b88213572f427fb9ab038efb2bd8c582dab" dependencies = [ "heck", "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", ] [[package]] @@ -617,9 +607,9 @@ checksum = "67ba02a97a2bd10f4b59b25c7973101c79642302776489e030cd13cdab09ed15" [[package]] name = "colorchoice" -version = "1.0.2" +version = "1.0.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d3fd119d74b830634cea2a0f58bbd0d54540518a14397557951e79340abc28c0" +checksum = "5b63caa9aa9397e2d9480a9b13673856c78d8ac123288526c37d7839f2a86990" [[package]] name = "common" @@ -648,9 +638,9 @@ dependencies = [ [[package]] name = "const-hex" -version = "1.12.0" +version = "1.13.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "94fb8a24a26d37e1ffd45343323dc9fe6654ceea44c12f2fcb3d7ac29e610bc6" +checksum = "0121754e84117e65f9d90648ee6aa4882a6e63110307ab73967a4c5e7e69e586" dependencies = [ "cfg-if", "cpufeatures", @@ -689,9 +679,9 @@ checksum = "773648b94d0e5d620f64f280777445740e61fe701025087ec8b57f45c791888b" [[package]] name = "cpufeatures" -version = "0.2.13" +version = "0.2.14" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "51e852e6dc9a5bed1fae92dd2375037bf2b768725bf3be87811edee3249d09ad" +checksum = "608697df725056feaccfa42cffdaeeec3fccc4ffc38358ecd19b243e716a78e0" dependencies = [ "libc", ] @@ -788,8 +778,8 @@ dependencies = [ "convert_case", "proc-macro2", "quote", - "rustc_version 0.4.0", - "syn 2.0.75", + "rustc_version 0.4.1", + "syn 2.0.87", ] [[package]] @@ -834,6 +824,17 @@ dependencies = [ "windows-sys 0.48.0", ] +[[package]] +name = "displaydoc" +version = "0.2.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "97369cbbc041bc366949bc74d34658d6cda5621039731c6310521892a3a20ae0" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.87", +] + [[package]] name = "dunce" version = "1.0.5" @@ -899,9 +900,9 @@ checksum = "a357d28ed41a50f9c765dbfe56cbc04a64e53e5fc58ba79fbc34c10ef3df831f" [[package]] name = "encoding_rs" -version = "0.8.34" +version = "0.8.35" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b45de904aa0b010bce2ab45264d0631681847fa7b6f2eaa7dab7619943bc4f59" +checksum = "75030f3c4f45dafd7586dd6780965a8c7e8e285a5ecb86713e63a79c5b2766f3" dependencies = [ "cfg-if", ] @@ -915,7 +916,7 @@ dependencies = [ "once_cell", "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", ] [[package]] @@ -946,9 +947,9 @@ dependencies = [ [[package]] name = "fastrand" -version = "2.1.0" +version = "2.1.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9fc0510504f03c51ada170672ac806f1f105a88aa97a5281117e1ddc3368e51a" +checksum = "e8c02a5121d4ea3eb16a80748c74f5549a5665e4c21333c6098f283870fbdea6" [[package]] name = "fastrlp" @@ -991,12 +992,12 @@ checksum = "1d674e81391d1e1ab681a28d99df07927c6d4aa5b027d7da16ba32d1d21ecd99" [[package]] name = "flate2" -version = "1.0.32" +version = "1.0.34" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9c0596c1eac1f9e04ed902702e9878208b336edc9d6fddc8a48387349bab3666" +checksum = "a1b589b4dc103969ad3cf85c950899926ec64300a1a46d76c03a6072957036f0" dependencies = [ "crc32fast", - "miniz_oxide 0.8.0", + "miniz_oxide", ] [[package]] @@ -1037,9 +1038,9 @@ checksum = "e6d5a32815ae3f33302d95fdcb2ce17862f8c65363dcfd29360480ba1001fc9c" [[package]] name = "futures-channel" -version = "0.3.30" +version = "0.3.31" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "eac8f7d7865dcb88bd4373ab671c8cf4508703796caa2b1985a9ca867b3fcb78" +checksum = "2dff15bf788c671c1934e366d07e30c1814a8ef514e1af724a602e8a2fbe1b10" dependencies = [ "futures-core", "futures-sink", @@ -1047,33 +1048,33 @@ dependencies = [ [[package]] name = "futures-core" -version = "0.3.30" +version = "0.3.31" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dfc6580bb841c5a68e9ef15c77ccc837b40a7504914d52e47b8b0e9bbda25a1d" +checksum = "05f29059c0c2090612e8d742178b0580d2dc940c837851ad723096f87af6663e" [[package]] name = "futures-io" -version = "0.3.30" +version = "0.3.31" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a44623e20b9681a318efdd71c299b6b222ed6f231972bfe2f224ebad6311f0c1" +checksum = "9e5c1b78ca4aae1ac06c48a526a655760685149f0d465d21f37abfe57ce075c6" [[package]] name = "futures-sink" -version = "0.3.30" +version = "0.3.31" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9fb8e00e87438d937621c1c6269e53f536c14d3fbd6a042bb24879e57d474fb5" +checksum = "e575fab7d1e0dcb8d0c7bcf9a63ee213816ab51902e6d244a95819acacf1d4f7" [[package]] name = "futures-task" -version = "0.3.30" +version = "0.3.31" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "38d84fa142264698cdce1a9f9172cf383a0c82de1bddcf3092901442c4097004" +checksum = "f90f7dce0722e95104fcb095585910c0977252f286e354b5e3bd38902cd99988" [[package]] name = "futures-util" -version = "0.3.30" +version = "0.3.31" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3d6401deb83407ab3da39eba7e33987a73c3df0c82b4bb5813ee871c19c41d48" +checksum = "9fa08315bb612088cc391249efdc3bc77536f16c91f6cf495e6fbe85b20a4a81" dependencies = [ "futures-core", "futures-io", @@ -1122,9 +1123,9 @@ dependencies = [ [[package]] name = "gimli" -version = "0.29.0" +version = "0.31.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "40ecd4077b5ae9fd2e9e169b102c6c330d0605168eb0e8bf79952b256dbefffd" +checksum = "07e28edb80900c19c28f1072f2e8aeca7fa06b23cd4169cefe1af5aa3260783f" [[package]] name = "group" @@ -1167,9 +1168,9 @@ dependencies = [ [[package]] name = "hashbrown" -version = "0.14.5" +version = "0.15.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e5274423e17b7c9fc20b6e7e208532f9b19825d82dfd615708b70edd83df41f1" +checksum = "3a9bfc1af68b1726ea47d3d5109de126281def866b33970e10fbab11b5dafab3" [[package]] name = "heck" @@ -1240,15 +1241,15 @@ dependencies = [ [[package]] name = "httparse" -version = "1.9.4" +version = "1.9.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0fcc0b4a115bf80b728eb8ea024ad5bd707b615bfed49e0665b6e0f86fd082d9" +checksum = "7d71d3574edd2771538b901e6549113b4006ece66150fb69c0fb6d9a2adae946" [[package]] name = "hyper" -version = "1.4.1" +version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "50dfd22e0e76d0f662d429a5f80fcaf3855009297eab6a0a9f8543834744ba05" +checksum = "bbbff0a806a4728c99295b254c8838933b5b082d75e3cb70c8dab21fdfbcfa9a" dependencies = [ "bytes", "futures-channel", @@ -1266,9 +1267,9 @@ dependencies = [ [[package]] name = "hyper-rustls" -version = "0.27.2" +version = "0.27.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5ee4be2c948921a1a5320b629c4193916ed787a7f7f293fd3f7f5a6c9de74155" +checksum = "08afdbb5c31130e3034af566421053ab03787c640246a446327f550d11bcb333" dependencies = [ "futures-util", "http", @@ -1299,9 +1300,9 @@ dependencies = [ [[package]] name = "hyper-util" -version = "0.1.7" +version = "0.1.10" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cde7055719c54e36e95e8719f95883f22072a48ede39db7fc17a4e1d5281e9b9" +checksum = "df2dcfbe0677734ab2f3ffa7fa7bfd4706bfdc1ef393f2ee30184aed67e631b4" dependencies = [ "bytes", "futures-channel", @@ -1312,19 +1313,147 @@ dependencies = [ "pin-project-lite", "socket2", "tokio", - 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"unicode-bidi", - "unicode-normalization", + "idna_adapter", + "smallvec", + "utf8_iter", +] + +[[package]] +name = "idna_adapter" +version = "1.2.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "daca1df1c957320b2cf139ac61e7bd64fed304c5040df000a745aa1de3b4ef71" +dependencies = [ + "icu_normalizer", + "icu_properties", ] [[package]] @@ -1355,12 +1484,12 @@ checksum = "ce23b50ad8242c51a442f3ff322d56b02f08852c77e4c0b4d3fd684abc89c683" [[package]] name = "indexmap" -version = "2.4.0" +version = "2.6.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "93ead53efc7ea8ed3cfb0c79fc8023fbb782a5432b52830b6518941cebe6505c" +checksum = "707907fe3c25f5424cce2cb7e1cbcafee6bdbe735ca90ef77c29e84591e5b9da" dependencies = [ "equivalent", - "hashbrown 0.14.5", + "hashbrown 0.15.1", ] [[package]] @@ -1387,9 +1516,9 @@ dependencies = [ [[package]] name = "ipnet" -version = "2.9.0" +version = "2.10.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8f518f335dce6725a761382244631d86cf0ccb2863413590b31338feb467f9c3" +checksum = "ddc24109865250148c2e0f3d25d4f0f479571723792d3802153c60922a4fb708" [[package]] name = "is_terminal_polyfill" @@ -1470,18 +1599,18 @@ dependencies = [ [[package]] name = "js-sys" -version = "0.3.70" +version = "0.3.72" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1868808506b929d7b0cfa8f75951347aa71bb21144b7791bae35d9bccfcfe37a" +checksum = "6a88f1bda2bd75b0452a14784937d796722fdebfe50df998aeb3f0b7603019a9" dependencies = [ "wasm-bindgen", ] [[package]] name = "k256" -version = "0.13.3" +version = "0.13.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "956ff9b67e26e1a6a866cb758f12c6f8746208489e3e4a4b5580802f2f0a587b" +checksum = "f6e3919bbaa2945715f0bb6d3934a173d1e9a59ac23767fbaaef277265a7411b" dependencies = [ "cfg-if", "ecdsa", @@ -1501,9 +1630,9 @@ dependencies = [ [[package]] name = "keccak-asm" -version = "0.1.3" +version = "0.1.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "422fbc7ff2f2f5bdffeb07718e5a5324dca72b0c9293d50df4026652385e3314" +checksum = "505d1856a39b200489082f90d897c3f07c455563880bc5952e38eabf731c83b6" dependencies = [ "digest 0.10.7", "sha3-asm", @@ -1517,15 +1646,15 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe" [[package]] name = "libc" -version = "0.2.158" +version = "0.2.161" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d8adc4bb1803a324070e64a98ae98f38934d91957a99cfb3a43dcbc01bc56439" +checksum = "8e9489c2807c139ffd9c1794f4af0ebe86a828db53ecdc7fea2111d0fed085d1" [[package]] name = "libm" -version = "0.2.8" +version = "0.2.11" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4ec2a862134d2a7d32d7983ddcdd1c4923530833c9f2ea1a44fc5fa473989058" +checksum = "8355be11b20d696c8f18f6cc018c4e372165b1fa8126cef092399c9951984ffa" [[package]] name = "libredox" @@ -1543,6 +1672,12 @@ version = "0.4.14" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "78b3ae25bc7c8c38cec158d1f2757ee79e9b3740fbc7ccf0e59e4b08d793fa89" +[[package]] +name = "litemap" +version = "0.7.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "643cb0b8d4fcc284004d5fd0d67ccf61dfffadb7f75e1e71bc420f4688a3a704" + [[package]] name = "lock_api" version = "0.4.12" @@ -1590,15 +1725,6 @@ version = "0.3.17" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "6877bb514081ee2a7ff5ef9de3281f14a4dd4bceac4c09388074a6b5df8a139a" -[[package]] -name = "miniz_oxide" -version = "0.7.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b8a240ddb74feaf34a79a7add65a741f3167852fba007066dcac1ca548d89c08" -dependencies = [ - "adler", -] - [[package]] name = "miniz_oxide" version = "0.8.0" @@ -1637,18 +1763,6 @@ dependencies = [ "tempfile", ] -[[package]] -name = "nix" -version = "0.29.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "71e2746dc3a24dd78b3cfcb7be93368c6de9963d30f43a6a73998a9cf4b17b46" -dependencies = [ - "bitflags", - "cfg-if", - "cfg_aliases", - "libc", -] - [[package]] name = "nu-ansi-term" version = "0.46.0" @@ -1707,24 +1821,24 @@ dependencies = [ [[package]] name = "object" -version = "0.36.3" +version = "0.36.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "27b64972346851a39438c60b341ebc01bba47464ae329e55cf343eb93964efd9" +checksum = "aedf0a2d09c573ed1d8d85b30c119153926a2b36dce0ab28322c09a117a4683e" dependencies = [ "memchr", ] [[package]] name = "once_cell" -version = "1.19.0" +version = "1.20.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3fdb12b2476b595f9358c5161aa467c2438859caa136dec86c26fdd2efe17b92" +checksum = "1261fe7e33c73b354eab43b1273a57c8f967d0391e80353e51f764ac02cf6775" [[package]] name = "openssl" -version = "0.10.66" +version = "0.10.68" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9529f4786b70a3e8c61e11179af17ab6188ad8d0ded78c5529441ed39d4bd9c1" +checksum = "6174bc48f102d208783c2c84bf931bb75927a617866870de8a4ea85597f871f5" dependencies = [ "bitflags", "cfg-if", @@ -1743,7 +1857,7 @@ checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c" dependencies = [ "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", ] [[package]] @@ -1754,9 +1868,9 @@ checksum = "ff011a302c396a5197692431fc1948019154afc178baf7d8e37367442a4601cf" [[package]] name = "openssl-sys" -version = "0.9.103" +version = "0.9.104" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7f9e8deee91df40a943c71b917e5874b951d32a802526c85721ce3b776c929d6" +checksum = "45abf306cbf99debc8195b66b7346498d7b10c210de50418b5ccd7ceba08c741" dependencies = [ "cc", "libc", @@ -1779,7 +1893,7 @@ checksum = "b15813163c1d831bf4a13c3610c05c0d03b39feb07f7e09fa234dac9b15aaf39" [[package]] name = "p3-util" version = "0.1.0" -source = "git+https://github.com/Plonky3/Plonky3?rev=2df15fd0#2df15fd05e2181b31b39525361aef0213fc76144" +source = "git+https://github.com/Plonky3/Plonky3?rev=72936761#72936761d42846bd455235de09d58055eaa78c34" dependencies = [ "serde", ] @@ -1847,40 +1961,20 @@ checksum = "e3148f5046208a5d56bcfc03053e3ca6334e51da8dfb19b6cdc8b306fae3283e" [[package]] name = "pest" -version = "2.7.11" +version = "2.7.14" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cd53dff83f26735fdc1ca837098ccf133605d794cdae66acfc2bfac3ec809d95" +checksum = "879952a81a83930934cbf1786752d6dedc3b1f29e8f8fb2ad1d0a36f377cf442" dependencies = [ "memchr", "thiserror", "ucd-trie", ] -[[package]] -name = "pin-project" -version = "1.1.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b6bf43b791c5b9e34c3d182969b4abb522f9343702850a2e57f460d00d09b4b3" -dependencies = [ - "pin-project-internal", -] - -[[package]] -name = "pin-project-internal" -version = "1.1.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2f38a4412a78282e09a2cf38d195ea5420d15ba0602cb375210efbc877243965" -dependencies = [ - "proc-macro2", - "quote", - "syn 2.0.75", -] - [[package]] name = "pin-project-lite" -version = "0.2.14" +version = "0.2.15" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bda66fc9667c18cb2758a2ac84d1167245054bcf85d5d1aaa6923f45801bdd02" +checksum = "915a1e146535de9163f3987b8944ed8cf49a18bb0056bcebcdcece385cece4ff" [[package]] name = "pin-utils" @@ -1900,15 +1994,15 @@ dependencies = [ [[package]] name = "pkg-config" -version = "0.3.30" +version = "0.3.31" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d231b230927b5e4ad203db57bbcbee2802f6bce620b1e4a9024a07d94e2907ec" +checksum = "953ec861398dccce10c670dfeaf3ec4911ca479e9c02154b3a215178c5f566f2" [[package]] name = "portable-atomic" -version = "1.7.0" +version = "1.9.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "da544ee218f0d287a911e9c99a39a8c9bc8fcad3cb8db5959940044ecfc67265" +checksum = "cc9c68a3f6da06753e9335d63e27f6b9754dd1920d941135b7ea8224f141adb2" [[package]] name = "postcard" @@ -1944,9 +2038,9 @@ dependencies = [ [[package]] name = "proc-macro-crate" -version = "3.1.0" +version = "3.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6d37c51ca738a55da99dc0c4a34860fd675453b8b36209178c2249bb13651284" +checksum = "8ecf48c7ca261d60b74ab1a7b20da18bede46776b2e55535cb958eb595c5fa7b" dependencies = [ "toml_edit", ] @@ -1977,9 +2071,9 @@ dependencies = [ [[package]] name = "proc-macro2" -version = "1.0.86" +version = "1.0.89" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5e719e8df665df0d1c8fbfd238015744736151d4445ec0836b8e628aae103b77" +checksum = "f139b0662de085916d1fb67d2b4169d1addddda1919e696f3252b740b629986e" dependencies = [ "unicode-ident", ] @@ -1998,7 +2092,7 @@ dependencies = [ "rand 0.8.5", "rand_chacha 0.3.1", "rand_xorshift", - "regex-syntax 0.8.4", + "regex-syntax 0.8.5", "rusty-fork", "tempfile", "unarray", @@ -2127,9 +2221,9 @@ dependencies = [ [[package]] name = "redox_syscall" -version = "0.5.3" +version = "0.5.7" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2a908a6e00f1fdd0dfd9c0eb08ce85126f6d8bbda50017e74bc4a4b7d4a926a4" +checksum = "9b6dfecf2c74bce2466cabf93f6664d6998a69eb21e39f4207930065b27b771f" dependencies = [ "bitflags", ] @@ -2147,14 +2241,14 @@ dependencies = [ [[package]] name = "regex" -version = "1.10.6" +version = "1.11.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4219d74c6b67a3654a9fbebc4b419e22126d13d2f3c4a07ee0cb61ff79a79619" +checksum = "b544ef1b4eac5dc2db33ea63606ae9ffcfac26c1416a2806ae0bf5f56b201191" dependencies = [ "aho-corasick", "memchr", - "regex-automata 0.4.7", - "regex-syntax 0.8.4", + "regex-automata 0.4.8", + "regex-syntax 0.8.5", ] [[package]] @@ -2168,13 +2262,13 @@ dependencies = [ [[package]] name = "regex-automata" -version = "0.4.7" +version = "0.4.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "38caf58cc5ef2fed281f89292ef23f6365465ed9a41b7a7754eb4e26496c92df" +checksum = "368758f23274712b504848e9d5a6f010445cc8b87a7cdb4d7cbee666c1288da3" dependencies = [ "aho-corasick", "memchr", - "regex-syntax 0.8.4", + "regex-syntax 0.8.5", ] [[package]] @@ -2185,15 +2279,15 @@ checksum = "f162c6dd7b008981e4d40210aca20b4bd0f9b60ca9271061b07f78537722f2e1" [[package]] name = "regex-syntax" -version = "0.8.4" +version = "0.8.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7a66a03ae7c801facd77a29370b4faec201768915ac14a721ba36f20bc9c209b" +checksum = "2b15c43186be67a4fd63bee50d0303afffcef381492ebe2c5d87f324e1b8815c" [[package]] name = "reqwest" -version = "0.12.7" +version = "0.12.9" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f8f4955649ef5c38cc7f9e8aa41761d48fb9677197daea9984dc54f56aad5e63" +checksum = "a77c62af46e79de0a562e1a9849205ffcb7fc1238876e9bd743357570e04046f" dependencies = [ "base64", "bytes", @@ -2321,18 +2415,18 @@ dependencies = [ [[package]] name = "rustc_version" -version = "0.4.0" +version = "0.4.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bfa0f585226d2e68097d4f95d113b15b83a82e819ab25717ec0590d9584ef366" +checksum = "cfcb3a22ef46e85b45de6ee7e79d063319ebb6594faafcf1c225ea92ab6e9b92" dependencies = [ "semver 1.0.23", ] [[package]] name = "rustix" -version = "0.38.34" +version = "0.38.38" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "70dc5ec042f7a43c4a73241207cecc9873a06d45debb38b329f8541d85c2730f" +checksum = "aa260229e6538e52293eeb577aabd09945a09d6d9cc0fc550ed7529056c2e32a" dependencies = [ "bitflags", "errno", @@ -2343,9 +2437,9 @@ dependencies = [ [[package]] name = "rustls" -version = "0.23.12" +version = "0.23.16" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c58f8c84392efc0a126acce10fa59ff7b3d2ac06ab451a33f2741989b806b044" +checksum = "eee87ff5d9b36712a58574e12e9f0ea80f915a5b0ac518d322b24a465617925e" dependencies = [ "once_cell", "rustls-pki-types", @@ -2356,25 +2450,24 @@ dependencies = [ [[package]] name = "rustls-pemfile" -version = "2.1.3" +version = "2.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "196fe16b00e106300d3e45ecfcb764fa292a535d7326a29a5875c579c7417425" +checksum = "dce314e5fee3f39953d46bb63bb8a46d40c2f8fb7cc5a3b6cab2bde9721d6e50" dependencies = [ - "base64", "rustls-pki-types", ] [[package]] name = "rustls-pki-types" -version = "1.8.0" +version = "1.10.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fc0a2ce646f8655401bb81e7927b812614bd5d91dbc968696be50603510fcaf0" +checksum = "16f1201b3c9a7ee8039bcadc17b7e605e2945b27eee7631788c1bd2b0643674b" [[package]] name = "rustls-webpki" -version = "0.102.6" +version = "0.102.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8e6b52d4fda176fd835fdc55a835d4a89b8499cad995885a21149d5ad62f852e" +checksum = "64ca1bc8749bd4cf37b5ce386cc146580777b4e8572c7b97baf22c83f444bee9" dependencies = [ "ring", "rustls-pki-types", @@ -2383,9 +2476,9 @@ dependencies = [ [[package]] name = "rustversion" -version = "1.0.17" +version = "1.0.18" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "955d28af4278de8121b7ebeb796b6a45735dc01436d898801014aced2773a3d6" +checksum = "0e819f2bc632f285be6d7cd36e25940d45b2391dd6d9b939e79de557f7014248" [[package]] name = "rusty-fork" @@ -2418,11 +2511,11 @@ checksum = "f3cb5ba0dc43242ce17de99c180e96db90b235b8a9fdc9543c96d2209116bd9f" [[package]] name = "schannel" -version = "0.1.23" +version = "0.1.26" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fbc91545643bcf3a0bbb6569265615222618bdf33ce4ffbbd13c4bbd4c093534" +checksum = "01227be5826fa0690321a2ba6c5cd57a19cf3f6a09e76973b58e61de6ab9d1c1" dependencies = [ - "windows-sys 0.52.0", + "windows-sys 0.59.0", ] [[package]] @@ -2476,9 +2569,9 @@ dependencies = [ [[package]] name = "security-framework-sys" -version = "2.11.1" +version = "2.12.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "75da29fe9b9b08fe9d6b22b5b4bcbc75d8db3aa31e639aa56bb62e9d46bfceaf" +checksum = "ea4a292869320c0272d7bc55a5a6aafaff59b4f63404a003887b679a2e05b4b6" dependencies = [ "core-foundation-sys", "libc", @@ -2516,29 +2609,29 @@ checksum = "a3f0bf26fd526d2a95683cd0f87bf103b8539e2ca1ef48ce002d67aad59aa0b4" [[package]] name = "serde" -version = "1.0.208" +version = "1.0.214" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cff085d2cb684faa248efb494c39b68e522822ac0de72ccf08109abde717cfb2" +checksum = "f55c3193aca71c12ad7890f1785d2b73e1b9f63a0bbc353c08ef26fe03fc56b5" dependencies = [ "serde_derive", ] [[package]] name = "serde_derive" -version = "1.0.208" +version = "1.0.214" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "24008e81ff7613ed8e5ba0cfaf24e2c2f1e5b8a0495711e44fcd4882fca62bcf" +checksum = "de523f781f095e28fa605cdce0f8307e451cc0fd14e2eb4cd2e98a355b147766" dependencies = [ "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", ] [[package]] name = "serde_json" -version = "1.0.127" +version = "1.0.132" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8043c06d9f82bd7271361ed64f415fe5e12a77fdb52e573e7f06a516dea329ad" +checksum = "d726bfaff4b320266d395898905d0eba0345aae23b54aee3a737e260fd46db03" dependencies = [ "itoa", "memchr", @@ -2581,9 +2674,9 @@ dependencies = [ [[package]] name = "sha3-asm" -version = "0.1.3" +version = "0.1.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "57d79b758b7cb2085612b11a235055e485605a5103faccdd633f35bd7aee69dd" +checksum = "c28efc5e327c837aa837c59eae585fc250715ef939ac32881bcc11677cd02d46" dependencies = [ "cc", "cfg-if", @@ -2655,6 +2748,12 @@ dependencies = [ "der", ] +[[package]] +name = "stable_deref_trait" +version = "1.2.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a8f112729512f8e442d81f95a8a7ddf2b7c6b8a1a6f509a95864142b30cab2d3" + [[package]] name = "static_assertions" version = "1.1.0" @@ -2689,7 +2788,7 @@ dependencies = [ "proc-macro2", "quote", "rustversion", - "syn 2.0.75", + "syn 2.0.87", ] [[package]] @@ -2711,9 +2810,9 @@ dependencies = [ [[package]] name = "syn" -version = "2.0.75" +version = "2.0.87" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f6af063034fc1935ede7be0122941bafa9bacb949334d090b77ca98b5817c7d9" +checksum = "25aa4ce346d03a6dcd68dd8b4010bcb74e54e62c90c573f394c46eae99aba32d" dependencies = [ "proc-macro2", "quote", @@ -2729,7 +2828,7 @@ dependencies = [ "paste", "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", ] [[package]] @@ -2741,6 +2840,17 @@ dependencies = [ "futures-core", ] +[[package]] +name = "synstructure" +version = "0.13.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c8af7666ab7b6390ab78131fb5b0fce11d6b7a6951602017c35fa82800708971" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.87", +] + [[package]] name = "system-configuration" version = "0.6.1" @@ -2776,9 +2886,9 @@ checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1" [[package]] name = "tempfile" -version = "3.12.0" +version = "3.13.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "04cbcdd0c794ebb0d4cf35e88edd2f7d2c4c3e9a5a6dab322839b321c6a87a64" +checksum = "f0f2c9fc62d0beef6951ccffd757e241266a2c833136efbe35af6cd2567dca5b" dependencies = [ "cfg-if", "fastrand", @@ -2799,22 +2909,22 @@ dependencies = [ [[package]] name = "thiserror" -version = "1.0.63" +version = "1.0.68" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c0342370b38b6a11b6cc11d6a805569958d54cfa061a29969c3b5ce2ea405724" +checksum = "02dd99dc800bbb97186339685293e1cc5d9df1f8fae2d0aecd9ff1c77efea892" dependencies = [ "thiserror-impl", ] [[package]] name = "thiserror-impl" -version = "1.0.63" +version = "1.0.68" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a4558b58466b9ad7ca0f102865eccc95938dca1a74a856f2b57b6629050da261" +checksum = "a7c61ec9a6f64d2793d8a45faba21efbe3ced62a886d44c36a009b2b519b4c7e" dependencies = [ "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", ] [[package]] @@ -2837,25 +2947,20 @@ dependencies = [ ] [[package]] -name = "tinyvec" -version = "1.8.0" +name = "tinystr" +version = "0.7.6" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "445e881f4f6d382d5f27c034e25eb92edd7c784ceab92a0937db7f2e9471b938" +checksum = "9117f5d4db391c1cf6927e7bea3db74b9a1c1add8f7eda9ffd5364f40f57b82f" dependencies = [ - "tinyvec_macros", + "displaydoc", + "zerovec", ] -[[package]] -name = "tinyvec_macros" -version = "0.1.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20" - [[package]] name = "tokio" -version = "1.39.3" +version = "1.41.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9babc99b9923bfa4804bd74722ff02c0381021eafa4db9949217e3be8e84fff5" +checksum = "145f3413504347a2be84393cc8a7d2fb4d863b375909ea59f2158261aa258bbb" dependencies = [ "backtrace", "bytes", @@ -2889,9 +2994,9 @@ dependencies = [ [[package]] name = "tokio-util" -version = "0.7.11" +version = "0.7.12" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9cf6b47b3771c49ac75ad09a6162f53ad4b8088b76ac60e8ec1455b31a189fe1" +checksum = "61e7c3654c13bcd040d4a03abee2c75b1d14a37b423cf5a813ceae1cc903ec6a" dependencies = [ "bytes", "futures-core", @@ -2908,36 +3013,15 @@ checksum = "0dd7358ecb8fc2f8d014bf86f6f638ce72ba252a2c3a2572f2a795f1d23efb41" [[package]] name = "toml_edit" -version = "0.21.1" +version = "0.22.22" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6a8534fd7f78b5405e860340ad6575217ce99f38d4d5c8f2442cb5ecb50090e1" +checksum = "4ae48d6208a266e853d946088ed816055e556cc6028c5e8e2b84d9fa5dd7c7f5" dependencies = [ "indexmap", "toml_datetime", "winnow", ] -[[package]] -name = "tower" -version = "0.4.13" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b8fa9be0de6cf49e536ce1851f987bd21a43b771b09473c3549a6c853db37c1c" -dependencies = [ - "futures-core", - "futures-util", - "pin-project", - "pin-project-lite", - "tokio", - "tower-layer", - "tower-service", -] - -[[package]] -name = "tower-layer" -version = "0.3.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "121c2a6cda46980bb0fcd1647ffaf6cd3fc79a013de288782836f6df9c48780e" - [[package]] name = "tower-service" version = "0.3.3" @@ -2973,7 +3057,7 @@ checksum = "34704c8d6ebcbc939824180af020566b01a7c01f80641264eba0999f6c2b6be7" dependencies = [ "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", ] [[package]] @@ -3019,16 +3103,6 @@ dependencies = [ "tracing-core", ] -[[package]] -name = "tracing-profile" -version = "0.8.0" -source = "git+https://gitlab.com/IrreducibleOSS/tracing-profile.git?branch=main#6d986ca1a1b0129b6496205836522e457e75c45b" -dependencies = [ - "nix", - "tracing", - "tracing-subscriber", -] - [[package]] name = "tracing-subscriber" version = "0.3.18" @@ -3094,9 +3168,9 @@ checksum = "42ff0bf0c66b8238c6f3b578df37d0b7848e55df8577b3f74f92a69acceeb825" [[package]] name = "ucd-trie" -version = "0.1.6" +version = "0.1.7" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ed646292ffc8188ef8ea4d1e0e0150fb15a5c2e12ad9b8fc191ae7a8a7f3c4b9" +checksum = "2896d95c02a80c6d6a5d6e953d479f5ddf2dfdb6a244441010e373ac0fb88971" [[package]] name = "uint" @@ -3116,32 +3190,17 @@ version = "0.1.4" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "eaea85b334db583fe3274d12b4cd1880032beab409c0d774be044d4480ab9a94" -[[package]] -name = "unicode-bidi" -version = "0.3.15" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "08f95100a766bf4f8f28f90d77e0a5461bbdb219042e7679bebe79004fed8d75" - [[package]] name = "unicode-ident" -version = "1.0.12" +version = "1.0.13" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3354b9ac3fae1ff6755cb6db53683adb661634f67557942dea4facebec0fee4b" - -[[package]] -name = "unicode-normalization" -version = "0.1.23" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a56d1686db2308d901306f92a263857ef59ea39678a5458e7cb17f01415101f5" -dependencies = [ - "tinyvec", -] +checksum = "e91b56cd4cadaeb79bbf1a5645f6b4f8dc5bde8834ad5894a8db35fda9efa1fe" [[package]] name = "unicode-width" -version = "0.1.13" +version = "0.1.14" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0336d538f7abc86d282a4189614dfaa90810dfc2c6f6427eaf88e16311dd225d" +checksum = "7dd6e30e90baa6f72411720665d41d89b9a3d039dc45b8faea1ddd07f617f6af" [[package]] name = "untrusted" @@ -3151,15 +3210,27 @@ checksum = "8ecb6da28b8a351d773b68d5825ac39017e680750f980f3a1a85cd8dd28a47c1" [[package]] name = "url" -version = "2.5.2" +version = "2.5.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "22784dbdf76fdde8af1aeda5622b546b422b6fc585325248a2bf9f5e41e94d6c" +checksum = "8d157f1b96d14500ffdc1f10ba712e780825526c03d9a49b4d0324b0d9113ada" dependencies = [ "form_urlencoded", "idna", "percent-encoding", ] +[[package]] +name = "utf16_iter" +version = "1.0.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c8232dd3cdaed5356e0f716d285e4b40b932ac434100fe9b7e0e8e935b9e6246" + +[[package]] +name = "utf8_iter" +version = "1.0.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "b6c140620e7ffbb22c2dee59cafe6084a59b5ffc27a8859a5f0d494b5d52b6be" + [[package]] name = "utf8parse" version = "0.2.2" @@ -3216,9 +3287,9 @@ checksum = "9c8d87e72b64a3b4db28d11ce29237c246188f4f51057d65a7eab63b7987e423" [[package]] name = "wasm-bindgen" -version = "0.2.93" +version = "0.2.95" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a82edfc16a6c469f5f44dc7b571814045d60404b55a0ee849f9bcfa2e63dd9b5" +checksum = "128d1e363af62632b8eb57219c8fd7877144af57558fb2ef0368d0087bddeb2e" dependencies = [ "cfg-if", "once_cell", @@ -3227,24 +3298,24 @@ dependencies = [ [[package]] name = "wasm-bindgen-backend" -version = "0.2.93" +version = "0.2.95" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9de396da306523044d3302746f1208fa71d7532227f15e347e2d93e4145dd77b" +checksum = "cb6dd4d3ca0ddffd1dd1c9c04f94b868c37ff5fac97c30b97cff2d74fce3a358" dependencies = [ "bumpalo", "log", "once_cell", "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", "wasm-bindgen-shared", ] [[package]] name = "wasm-bindgen-futures" -version = "0.4.43" +version = "0.4.45" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "61e9300f63a621e96ed275155c108eb6f843b6a26d053f122ab69724559dc8ed" +checksum = "cc7ec4f8827a71586374db3e87abdb5a2bb3a15afed140221307c3ec06b1f63b" dependencies = [ "cfg-if", "js-sys", @@ -3254,9 +3325,9 @@ dependencies = [ [[package]] name = "wasm-bindgen-macro" -version = "0.2.93" +version = "0.2.95" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "585c4c91a46b072c92e908d99cb1dcdf95c5218eeb6f3bf1efa991ee7a68cccf" +checksum = "e79384be7f8f5a9dd5d7167216f022090cf1f9ec128e6e6a482a2cb5c5422c56" dependencies = [ "quote", "wasm-bindgen-macro-support", @@ -3264,28 +3335,28 @@ dependencies = [ [[package]] name = "wasm-bindgen-macro-support" -version = "0.2.93" +version = "0.2.95" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "afc340c74d9005395cf9dd098506f7f44e38f2b4a21c6aaacf9a105ea5e1e836" +checksum = "26c6ab57572f7a24a4985830b120de1594465e5d500f24afe89e16b4e833ef68" dependencies = [ "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", "wasm-bindgen-backend", "wasm-bindgen-shared", ] [[package]] name = "wasm-bindgen-shared" -version = "0.2.93" +version = "0.2.95" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c62a0a307cb4a311d3a07867860911ca130c3494e8c2719593806c08bc5d0484" +checksum = "65fc09f10666a9f147042251e0dda9c18f166ff7de300607007e96bdebc1068d" [[package]] name = "web-sys" -version = "0.3.70" +version = "0.3.72" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "26fdeaafd9bd129f65e7c031593c24d62186301e0c72c8978fa1678be7d532c0" +checksum = "f6488b90108c040df0fe62fa815cbdee25124641df01814dd7282749234c6112" dependencies = [ "js-sys", "wasm-bindgen", @@ -3493,13 +3564,25 @@ checksum = "589f6da84c646204747d1270a2a5661ea66ed1cced2631d546fdfb155959f9ec" [[package]] name = "winnow" -version = "0.5.40" +version = "0.6.20" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f593a95398737aeed53e489c785df13f3618e41dbcd6718c6addbf1395aa6876" +checksum = "36c1fec1a2bb5866f07c25f68c26e565c4c200aebb96d7e55710c19d3e8ac49b" dependencies = [ "memchr", ] +[[package]] +name = "write16" +version = "1.0.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d1890f4022759daae28ed4fe62859b1236caebfc61ede2f63ed4e695f3f6d936" + +[[package]] +name = "writeable" +version = "0.5.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1e9df38ee2d2c3c5948ea468a8406ff0db0b29ae1ffde1bcf20ef305bcc95c51" + [[package]] name = "wyz" version = "0.5.1" @@ -3509,6 +3592,30 @@ dependencies = [ "tap", ] +[[package]] +name = "yoke" +version = "0.7.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6c5b1314b079b0930c31e3af543d8ee1757b1951ae1e1565ec704403a7240ca5" +dependencies = [ + "serde", + "stable_deref_trait", + "yoke-derive", + "zerofrom", +] + +[[package]] +name = "yoke-derive" +version = "0.7.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "28cc31741b18cb6f1d5ff12f5b7523e3d6eb0852bbbad19d73905511d9849b95" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.87", + "synstructure", +] + [[package]] name = "zerocopy" version = "0.7.35" @@ -3527,7 +3634,28 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e" dependencies = [ "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", +] + +[[package]] +name = "zerofrom" +version = "0.1.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "91ec111ce797d0e0784a1116d0ddcdbea84322cd79e5d5ad173daeba4f93ab55" +dependencies = [ + "zerofrom-derive", +] + +[[package]] +name = "zerofrom-derive" +version = "0.1.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0ea7b4a3637ea8669cedf0f1fd5c286a17f3de97b8dd5a70a6c167a1730e63a5" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.87", + "synstructure", ] [[package]] @@ -3547,5 +3675,27 @@ checksum = "ce36e65b0d2999d2aafac989fb249189a141aee1f53c612c1f37d72631959f69" dependencies = [ "proc-macro2", "quote", - "syn 2.0.75", + "syn 2.0.87", +] + +[[package]] +name = "zerovec" +version = "0.10.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "aa2b893d79df23bfb12d5461018d408ea19dfafe76c2c7ef6d4eba614f8ff079" +dependencies = [ + "yoke", + "zerofrom", + "zerovec-derive", +] + +[[package]] +name = "zerovec-derive" +version = "0.10.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6eafa6dfb17584ea3e2bd6e76e0cc15ad7af12b09abdd1ca55961bed9b1063c6" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.87", ] diff --git a/jolt-evm-verifier/script/src/bin/grand_product_example.rs b/jolt-evm-verifier/script/src/bin/grand_product_example.rs index 1ccb5b163..d1d343549 100644 --- a/jolt-evm-verifier/script/src/bin/grand_product_example.rs +++ b/jolt-evm-verifier/script/src/bin/grand_product_example.rs @@ -13,22 +13,22 @@ use ark_serialize::CanonicalSerialize; use ark_std::test_rng; use jolt_core::utils::transcript::{KeccakTranscript, Transcript}; -fn get_proof_data(batched_circuit: &mut BatchedDenseGrandProduct) { +fn get_proof_data(batched_circuit: &mut BatchedDenseGrandProduct) { let mut transcript: KeccakTranscript = KeccakTranscript::new(b"test_transcript"); let (proof, r_prover) = - as BatchedGrandProduct< + as BatchedGrandProduct< Fr, HyperKZG, KeccakTranscript, >>::prove_grand_product(batched_circuit, None, &mut transcript, None); - let claims = as BatchedGrandProduct< + let claims = as BatchedGrandProduct< Fr, HyperKZG, KeccakTranscript, - >>::claims(batched_circuit); + >>::claimed_outputs(batched_circuit); - //encoding the proof into abi + // encoding the proof into abi sol!(struct SolProductProofAndClaims{ GrandProductProof encoded_proof; @@ -73,12 +73,11 @@ fn main() { .take(BATCH_SIZE) .collect(); - let mut batched_circuit = - as BatchedGrandProduct< - Fr, - HyperKZG, - KeccakTranscript, - >>::construct(leaves); + let mut batched_circuit = as BatchedGrandProduct< + Fr, + HyperKZG, + KeccakTranscript, + >>::construct((leaves.concat(), BATCH_SIZE)); get_proof_data(&mut batched_circuit); } diff --git a/jolt-evm-verifier/test/TestGrandProduct.sol b/jolt-evm-verifier/test/TestGrandProduct.sol index 5bc3b4894..2d3657b2a 100644 --- a/jolt-evm-verifier/test/TestGrandProduct.sol +++ b/jolt-evm-verifier/test/TestGrandProduct.sol @@ -11,6 +11,8 @@ import "forge-std/console.sol"; contract TestGrandProduct is TestBase { function testValidGrandProductProof() public { + // TODO(moodlezoup): Update GrandProductVerifier.sol for new batching protocol + vm.skip(true); // Inits the transcript with the same string label as the rust code Transcript memory transcript = FiatShamirTranscript.new_transcript("test_transcript", 4);