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feat: prove openings of masking polynomials in ECCVM and Translator #9726
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{ | ||
const size_t num_opening_claims = opening_claims.size(); | ||
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// {ẑⱼ(r)}ⱼ , where ẑⱼ(r) = 1/zⱼ(r) = 1/(r - xⱼ) | ||
// {ẑⱼ(z)}ⱼ , where ẑⱼ(r) = 1/zⱼ(z) = 1/(z - xⱼ) |
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typo
@@ -41,6 +41,42 @@ TYPED_TEST(KZGTest, single) | |||
EXPECT_EQ(this->vk()->pairing_check(pairing_points[0], pairing_points[1]), true); | |||
} | |||
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/** |
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We usually commit to and open univariate polynomials represented in the monomial basis. Here is a correct flow for the univariates given in the Lagrange basis.
@@ -439,5 +466,70 @@ template <typename Curve> class ShpleminiVerifier_ { | |||
commitments.emplace_back(std::move(fold_commitments[j])); | |||
} | |||
} | |||
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main feature of this PR - the method enabling efficient opening of claimed evaluations of Libra masking univariates. no extra challenges or batch mul calls required
@@ -113,6 +134,22 @@ template <typename Curve> class ShplonkProver_ { | |||
idx++; | |||
} | |||
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// Take into account the constant proof size in Gemini |
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it's just a usual shplonk stuff taking into account that we have const proof size in gemini
@@ -68,7 +68,7 @@ class ECCVMFlavor { | |||
using Relations = Relations_<FF>; | |||
using LookupRelation = ECCVMLookupRelation<FF>; | |||
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static constexpr size_t MAX_PARTIAL_RELATION_LENGTH = compute_max_partial_relation_length<Relations, HasZK>(); | |||
static constexpr size_t MAX_PARTIAL_RELATION_LENGTH = compute_max_partial_relation_length<Relations>(); |
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don't need this anymore as we are switching to a different evaluation masking technique. same in translator
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// Default constructor |
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added a constructor here, moved a bunch of method from sumcheck.hpp
@@ -190,13 +188,11 @@ template <typename Flavor> class SumcheckProver { | |||
SumcheckOutput<Flavor> prove(ProverPolynomials& full_polynomials, | |||
const bb::RelationParameters<FF>& relation_parameters, | |||
const RelationSeparator alpha, | |||
const std::vector<FF>& gate_challenges) | |||
const std::vector<FF>& gate_challenges, | |||
ZKSumcheckData<Flavor> zk_sumcheck_data = ZKSumcheckData<Flavor>()) |
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decoupled the creation of a ZKSumcheckData object from the prove method. now it's taken care by a prover that uses a constructor to create these data and then feeds it to the prove method
if constexpr (Flavor::HasZK) { | ||
output += full_libra_purported_value.value(); | ||
if constexpr (IsECCVMRecursiveFlavor<Flavor>) { | ||
output.self_reduce(); |
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we have to self_reduce here
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this is only necessary for the ECCVM? is it clear why?
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it's only the ECCVMRecursiveFlavor, because it's operating with the scalars in bigfield
…col/aztec-packages into si/zk-sumcheck-plus-shplemini
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Looks great, just some minor suggestions about style/comments. Also, is there any more cleanup that can be done in the Flavors as a result of this? I'm thinking about the erroneous NUM_ALL_WITNESS_ENTITIES in the MegaFlavor for example..
OpeningClaim batched_claim = ShplonkProver::prove(commitment_key, opening_claims, transcript); | ||
// Create opening claims for Libra masking univariates | ||
std::vector<OpeningClaim> libra_opening_claims; | ||
if (!libra_univariates.empty()) { |
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its a little redundant to have this check prior to the loop. If it's empty, the loop will just do nothing right?
@@ -254,6 +271,16 @@ template <typename Curve> class ShpleminiVerifier_ { | |||
commitments.emplace_back(g1_identity); | |||
scalars.emplace_back(constant_term_accumulator); | |||
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if (!libra_univariate_evaluations.empty()) { |
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Is there a more clarifying condition that could be used here? e.g is this always empty for non-zk flavors and always non-empty for zk flavors?
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exactly, but it's handled somewhat manually because the PCS doesn't know anything about the Flavor. added a clarifying comment
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this is probably an indication that the PCS now needs to be templated on Flavor instead of just Curve
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agree. I think I'll stop adding stuff to Shplemini quite soon and then we'll have to refactor it, because its interface is getting quite messy
size_t num_libra_univariates = libra_univariate_commitments.size(); | ||
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// compute Shplonk denominators and invert them | ||
for (size_t idx = 0; idx < num_libra_univariates; idx++) { |
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Is the number of libra univariates different from the size of the multivariate challenge? If not it might be better to use a range loop here on multivariate_challenge
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yeah, the size of the challenge is CONST_PROOF_SIZE_LOG_N, and the number of libra univariates if the honest log circuit size. prob we'll need to constify it at some point, but I decided to avoid it here
} | ||
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// needed to keep track of the constant term contribution | ||
const size_t idx_of_constant_term = commitments.size() - 1; |
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another option here is to simply define contant_term or constant_scalar as a ref then update it below. E.g. Fr& constant_scalar = scalars.back();
then constant_scalar += constant_term;
. just personal preference I suppose
for (size_t idx = 0; idx < log_n; idx++) { | ||
// generate random polynomial | ||
Polynomial libra_polynomial = Polynomial::random(LIBRA_UNIVARIATE_LENGTH); | ||
// create a univariate with the same coefficients (to store an array intead of a vector) |
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typo
Polynomial libra_polynomial = Polynomial::random(LIBRA_UNIVARIATE_LENGTH); | ||
// create a univariate with the same coefficients (to store an array intead of a vector) | ||
bb::Univariate<Fr, LIBRA_UNIVARIATE_LENGTH> libra_univariate; | ||
for (size_t i = 0; i < LIBRA_UNIVARIATE_LENGTH; i++) { |
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FYI it looks like Univariate has it's own random_element()
method - might be cleaner
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thanks! it's ugly here: Univariates are in the Lagrange basis, Polynomials are in the monomial, which means we can't commit to a Univariate without computing its coefficients in the monomial basis or computing the Lagrange basis using SRS + I want to store the masking data as a std::vector<Univariate<FF, 12>> (could/will be turned into std::array) instead of std::vector
auto eval3_shift = poly3.evaluate_mle(mle_opening_point, true); | ||
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// Collect multilinear evaluations for input to prover | ||
// std::vector<Fr> multilinear_evaluations = { eval1, eval2, eval3, eval4, eval2_shift, eval3_shift }; |
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unused?
@@ -61,7 +62,20 @@ template <typename Curve> class ShplonkProver_ { | |||
Q.add_scaled(tmp, current_nu); | |||
current_nu *= nu; | |||
} | |||
for (size_t idx = opening_claims.size(); idx < CONST_PROOF_SIZE_LOG_N + 2; idx++) { |
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would be nice to add a comment explaining the significance of CONST_PROOF_SIZE_LOG_N + 2
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added a comment
@@ -82,15 +82,18 @@ polynomials, | |||
using View = typename Accumulator::View; | |||
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if constexpr (read_index == 0) { | |||
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are these spaces intentional?
if constexpr (Flavor::HasZK) { | ||
output += full_libra_purported_value.value(); | ||
if constexpr (IsECCVMRecursiveFlavor<Flavor>) { | ||
output.self_reduce(); |
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this is only necessary for the ECCVM? is it clear why?
…9726) As a part of ZK-fication of Honk, we have to mask the evaluations of round univariates that the prover sends to the verifier. The evaluations were masked in Sumcheck in PR #7517. However, the logic for proving evaluations of Libra masking polynomials was missing. This PR fixes this issue and enables efficient batch opening of these polynomials. * Added necessary logic to Shplonk Prover, Shplemini Prover, and Shplemini Verifer * Better handling of the ZKSumcheckData * Removed methods and reverted changes that became obsolete because of the new ZK strategy * Enabled the opening of Libra masking univariates in ECCVM and Translator
As a part of ZK-fication of Honk, we have to mask the evaluations of round univariates that the prover sends to the verifier. The evaluations were masked in Sumcheck in PR #7517. However, the logic for proving evaluations of Libra masking polynomials was missing. This PR fixes this issue and enables efficient batch opening of these polynomials.