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Polynomial Commitment Scheme Prover

In this section, we will see the implementation of the commitment scheme prover. We will start by looking at the building blocks.

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Commitment Tree Prover

The CommitmentTreeProver struct represents the data for a single Merkle tree commitment. As we have seen in the Merkle tree section, we can commit to multiple polynomials of different degrees in the same Merkle tree. It is implemented as follows: 
pub struct CommitmentTreeProver<B: BackendForChannel<MC>, MC: MerkleChannel> {
    pub polynomials: ColumnVec<CirclePoly<B>>,
    pub evaluations: ColumnVec<CircleEvaluation<B, BaseField, BitReversedOrder>>,
    pub commitment: MerkleProver<B, MC::H>,
}
Here, pub type ColumnVec<T> = Vec<T>. It contains the following fields:
  • polynomials: The set of polynomials committed in a single Merkle tree.
  • evaluations: The evaluations of these polynomials over their respective domains.
  • commitment: The MerkleProver struct as described in the Merkle tree section.
It is initialized as follows: 
    pub fn new(
        polynomials: ColumnVec<CirclePoly<B>>,
        log_blowup_factor: u32,
        channel: &mut MC::C,
        twiddles: &TwiddleTree<B>,
    ) -> Self {
        let span = span!(Level::INFO, "Extension").entered();
        let evaluations = B::evaluate_polynomials(&polynomials, log_blowup_factor, twiddles);
        span.exit();

        let _span = span!(Level::INFO, "Merkle").entered();
        let tree = MerkleProver::commit(evaluations.iter().map(|eval| &eval.values).collect());
        MC::mix_root(channel, tree.root());

        CommitmentTreeProver {
            polynomials,
            evaluations,
            commitment: tree,
        }
    }
It proceeds as follows. First, given the polynomials, we evaluate them on the evaluation domain using circle FFT to compute evaluations. Then we commit to those evaluations using the MerkleProver struct. Finally, we create and output the CommitmentTreeProver struct.

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Commitment Scheme Prover

The CommitmentSchemeProver struct is the key struct which maintains a vector of commitment trees. It implements functionalities to open the committed polynomials, compute quotients, and then apply the FRI protocol. It contains the following fields: 
pub struct CommitmentSchemeProver<'a, B: BackendForChannel<MC>, MC: MerkleChannel> {
    pub trees: TreeVec<CommitmentTreeProver<B, MC>>,
    pub config: PcsConfig,
    twiddles: &'a TwiddleTree<B>,
}
It contains the following fields:
  • tree: This contains a vector of commitment trees. Here, pub struct TreeVec<T>(pub Vec<T>).
  • config: This is the PcsConfig, which contains the fri_config and pow_bits.
pub struct PcsConfig {
    pub pow_bits: u32,
    pub fri_config: FriConfig,
}
The security of the polynomial commitment scheme is computed as: 
    pub const fn security_bits(&self) -> u32 {
        self.pow_bits + self.fri_config.security_bits()
    }
Now we will see some key functions defined on the CommitmentSchemeProver struct.

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Commit

The commit function, given a batch of polynomials, computes the CommitmentTreeProver struct which commits to the input polynomials and then appends the tree struct to the vector of stored trees. 
    fn commit(&mut self, polynomials: ColumnVec<CirclePoly<B>>, channel: &mut MC::C) {
        let _span = span!(Level::INFO, "Commitment").entered();
        let tree = CommitmentTreeProver::new(
            polynomials,
            self.config.fri_config.log_blowup_factor,
            channel,
            self.twiddles,
        );
        self.trees.push(tree);
    }

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Trace

The trace function returns a Trace struct containing all polynomials and their evaluations corresponding to all the commitment trees. It is implemented as follows: 
    pub fn trace(&self) -> Trace<'_, B> {
        let polys = self.polynomials();
        let evals = self.evaluations();
        Trace { polys, evals }
    }

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Commitment Tree Builder

The tree_builder function outputs the TreeBuilder struct. 
    pub fn tree_builder(&mut self) -> TreeBuilder<'_, 'a, B, MC> {
        TreeBuilder {
            tree_index: self.trees.len(),
            commitment_scheme: self,
            polys: Vec::default(),
        }
    }
The TreeBuilder struct is a helper for aggregating polynomials and evaluations before committing them in a Merkle tree. It allows the prover to collect columns (polynomials) and then commit them together as a batch. It is implemented as follows: 
pub struct TreeBuilder<'a, 'b, B: BackendForChannel<MC>, MC: MerkleChannel> {
    tree_index: usize,
    commitment_scheme: &'a mut CommitmentSchemeProver<'b, B, MC>,
    polys: ColumnVec<CirclePoly<B>>,
}

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Prove

The prove_values function is central to the protocol, handling the opening of committed polynomials at specified sample points and integrating with the FRI protocol for low-degree testing. It is implemented as follows: 
    pub fn prove_values(
        self,
        sampled_points: TreeVec<ColumnVec<Vec<CirclePoint<SecureField>>>>,
        channel: &mut MC::C,
    ) -> CommitmentSchemeProof<MC::H> {
        // Evaluate polynomials on open points.
        let span = span!(
            Level::INFO,
            "Evaluate columns out of domain",
            class = "EvaluateOutOfDomain"
        )
        .entered();
        let samples = self
            .polynomials()
            .zip_cols(&sampled_points)
            .map_cols(|(poly, points)| {
                points
                    .iter()
                    .map(|&point| PointSample {
                        point,
                        value: poly.eval_at_point(point),
                    })
                    .collect_vec()
            });
        span.exit();
        let sampled_values = samples
            .as_cols_ref()
            .map_cols(|x| x.iter().map(|o| o.value).collect());
        channel.mix_felts(&sampled_values.clone().flatten_cols());

        // Compute oods quotients for boundary constraints on the sampled points.
        let columns = self.evaluations().flatten();
        let quotients = compute_fri_quotients(
            &columns,
            &samples.flatten(),
            channel.draw_secure_felt(),
            self.config.fri_config.log_blowup_factor,
        );

        // Run FRI commitment phase on the oods quotients.
        let fri_prover =
            FriProver::<B, MC>::commit(channel, self.config.fri_config, &quotients, self.twiddles);

        // Proof of work.
        let span1 = span!(Level::INFO, "Grind", class = "Queries POW").entered();
        let proof_of_work = B::grind(channel, self.config.pow_bits);
        span1.exit();
        channel.mix_u64(proof_of_work);

        // FRI decommitment phase.
        let (fri_proof, query_positions_per_log_size) = fri_prover.decommit(channel);

        // Decommit the FRI queries on the merkle trees.
        let decommitment_results = self
            .trees
            .as_ref()
            .map(|tree| tree.decommit(&query_positions_per_log_size));

        let queried_values = decommitment_results.as_ref().map(|(v, _)| v.clone());
        let decommitments = decommitment_results.map(|(_, d)| d);

        CommitmentSchemeProof {
            commitments: self.roots(),
            sampled_values,
            decommitments,
            queried_values,
            proof_of_work,
            fri_proof,
            config: self.config,
        }
    }
}
Here is a detailed breakdown:
  1. Evaluate Polynomials at Sample Points:
    • For each committed polynomial and each sample point (including out-of-domain points and mask points which contain constraint offsets), the function evaluates the polynomials and collects the results in samples.
    • The sampled_values are mixed into the channel, ensuring they are bound to the proof and used for subsequent randomness generation.
  2. Compute FRI Quotients:
    • The function computes FRI quotient polynomials using compute_fri_quotients to open the committed polynomials at sampled points in samples. This follows the same quotienting process as described in the overview section.
  3. FRI Commitment Phase:
    • The FRI protocol is run on the quotient polynomials, committing to their evaluations in Merkle trees and initializing the fri_prover. For more details, refer to the FRI prover section.
  4. Proof of Work:
    • A proof-of-work step is performed, with the result mixed into the channel.
  5. FRI Decommitment Phase:
    • The function generates random query positions using the channel and decommits the FRI layers at those positions, providing Merkle decommitments for all queried values. For more details, refer to the FRI prover section.
  6. Decommitment of Committed Trees:
    • For each commitment tree, the function decommits the Merkle tree at the FRI query positions, providing the queried values and authentication paths.
  7. Return Proof Object:
    • The function returns a CommitmentSchemeProof object containing:
      • Merkle roots of all commitments
      • Sampled values at all sample points
      • Merkle decommitments for all queries
      • Queried values
      • Proof-of-work result
      • FRI proof
      • Protocol configuration
We will now look into the proof verifier implementation.